vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

merge upstream

Browse source
This commit is contained in:
zhangkaihuo 2024-04-16 20:29:29 +08:00
commit 4da6e3e3e4
139 changed files with 38377 additions and 11522 deletions
Download patch file Download diff file Expand all files Collapse all files

4
.devops/full-cuda.Dockerfile
View file

@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build
ARG CUDA_DOCKER_ARCH=all
RUN apt-get update && \
apt-get install -y build-essential python3 python3-pip git
apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
COPY requirements.txt requirements.txt
COPY requirements requirements
@ -28,6 +28,8 @@ COPY . .
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable CUDA
ENV LLAMA_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1
RUN make

5
.devops/full-rocm.Dockerfile
View file

@ -40,6 +40,11 @@ ENV LLAMA_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
# Enable cURL
ENV LLAMA_CURL=1
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
RUN make
ENTRYPOINT ["/app/.devops/tools.sh"]

5
.devops/full.Dockerfile
View file

@ -3,7 +3,7 @@ ARG UBUNTU_VERSION=22.04
FROM ubuntu:$UBUNTU_VERSION as build
RUN apt-get update && \
apt-get install -y build-essential python3 python3-pip git
apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
COPY requirements.txt requirements.txt
COPY requirements requirements
@ -15,6 +15,9 @@ WORKDIR /app
COPY . .
ENV LLAMA_CURL=1
RUN make
ENV LC_ALL=C.utf8

2
.devops/llama-cpp-clblast.srpm.spec
View file

@ -1,5 +1,5 @@
# SRPM for building from source and packaging an RPM for RPM-based distros.
# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
# Built and maintained by John Boero - boeroboy@gmail.com
# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal

2
.devops/llama-cpp-cuda.srpm.spec
View file

@ -1,5 +1,5 @@
# SRPM for building from source and packaging an RPM for RPM-based distros.
# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
# Built and maintained by John Boero - boeroboy@gmail.com
# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal

2
.devops/llama-cpp.srpm.spec
View file

@ -1,5 +1,5 @@
# SRPM for building from source and packaging an RPM for RPM-based distros.
# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
# Built and maintained by John Boero - boeroboy@gmail.com
# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal

7
.devops/server-cuda.Dockerfile
View file

@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build
ARG CUDA_DOCKER_ARCH=all
RUN apt-get update && \
apt-get install -y build-essential git
apt-get install -y build-essential git libcurl4-openssl-dev
WORKDIR /app
@ -22,11 +22,16 @@ COPY . .
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable CUDA
ENV LLAMA_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1
RUN make
FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
COPY --from=build /app/server /server
ENTRYPOINT [ "/server" ]

7
.devops/server-intel.Dockerfile
View file

@ -4,7 +4,7 @@ FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
ARG LLAMA_SYCL_F16=OFF
RUN apt-get update && \
apt-get install -y git
apt-get install -y git libcurl4-openssl-dev
WORKDIR /app
@ -16,11 +16,14 @@ RUN mkdir build && \
echo "LLAMA_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
fi && \
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake --build . --config Release --target server
FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
COPY --from=build /app/build/bin/server /server
ENV LC_ALL=C.utf8

5
.devops/server-rocm.Dockerfile
View file

@ -40,6 +40,11 @@ ENV LLAMA_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
# Enable cURL
ENV LLAMA_CURL=1
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
RUN make
ENTRYPOINT [ "/app/server" ]

6
.devops/server-vulkan.Dockerfile
View file

@ -11,12 +11,16 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
apt update -y && \
apt-get install -y vulkan-sdk
# Install cURL
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
# Build it
WORKDIR /app
COPY . .
RUN mkdir build && \
cd build && \
cmake .. -DLLAMA_VULKAN=1 && \
cmake .. -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
cmake --build . --config Release --target server
# Clean up

7
.devops/server.Dockerfile
View file

@ -3,16 +3,21 @@ ARG UBUNTU_VERSION=22.04
FROM ubuntu:$UBUNTU_VERSION as build
RUN apt-get update && \
apt-get install -y build-essential git
apt-get install -y build-essential git libcurl4-openssl-dev
WORKDIR /app
COPY . .
ENV LLAMA_CURL=1
RUN make
FROM ubuntu:$UBUNTU_VERSION as runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
COPY --from=build /app/server /server
ENV LC_ALL=C.utf8

65
.github/workflows/bench.yml vendored
View file

@ -24,15 +24,15 @@ on:
push:
branches:
- master
paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
pull_request:
paths: ['llama.cpp', 'ggml.c', 'ggml-backend.c', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
pull_request_target:
types: [opened, synchronize, reopened]
paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
paths: ['llama.cpp', 'ggml.c', 'ggml-backend.c', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
schedule:
- cron: '04 2 * * *'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}-${{ github.event.inputs.sha }}
cancel-in-progress: true
jobs:
@ -42,11 +42,21 @@ jobs:
RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
N_USERS: 8
DURATION: 10m
if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.event.push.ref == 'refs/heads/master' }}
strategy:
matrix:
model: [phi-2]
ftype: [q4_0, q8_0, f16]
include:
- model: phi-2
ftype: q4_0
pr_comment_enabled: "true"
if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.head_ref == 'master' || github.ref_name == 'master' || github.event.push.ref == 'refs/heads/master' }}
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
with:
fetch-depth: 0
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
@ -69,12 +79,18 @@ jobs:
sleep 0.1
done
- name: Install k6
- name: Set up Go
uses: actions/setup-go@v5
with:
go-version: '1.21'
- name: Install k6 and xk6-sse
id: k6_installation
run: |
cd examples/server/bench
wget --quiet https://github.com/grafana/k6/releases/download/v0.49.0/k6-v0.49.0-linux-amd64.tar.gz
tar xzf k6*.tar.gz --strip-components=1
go install go.k6.io/xk6/cmd/xk6@latest
xk6 build master \
--with github.com/phymbert/xk6-sse
- name: Build
id: cmake_build
@ -108,7 +124,7 @@ jobs:
cd examples/server/bench
source venv/bin/activate
BENCH_K6_BIN_PATH=./k6 python bench.py \
python bench.py \
--runner-label ${{ env.RUNNER_LABEL }} \
--name ${{ github.job }} \
--branch ${{ github.head_ref || github.ref_name }} \
@ -116,7 +132,7 @@ jobs:
--scenario script.js \
--duration ${{ github.event.inputs.duration || env.DURATION }} \
--hf-repo ggml-org/models \
--hf-file phi-2/ggml-model-q4_0.gguf \
--hf-file ${{ matrix.model }}/ggml-model-${{ matrix.ftype }}.gguf \
--model-path-prefix /models \
--parallel ${{ env.N_USERS }} \
-ngl 33 \
@ -134,7 +150,7 @@ jobs:
- uses: actions/upload-artifact@v4
with:
name: benchmark-results
name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
compression-level: 9
path: |
examples/server/bench/*.jpg
@ -146,7 +162,7 @@ jobs:
with:
authToken: ${{secrets.GITHUB_TOKEN}}
sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
context: bench-server-baseline
context: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
description: |
${{ env.BENCH_RESULTS }}
state: 'success'
@ -203,21 +219,26 @@ jobs:
- name: Comment PR
uses: mshick/add-pr-comment@v2
id: comment_pr
if: ${{ github.event.pull_request != '' }}
if: ${{ github.event.pull_request != '' && matrix.pr_comment_enabled == 'true' }}
with:
message-id: bench-${{ github.job }}-${{ env.RUNNER_LABEL }}
message-id: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
message: |
📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
<p align="center">
- Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
- HTTP request : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms p(90)=${{ env.HTTP_REQ_DURATION_P_90_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
- Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_TOKENS_AVG }}tk/s p(90)=${{ env.LLAMACPP_PROMPT_TOKENS_P_90_ }}tk/s **total=${{ env.LLAMACPP_PROMPT_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
- Token generation (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(90)=${{ env.LLAMACPP_TOKENS_SECOND_P_90_ }}tk/s **total=${{ env.LLAMACPP_COMPLETION_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
- ${{ env.BENCH_GRAPH_XLABEL }}
📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_ for `${{ matrix.model }}`-`${{ matrix.ftype }}`: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
</p>
<details>
<summary>Time series</summary>
<summary>Expand details for performance related PR only</summary>
- Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
- HTTP request : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms p(95)=${{ env.HTTP_REQ_DURATION_P_95_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
- Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_P_95_ }}tk/s
- Token generation (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_TOKENS_SECOND_P_95_ }}tk/s
- ${{ env.BENCH_GRAPH_XLABEL }}
<p align="center">

118
.github/workflows/build.yml vendored
View file

@ -16,7 +16,7 @@ on:
paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
env:
@ -31,7 +31,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
@ -52,7 +52,7 @@ jobs:
id: cmake_test
run: |
cd build
ctest -L main --verbose --timeout 900
ctest -L 'main|curl' --verbose --timeout 900
- name: Determine tag name
id: tag
@ -76,10 +76,10 @@ jobs:
- name: Upload artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
uses: actions/upload-artifact@v3
uses: actions/upload-artifact@v4
with:
path: |
llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip
path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip
name: llama-bin-macos-arm64.zip
macOS-latest-cmake-x64:
runs-on: macos-latest
@ -87,7 +87,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
@ -101,7 +101,9 @@ jobs:
sysctl -a
mkdir build
cd build
cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON ..
# 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 -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF -DLLAMA_CURL=ON ..
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -132,10 +134,10 @@ jobs:
- name: Upload artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
uses: actions/upload-artifact@v3
uses: actions/upload-artifact@v4
with:
path: |
llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
name: llama-bin-macos-x64.zip
ubuntu-focal-make:
runs-on: ubuntu-20.04
@ -146,7 +148,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
@ -158,7 +160,7 @@ jobs:
with:
node-version: "20"
- uses: actions/setup-python@v4
- uses: actions/setup-python@v5
with:
python-version: "3.11"
@ -181,7 +183,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
@ -203,27 +205,27 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential
sudo apt-get install build-essential libcurl4-openssl-dev
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake .. -DLLAMA_FATAL_WARNINGS=ON
cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON
cmake --build . --config Release -j $(nproc)
- name: Test
id: cmake_test
run: |
cd build
ctest -L main --verbose --timeout 900
ctest -L 'main|curl' --verbose --timeout 900
- name: Test llama2c conversion
id: llama2c_test
@ -249,7 +251,7 @@ jobs:
# steps:
# - name: Clone
# id: checkout
# uses: actions/checkout@v3
# uses: actions/checkout@v4
#
# - name: Dependencies
# id: depends
@ -283,7 +285,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
@ -311,7 +313,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
@ -357,7 +359,7 @@ jobs:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Build
id: cmake_build
@ -398,7 +400,7 @@ jobs:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Build
id: cmake_build
@ -418,7 +420,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
@ -449,7 +451,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
id: depends
@ -593,7 +595,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
with:
fetch-depth: 0
@ -723,10 +725,10 @@ jobs:
- name: Upload artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
uses: actions/upload-artifact@v3
uses: actions/upload-artifact@v4
with:
path: |
llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip
path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip
name: llama-bin-win-${{ matrix.build }}-x64.zip
windows-latest-cmake-cuda:
runs-on: windows-latest
@ -739,7 +741,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
with:
fetch-depth: 0
@ -779,10 +781,10 @@ jobs:
- name: Upload artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
uses: actions/upload-artifact@v3
uses: actions/upload-artifact@v4
with:
path: |
llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
name: llama-bin-win-cu${{ matrix.cuda }}-x64.zip
- name: Copy and pack Cuda runtime
run: |
@ -793,10 +795,10 @@ jobs:
- name: Upload Cuda runtime
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
uses: actions/upload-artifact@v3
uses: actions/upload-artifact@v4
with:
path: |
cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
path: cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
name: cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
windows-latest-cmake-sycl:
runs-on: windows-latest
@ -812,7 +814,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
with:
fetch-depth: 0
@ -844,17 +846,17 @@ jobs:
- name: Upload artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
uses: actions/upload-artifact@v3
uses: actions/upload-artifact@v4
with:
path: |
llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip
path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip
name: llama-bin-win-sycl-x64.zip
ios-xcode-build:
runs-on: macos-latest
steps:
- name: Checkout code
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Build Xcode project
run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
@ -864,7 +866,7 @@ jobs:
steps:
- name: Clone
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Set up JDK
uses: actions/setup-java@v3
@ -887,7 +889,7 @@ jobs:
# runs-on: macos-12
# steps:
# - name: Clone
# uses: actions/checkout@v3
# uses: actions/checkout@v4
#
# - name: Build
# uses: cross-platform-actions/action@v0.19.0
@ -918,7 +920,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
with:
fetch-depth: 0
@ -937,7 +939,13 @@ jobs:
- name: Download artifacts
id: download-artifact
uses: actions/download-artifact@v3
uses: actions/download-artifact@v4
with:
path: ./artifact
- name: Move artifacts
id: move_artifacts
run: mkdir -p ./artifact/release && mv ./artifact/*/*.zip ./artifact/release
- name: Create release
id: create_release
@ -956,7 +964,7 @@ jobs:
const path = require('path');
const fs = require('fs');
const release_id = '${{ steps.create_release.outputs.id }}';
for (let file of await fs.readdirSync('./artifact')) {
for (let file of await fs.readdirSync('./artifact/release')) {
if (path.extname(file) === '.zip') {
console.log('uploadReleaseAsset', file);
await github.repos.uploadReleaseAsset({
@ -964,7 +972,7 @@ jobs:
repo: context.repo.repo,
release_id: release_id,
name: file,
data: await fs.readFileSync(`./artifact/${file}`)
data: await fs.readFileSync(`./artifact/release/${file}`)
});
}
}
@ -978,7 +986,7 @@ jobs:
#
# steps:
# - name: Clone
# uses: actions/checkout@v3
# uses: actions/checkout@v4
#
# - name: Dependencies
# run: |
@ -1002,7 +1010,7 @@ jobs:
#
# steps:
# - name: Clone
# uses: actions/checkout@v3
# uses: actions/checkout@v4
#
# - name: Dependencies
# run: |
@ -1026,7 +1034,7 @@ jobs:
#
# steps:
# - name: Clone
# uses: actions/checkout@v3
# uses: actions/checkout@v4
#
# - name: Dependencies
# run: |
@ -1056,7 +1064,7 @@ jobs:
#
# steps:
# - name: Clone
# uses: actions/checkout@v3
# uses: actions/checkout@v4
#
# - name: Add msbuild to PATH
# uses: microsoft/setup-msbuild@v1
@ -1072,7 +1080,7 @@ jobs:
# msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }}
#
# - name: Upload binaries
# uses: actions/upload-artifact@v1
# uses: actions/upload-artifact@v4
# with:
# name: llama-bin-${{ matrix.arch }}
# path: build/bin/${{ matrix.build }}
@ -1095,7 +1103,7 @@ jobs:
#
# steps:
# - name: Clone
# uses: actions/checkout@v3
# uses: actions/checkout@v4
#
# - name: Add msbuild to PATH
# uses: microsoft/setup-msbuild@v1
@ -1127,7 +1135,7 @@ jobs:
#
# - name: Upload binaries
# if: matrix.blas == 'ON'
# uses: actions/upload-artifact@v1
# uses: actions/upload-artifact@v4
# with:
# name: llama-blas-bin-${{ matrix.arch }}
# path: build/bin/${{ matrix.build }}
@ -1141,7 +1149,7 @@ jobs:
#
# steps:
# - name: Clone
# uses: actions/checkout@v3
# uses: actions/checkout@v4
#
# - name: Dependencies
# run: |

4
.github/workflows/code-coverage.yml vendored
View file

@ -6,7 +6,7 @@ env:
GGML_N_THREADS: 1
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:
@ -14,7 +14,7 @@ jobs:
runs-on: ubuntu-20.04
steps:
- name: Checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Dependencies
run: |

14
.github/workflows/docker.yml vendored
View file

@ -16,7 +16,7 @@ on:
- master
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:
@ -46,7 +46,7 @@ jobs:
- { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" }
steps:
- name: Check out the repo
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Set up QEMU
uses: docker/setup-qemu-action@v2
@ -91,6 +91,12 @@ jobs:
echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
fi
- name: Downcase github.repository_owner
run: |
echo "repository_owner_lowercase=${GITHUB_REPOSITORY_OWNER@L}" >> $GITHUB_ENV
env:
GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
- name: Build and push Docker image (versioned)
if: github.event_name == 'push'
uses: docker/build-push-action@v4
@ -98,7 +104,7 @@ jobs:
context: .
push: true
platforms: ${{ matrix.config.platforms }}
tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}"
tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}"
file: ${{ matrix.config.dockerfile }}
- name: Build and push Docker image (tagged)
@ -107,5 +113,5 @@ jobs:
context: .
push: ${{ github.event_name == 'push' }}
platforms: ${{ matrix.config.platforms }}
tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
file: ${{ matrix.config.dockerfile }}

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

@ -15,13 +15,13 @@ on:
- master
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:
editorconfig:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v4
- uses: editorconfig-checker/action-editorconfig-checker@main
- run: editorconfig-checker

4
.github/workflows/gguf-publish.yml vendored
View file

@ -24,9 +24,9 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v2
uses: actions/setup-python@v5
with:
python-version: '3.9.x'
- name: Install dependencies

2
.github/workflows/nix-ci-aarch64.yml vendored
View file

@ -18,7 +18,7 @@ on:
paths: ['**/*.nix', 'flake.lock']
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:

2
.github/workflows/nix-ci.yml vendored
View file

@ -9,7 +9,7 @@ on:
types: [opened, synchronize, reopened]
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:

6
.github/workflows/python-check-requirements.yml vendored
View file

@ -17,7 +17,7 @@ on:
- 'requirements/*.txt'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:
@ -26,9 +26,9 @@ jobs:
name: check-requirements
steps:
- name: Check out source repository
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Set up Python environment
uses: actions/setup-python@v4
uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Run check-requirements.sh script

6
.github/workflows/python-lint.yml vendored
View file

@ -3,7 +3,7 @@ name: flake8 Lint
on: [push, pull_request]
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:
@ -12,9 +12,9 @@ jobs:
name: Lint
steps:
- name: Check out source repository
uses: actions/checkout@v3
uses: actions/checkout@v4
- name: Set up Python environment
uses: actions/setup-python@v4
uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: flake8 Lint

48
.github/workflows/server.yml vendored
View file

@ -4,6 +4,10 @@ name: Server
on:
workflow_dispatch: # allows manual triggering
inputs:
sha:
description: 'Commit SHA1 to build'
required: false
type: string
slow_tests:
description: 'Run slow tests'
required: true
@ -11,15 +15,15 @@ on:
push:
branches:
- master
paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
pull_request:
paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
pull_request_target:
types: [opened, synchronize, reopened]
paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
schedule:
- cron: '0 0 * * *'
- cron: '2 4 * * *'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:
@ -44,25 +48,45 @@ jobs:
options: --cpus 4
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Dependencies
id: depends
run: |
apt-get update
apt-get -y install \
build-essential \
xxd \
git \
cmake \
python3-pip \
curl \
wget \
language-pack-en \
libcurl4-openssl-dev
- name: Clone
id: checkout
uses: actions/checkout@v4
with:
fetch-depth: 0
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
- name: Verify server deps
id: verify_server_deps
run: |
git config --global --add safe.directory $(realpath .)
cd examples/server
git ls-files --others --modified
git status
./deps.sh
git status
not_ignored_files="$(git ls-files --others --modified)"
echo "Modified files: ${not_ignored_files}"
if [ -n "${not_ignored_files}" ]; then
echo "Repository is dirty or server deps are not built as expected"
echo "${not_ignored_files}"
exit 1
fi
- name: Build
id: cmake_build
run: |
@ -102,7 +126,7 @@ jobs:
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
uses: actions/checkout@v4
with:
fetch-depth: 0

4
.github/workflows/zig-build.yml vendored
View file

@ -7,7 +7,7 @@ on:
- master
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
cancel-in-progress: true
jobs:
@ -18,7 +18,7 @@ jobs:
runs-on: [ubuntu-latest, macos-latest, windows-latest]
runs-on: ${{ matrix.runs-on }}
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v4
with:
submodules: recursive
fetch-depth: 0

1
.gitignore vendored
View file

@ -48,6 +48,7 @@ models-mnt
/convert-llama2c-to-ggml
/embd-input-test
/embedding
/eval-callback
/gguf
/gguf-llama-simple
/gguf-split

655
AUTHORS Normal file
View file

@ -0,0 +1,655 @@
# date: Tue Apr 9 09:17:14 EEST 2024
# this file is auto-generated by scripts/gen-authors.sh
0cc4m <picard12@live.de>
0xspringtime <110655352+0xspringtime@users.noreply.github.com>
2f38b454 <dxf@protonmail.com>
3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com>
44670 <44670@users.noreply.github.com>
AN Long <aisk@users.noreply.github.com>
AT <manyoso@users.noreply.github.com>
Aarni Koskela <akx@iki.fi>
Aaron Miller <apage43@ninjawhale.com>
Aaryaman Vasishta <aaryaman.vasishta@amd.com>
Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
Abhishek Gopinath K <31348521+overtunned@users.noreply.github.com>
Adithya Balaji <adithya.b94@gmail.com>
AdithyanI <adithyan.i4internet@gmail.com>
Adrian <smith.adriane@gmail.com>
Adrian Hesketh <a-h@users.noreply.github.com>
AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
Aisuko <urakiny@gmail.com>
Alberto <57916483+albbus-stack@users.noreply.github.com>
Alex <awhill19@icloud.com>
Alex Azarov <alex@azarov.by>
Alex Azarov <alexander.azarov@mapbox.com>
Alex Klinkhamer <from.github.com.917@grencez.dev>
Alex Klinkhamer <git@grencez.dev>
Alex Nguyen <tiendung@users.noreply.github.com>
Alex Petenchea <alex.petenchea@gmail.com>
Alex Renda <alexrenda@users.noreply.github.com>
Alex von Gluck IV <kallisti5@unixzen.com>
Alexey Parfenov <zxed@alkatrazstudio.net>
Ali Chraghi <63465728+alichraghi@users.noreply.github.com>
Ali Nehzat <ali.nehzat@thanks.dev>
Ali Tariq <ali.tariq@10xengineers.ai>
Alon <alonfaraj@gmail.com>
AlpinDale <52078762+AlpinDale@users.noreply.github.com>
AmirAli Mirian <37371367+amiralimi@users.noreply.github.com>
Ananta Bastola <anantarajbastola@gmail.com>
Anas Ahouzi <112881240+aahouzi@users.noreply.github.com>
András Salamon <ott2@users.noreply.github.com>
Andrei <abetlen@gmail.com>
Andrew Canis <andrew.canis@gmail.com>
Andrew Duffy <a10y@users.noreply.github.com>
Andrew Godfrey <AndrewGodfrey@users.noreply.github.com>
Arik Poznanski <arikpoz@users.noreply.github.com>
Artem <guinmoon@gmail.com>
Artyom Lebedev <vagran.ast@gmail.com>
Asbjørn Olling <asbjornolling@gmail.com>
Ásgeir Bjarni Ingvarsson <asgeir@fundinn.org>
Ashok Gelal <401055+ashokgelal@users.noreply.github.com>
Ashraful Islam <ashraful.meche@gmail.com>
Atsushi Tatsuma <yoshoku@outlook.com>
Austin <77757836+teleprint-me@users.noreply.github.com>
AustinMroz <austinmroz@utexas.edu>
BADR <contact@pythops.com>
Bach Le <bach@bullno1.com>
Bailey Chittle <39804642+bachittle@users.noreply.github.com>
BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com>
Behnam M <58621210+ibehnam@users.noreply.github.com>
Ben Garney <bengarney@users.noreply.github.com>
Ben Siraphob <bensiraphob@gmail.com>
Ben Williams <ben@719ben.com>
Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com>
Bernat Vadell <hounter.caza@gmail.com>
Bodo Graumann <mail@bodograumann.de>
Bono Lv <lvscar@users.noreply.github.com>
Borislav Stanimirov <b.stanimirov@abv.bg>
Branden Butler <bwtbutler@hotmail.com>
Brian <mofosyne@gmail.com>
Bruce MacDonald <brucewmacdonald@gmail.com>
CJ Pais <cj@cjpais.com>
CRD716 <crd716@gmail.com>
Cameron <csteele@steelecameron.com>
Cameron Kaiser <classilla@users.noreply.github.com>
Casey Primozic <casey@cprimozic.net>
Casey Primozic <me@ameo.link>
CausalLM <148736309+CausalLM@users.noreply.github.com>
Cebtenzzre <cebtenzzre@gmail.com>
Chad Brewbaker <crb002@gmail.com>
Cheng Shao <terrorjack@type.dance>
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 Kögler <ck3d@gmx.de>
Clark Saben <76020733+csaben@users.noreply.github.com>
Clint Herron <hanclinto@gmail.com>
Cuong Trinh Manh <nguoithichkhampha@gmail.com>
DAN™ <dranger003@gmail.com>
Damian Stewart <d@damianstewart.com>
Dane Madsen <dane_madsen@hotmail.com>
DaniAndTheWeb <57776841+DaniAndTheWeb@users.noreply.github.com>
Daniel Bevenius <daniel.bevenius@gmail.com>
Daniel Drake <drake@endlessos.org>
Daniel Hiltgen <dhiltgen@users.noreply.github.com>
Daniel Illescas Romero <illescas.daniel@protonmail.com>
DannyDaemonic <DannyDaemonic@gmail.com>
Dat Quoc Nguyen <2412555+datquocnguyen@users.noreply.github.com>
Dave Della Costa <ddellacosta+github@gmail.com>
David Friehs <david@friehs.info>
David Kennedy <dakennedyd@gmail.com>
David Pflug <david@pflug.email>
David Renshaw <dwrenshaw@gmail.com>
David Sommers <12738+databyte@users.noreply.github.com>
David Yang <davidyang6us@gmail.com>
Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com>
Dean <Dean.Sinaean@gmail.com>
Deins <deinsegle@gmail.com>
Didzis Gosko <didzis@users.noreply.github.com>
Don Mahurin <dmahurin@users.noreply.github.com>
DooWoong Lee (David) <manics99@naver.com>
Doomsdayrs <38189170+Doomsdayrs@users.noreply.github.com>
Douglas Hanley <thesecretaryofwar@gmail.com>
Dr. Tom Murphy VII Ph.D <499244+tom7@users.noreply.github.com>
Ebey Abraham <ebey97@gmail.com>
Ed Lee <edilee@mozilla.com>
Ed Lepedus <ed.lepedus@googlemail.com>
Edward Taylor <edeetee@gmail.com>
Elbios <141279586+Elbios@users.noreply.github.com>
Engininja2 <139037756+Engininja2@users.noreply.github.com>
Equim <sayaka@ekyu.moe>
Eric Sommerlade <es0m@users.noreply.github.com>
Eric Zhang <34133756+EZForever@users.noreply.github.com>
Erik Garrison <erik.garrison@gmail.com>
Erik Scholz <Green-Sky@users.noreply.github.com>
Ettore Di Giacinto <mudler@users.noreply.github.com>
Evan Jones <evan.q.jones@gmail.com>
Evan Miller <emmiller@gmail.com>
Eve <139727413+netrunnereve@users.noreply.github.com>
Evgeny Kurnevsky <kurnevsky@gmail.com>
Ewout ter Hoeven <E.M.terHoeven@student.tudelft.nl>
ExtReMLapin <3909752+ExtReMLapin@users.noreply.github.com>
FK <sozforex@gmail.com>
Fabian <cmdrf@users.noreply.github.com>
Fabio R. Sluzala <Fabio3rs@users.noreply.github.com>
Faez Shakil <faez.shakil@gmail.com>
FantasyGmm <16450052+FantasyGmm@users.noreply.github.com>
Fattire <528174+fat-tire@users.noreply.github.com>
Felix <stenbackfelix@gmail.com>
Finn Voorhees <finnvoorhees@gmail.com>
Firat <firatkiral@gmail.com>
Folko-Ven <71110216+Folko-Ven@users.noreply.github.com>
Foul-Tarnished <107711110+Foul-Tarnished@users.noreply.github.com>
Francisco Melo <43780565+francis2tm@users.noreply.github.com>
FrankHB <frankhb1989@gmail.com>
Frederik Vogel <Schaltfehler@users.noreply.github.com>
Gabe Goodhart <gabe.l.hart@gmail.com>
GainLee <perfecter.gen@gmail.com>
Galunid <karolek1231456@gmail.com>
Gary Linscott <glinscott@gmail.com>
Gary Mulder <gjmulder@gmail.com>
Genkagaku.GPT <hlhr202@163.com>
Georgi Gerganov <ggerganov@gmail.com>
Gilad S <giladgd@users.noreply.github.com>
GiviMAD <GiviMAD@users.noreply.github.com>
Govlzkoy <gotope@users.noreply.github.com>
Guillaume "Vermeille" Sanchez <Guillaume.V.Sanchez@gmail.com>
Guillaume Wenzek <gwenzek@users.noreply.github.com>
Guoteng <32697156+SolenoidWGT@users.noreply.github.com>
Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com>
Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
Haohui Mai <ricetons@gmail.com>
Haoxiang Fei <tonyfettes@tonyfettes.com>
Harald Fernengel <harald.fernengel@here.com>
Hatsune Miku <129688334+at8u@users.noreply.github.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>
Hoang Nguyen <hugo53@users.noreply.github.com>
Hongyu Ouyang <96765450+casavaca@users.noreply.github.com>
Howard Su <howard0su@gmail.com>
Hua Jiang <allenhjiang@outlook.com>
Huawei Lin <huaweilin.cs@gmail.com>
Ian Bull <irbull@eclipsesource.com>
Ian Bull <irbull@gmail.com>
Ian Scrivener <github@zilogy.asia>
Ido S <ido.pluto@gmail.com>
IgnacioFDM <ignaciofdm@gmail.com>
Igor Okulist <okigan@gmail.com>
Ikko Eltociear Ashimine <eltociear@gmail.com>
Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com>
Ionoclast Laboratories <brigham@ionoclast.com>
Isaac McFadyen <isaac@imcf.me>
IsaacDynamo <61521674+IsaacDynamo@users.noreply.github.com>
Ivan Komarov <Ivan.Komarov@dfyz.info>
Ivan Stepanov <ivanstepanovftw@gmail.com>
JH23X <165871467+JH23X@users.noreply.github.com>
Jack Mousseau <jmousseau@users.noreply.github.com>
JackJollimore <130917767+JackJollimore@users.noreply.github.com>
Jag Chadha <jagtesh@gmail.com>
Jakub N <jakubniemczyk97@gmail.com>
James Reynolds <magnusviri@users.noreply.github.com>
Jan Boon <jan.boon@kaetemi.be>
Jan Boon <kaetemi@gmail.com>
Jan Ploski <jpl@plosquare.com>
Jannis Schönleber <joennlae@gmail.com>
Jared Van Bortel <cebtenzzre@gmail.com>
Jared Van Bortel <jared@nomic.ai>
Jason McCartney <jmac@theroot.org>
Jean-Christophe Hoelt <hoelt@fovea.cc>
Jean-Michaël Celerier <jeanmichael.celerier+github@gmail.com>
Jed Fox <git@jedfox.com>
Jeffrey Quesnelle <emozilla@nousresearch.com>
Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
Jhen-Jie Hong <iainst0409@gmail.com>
Jiahao Li <liplus17@163.com>
Jian Liao <jianliao@users.noreply.github.com>
JidongZhang-THU <1119708529@qq.com>
Jinwoo Jeong <33892306+williamjeong2@users.noreply.github.com>
Jiří Podivín <66251151+jpodivin@users.noreply.github.com>
Johannes Gäßler <johannesg@5d6.de>
Johannes Rudolph <johannes.rudolph@gmail.com>
John <78893154+cmp-nct@users.noreply.github.com>
John Balis <phobossystems@gmail.com>
John Smith <67539080+kingsidelee@users.noreply.github.com>
JohnnyB <jboero@users.noreply.github.com>
Jonas Wunderlich <32615971+jonas-w@users.noreply.github.com>
Jorge A <161275481+jorgealias@users.noreply.github.com>
Jose Maldonado <63384398+yukiteruamano@users.noreply.github.com>
Joseph Stahl <1269177+josephst@users.noreply.github.com>
Joyce <joycebrum@google.com>
Juan Calderon-Perez <835733+gaby@users.noreply.github.com>
Judd <foldl@users.noreply.github.com>
Julius Arkenberg <arki05@users.noreply.github.com>
Jun Jie <71215065+junnjiee16@users.noreply.github.com>
Juraj Bednar <juraj@bednar.io>
Justin Parker <jparkerweb@gmail.com>
Justin Suess <justin.suess@westpoint.edu>
Justine Tunney <jtunney@gmail.com>
Juuso Alasuutari <juuso.alasuutari@gmail.com>
KASR <karim.asrih@gmail.com>
Kamil Tomšík <info@tomsik.cz>
Karsten Weiss <knweiss@gmail.com>
Karthick <j.karthic2004@gmail.com>
Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com>
Karthik Sethuraman <k.seth1993@gmail.com>
Kasumi <90275229+kasumi-1@users.noreply.github.com>
Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Keiichi Tabata <keiichi.tabata@outlook.com>
Kenvix ⭐ <kenvixzure@live.com>
Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Kevin Ji <1146876+kevinji@users.noreply.github.com>
Kevin Kwok <antimatter15@gmail.com>
Kevin Lo <kevlo@kevlo.org>
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 Liang <liangmanlai@gmail.com>
Kyle Mistele <kyle@mistele.com>
Kylin <56434533+KyL0N@users.noreply.github.com>
Lars Grammel <lars.grammel@gmail.com>
Laura <Tijntje_7@msn.com>
Lee <44310445+lx200916@users.noreply.github.com>
Lee Drake <b.lee.drake@gmail.com>
Leng Yue <lengyue@lengyue.me>
LeonEricsson <70749762+LeonEricsson@users.noreply.github.com>
Leonardo Neumann <leonardo@neumann.dev.br>
Li Tan <tanliboy@gmail.com>
Linwei Wang <wanix1988@gmail.com>
LoganDark <github@logandark.mozmail.com>
LostRuins <39025047+LostRuins@users.noreply.github.com>
Luciano <lucianostrika44@gmail.com>
Luo Tian <lt@basecity.com>
M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
Maarten ter Huurne <maarten@treewalker.org>
Mack Straight <eiz@users.noreply.github.com>
Maël Kerbiriou <m431.kerbiriou@gmail.com>
MaggotHATE <clay1326@gmail.com>
Marc Köhlbrugge <subscriptions@marckohlbrugge.com>
Marco Matthies <71844+marcom@users.noreply.github.com>
Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com>
Marian Cepok <marian.cepok@gmail.com>
Mark Fairbairn <thebaron88@gmail.com>
Marko Tasic <mtasic85@gmail.com>
Martin Krasser <krasserm@googlemail.com>
Martin Schwaighofer <mschwaig@users.noreply.github.com>
Marvin Gießing <marvin.giessing@gmail.com>
Mateusz Charytoniuk <mateusz.charytoniuk@protonmail.com>
Matheus C. França <matheus-catarino@hotmail.com>
Matheus Gabriel Alves Silva <matheusgasource@gmail.com>
Mathieu Nayrolles <MathieuNls@users.noreply.github.com>
Mathijs de Bruin <mathijs@mathijsfietst.nl>
Matt Clayton <156335168+mattjcly@users.noreply.github.com>
Matt Pulver <matt.pulver@heavy.ai>
Matteo Boschini <12133566+mbosc@users.noreply.github.com>
Matthew Tejo <matthew.tejo@gmail.com>
Matvey Soloviev <blackhole89@gmail.com>
Maxime <672982+maximegmd@users.noreply.github.com>
Maximilian Winter <maximilian.winter.91@gmail.com>
Meng Zhang <meng@tabbyml.com>
Meng, Hengyu <hengyu.meng@intel.com>
Merrick Christensen <merrick.christensen@gmail.com>
Michael Coppola <m18coppola@gmail.com>
Michael Hueschen <m@mhueschen.dev>
Michael Kesper <mkesper@schokokeks.org>
Michael Klimenko <mklimenko29@gmail.com>
Michael Podvitskiy <podvitskiymichael@gmail.com>
Michael Potter <NanoTekGuy@Gmail.com>
Michaël de Vries <vriesdemichael@gmail.com>
Mihai <mihai.chirculescu@yahoo.com>
Mike <ytianhui2004@gmail.com>
Minsoo Cheong <54794500+mscheong01@users.noreply.github.com>
Mirko185 <mirkosig@gmail.com>
Mirror Azure <54669636+MirrorAzure@users.noreply.github.com>
Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com>
Mohammadreza Hendiani <hendiani.mohammadreza@gmail.com>
Murilo Santana <mvrilo@gmail.com>
Musab Gultekin <musabgultekin@users.noreply.github.com>
Nam D. Tran <42194884+namtranase@users.noreply.github.com>
NawafAlansari <72708095+NawafAlansari@users.noreply.github.com>
Nebula <infinitewormhole@gmail.com>
Neo Zhang Jianyu <jianyu.zhang@intel.com>
Neuman Vong <neuman.vong@gmail.com>
Nexesenex <124105151+Nexesenex@users.noreply.github.com>
Niall Coates <1349685+Niall-@users.noreply.github.com>
Nicolai Weitkemper <kontakt@nicolaiweitkemper.de>
Nigel Bosch <pnigelb@gmail.com>
Niklas Korz <niklas@niklaskorz.de>
Nindaleth <Nindaleth@users.noreply.github.com>
Oleksandr Nikitin <oleksandr@tvori.info>
Oleksii Maryshchenko <oleksii.maryshchenko@gmail.com>
Olivier Chafik <ochafik@users.noreply.github.com>
Ondřej Čertík <ondrej@certik.us>
Ouadie EL FAROUKI <ouadie.elfarouki@codeplay.com>
Paul Tsochantaris <ptsochantaris@icloud.com>
Pavol Rusnak <pavol@rusnak.io>
Pedro Cuenca <pedro@huggingface.co>
Peter Sugihara <peter@campsh.com>
Phil H <5756783+phiharri@users.noreply.github.com>
Philip Taron <philip.taron@gmail.com>
Phillip Kravtsov <phillip@kravtsov.net>
Pierre Alexandre SCHEMBRI <pa.schembri@gmail.com>
Pierrick Hymbert <pierrick.hymbert@gmail.com>
Przemysław Pawełczyk <przemoc@gmail.com>
Qin Yue Chen <71813199+chenqiny@users.noreply.github.com>
Qingyou Meng <meng.qingyou@gmail.com>
Qu Zongfu <43257352+yancaoweidaode@users.noreply.github.com>
RJ Adriaansen <adriaansen@eshcc.eur.nl>
Radoslav Gerganov <rgerganov@gmail.com>
Radosław Gryta <radek.gryta@gmail.com>
Rahul Vivek Nair <68507071+RahulVivekNair@users.noreply.github.com>
Rand Xie <randxiexyy29@gmail.com>
Randall Fitzgerald <randall@dasaku.net>
Reinforce-II <fate@eastal.com>
Riceball LEE <snowyu.lee@gmail.com>
Richard Kiss <him@richardkiss.com>
Richard Roberson <richardr1126@gmail.com>
Rick G <26732651+TheFlipbook@users.noreply.github.com>
Rickard Edén <rickardeden@gmail.com>
Rickard Hallerbäck <rickard.hallerback@gmail.com>
Rickey Bowers Jr <bitRAKE@gmail.com>
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 Sung-wook Shin <edp1096@users.noreply.github.com>
Robey Holderith <robey@flaminglunchbox.net>
Robyn <robyngraf@users.noreply.github.com>
Roger Meier <r.meier@siemens.com>
Roland <14355895+rbur0425@users.noreply.github.com>
Romain D <90720+Artefact2@users.noreply.github.com>
Romain Neutron <romain@neutron.io>
Roman Parykin <donderom@gmail.com>
Ron Evans <ron@hybridgroup.com>
Ron Jailall <rojailal@gmail.com>
Ronny Brendel <ronnybrendel@gmail.com>
Ronsor <ronsor@ronsor.pw>
Rowan Hart <rowanbhart@gmail.com>
Rune <43761327+Rune-AI@users.noreply.github.com>
Ryan Landay <rlanday@gmail.com>
Ryder Wishart <ryderwishart@gmail.com>
Rőczey Barnabás <31726601+An0nie@users.noreply.github.com>
SakuraUmi <yukinon244@gmail.com>
Salvador E. Tropea <stropea@inti.gob.ar>
Sam Spilsbury <smspillaz@gmail.com>
Sami Farin <3876865+Safari77@users.noreply.github.com>
Samuel Maynard <samwmaynard@gmail.com>
Sang-Kil Park <sang.park@42dot.ai>
Seb C <47074056+Sebby37@users.noreply.github.com>
Sebastián A <sebastian.aedo29@gmail.com>
SebastianApel <13675545+SebastianApel@users.noreply.github.com>
Senemu <10880819+Senemu@users.noreply.github.com>
Sergey Alirzaev <zl29ah@gmail.com>
Sergio López <slp@sinrega.org>
SeungWon Jeong <65549245+redlion0929@users.noreply.github.com>
ShadovvBeast <ShadovvBeast@gmail.com>
Shakhar Dasgupta <shakhardasgupta@gmail.com>
Shangning Xu <32517059+xushangning@users.noreply.github.com>
Shijie <821898965@qq.com>
Shintarou Okada <kokuzen@gmail.com>
Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com>
Shouzheng Liu <lshzh.hi@gmail.com>
Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Simon Willison <swillison@gmail.com>
Siwen Yu <yusiwen@gmail.com>
Sky Yan <skyan83@gmail.com>
Slaren <2141330+slaren@users.noreply.github.com>
Slava Primenko <primenko.s@gmail.com>
SoftwareRenderer <138734813+SoftwareRenderer@users.noreply.github.com>
Someone <sergei.kozlukov@aalto.fi>
Someone Serge <sergei.kozlukov@aalto.fi>
Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>
Spencer Sutton <spencersutton@users.noreply.github.com>
Srinivas Billa <nivibilla@gmail.com>
Stefan Sydow <stefan@sydow.email>
Stephan Walter <stephan@walter.name>
Stephen Nichols <snichols@users.noreply.github.com>
Steve Grubb <ausearch.1@gmail.com>
Steven Roussey <sroussey@gmail.com>
Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com>
SuperUserNameMan <yoann@terminajones.com>
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>
Thái Hoàng Tâm <75922889+RoyalHeart@users.noreply.github.com>
Thatcher Chamberlin <j.thatcher.c@gmail.com>
Theia Vogel <theia@vgel.me>
Thérence <13496987+Royalphax@users.noreply.github.com>
Thibault Terrasson <thibault.terrasson@gmail.com>
Thomas Klausner <wiz@gatalith.at>
Tim Miller <drasticactions@users.noreply.github.com>
Timmy Knight <r2d2fish@gmail.com>
Timothy Cronin <40186632+4imothy@users.noreply.github.com>
Ting Lou <ting.lou@gmail.com>
Ting Sun <suntcrick@gmail.com>
Tobias Lütke <tobi@shopify.com>
Tom C <tom.corelis@gmail.com>
Tom Jobbins <784313+TheBloke@users.noreply.github.com>
Tomas <tom.tomas.36478119@gmail.com>
Tomáš Pazdiora <tomas.pazdiora@gmail.com>
Tristan Ross <rosscomputerguy@protonmail.com>
Tungsten842 <886724vf@anonaddy.me>
Tungsten842 <quantmint@protonmail.com>
Tushar <ditsuke@protonmail.com>
UEXTM.com <84163508+uextm@users.noreply.github.com>
Uzo Nweke <uzoechi@gmail.com>
Vaibhav Srivastav <vaibhavs10@gmail.com>
Val Kharitonov <mail@kharvd.com>
Valentin Konovalov <valle.ketsujin@gmail.com>
Valentyn Bezshapkin <61702053+valentynbez@users.noreply.github.com>
Victor Z. Peng <ziliangdotme@gmail.com>
Vlad <spitfireage@gmail.com>
Vladimir <bogdad@gmail.com>
Vladimir Malyutin <first-leon@yandex.ru>
Vladimir Zorin <vladimir@deviant.guru>
Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com>
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>
Willy Tarreau <w@1wt.eu>
Wu Jian Ping <wujjpp@hotmail.com>
Wu Jian Ping <wujp@greatld.com>
Xiake Sun <xiake.sun@intel.com>
Xiang (Kevin) Li <kevinli020508@gmail.com>
Xiao-Yong Jin <jinxiaoyong@gmail.com>
XiaotaoChen <chenxiaotao1234@gmail.com>
Xiaoyi Chen <cxychina@gmail.com>
Xingchen Song(宋星辰) <xingchensong1996@163.com>
Xuan Son Nguyen <thichthat@gmail.com>
Yann Follet <131855179+YannFollet@users.noreply.github.com>
Yiming Cui <conandiy@vip.qq.com>
Yishuo Wang <MeouSker77@outlook.com>
Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com>
Yui <dev@sleepyyui.com>
Yusuf Kağan Hanoğlu <hanoglu@yahoo.com>
Yuval Peled <31162840+Yuval-Peled@users.noreply.github.com>
ZHAOKAI WANG <sanxianwei@163.com>
Zane Shannon <z@zcs.me>
Zay <95888118+isaiahbjork@users.noreply.github.com>
Zenix <zenixls2@gmail.com>
Zhang Peiyuan <a1286225768@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>
adel boussaken <netdur@gmail.com>
afrideva <95653597+afrideva@users.noreply.github.com>
akawrykow <142945436+akawrykow@users.noreply.github.com>
alexpinel <93524949+alexpinel@users.noreply.github.com>
alonfaraj <alonfaraj@gmail.com>
andrijdavid <david@geek.mg>
anon998 <131767832+anon998@users.noreply.github.com>
anzz1 <anzz1@live.com>
apaz <aarpazdera@gmail.com>
apcameron <37645737+apcameron@users.noreply.github.com>
arcrank <arcrank@gmail.com>
arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com>
at8u <129688334+at8u@users.noreply.github.com>
automaticcat <daogiatuank54@gmail.com>
bandoti <141645996+bandoti@users.noreply.github.com>
beiller <beiller@gmail.com>
bhubbb <79117352+bhubbb@users.noreply.github.com>
bmwl <brian.marshall@tolko.com>
bobqianic <129547291+bobqianic@users.noreply.github.com>
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>
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>
coezbek <c.oezbek@gmail.com>
comex <comexk@gmail.com>
compilade <113953597+compilade@users.noreply.github.com>
crasm <crasm@git.vczf.net>
crasm <crasm@git.vczf.us>
daboe01 <daboe01@googlemail.com>
david raistrick <keen99@users.noreply.github.com>
ddpasa <112642920+ddpasa@users.noreply.github.com>
deepdiffuser <112834445+deepdiffuser@users.noreply.github.com>
divinity76 <divinity76@gmail.com>
dotpy314 <33351922+dotpy314@users.noreply.github.com>
drbh <david.richard.holtz@gmail.com>
ds5t5 <145942675+ds5t5@users.noreply.github.com>
dylan <canardleteer@users.noreply.github.com>
eastriver <lee@eastriver.dev>
ebraminio <ebraminio@gmail.com>
eiery <19350831+eiery@users.noreply.github.com>
eric8607242 <e0928021388@gmail.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>
goerch <jhr.walter@t-online.de>
grahameth <96447521+grahameth@users.noreply.github.com>
gwjr <502526+gwjr@users.noreply.github.com>
h-h-h-h <13482553+h-h-h-h@users.noreply.github.com>
hankcs <cnhankmc@gmail.com>
hoangmit <hoangmit@users.noreply.github.com>
hongbo.mo <352280764@qq.com>
howlger <eclipse@voormann.de>
howlger <github@voormann.de>
hutli <6594598+hutli@users.noreply.github.com>
hutli <hutli@hutli.hu>
hutli <jensstaermose@hotmail.com>
hxer7963 <hxer7963@gmail.com>
hydai <z54981220@gmail.com>
iSma <ismail.senhaji@gmail.com>
iacore <74560659+iacore@users.noreply.github.com>
igarnier <igarnier@protonmail.com>
iohub <rickyang.pro@gmail.com>
jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com>
jameswu2014 <545426914@qq.com>
jneem <joeneeman@gmail.com>
johnson442 <56517414+johnson442@users.noreply.github.com>
jon-chuang <9093549+jon-chuang@users.noreply.github.com>
jp-x-g <jpxg-dev@protonmail.com>
jwj7140 <32943891+jwj7140@users.noreply.github.com>
kaizau <kaizau@users.noreply.github.com>
kalomaze <66376113+kalomaze@users.noreply.github.com>
kang <tpdns9032100@gmail.com>
katsu560 <118887472+katsu560@users.noreply.github.com>
kchro3 <62481661+kchro3@users.noreply.github.com>
khimaros <me@khimaros.com>
kiltyj <kiltyj@gmail.com>
klosax <131523366+klosax@users.noreply.github.com>
kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
kunnis <kunnis@users.noreply.github.com>
kuronekosaiko <EvanChanJ@163.com>
kuvaus <22169537+kuvaus@users.noreply.github.com>
kwin1412 <42286931+kwin1412@users.noreply.github.com>
l3utterfly <gc.pthzfoldr@gmail.com>
ldwang <ftgreat@163.com>
le.chang <cljs118@126.com>
leejet <leejet714@gmail.com>
limitedAtonement <limitedAtonement@users.noreply.github.com>
lon <114724657+longregen@users.noreply.github.com>
m3ndax <adrian.goessl@outlook.com>
maddes8cht <55592906+maddes8cht@users.noreply.github.com>
makomk <makosoft@googlemail.com>
manikbhandari <mbbhandarimanik2@gmail.com>
mdrokz <mohammadmunshi@gmail.com>
mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com>
minarchist <minarchist@users.noreply.github.com>
mj-shifu <77107165+mj-shifu@users.noreply.github.com>
mmyjona <jonathan.gonse@gmail.com>
momonga <115213907+mmnga@users.noreply.github.com>
moritzbrantner <31051084+moritzbrantner@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>
nhamanasu <45545786+nhamanasu@users.noreply.github.com>
niansa/tuxifan <anton-sa@web.de>
niansa/tuxifan <tuxifan@posteo.de>
ningshanwutuobang <ningshanwutuobang@gmail.com>
nold <Nold360@users.noreply.github.com>
nopperl <54780682+nopperl@users.noreply.github.com>
nusu-github <29514220+nusu-github@users.noreply.github.com>
olexiyb <olexiyb@gmail.com>
oobabooga <112222186+oobabooga@users.noreply.github.com>
opparco <parco.opaai@gmail.com>
ostix360 <55257054+ostix360@users.noreply.github.com>
perserk <perserk@gmail.com>
postmasters <namnguyen@google.com>
pudepiedj <pudepiedj@gmail.com>
qingfengfenga <41416092+qingfengfenga@users.noreply.github.com>
qouoq <qouoq@fastmail.com>
qunash <anzoria@gmail.com>
rabidcopy <rabidcopy@yahoo.com>
rankaiyx <rankaiyx@rankaiyx.com>
rhjdvsgsgks <26178113+rhjdvsgsgks@users.noreply.github.com>
rhuddleston <ryan.huddleston@percona.com>
rimoliga <53384203+rimoliga@users.noreply.github.com>
runfuture <runfuture@users.noreply.github.com>
sandyiscool <sandyiscool@gmail.com>
semidark <me@semidark.net>
sharpHL <132747147+sharpHL@users.noreply.github.com>
shibe2 <shibe@tuta.io>
singularity <12184989+singularity-s0@users.noreply.github.com>
sjinzh <sjinzh@gmail.com>
slaren <2141330+slaren@users.noreply.github.com>
slaren <slarengh@gmail.com>
snadampal <87143774+snadampal@users.noreply.github.com>
staviq <staviq@gmail.com>
stduhpf <stephduh@live.fr>
swittk <switt1995@gmail.com>
takov751 <40316768+takov751@users.noreply.github.com>
tarcey <cey.tarik@gmail.com>
texmex76 <40733439+texmex76@users.noreply.github.com>
thement <40525767+thement@users.noreply.github.com>
tjohnman <tjohnman@users.noreply.github.com>
tslmy <tslmy@users.noreply.github.com>
ubik2 <ubik2@users.noreply.github.com>
uint256_t <konndennsa@gmail.com>
uint256_t <maekawatoshiki1017@gmail.com>
unbounded <haakon@likedan.net>
valiray <133289098+valiray@users.noreply.github.com>
vodkaslime <646329483@qq.com>
vvhg1 <94630311+vvhg1@users.noreply.github.com>
vxiiduu <73044267+vxiiduu@users.noreply.github.com>
wbpxre150 <100937007+wbpxre150@users.noreply.github.com>
whoreson <139810751+whoreson@users.noreply.github.com>
wonjun Jang <strutive07@gmail.com>
wzy <32936898+Freed-Wu@users.noreply.github.com>
xaedes <xaedes@gmail.com>
xaedes <xaedes@googlemail.com>
xloem <0xloem@gmail.com>
yangli2 <yangli2@gmail.com>
yuiseki <yuiseki@gmail.com>
zakkor <edward.partenie@gmail.com>
zhouwg <6889919+zhouwg@users.noreply.github.com>
zrm <trustiosity.zrm@gmail.com>
源文雨 <41315874+fumiama@users.noreply.github.com>
Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>

13
CMakeLists.txt
View file

@ -113,6 +113,9 @@ option(LLAMA_METAL "llama: use Metal"
option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF)
option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF)
option(LLAMA_METAL_EMBED_LIBRARY "llama: embed Metal library" OFF)
set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING
"llama: metal minimum macOS version")
set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)")
option(LLAMA_KOMPUTE "llama: use Kompute" OFF)
option(LLAMA_MPI "llama: use MPI" OFF)
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
@ -250,6 +253,16 @@ if (LLAMA_METAL)
set(XC_FLAGS -O3)
endif()
# Append macOS metal versioning flags
if (LLAMA_METAL_MACOSX_VERSION_MIN)
message(STATUS "Adding -mmacosx-version-min=${LLAMA_METAL_MACOSX_VERSION_MIN} flag to metal compilation")
list(APPEND XC_FLAGS -mmacosx-version-min=${LLAMA_METAL_MACOSX_VERSION_MIN})
endif()
if (LLAMA_METAL_STD)
message(STATUS "Adding -std=${LLAMA_METAL_STD} flag to metal compilation")
list(APPEND XC_FLAGS -std=${LLAMA_METAL_STD})
endif()
add_custom_command(
OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air

2
LICENSE
View file

@ -1,6 +1,6 @@
MIT License
Copyright (c) 2023 Georgi Gerganov
Copyright (c) 2023-2024 The ggml authors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal

24
Makefile
View file

@ -1,7 +1,7 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = \
main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf gguf-split llama-bench libllava.a llava-cli baby-llama beam-search \
simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama beam-search \
retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
# Binaries only useful for tests
@ -10,7 +10,7 @@ TEST_TARGETS = \
tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope \
tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease \
tests/test-json-schema-to-grammar
tests/test-json-schema-to-grammar tests/test-grammar-integration
# Code coverage output files
COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@ -646,7 +646,7 @@ CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])'
ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1)
ifndef CUDA_DOCKER_ARCH
ifndef CUDA_POWER_ARCH
$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via CUDA_DOCKER_ARCH)
$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via environment variable CUDA_DOCKER_ARCH, e.g. by running "export CUDA_DOCKER_ARCH=compute_XX" on Unix-like systems, where XX is the minimum compute capability that the code needs to run on. A list with compute capabilities can be found here: https://developer.nvidia.com/cuda-gpus )
endif # CUDA_POWER_ARCH
endif # CUDA_DOCKER_ARCH
endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1)
@ -688,7 +688,7 @@ llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml
$(CXX) $(CXXFLAGS) -c $< -o $@
COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o
COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o
common.o: common/common.cpp $(COMMON_H_DEPS)
$(CXX) $(CXXFLAGS) -c $< -o $@
@ -756,7 +756,7 @@ batched: examples/batched/batched.cpp ggml.o llama.o $(C
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS)
batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@ -788,7 +788,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp json-schema-to-grammar.o common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
@ -800,6 +800,10 @@ gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(O
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
eval-callback: examples/eval-callback/eval-callback.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@ -867,6 +871,10 @@ passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
ifeq ($(UNAME_S),Darwin)
swift: examples/batched.swift
(cd examples/batched.swift; make build)
@ -914,6 +922,10 @@ tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-grammar-integration: tests/test-grammar-integration.cpp ggml.o llama.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

69
Package.swift
View file

@ -2,6 +2,44 @@
import PackageDescription
var sources = [
"ggml.c",
"llama.cpp",
"unicode.cpp",
"unicode-data.cpp",
"ggml-alloc.c",
"ggml-backend.c",
"ggml-quants.c",
]
var resources: [Resource] = []
var linkerSettings: [LinkerSetting] = []
var cSettings: [CSetting] = [
.unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
.unsafeFlags(["-fno-objc-arc"]),
// NOTE: NEW_LAPACK will required iOS version 16.4+
// We should consider add this in the future when we drop support for iOS 14
// (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
// .define("ACCELERATE_NEW_LAPACK"),
// .define("ACCELERATE_LAPACK_ILP64")
]
#if canImport(Darwin)
sources.append("ggml-metal.m")
resources.append(.process("ggml-metal.metal"))
linkerSettings.append(.linkedFramework("Accelerate"))
cSettings.append(
contentsOf: [
.define("GGML_USE_ACCELERATE"),
.define("GGML_USE_METAL")
]
)
#endif
#if os(Linux)
cSettings.append(.define("_GNU_SOURCE"))
#endif
let package = Package(
name: "llama",
platforms: [
@ -28,34 +66,11 @@ let package = Package(
"ggml-cuda.h",
"Makefile"
],
sources: [
"ggml.c",
"llama.cpp",
"unicode.cpp",
"unicode-data.cpp",
"ggml-alloc.c",
"ggml-backend.c",
"ggml-quants.c",
"ggml-metal.m",
],
resources: [
.process("ggml-metal.metal")
],
sources: sources,
resources: resources,
publicHeadersPath: "spm-headers",
cSettings: [
.unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
.define("GGML_USE_ACCELERATE"),
.unsafeFlags(["-fno-objc-arc"]),
.define("GGML_USE_METAL"),
// NOTE: NEW_LAPACK will required iOS version 16.4+
// We should consider add this in the future when we drop support for iOS 14
// (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
// .define("ACCELERATE_NEW_LAPACK"),
// .define("ACCELERATE_LAPACK_ILP64")
],
linkerSettings: [
.linkedFramework("Accelerate")
]
cSettings: cSettings,
linkerSettings: linkerSettings
)
],
cxxLanguageStandard: .cxx11

511
README-sycl.md
View file

@ -3,32 +3,41 @@
- [Background](#background)
- [News](#news)
- [OS](#os)
- [Intel GPU](#intel-gpu)
- [Hardware](#hardware)
- [Docker](#docker)
- [Linux](#linux)
- [Windows](#windows)
- [Environment Variable](#environment-variable)
- [Known Issue](#known-issue)
- [Q&A](#q&a)
- [Todo](#todo)
- [Known Issue](#known-issues)
- [Q&A](#qa)
- [TODO](#todo)
## Background
SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators—such as CPUs, GPUs, and FPGAs. It is a single-source embedded domain-specific language based on pure C++17.
**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.
oneAPI is a specification that is open and standards-based, supporting multiple architecture types including but not limited to GPU, CPU, and FPGA. The spec has both direct programming and API-based programming paradigms.
**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
Intel uses the SYCL as direct programming language to support CPU, GPUs and FPGAs.
- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
To avoid to re-invent the wheel, this code refer other code paths in llama.cpp (like OpenBLAS, cuBLAS, CLBlast). We use a open-source tool [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) migrate to SYCL.
### Llama.cpp + SYCL
The llama.cpp for SYCL is used to support Intel GPUs.
The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it could support other vendor GPUs: Nvidia GPU (*AMD GPU coming*).
For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
## News
- 2024.4
- Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M.
- 2024.3
- Release binary files of Windows.
- A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd).
- New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437).
- Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
@ -45,216 +54,234 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
## OS
|OS|Status|Verified|
|-|-|-|
|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39|
|Windows|Support|Windows 11|
| OS | Status | Verified |
|---------|---------|------------------------------------|
| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39 |
| Windows | Support | Windows 11 |
## Intel GPU
## Hardware
### Verified
### Intel GPU
|Intel GPU| Status | Verified Model|
|-|-|-|
|Intel Data Center Max Series| Support| Max 1550|
|Intel Data Center Flex Series| Support| Flex 170|
|Intel Arc Series| Support| Arc 770, 730M|
|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7|
**Verified devices**
Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use.
| Intel GPU | Status | Verified Model |
|-------------------------------|---------|---------------------------------------|
| Intel Data Center Max Series | Support | Max 1550, 1100 |
| Intel Data Center Flex Series | Support | Flex 170 |
| Intel Arc Series | Support | Arc 770, 730M |
| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake |
| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |
### Memory
*Notes:*
The memory is a limitation to run LLM on GPUs.
- **Memory**
- The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/main`.
When run llama.cpp, there is print log to show the applied memory on GPU. You could know how much memory to be used in your case. Like `llm_load_tensors: buffer size = 3577.56 MiB`.
- Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.
For iGPU, please make sure the shared memory from host memory is enough. For llama-2-7b.Q4_0, recommend the host memory is 8GB+.
- **Execution Unit (EU)**
- If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use.
For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+.
### Other Vendor GPU
## Nvidia GPU
**Verified devices**
### Verified
|Intel GPU| Status | Verified Model|
|-|-|-|
|Ampere Series| Support| A100|
### oneMKL for CUDA
The current oneMKL release does not contain the oneMKL cuBlas backend.
As a result for Nvidia GPU's oneMKL must be built from source.
```
git clone https://github.com/oneapi-src/oneMKL
cd oneMKL
mkdir build
cd build
cmake -G Ninja .. -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON
ninja
// Add paths as necessary
```
| Nvidia GPU | Status | Verified Model |
|--------------------------|---------|----------------|
| Ampere Series | Support | A100, A4000 |
| Ampere Series *(Mobile)* | Support | RTX 40 Series |
## Docker
The docker build option is currently limited to *intel GPU* targets.
Note:
- Only docker on Linux is tested. Docker on WSL may not work.
- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that)
### Build the image
You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
### Build image
```sh
# For F16:
#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
# Or, for F32:
docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile .
# Note: you can also use the ".devops/server-intel.Dockerfile", which compiles the "server" example
# Using FP16
docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
```
### Run
*Notes*:
To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="LLAMA_SYCL_F16=ON"` argument from the previous command.
You can also use the `.devops/server-intel.Dockerfile`, which builds the *"server"* alternative.
### Run container
```sh
# Firstly, find all the DRI cards:
# First, find all the DRI cards
ls -la /dev/dri
# Then, pick the card that you want to use.
# For example with "/dev/dri/card1"
# Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
```
*Notes:*
- Docker has been tested successfully on native Linux. WSL support has not been verified yet.
- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*.
## Linux
### Setup Environment
### I. Setup Environment
1. Install Intel GPU driver.
1. **Install GPU drivers**
a. Please install Intel GPU driver by official guide: [Install GPU Drivers](https://dgpu-docs.intel.com/driver/installation.html).
- **Intel GPU**
Note: for iGPU, please install the client GPU driver.
Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).
b. Add user to group: video, render.
*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html).
Once installed, add the user(s) to the `video` and `render` groups.
```sh
sudo usermod -aG render username
sudo usermod -aG video username
sudo usermod -aG render $USER
sudo usermod -aG video $USER
```
Note: re-login to enable it.
*Note*: logout/re-login for the changes to take effect.
c. Check
Verify installation through `clinfo`:
```sh
sudo apt install clinfo
sudo clinfo -l
```
Output (example):
Sample output:
```
```sh
Platform #0: Intel(R) OpenCL Graphics
`-- Device #0: Intel(R) Arc(TM) A770 Graphics
Platform #0: Intel(R) OpenCL HD Graphics
`-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
```
2. Install Intel® oneAPI Base toolkit.
- **Nvidia GPU**
a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed.
Recommend to install to default folder: **/opt/intel/oneapi**.
2. **Install Intel® oneAPI Base toolkit**
Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder.
- **For Intel GPU**
b. Check
The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*.
Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.
- **Adding support to Nvidia GPUs**
**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.
**oneMKL for cuBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs.
```sh
source /opt/intel/oneapi/setvars.sh
git clone https://github.com/oneapi-src/oneMKL
cd oneMKL
mkdir -p buildWithCublas && cd buildWithCublas
cmake ../ -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
make
```
3. **Verify installation and environment**
In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
```sh
source /opt/intel/oneapi/setvars.sh
sycl-ls
```
There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**.
- **Intel GPU**
When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`ext_oneapi_level_zero:gpu:0`] in the sample output below:
Output (example):
```
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000]
[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
```
2. Build locally:
- **Nvidia GPU**
Note:
- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow:
```
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix]
[opencl:cpu:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.2]
```
### II. Build llama.cpp
#### Intel GPU
```sh
mkdir -p build
cd build
# Export relevant ENV variables
source /opt/intel/oneapi/setvars.sh
# For FP16:
#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
# Build LLAMA with MKL BLAS acceleration for intel GPU
mkdir -p build && cd build
# Or, for FP32:
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 1: Use FP16 for better performance in long-prompt inference
cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
# Or without "--build", run "make" next
# For Nvidia GPUs
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 2: Use FP32 by default
cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Build example/main only
#cmake --build . --config Release --target main
# Or, build all binary
cmake --build . --config Release -v
cd ..
#build all binary
cmake --build . --config Release -j -v
```
or
#### Nvidia GPU
```sh
./examples/sycl/build.sh
# Export relevant ENV variables
export LD_LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LD_LIBRARY_PATH
export LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LIBRARY_PATH
export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_DIR
export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
# Build LLAMA with Nvidia BLAS acceleration through SYCL
mkdir -p build && cd build
# Option 1: Use FP16 for better performance in long-prompt inference
cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
# Option 2: Use FP32 by default
cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
#build all binary
cmake --build . --config Release -j -v
```
### Run
### III. Run the inference
1. Put model file to folder **models**
1. Retrieve and prepare model
You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example.
You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
2. Enable oneAPI running environment
```
```sh
source /opt/intel/oneapi/setvars.sh
```
3. List device ID
3. List devices information
Run without parameter:
Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
```sh
./build/bin/ls-sycl-device
# or running the "main" executable and look at the output log:
./build/bin/main
```
Check the ID in startup log, like:
A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following:
```
found 6 SYCL devices:
| | | |Compute |Max compute|Max work|Max sub| |
@ -268,22 +295,22 @@ found 6 SYCL devices:
| 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616|
```
|Attribute|Note|
|-|-|
|compute capability 1.3|Level-zero running time, recommended |
|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
| Attribute | Note |
|------------------------|-------------------------------------------------------------|
| compute capability 1.3 | Level-zero driver/runtime, recommended |
| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases |
4. Device selection and execution of llama.cpp
4. Launch inference
There are two device selection modes:
- Single device: Use one device assigned by user.
- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
- Single device: Use one device target specified by the user.
- Multiple devices: Automatically select the devices with the same largest Max compute-units.
|Device selection|Parameter|
|-|-|
|Single device|--split-mode none --main-gpu DEVICE_ID |
|Multiple devices|--split-mode layer (default)|
| Device selection | Parameter |
|------------------|----------------------------------------|
| Single device | --split-mode none --main-gpu DEVICE_ID |
| Multiple devices | --split-mode layer (default) |
Examples:
@ -303,74 +330,63 @@ or run by script:
```sh
ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
```
or run by script:
Otherwise, you can run the script:
```sh
./examples/sycl/run_llama2.sh
```
Note:
*Notes:*
- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
5. Verify the device ID in output
Verify to see if the selected GPU is shown in the output, like:
```
```sh
detect 1 SYCL GPUs: [0] with top Max compute units:512
```
Or
```
```sh
use 1 SYCL GPUs: [0] with Max compute units:512
```
## Windows
### Setup Environment
### I. Setup Environment
1. Install Intel GPU driver.
1. Install GPU driver
Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
Note: **The driver is mandatory for compute function**.
2. Install Visual Studio
2. Install Visual Studio.
If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/).
Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact oneAPI environment enabling in Windows.
3. Install Intel® oneAPI Base toolkit
3. Install Intel® oneAPI Base toolkit.
The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*.
Recommend to install to default folder: **C:\Program Files (x86)\Intel\oneAPI**.
Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder.
Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
b. Enable oneAPI running environment:
- In Search, input 'oneAPI'.
- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App.
Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022"
- In Run:
In CMD:
- On the command prompt, enable the runtime environment with the following:
```
"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
```
c. Check GPU
c. Verify installation
In oneAPI command line:
In the oneAPI command line, run the following to print the available SYCL devices:
```
sycl-ls
```
There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**.
There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device:
Output (example):
```
@ -380,7 +396,7 @@ Output (example):
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
```
4. Install cmake & make
4. Install build tools
a. Download & install cmake for Windows: https://cmake.org/download/
@ -390,76 +406,53 @@ b. Download & install mingw-w64 make for Windows provided by w64devkit
- Extract `w64devkit` on your pc.
- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`.
- Add the **bin** folder path in the Windows system PATH environment (for e.g. `C:\xxx\w64devkit\bin\`).
### Build locally:
### II. Build llama.cpp
In oneAPI command line window:
On the oneAPI command line window, step into the llama.cpp main directory and run the following:
```
mkdir -p build
cd build
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
:: for FP16
:: faster for long-prompt inference
:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
:: for FP32
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
:: build example/main only
:: make main
:: build all binary
make -j
cd ..
```
or
```
Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
```sh
.\examples\sycl\win-build-sycl.bat
```
Note:
*Notes:*
- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
- By default, calling `make` will build all target binary files. In case of a minimal experimental setup, the user can build the inference executable only through `make main`.
### Run
### III. Run the inference
1. Put model file to folder **models**
1. Retrieve and prepare model
You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example.
You can refer to the general [*Prepare and Quantize*](README#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
2. Enable oneAPI running environment
- In Search, input 'oneAPI'.
Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022"
- In Run:
In CMD:
On the oneAPI command line window, run the following and step into the llama.cpp directory:
```
"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
```
3. List device ID
3. List devices information
Run without parameter:
Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
```
build\bin\ls-sycl-device.exe
or
build\bin\main.exe
```
Check the ID in startup log, like:
The output of this command in a system with 1 *intel CPU* and 1 *intel GPU* would look like the following:
```
found 6 SYCL devices:
| | | |Compute |Max compute|Max work|Max sub| |
@ -474,23 +467,23 @@ found 6 SYCL devices:
```
|Attribute|Note|
|-|-|
|compute capability 1.3|Level-zero running time, recommended |
|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
| Attribute | Note |
|------------------------|-----------------------------------------------------------|
| compute capability 1.3 | Level-zero running time, recommended |
| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases |
4. Device selection and execution of llama.cpp
4. Launch inference
There are two device selection modes:
- Single device: Use one device assigned by user.
- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
|Device selection|Parameter|
|-|-|
|Single device|--split-mode none --main-gpu DEVICE_ID |
|Multiple devices|--split-mode layer (default)|
| Device selection | Parameter |
|------------------|----------------------------------------|
| Single device | --split-mode none --main-gpu DEVICE_ID |
| Multiple devices | --split-mode layer (default) |
Examples:
@ -505,7 +498,7 @@ build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be
```
build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
```
or run by script:
Otherwise, run the following wrapper script:
```
.\examples\sycl\win-run-llama2.bat
@ -513,19 +506,13 @@ or run by script:
Note:
- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
5. Verify the device ID in output
Verify to see if the selected GPU is shown in the output, like:
```
```sh
detect 1 SYCL GPUs: [0] with top Max compute units:512
```
Or
```
```sh
use 1 SYCL GPUs: [0] with Max compute units:512
```
@ -533,67 +520,51 @@ use 1 SYCL GPUs: [0] with Max compute units:512
#### Build
|Name|Value|Function|
|-|-|-|
|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path. <br>For FP32/FP16, LLAMA_SYCL=ON is mandatory.|
|LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference. <br>For FP32, not set it.|
|CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path|
|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path|
| Name | Value | Function |
|--------------------|-----------------------------------|---------------------------------------------|
| LLAMA_SYCL | ON (mandatory) | Enable build with SYCL code path. |
| LLAMA_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. |
| LLAMA_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. |
| CMAKE_C_COMPILER | icx | Set *icx* compiler for SYCL code path. |
| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |
#### Running
#### Runtime
| Name | Value | Function |
|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG |
| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
|Name|Value|Function|
|-|-|-|
|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG|
|ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer|
## Known Issues
## Known Issue
- Hang during startup
llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block.
Solution: add **--no-mmap** or **--mmap 0**.
- Split-mode: [row] is not supported
It's on developing.
- `Split-mode:[row]` is not supported.
## Q&A
Note: please add prefix **[SYCL]** in issue title, so that we will check it as soon as possible.
- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
Miss to enable oneAPI running environment.
- Potential cause: Unavailable oneAPI installation or not set ENV variables.
- Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.
Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`.
- General compiler error:
- In Windows, no result, not error.
- Remove **build** folder or try a clean-build.
Miss to enable oneAPI running environment.
- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux.
- Meet compile error.
Please double-check with `sudo sycl-ls`.
Remove folder **build** and try again.
- I can **not** see **[ext_oneapi_level_zero:gpu:0]** afer install GPU driver in Linux.
Please run **sudo sycl-ls**.
If you see it in result, please add video/render group to your ID:
If it's present in the list, please add video/render group to your user then **logout/login** or restart your system:
```
sudo usermod -aG render username
sudo usermod -aG video username
sudo usermod -aG render $USER
sudo usermod -aG video $USER
```
Otherwise, please double-check the GPU driver installation steps.
Then **relogin**.
### **GitHub contribution**:
Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
If you do not see it, please check the installation GPU steps again.
## Todo
## TODO
- Support row layer split for multiple card runs.

72
README.md
View file

@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Recent API changes
- [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341
- [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
@ -18,12 +19,13 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Hot topics
- **MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387**
- Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
- Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225
- Multi-GPU pipeline parallelizm support https://github.com/ggerganov/llama.cpp/pull/6017
- Multi-GPU pipeline parallelism support https://github.com/ggerganov/llama.cpp/pull/6017
- Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981
- Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962
- Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328
- Support loading sharded model, using `gguf-split` CLI https://github.com/ggerganov/llama.cpp/pull/6187
----
@ -92,6 +94,7 @@ Typically finetunes of the base models below are supported as well.
- [x] LLaMA 2 🦙🦙
- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
- [X] Falcon
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
@ -115,7 +118,12 @@ Typically finetunes of the base models below are supported as well.
- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
- [x] [Gemma](https://ai.google.dev/gemma)
- [x] [Mamba](https://github.com/state-spaces/mamba)
- [x] [Xverse](https://huggingface.co/models?search=xverse)
- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md))
**Multimodal models:**
@ -139,6 +147,7 @@ Typically finetunes of the base models below are supported as well.
- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
- 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)
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
@ -175,6 +184,12 @@ Unless otherwise noted these projects are open-source with permissive licensing:
- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
- [Msty](https://msty.app) (proprietary)
- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file)(Apachev2.0 or later)
- [Dot](https://github.com/alexpinel/Dot) (GPL)
- [MindMac](https://mindmac.app) (proprietary)
- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
---
@ -471,20 +486,20 @@ Building the program with BLAS support may lead to some performance improvements
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. The following compilation options are also available to tweak performance:
| Option | Legal values | Default | Description |
|--------------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
| LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
| Option | Legal values | Default | Description |
|--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
| LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
- #### hipBLAS
This provides BLAS acceleration on HIP-supported AMD GPUs.
Make sure to have ROCm installed.
You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html).
You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
- Using `make`:
```bash
@ -501,7 +516,7 @@ Building the program with BLAS support may lead to some performance improvements
- Using `make` (example for target gfx1030, build with 16 CPU threads):
```bash
make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gxf1030
make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
```
- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
@ -509,7 +524,7 @@ Building the program with BLAS support may lead to some performance improvements
set PATH=%HIP_PATH%\bin;%PATH%
mkdir build
cd build
cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..
cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release ..
cmake --build .
```
Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
@ -520,11 +535,11 @@ Building the program with BLAS support may lead to some performance improvements
If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
- #### CLBlast
@ -632,15 +647,6 @@ Building the program with BLAS support may lead to some performance improvements
- #### Vulkan
> [!WARNING]
>
> Vulkan support has been broken in https://github.com/ggerganov/llama.cpp/pull/6122
> due to relying on `GGML_OP_GET_ROWS` which is not yet properly supported by the Vulkan backend,
> but should be fixed relatively soon (possibly in https://github.com/ggerganov/llama.cpp/pull/6155
> (ref: https://github.com/ggerganov/llama.cpp/pull/6122#issuecomment-2015327635)).
>
> Meanwhile, if you want to use the Vulkan backend, you should use the commit right before the breaking change, https://github.com/ggerganov/llama.cpp/commit/55c1b2a3bbd470e9e2a3a0618b92cf64a885f806
**With docker**:
You don't need to install Vulkan SDK. It will be installed inside the container.
@ -741,11 +747,11 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver
As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
| Model | Original size | Quantized size (Q4_0) |
|------:|--------------:|-----------------------:|
| 7B | 13 GB | 3.9 GB |
| 13B | 24 GB | 7.8 GB |
| 30B | 60 GB | 19.5 GB |
| 65B | 120 GB | 38.5 GB |
|------:|--------------:|----------------------:|
| 7B | 13 GB | 3.9 GB |
| 13B | 24 GB | 7.8 GB |
| 30B | 60 GB | 19.5 GB |
| 65B | 120 GB | 38.5 GB |
### Quantization
@ -753,7 +759,7 @@ Several quantization methods are supported. They differ in the resulting model d
*(outdated)*
| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 |
| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 |
|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
| 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G |

67
SECURITY.md Normal file
View file

@ -0,0 +1,67 @@
# Security Policy
- [**Using llama.cpp securely**](#using-llamacpp-securely)
- [Untrusted models](#untrusted-models)
- [Untrusted inputs](#untrusted-inputs)
- [Data privacy](#data-privacy)
- [Untrusted environments or networks](#untrusted-environments-or-networks)
- [Multi-Tenant environments](#multi-tenant-environments)
- [**Reporting a vulnerability**](#reporting-a-vulnerability)
## Using llama.cpp securely
### Untrusted models
Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources.
*Always execute untrusted models within a secure, isolated environment such as a sandbox* (e.g., containers, virtual machines). This helps protect your system from potentially malicious code.
> [!NOTE]
> The trustworthiness of a model is not binary. You must always determine the proper level of caution depending on the specific model and how it matches your use case and risk tolerance.
### Untrusted inputs
Some models accept various input formats (text, images, audio, etc.). The libraries converting these inputs have varying security levels, so it's crucial to isolate the model and carefully pre-process inputs to mitigate script injection risks.
For maximum security when handling untrusted inputs, you may need to employ the following:
* Sandboxing: Isolate the environment where the inference happens.
* Pre-analysis: Check how the model performs by default when exposed to prompt injection (e.g. using [fuzzing for prompt injection](https://github.com/FonduAI/awesome-prompt-injection?tab=readme-ov-file#tools)). This will give you leads on how hard you will have to work on the next topics.
* Updates: Keep both LLaMA C++ and your libraries updated with the latest security patches.
* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as:
* Validation: Enforce strict rules on allowed characters and data types.
* Filtering: Remove potentially malicious scripts or code fragments.
* Encoding: Convert special characters into safe representations.
* Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)).
### Data privacy
To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the model execution. This means running the model in a secure, isolated environment, which helps mitigate many attack vectors.
### Untrusted environments or networks
If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions:
* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value
* Encrypt your data if sending it over the network.
### Multi-Tenant environments
If you intend to run multiple models in parallel with shared memory, it is your responsibility to ensure the models do not interact or access each other's data. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks.
1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity.
2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
## Reporting a vulnerability
Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++.
<!-- normal version -->
However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
Please disclose it as a private [security advisory](https://github.com/ggerganov/llama.cpp/security/advisories/new).
A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.

14
build.zig
View file

@ -128,14 +128,14 @@ pub fn build(b: *std.build.Builder) !void {
const clip = make.obj("clip", "examples/llava/clip.cpp");
const llava = make.obj("llava", "examples/llava/llava.cpp");
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, train });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, train });
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo });
_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, grammar_parser, json_schema_to_grammar, clip, llava });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava });
if (server.target.isWindows()) {
server.linkSystemLibrary("ws2_32");
}

51
ci/run.sh
View file

@ -153,6 +153,52 @@ function gg_sum_ctest_release {
gg_printf '```\n'
}
# test_scripts_debug
function gg_run_test_scripts_debug {
cd ${SRC}
set -e
(cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
set +e
}
function gg_sum_test_scripts_debug {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'Runs test scripts in debug mode\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '```\n'
gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
gg_printf '```\n'
gg_printf '\n'
}
# test_scripts_release
function gg_run_test_scripts_release {
cd ${SRC}
set -e
(cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
set +e
}
function gg_sum_test_scripts_release {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'Runs test scripts in release mode\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '```\n'
gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
gg_printf '```\n'
gg_printf '\n'
}
function gg_get_model {
local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf"
local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
@ -575,7 +621,7 @@ function gg_run_embd_bge_small {
cd ${SRC}
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/tokenizer.model
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer.json
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin
@ -642,6 +688,9 @@ test $ret -eq 0 && gg_run ctest_release
if [ -z ${GG_BUILD_LOW_PERF} ]; then
test $ret -eq 0 && gg_run embd_bge_small
test $ret -eq 0 && gg_run test_scripts_debug
test $ret -eq 0 && gg_run test_scripts_release
if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
if [ -z ${GG_BUILD_CUDA} ]; then
test $ret -eq 0 && gg_run open_llama_3b_v2

4
common/CMakeLists.txt
View file

@ -47,9 +47,6 @@ if (BUILD_SHARED_LIBS)
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
endif()
set(TARGET json-schema-to-grammar)
add_library(${TARGET} OBJECT json-schema-to-grammar.cpp json-schema-to-grammar.h)
set(TARGET common)
add_library(${TARGET} STATIC
@ -63,6 +60,7 @@ add_library(${TARGET} STATIC
grammar-parser.h
grammar-parser.cpp
json.hpp
json-schema-to-grammar.cpp
train.h
train.cpp
ngram-cache.h

113
common/common.cpp
View file

@ -1,4 +1,6 @@
#include "common.h"
#include "json.hpp"
#include "json-schema-to-grammar.h"
#include "llama.h"
#include <algorithm>
@ -16,6 +18,7 @@
#include <unordered_set>
#include <vector>
#include <cinttypes>
#include <codecvt>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
@ -27,7 +30,6 @@
#ifndef NOMINMAX
# define NOMINMAX
#endif
#include <codecvt>
#include <locale>
#include <windows.h>
#include <fcntl.h>
@ -68,6 +70,8 @@
#define LLAMA_CURL_MAX_HEADER_LENGTH 256
#endif // LLAMA_USE_CURL
using json = nlohmann::ordered_json;
int32_t get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
@ -1148,6 +1152,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
);
return true;
}
if (arg == "-j" || arg == "--json-schema") {
if (++i >= argc) {
invalid_param = true;
return true;
}
sparams.grammar = json_schema_to_grammar(json::parse(argv[i]));
return true;
}
if (arg == "--override-kv") {
if (++i >= argc) {
invalid_param = true;
@ -1353,6 +1365,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
printf(" --grammar-file FNAME file to read grammar from\n");
printf(" -j SCHEMA, --json-schema SCHEMA\n");
printf(" JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object.\n");
printf(" For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead\n");
printf(" --cfg-negative-prompt PROMPT\n");
printf(" negative prompt to use for guidance. (default: empty)\n");
printf(" --cfg-negative-prompt-file FNAME\n");
@ -1500,6 +1515,77 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
GGML_UNREACHABLE();
}
// Validate if a filename is safe to use
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
bool validate_file_name(const std::string & filename) {
if (!filename.length()) {
// Empty filename invalid
return false;
}
if (filename.length() > 255) {
// Limit at common largest possible filename on Linux filesystems
// to avoid unnecessary further validation
// (On systems with smaller limits it will be caught by the OS)
return false;
}
std::u32string filename_utf32;
try {
std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
filename_utf32 = converter.from_bytes(filename);
// If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
// or invalid encodings were encountered. Reject such attempts
std::string filename_reencoded = converter.to_bytes(filename_utf32);
if (filename_reencoded != filename) {
return false;
}
} catch (const std::exception &) {
return false;
}
// Check for forbidden codepoints:
// - Control characters
// - Unicode equivalents of illegal characters
// - UTF-16 surrogate pairs
// - UTF-8 replacement character
// - Byte order mark (BOM)
// - Illegal characters: / \ : * ? " < > |
for (char32_t c : filename_utf32) {
if (c <= 0x1F // Control characters (C0)
|| c == 0x7F // Control characters (DEL)
|| (c >= 0x80 && c <= 0x9F) // Control characters (C1)
|| c == 0xFF0E // Fullwidth Full Stop (period equivalent)
|| c == 0x2215 // Division Slash (forward slash equivalent)
|| c == 0x2216 // Set Minus (backslash equivalent)
|| (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
|| c == 0xFFFD // Replacement Character (UTF-8)
|| c == 0xFEFF // Byte Order Mark (BOM)
|| c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
|| c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
return false;
}
}
// Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
// Unicode and other whitespace is not affected, only 0x20 space
if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
return false;
}
// Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
if (filename.find("..") != std::string::npos) {
return false;
}
// Reject "."
if (filename == ".") {
return false;
}
return true;
}
//
// String utils
//
@ -1674,6 +1760,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
cparams.pooling_type = params.pooling_type;
cparams.defrag_thold = params.defrag_thold;
cparams.cb_eval = params.cb_eval;
cparams.cb_eval_user_data = params.cb_eval_user_data;
cparams.offload_kqv = !params.no_kv_offload;
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
@ -1928,11 +2016,6 @@ struct llama_model * llama_load_model_from_url(
return NULL;
}
if (!curl) {
fprintf(stderr, "%s: error initializing libcurl\n", __func__);
return NULL;
}
if (!llama_download_file(curl, model_url, path_model)) {
return NULL;
}
@ -2126,7 +2209,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
}
{
if (params.warmup) {
LOG("warming up the model with an empty run\n");
std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
@ -2146,23 +2229,23 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
std::vector<llama_token> llama_tokenize(
const struct llama_context * ctx,
const std::string & text,
bool add_bos,
bool special) {
return llama_tokenize(llama_get_model(ctx), text, add_bos, special);
bool add_special,
bool parse_special) {
return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
}
std::vector<llama_token> llama_tokenize(
const struct llama_model * model,
const std::string & text,
bool add_bos,
bool special) {
bool add_special,
bool parse_special) {
// upper limit for the number of tokens
int n_tokens = text.length() + add_bos;
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_bos, special);
n_tokens = llama_tokenize(model, 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_bos, special);
int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);

14
common/common.h
View file

@ -80,6 +80,9 @@ struct gpt_params {
int32_t yarn_orig_ctx = 0; // YaRN original context length
float defrag_thold = -1.0f; // KV cache defragmentation threshold
ggml_backend_sched_eval_callback cb_eval = nullptr;
void * cb_eval_user_data = nullptr;
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
@ -156,6 +159,7 @@ struct gpt_params {
bool infill = false; // use infill mode
bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes
bool no_kv_offload = false; // disable KV offloading
bool warmup = true; // warmup run
std::string cache_type_k = "f16"; // KV cache data type for the K
std::string cache_type_v = "f16"; // KV cache data type for the V
@ -179,6 +183,8 @@ std::string gpt_random_prompt(std::mt19937 & rng);
void process_escapes(std::string& input);
bool validate_file_name(const std::string & filename);
//
// String utils
//
@ -221,14 +227,14 @@ void llama_batch_add(
std::vector<llama_token> llama_tokenize(
const struct llama_context * ctx,
const std::string & text,
bool add_bos,
bool special = false);
bool add_special,
bool parse_special = false);
std::vector<llama_token> llama_tokenize(
const struct llama_model * model,
const std::string & text,
bool add_bos,
bool special = false);
bool add_special,
bool parse_special = false);
// tokenizes a token into a piece
// should work similar to Python's `tokenizer.id_to_piece`

237
common/json-schema-to-grammar.cpp
View file

@ -11,35 +11,101 @@
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 = "", bool item_rule_is_literal = false) {
if (separator_rule.empty()) {
if (min_items == 0 && max_items == 1) {
return item_rule + "?";
} else if (min_items == 1 && max_items == std::numeric_limits<int>::max()) {
return item_rule + "+";
}
}
std::string result;
if (min_items > 0) {
if (item_rule_is_literal && separator_rule.empty()) {
result = "\"" + repeat(std::string(item_rule.begin() + 1, item_rule.end() - 1), min_items) + "\"";
} else {
std::vector<std::string> items(min_items, item_rule);
result = join(items.begin(), items.end(), separator_rule.empty() ? " " : " " + separator_rule + " ");
}
}
std::function<std::string(int, bool)> opt_repetitions = [&](int up_to_n, bool prefix_with_sep) -> std::string {
auto content = prefix_with_sep && !separator_rule.empty() ? separator_rule + " " + item_rule : item_rule;
if (up_to_n == 0) {
return "";
} else if (up_to_n == 1) {
return "(" + content + ")?";
} else if (!separator_rule.empty() && !prefix_with_sep) {
return "(" + content + " " + opt_repetitions(up_to_n - 1, true) + ")?";
} else {
std::string res = repeat("(" + content + " ", up_to_n);
// strip trailing space
res = res.substr(0, res.length() - 1);
res += repeat(")?", up_to_n);
return res;
}
};
if (min_items > 0 && max_items != min_items) {
result += " ";
}
if (max_items != std::numeric_limits<int>::max()) {
result += opt_repetitions(max_items - min_items, min_items > 0);
} else {
std::string item_operator = "(" + (separator_rule.empty() ? "" : separator_rule + " ") + item_rule + ")";
if (min_items == 0 && !separator_rule.empty()) {
result = "(" + item_rule + " " + item_operator + "*)?";
} else {
result += item_operator + "*";
}
}
return result;
}
const std::string SPACE_RULE = "\" \"?";
std::unordered_map<std::string, std::string> PRIMITIVE_RULES = {
{"boolean", "(\"true\" | \"false\") space"},
{"number", "(\"-\"? ([0-9] | [1-9] [0-9]*)) (\".\" [0-9]+)? ([eE] [-+]? [0-9]+)? space"},
{"integer", "(\"-\"? ([0-9] | [1-9] [0-9]*)) space"},
{"value", "object | array | string | number | boolean"},
{"object", "\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space"},
{"array", "\"[\" space ( value (\",\" space value)* )? \"]\" space"},
{"uuid", "\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space"},
{"string", " \"\\\"\" (\n"
" [^\"\\\\] |\n"
" \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])\n"
" )* \"\\\"\" space"},
{"null", "\"null\" space"}
struct BuiltinRule {
std::string content;
std::vector<std::string> deps;
};
std::vector<std::string> OBJECT_RULE_NAMES = {"object", "array", "string", "number", "boolean", "null", "value"};
std::unordered_map<std::string, std::string> DATE_RULES = {
{"date", "[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )"},
{"time", "([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )"},
{"date-time", "date \"T\" time"},
{"date-string", "\"\\\"\" date \"\\\"\" space"},
{"time-string", "\"\\\"\" time \"\\\"\" space"},
{"date-time-string", "\"\\\"\" date-time \"\\\"\" space"}
const std::string _up_to_15_digits = build_repetition("[0-9]", 0, 15);
std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
{"boolean", {"(\"true\" | \"false\") space", {}}},
{"decimal-part", {"[0-9] " + _up_to_15_digits, {}}},
{"integral-part", {"[0-9] | [1-9] " + _up_to_15_digits, {}}},
{"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}},
{"integer", {"(\"-\"? integral-part) space", {"integral-part"}}},
{"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}},
{"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
{"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
{"uuid", {"\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space", {}}},
{"char", {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])", {}}},
{"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
{"null", {"\"null\" space", {}}},
};
std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
{"date", {"[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
{"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
{"date-time", {"date \"T\" time", {"date", "time"}}},
{"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}},
{"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}},
{"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}}
};
static bool is_reserved_name(const std::string & name) {
@ -47,7 +113,7 @@ static bool is_reserved_name(const std::string & name) {
if (RESERVED_NAMES.empty()) {
RESERVED_NAMES.insert("root");
for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first);
for (const auto &p : DATE_RULES) RESERVED_NAMES.insert(p.first);
for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first);
}
return RESERVED_NAMES.find(name) != RESERVED_NAMES.end();
}
@ -192,7 +258,7 @@ private:
if (_dotall) {
rule = "[\\U00000000-\\U0010FFFF]";
} else {
rule = "[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]";
rule = "[^\\x0A\\x0D]";
}
return _add_rule("dot", rule);
};
@ -308,47 +374,21 @@ private:
auto &sub = last.first;
auto sub_is_literal = last.second;
if (min_times == 0 && max_times == std::numeric_limits<int>::max()) {
sub += "*";
} else if (min_times == 0 && max_times == 1) {
sub += "?";
} else if (min_times == 1 && max_times == std::numeric_limits<int>::max()) {
sub += "+";
} else {
if (!sub_is_literal) {
std::string & sub_id = sub_rule_ids[sub];
if (sub_id.empty()) {
sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub);
}
sub = sub_id;
if (!sub_is_literal) {
std::string & sub_id = sub_rule_ids[sub];
if (sub_id.empty()) {
sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub);
}
std::string result;
if (sub_is_literal && min_times > 0) {
result = "\"" + repeat(sub.substr(1, sub.length() - 2), min_times) + "\"";
} else {
for (int j = 0; j < min_times; j++) {
if (j > 0) {
result += " ";
}
result += sub;
}
}
if (min_times > 0 && min_times < max_times) {
result += " ";
}
if (max_times == std::numeric_limits<int>::max()) {
result += sub + "*";
} else {
for (int j = min_times; j < max_times; j++) {
if (j > min_times) {
result += " ";
}
result += sub + "?";
}
}
seq.back().first = result;
seq.back().second = false;
sub = sub_id;
}
seq.back().first = build_repetition(
sub_is_literal ? "\"" + sub + "\"" : sub,
min_times,
max_times,
"",
sub_is_literal
);
seq.back().second = false;
} else {
std::string literal;
auto is_non_literal = [&](char c) {
@ -424,7 +464,7 @@ private:
if (additional_properties.is_object() || (additional_properties.is_boolean() && additional_properties.get<bool>())) {
std::string sub_name = name + (name.empty() ? "" : "-") + "additional";
std::string value_rule = visit(additional_properties.is_object() ? additional_properties : json::object(), sub_name + "-value");
std::string kv_rule = _add_rule(sub_name + "-kv", _add_rule("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule);
std::string kv_rule = _add_rule(sub_name + "-kv", _add_primitive("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule);
prop_kv_rule_names["*"] = kv_rule;
optional_props.push_back("*");
}
@ -486,6 +526,25 @@ private:
return rule;
}
std::string _add_primitive(const std::string & name, const BuiltinRule & rule) {
auto n = _add_rule(name, rule.content);
for (const auto & dep : rule.deps) {
BuiltinRule dep_rule;
auto it = PRIMITIVE_RULES.find(dep);
if (it == PRIMITIVE_RULES.end()) {
it = STRING_FORMAT_RULES.find(dep);
if (it == STRING_FORMAT_RULES.end()) {
_errors.push_back("Rule " + dep + " not known");
continue;
}
}
if (_rules.find(dep) == _rules.end()) {
_add_primitive(dep, it->second);
}
}
return n;
}
public:
SchemaConverter(
const std::function<json(const std::string &)> & fetch_json,
@ -647,49 +706,33 @@ public:
return _add_rule(rule_name, rule);
} else {
std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item");
std::string list_item_operator = "( \",\" space " + item_rule_name + " )";
std::string successive_items;
int min_items = schema.contains("minItems") ? schema["minItems"].get<int>() : 0;
json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json();
int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : -1;
if (min_items > 0) {
successive_items += repeat(list_item_operator, min_items - 1);
min_items--;
}
if (max_items >= 0 && max_items > min_items) {
successive_items += repeat(list_item_operator + "?", max_items - min_items - 1);
} else {
successive_items += list_item_operator + "*";
}
std::string rule;
if (min_items == 0) {
rule = "\"[\" space ( " + item_rule_name + " " + successive_items + " )? \"]\" space";
} else {
rule = "\"[\" space " + item_rule_name + " " + successive_items + " \"]\" space";
}
return _add_rule(rule_name, rule);
int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : std::numeric_limits<int>::max();
return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space");
}
} else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) {
return _visit_pattern(schema["pattern"], rule_name);
} else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) {
return _add_rule(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid"));
} else if ((schema_type.is_null() || schema_type == "string") && DATE_RULES.find(schema_format) != DATE_RULES.end()) {
for (const auto & kv : DATE_RULES) {
_add_rule(kv.first, kv.second);
}
return schema_format + "-string";
return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid"));
} else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) {
auto prim_name = schema_format + "-string";
return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name)));
} else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) {
std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char"));
int min_len = schema.contains("minLength") ? schema["minLength"].get<int>() : 0;
int max_len = schema.contains("maxLength") ? schema["maxLength"].get<int>() : std::numeric_limits<int>::max();
return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space");
} else if (schema.empty() || schema_type == "object") {
for (const auto & n : OBJECT_RULE_NAMES) {
_add_rule(n, PRIMITIVE_RULES.at(n));
}
return _add_rule(rule_name, "object");
return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object")));
} else {
if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get<std::string>()) == PRIMITIVE_RULES.end()) {
_errors.push_back("Unrecognized schema: " + schema.dump());
return "";
}
// TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
return _add_rule(rule_name == "root" ? "root" : schema_type.get<std::string>(), PRIMITIVE_RULES.at(schema_type.get<std::string>()));
return _add_primitive(rule_name == "root" ? "root" : schema_type.get<std::string>(), PRIMITIVE_RULES.at(schema_type.get<std::string>()));
}
}

2
common/sampling.h
View file

@ -129,7 +129,7 @@ llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
int idx = 0);
int idx = -1);
// Prepares and adjusts the set of token candidates for sampling based on penalties, biases, and sampling parameters.
llama_token_data_array llama_sampling_prepare(

560
convert-hf-to-gguf.py
View file

@ -23,7 +23,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
from convert import HfVocab
from convert import LlamaHfVocab, permute
###### MODEL DEFINITIONS ######
@ -43,17 +43,18 @@ AnyModel = TypeVar("AnyModel", bound="type[Model]")
class Model(ABC):
_model_classes: dict[str, type[Model]] = {}
def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool):
def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool):
self.dir_model = dir_model
self.ftype = ftype
self.fname_out = fname_out
self.is_big_endian = is_big_endian
self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
self.use_temp_file = use_temp_file
self.is_safetensors = self._is_model_safetensors()
self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
self.part_names = self._get_part_names()
self.hparams = Model.load_hparams(self.dir_model)
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
@property
@ -160,7 +161,7 @@ class Model(ABC):
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")):
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
@ -227,15 +228,14 @@ class Model(ABC):
return ("pytorch_model.bin",)
return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
def _set_vocab_gpt2(self):
dir_model = self.dir_model
hparams = self.hparams
tokens: list[bytearray] = []
# used for GPT-2 BPE and WordPiece vocabs
def get_basic_vocab(self) -> tuple[list[str], list[int]]:
tokens: list[str] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_model)
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
assert max(tokenizer.vocab.values()) < vocab_size
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
@ -243,8 +243,7 @@ class Model(ABC):
for i in range(vocab_size):
if i not in reverse_vocab:
pad_token = f"[PAD{i}]".encode('utf-8')
tokens.append(bytearray(pad_token))
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
@ -256,17 +255,21 @@ class Model(ABC):
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
return tokens, toktypes
def _set_vocab_gpt2(self) -> None:
tokens, toktypes = self.get_basic_vocab()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_qwen(self):
dir_model = self.dir_model
hparams = self.hparams
tokens: list[bytearray] = []
tokens: list[str] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
@ -291,8 +294,7 @@ class Model(ABC):
for i in range(vocab_size):
if i not in reverse_vocab:
pad_token = f"[PAD{i}]".encode("utf-8")
tokens.append(bytearray(pad_token))
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
@ -325,8 +327,7 @@ class Model(ABC):
toktypes: list[int] = []
if not tokenizer_path.is_file():
print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
sys.exit(1)
raise FileNotFoundError(f"File not found: {tokenizer_path}")
tokenizer = SentencePieceProcessor(str(tokenizer_path))
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
@ -372,12 +373,8 @@ class Model(ABC):
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_hf(self):
path = self.dir_model
added_tokens_path = self.dir_model
vocab = HfVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
def _set_vocab_llama_hf(self):
vocab = LlamaHfVocab(self.dir_model)
tokens = []
scores = []
toktypes = []
@ -517,6 +514,17 @@ class BloomModel(Model):
class MPTModel(Model):
model_arch = gguf.MODEL_ARCH.MPT
def set_vocab(self):
try:
self._set_vocab_gpt2()
except Exception:
# Fallback for SEA-LION model
self._set_vocab_sentencepiece()
self.gguf_writer.add_add_bos_token(False)
self.gguf_writer.add_pad_token_id(3)
self.gguf_writer.add_eos_token_id(1)
self.gguf_writer.add_unk_token_id(0)
def set_gguf_parameters(self):
block_count = self.hparams["n_layers"]
self.gguf_writer.add_name(self.dir_model.name)
@ -530,7 +538,10 @@ class MPTModel(Model):
self.gguf_writer.add_layer_norm_eps(1e-5)
if self.hparams["attn_config"]["clip_qkv"] is not None:
self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
if self.hparams["attn_config"]["alibi"]:
self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
else:
self.gguf_writer.add_max_alibi_bias(0.0)
def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))
@ -779,6 +790,148 @@ class BaichuanModel(Model):
return weights[r * n_part:r * n_part + r, ...]
@Model.register("XverseForCausalLM")
class XverseModel(Model):
model_arch = gguf.MODEL_ARCH.XVERSE
def set_vocab(self):
assert (self.dir_model / "tokenizer.json").is_file()
dir_model = self.dir_model
hparams = self.hparams
tokens: list[bytearray] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_model)
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
assert max(tokenizer.vocab.values()) < vocab_size
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
added_vocab = tokenizer.get_added_vocab()
for token_id in range(vocab_size):
token_text = reverse_vocab[token_id].encode('utf-8')
# replace "\x00" to string with length > 0
if token_text == b"\x00":
toktype = gguf.TokenType.BYTE # special
token_text = f"<{token_text}>".encode('utf-8')
elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
toktype = gguf.TokenType.BYTE # special
elif reverse_vocab[token_id] in added_vocab:
if tokenizer.added_tokens_decoder[token_id].special:
toktype = gguf.TokenType.CONTROL
else:
toktype = gguf.TokenType.USER_DEFINED
else:
toktype = gguf.TokenType.NORMAL
tokens.append(token_text)
toktypes.append(toktype)
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
hf_repo = self.hparams.get("_name_or_path", "")
ctx_length = 0
if "max_sequence_length" in self.hparams:
ctx_length = self.hparams["max_sequence_length"]
elif "max_position_embeddings" in self.hparams:
ctx_length = self.hparams["max_position_embeddings"]
elif "model_max_length" in self.hparams:
ctx_length = self.hparams["model_max_length"]
else:
print("gguf: can not find ctx length parameter.")
sys.exit()
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_source_hf_repo(hf_repo)
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_context_length(ctx_length)
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("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 write_tensors(self):
# Collect tensors from generator object
model_kv = dict(self.get_tensors())
block_count = self.hparams["num_hidden_layers"]
head_count = self.hparams["num_attention_heads"]
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
for name, data_torch in model_kv.items():
# we don't need these
if name.endswith(".rotary_emb.inv_freq"):
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
# HF models permute some of the tensors, so we need to undo that
if name.endswith(("q_proj.weight")):
data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
if name.endswith(("k_proj.weight")):
data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
data = data_torch.squeeze().numpy()
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape)
)
@Model.register("FalconForCausalLM", "RWForCausalLM")
class FalconModel(Model):
model_arch = gguf.MODEL_ARCH.FALCON
@ -1058,12 +1211,128 @@ class StableLMModel(Model):
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
@Model.register("MixtralForCausalLM")
class MixtralModel(Model):
@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
class LlamaModel(Model):
model_arch = gguf.MODEL_ARCH.LLAMA
def set_vocab(self):
self._set_vocab_sentencepiece()
try:
self. _set_vocab_sentencepiece()
except FileNotFoundError:
self._set_vocab_llama_hf()
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
special_vocab._set_special_token("prefix", 32007)
special_vocab._set_special_token("suffix", 32008)
special_vocab._set_special_token("middle", 32009)
special_vocab._set_special_token("eot", 32010)
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"])
self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
# Same as super class, but permuting q_proj, k_proj
def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
n_head = self.hparams.get("num_attention_heads")
n_kv_head = self.hparams.get("num_key_value_heads")
n_experts = self.hparams.get("num_local_experts")
experts = dict()
for name, data_torch in self.get_tensors():
# we don't need these
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.numpy()
if name.endswith("q_proj.weight"):
data = permute(data, n_head, n_head)
if name.endswith("k_proj.weight"):
data = permute(data, n_head, n_kv_head)
data = data.squeeze()
# process the experts separately
if name.find("block_sparse_moe.experts") != -1:
experts[name] = data
if len(experts) >= n_experts:
# merge the experts into a single 3d tensor
for bid in range(block_count):
for wid in range(1, 4):
full = True
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight"
if ename not in experts:
full = False
break
if not full:
continue
datas = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight"
datas.append(experts[ename])
del experts[ename]
data = np.stack(datas, axis=0)
data_dtype = data.dtype
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
if self.ftype == 1 and data_dtype == np.float32:
data = data.astype(np.float16)
merged_name = f"layers.{bid}.feed_forward.experts.w{wid}.weight"
new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
continue
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# 1d tensors need to be converted to float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts.keys()}")
@Model.register("GrokForCausalLM")
@ -1080,6 +1349,188 @@ class GrokModel(Model):
super().set_gguf_parameters()
self.gguf_writer.add_name("Grok")
def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
n_experts = self.hparams.get("num_local_experts")
experts = dict()
for name, data_torch in self.get_tensors():
# we don't need these
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.squeeze().numpy()
# process the experts separately
if name.find(".moe.") != -1:
experts[name] = data
if len(experts) >= n_experts:
# merge the experts into a single 3d tensor
for bid in range(block_count):
for wid in ["linear", "linear_1", "linear_v"]:
full = True
for xid in range(n_experts):
ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
if ename not in experts:
full = False
break
if not full:
continue
datas = []
for xid in range(n_experts):
ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
datas.append(experts[ename])
del experts[ename]
data = np.stack(datas, axis=0)
data_dtype = data.dtype
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
if self.ftype == 1 and data_dtype == np.float32:
data = data.astype(np.float16)
merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
continue
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
@Model.register("DbrxForCausalLM")
class DbrxModel(Model):
model_arch = gguf.MODEL_ARCH.DBRX
def set_gguf_parameters(self):
ffn_config = self.hparams["ffn_config"]
attn_config = self.hparams["attn_config"]
self.gguf_writer.add_name(self.hparams["model_type"])
self.gguf_writer.add_block_count(self.hparams["n_layers"])
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
self.gguf_writer.add_head_count(self.hparams["n_heads"])
self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
self.gguf_writer.add_layer_norm_eps(1e-5)
self.gguf_writer.add_file_type(self.ftype)
print(f"gguf: file type = {self.ftype}")
def write_tensors(self):
block_count = self.hparams.get("n_layers")
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
for name, data_torch in self.get_tensors():
n_expert = self.hparams["ffn_config"]["moe_num_experts"]
n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
n_embd = self.hparams["d_model"]
# Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
# original implementation expects (n_expert, n_ff, n_embd) for all experts weights
# But llama.cpp moe graph works differently
# AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
# so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
"ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert}
"ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
experts = False
for exp_tensor_name in exp_tensor_names.keys():
if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
experts = True
data_torch = data_torch.view(n_expert, n_ff, n_embd)
if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
data_torch = data_torch.permute(*permute_tensor)
break
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.squeeze().numpy()
# map tensor names
# In MoE models the ffn tensors are typically most of the model weights,
# and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
# Every other model has the weight names ending in .weight,
# let's assume that is the convention which is not the case for dbrx:
# https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# Most of the codebase that takes in 1D tensors only handles F32 tensors
# and most of the outputs tensors are F32.
if data_dtype != np.float32 and n_dims == 1:
print(f"Can not map tensor {name!r}: all 1D tensors must be F32")
sys.exit()
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and n_dims > 1:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
@Model.register("MiniCPMForCausalLM")
class MiniCPMModel(Model):
@ -1101,7 +1552,7 @@ class MiniCPMModel(Model):
self.gguf_writer.add_tie_lm_head(self.hparams["tie_lm_head"])
def set_vocab(self):
self._set_vocab_hf()
self._set_vocab_llama_hf()
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
if n_kv_head is not None and n_head != n_kv_head:
@ -1702,37 +2153,25 @@ class BertModel(Model):
self.gguf_writer.add_pooling_type(pooling_type)
def set_vocab(self):
path = self.dir_model
added_tokens_path = self.dir_model if self.dir_model.exists() else None
# use huggingface vocab to get all tokens
vocab = HfVocab(path, added_tokens_path)
tokens, scores, toktypes = zip(*vocab.all_tokens())
assert len(tokens) == vocab.vocab_size
self.vocab_size = vocab.vocab_size
tokens, toktypes = self.get_basic_vocab()
self.vocab_size = len(tokens)
# we need this to validate the size of the token_type embeddings
# though currently we are passing all zeros to the token_type embeddings
n_token_types = len(set(toktypes))
self.gguf_writer.add_token_type_count(n_token_types)
self.gguf_writer.add_token_type_count(2) # "Sequence A" or "Sequence B"
# convert to phantom space vocab
def phantom(tok, typ):
if tok.startswith(b"[") and tok.endswith(b"]"):
def phantom(tok):
if tok.startswith("[") and tok.endswith("]"):
return tok
if tok.startswith(b"##"):
if tok.startswith("##"):
return tok[2:]
return b"\xe2\x96\x81" + tok
tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes))
# set up bos and eos tokens (cls and sep)
self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id)
self.gguf_writer.add_eos_token_id(vocab.tokenizer.sep_token_id)
return "\u2581" + tok
tokens = list(map(phantom, tokens))
# add vocab to gguf
self.gguf_writer.add_tokenizer_model("bert")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
# handle special tokens
@ -1804,16 +2243,6 @@ class NomicBertModel(BertModel):
super().set_gguf_parameters()
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
def get_tensors(self):
assert self.vocab_size is not None
for name, data in super().get_tensors():
# Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly.
if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size:
rounded_vocab_size = (self.vocab_size + 63) // 64 * 64
assert data.shape == (rounded_vocab_size, self.hparams["n_embd"])
data = data[:self.vocab_size, :]
yield name, data
@Model.register("GemmaForCausalLM")
class GemmaModel(Model):
@ -1821,6 +2250,13 @@ class GemmaModel(Model):
def set_vocab(self):
self._set_vocab_sentencepiece()
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
special_vocab._set_special_token("prefix", 67)
special_vocab._set_special_token("suffix", 69)
special_vocab._set_special_token("middle", 68)
special_vocab._set_special_token("eot", 70)
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
hparams = self.hparams
@ -1989,7 +2425,8 @@ class MambaModel(Model):
data = data.astype(np.float32)
# if f16 desired, convert big float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and new_name.removesuffix(".weight").endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2:
new_weight_name = new_name[:-len(".weight")] if new_name.endswith(".weight") else ""
if self.ftype == 1 and data_dtype == np.float32 and new_weight_name.endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
@ -2040,6 +2477,7 @@ def parse_args() -> argparse.Namespace:
"model", type=Path,
help="directory containing model file",
)
parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)")
return parser.parse_args()
@ -2083,7 +2521,7 @@ def main() -> None:
with torch.inference_mode():
model_class = Model.from_model_architecture(hparams["architectures"][0])
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file)
print("Set model parameters")
model_instance.set_gguf_parameters()

8
convert-persimmon-to-gguf.py
View file

@ -1,4 +1,6 @@
#!/usr/bin/env python3
from __future__ import annotations
import argparse
import os
import sys
@ -106,12 +108,12 @@ def main():
tensor_map = gguf.get_tensor_name_map(arch, block_count)
print(tensor_map)
for name in tensors.keys():
data = tensors[name]
data_torch = tensors[name]
if name.endswith(".self_attention.rotary_emb.inv_freq"):
continue
old_dtype = data.dtype
old_dtype = data_torch.dtype
# TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
data = data.to(torch.float32).squeeze().numpy()
data = data_torch.to(torch.float32).squeeze().numpy()
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
print("Can not map tensor '" + name + "'")

384
convert.py
View file

@ -16,13 +16,14 @@ import re
import signal
import struct
import sys
import textwrap
import time
import zipfile
from abc import ABCMeta, abstractmethod
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass
from pathlib import Path
from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar
from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable
import numpy as np
from sentencepiece import SentencePieceProcessor
@ -32,7 +33,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
import gguf
if TYPE_CHECKING:
from typing import TypeAlias
from typing_extensions import Self, TypeAlias
if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
faulthandler.register(signal.SIGUSR1)
@ -43,6 +44,9 @@ ARCH = gguf.MODEL_ARCH.LLAMA
DEFAULT_CONCURRENCY = 8
ADDED_TOKENS_FILE = 'added_tokens.json'
FAST_TOKENIZER_FILE = 'tokenizer.json'
#
# data types
#
@ -135,7 +139,8 @@ class GGMLFileType(enum.IntEnum):
dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
if dt is None:
raise ValueError(self)
# 1D tensors are always F32.
# Convert all 1D tensors to F32. Most of the codebase that takes in 1D tensors only handles F32 tensors, and most of the outputs tensors are F32.
# Also The 1d tensors aren't much of a performance/size issue. So instead of having to have separate F32 and F16 implementations of both, just convert everything to F32 for now.
return dt if len(tensor.shape) > 1 else DT_F32
@ -188,8 +193,10 @@ class Params:
n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
if n_layer < 1:
raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
msg = """\
failed to guess 'n_layer'. This model is unknown or unsupported.
Suggestion: provide 'config.json' of the model in the same directory containing model files."""
raise KeyError(textwrap.dedent(msg))
n_head = n_embd // 128 # guessed
n_mult = 256 # guessed
@ -211,7 +218,8 @@ class Params:
@staticmethod
def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
config = json.load(open(config_path))
with open(config_path) as f:
config = json.load(f)
rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
rope_scaling = config.get("rope_scaling")
@ -233,8 +241,10 @@ class Params:
elif "max_position_embeddings" in config:
n_ctx = config["max_position_embeddings"]
else:
raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
msg = """\
failed to guess 'n_ctx'. This model is unknown or unsupported.
Suggestion: provide 'config.json' of the model in the same directory containing model files."""
raise KeyError(textwrap.dedent(msg))
n_experts = None
n_experts_used = None
@ -265,7 +275,8 @@ class Params:
# {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1}
@staticmethod
def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
config = json.load(open(config_path))
with open(config_path) as f:
config = json.load(f)
n_experts = None
n_experts_used = None
@ -331,47 +342,86 @@ class Params:
# vocab
#
class BpeVocab:
@runtime_checkable
class BaseVocab(Protocol):
tokenizer_model: ClassVar[str]
name: ClassVar[str]
class NoVocab(BaseVocab):
tokenizer_model = "no_vocab"
name = "no_vocab"
def __repr__(self) -> str:
return "<NoVocab for a model without integrated vocabulary>"
@runtime_checkable
class Vocab(BaseVocab, Protocol):
vocab_size: int
added_tokens_dict: dict[str, int]
added_tokens_list: list[str]
fname_tokenizer: Path
def __init__(self, base_path: Path): ...
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
class BpeVocab(Vocab):
tokenizer_model = "gpt2"
name = "bpe"
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
if isinstance(self.bpe_tokenizer.get('model'), dict):
self.vocab = self.bpe_tokenizer["model"]["vocab"]
else:
self.vocab = self.bpe_tokenizer
added_tokens: dict[str, int]
if fname_added_tokens is not None:
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
else:
# Fall back to trying to find the added tokens in tokenizer.json
tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json'
if not tokenizer_json_file.is_file():
added_tokens = {}
else:
tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8"))
added_tokens = dict(
(item['content'], item['id'])
for item in tokenizer_json.get('added_tokens', [])
# Added tokens here can be duplicates of the main vocabulary.
if item['content'] not in self.bpe_tokenizer)
def __init__(self, base_path: Path):
added_tokens: dict[str, int] = {}
vocab_size: int = len(self.vocab)
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
actual_ids = sorted(added_tokens.values())
if (fname_tokenizer := base_path / 'vocab.json').exists():
# "slow" tokenizer
with open(fname_tokenizer, encoding="utf-8") as f:
self.vocab = json.load(f)
try:
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
added_tokens = json.load(f)
except FileNotFoundError:
pass
else:
# "fast" tokenizer
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
# if this fails, FileNotFoundError propagates to caller
with open(fname_tokenizer, encoding="utf-8") as f:
tokenizer_json = json.load(f)
tokenizer_model: dict[str, Any] = tokenizer_json['model']
if (
tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
or tokenizer_json['decoder']['type'] != 'ByteLevel'
):
raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
self.vocab = tokenizer_model["vocab"]
if (added := tokenizer_json.get('added_tokens')) is not None:
# Added tokens here can be duplicates of the main vocabulary.
added_tokens = {item['content']: item['id']
for item in added
if item['content'] not in self.vocab}
vocab_size = len(self.vocab)
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
actual_ids = sorted(added_tokens.values())
if expected_ids != actual_ids:
expected_end_id = vocab_size + len(actual_ids) - 1
raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}")
raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
f"{vocab_size} - {expected_end_id}; got {actual_ids}")
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
self.added_tokens_dict = added_tokens
self.added_tokens_list = [text for (text, idx) in items]
self.vocab_size_base: int = vocab_size
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
self.fname_added_tokens = fname_added_tokens
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
@ -392,19 +442,25 @@ class BpeVocab:
return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class SentencePieceVocab:
class SentencePieceVocab(Vocab):
tokenizer_model = "llama"
name = "spm"
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
added_tokens: dict[str, int]
if fname_added_tokens is not None:
added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
else:
added_tokens = {}
def __init__(self, base_path: Path):
added_tokens: dict[str, int] = {}
if (fname_tokenizer := base_path / 'tokenizer.model').exists():
# normal location
try:
with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
added_tokens = json.load(f)
except FileNotFoundError:
pass
elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
# not found in alternate location either
raise FileNotFoundError('Cannot find tokenizer.model')
vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
vocab_size = self.sentencepiece_tokenizer.vocab_size()
new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
@ -414,18 +470,17 @@ class SentencePieceVocab:
raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
# Token pieces that were added to the base vocabulary.
self.added_tokens_dict = added_tokens
self.added_tokens_dict = added_tokens
self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
self.fname_added_tokens = fname_added_tokens
def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.sentencepiece_tokenizer
for i in range(tokenizer.vocab_size()):
piece = tokenizer.id_to_piece(i)
text: bytes = piece.encode("utf-8")
text = piece.encode("utf-8")
score: float = tokenizer.get_score(i)
toktype = gguf.TokenType.NORMAL
@ -458,27 +513,40 @@ class SentencePieceVocab:
return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class HfVocab:
class LlamaHfVocab(Vocab):
tokenizer_model = "llama"
name = "hfft"
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None:
def __init__(self, base_path: Path):
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
# if this fails, FileNotFoundError propagates to caller
with open(fname_tokenizer, encoding='utf-8') as f:
tokenizer_json = json.load(f)
# pre-check so we know if we need transformers
tokenizer_model: dict[str, Any] = tokenizer_json['model']
if (
tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
or tokenizer_json['decoder']['type'] != 'Sequence'
):
raise FileNotFoundError('Cannot find Llama BPE tokenizer')
try:
from transformers import AutoTokenizer
except ImportError as e:
raise ImportError(
"To use HfVocab, please install the `transformers` package. "
"To use LlamaHfVocab, please install the `transformers` package. "
"You can install it with `pip install transformers`."
) from e
print("fname_tokenizer:", fname_tokenizer)
# Allow the tokenizer to default to slow or fast versions.
# Explicitly set tokenizer to use local paths.
self.tokenizer = AutoTokenizer.from_pretrained(
fname_tokenizer,
cache_dir=fname_tokenizer,
base_path,
cache_dir=base_path,
local_files_only=True,
)
assert self.tokenizer.is_fast # assume tokenizer.json is used
# Initialize lists and dictionaries for added tokens
self.added_tokens_list = []
@ -506,8 +574,7 @@ class HfVocab:
self.vocab_size_base = self.tokenizer.vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
self.fname_added_tokens = fname_added_tokens
self.fname_tokenizer = fname_tokenizer
def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
reverse_vocab = {
@ -559,18 +626,7 @@ class HfVocab:
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class NoVocab:
tokenizer_model = "no_vocab"
name = "no_vocab"
def __repr__(self) -> str:
return "<NoVocab for a model without integrated vocabulary>"
Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab | NoVocab"
return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
#
@ -588,17 +644,18 @@ def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
.reshape(weights.shape))
class Tensor(metaclass=ABCMeta):
class Tensor(ABC):
ndarray: NDArray
data_type: DataType
@abstractmethod
def astype(self, data_type: DataType) -> Tensor: ...
def astype(self, data_type: DataType) -> Self: ...
@abstractmethod
def permute(self, n_head: int, n_head_kv: int) -> Tensor: ...
def permute(self, n_head: int, n_head_kv: int) -> Self: ...
@abstractmethod
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ...
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ...
@abstractmethod
def part(self, n_part: int) -> UnquantizedTensor: ...
def part(self, n_part: int) -> Self: ...
@abstractmethod
def to_ggml(self) -> GGMLCompatibleTensor: ...
@ -610,18 +667,18 @@ def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:
class UnquantizedTensor(Tensor):
def __init__(self, ndarray: NDArray) -> None:
def __init__(self, ndarray: NDArray):
assert isinstance(ndarray, np.ndarray)
self.ndarray = ndarray
self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
def astype(self, data_type: DataType) -> Tensor:
def astype(self, data_type: DataType) -> UnquantizedTensor:
dtype = data_type.dtype
if self.data_type == DT_BF16:
self.ndarray = bf16_to_fp32(self.ndarray)
return UnquantizedTensor(self.ndarray.astype(dtype))
def to_ggml(self) -> UnquantizedTensor:
def to_ggml(self) -> Self:
return self
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor:
@ -689,7 +746,7 @@ class ModelPlus:
model: LazyModel
paths: list[Path] # Where this was read from.
format: Literal['ggml', 'torch', 'safetensors', 'none']
vocab: Vocab | None # For GGML models (which have vocab built in), the vocab.
vocab: BaseVocab | None # For GGML models (which have vocab built in), the vocab.
def merge_sharded(models: list[LazyModel]) -> LazyModel:
@ -698,7 +755,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:
names = {name: None for model in models for name in model}
def convert(name: str) -> LazyTensor:
lazy_tensors: list[LazyTensor] = [model[name] for model in models]
lazy_tensors = [model[name] for model in models]
if len(lazy_tensors) == 1:
# only one file; don't go through this procedure since there might
# be quantized tensors
@ -719,7 +776,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:
def load() -> UnquantizedTensor:
ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors]
concatenated: NDArray = np.concatenate(ndarrays, axis=axis)
concatenated = np.concatenate(ndarrays, axis=axis)
return UnquantizedTensor(concatenated)
description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]'
return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description)
@ -771,6 +828,15 @@ def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)
def pack_experts_lazy(lazy_tensors: list[LazyTensor]) -> LazyTensor:
def load() -> Tensor:
tensors = [lazy_tensor.load() for lazy_tensor in lazy_tensors]
return UnquantizedTensor(np.array([tensor.ndarray for tensor in tensors]))
s = lazy_tensors[0].shape.copy()
s.insert(0, len(lazy_tensors))
return LazyTensor(load, s, lazy_tensors[0].data_type, 'pack_experts ' + ' | '.join(lt.description for lt in lazy_tensors))
# Functionality that simulates `torch.load` but where individual tensors are
# only loaded into memory on demand, not all at once.
# PyTorch can't do this natively as of time of writing:
@ -807,10 +873,10 @@ class LazyUnpickler(pickle.Unpickler):
def load(offset: int, elm_count: int) -> NDArray:
dtype = data_type.dtype
fp = self.zip_file.open(info)
fp.seek(offset * dtype.itemsize)
size = elm_count * dtype.itemsize
data = fp.read(size)
with self.zip_file.open(info) as fp:
fp.seek(offset * dtype.itemsize)
size = elm_count * dtype.itemsize
data = fp.read(size)
assert len(data) == size
return np.frombuffer(data, dtype)
description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'
@ -831,7 +897,7 @@ class LazyUnpickler(pickle.Unpickler):
def rebuild_from_type_v2(func, new_type, args, state):
return func(*args)
CLASSES: dict[tuple[str, str], Any] = {
CLASSES = {
# getattr used here as a workaround for mypy not being smart enough to determine
# the staticmethods have a __func__ attribute.
('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
@ -890,7 +956,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
def must_read(fp: IO[bytes], length: int) -> bytes:
ret = fp.read(length)
if len(ret) < length:
raise Exception("unexpectedly reached end of file")
raise EOFError("unexpectedly reached end of file")
return ret
@ -948,13 +1014,14 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
yield result
def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None:
def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) -> None:
# Handle special case where the model's vocab size is not set
if params.n_vocab == -1:
raise ValueError(
f"The model's vocab size is set to -1 in params.json. Please update it manually.{f' Maybe {vocab.vocab_size}?' if hasattr(vocab, 'vocab_size') else ''}"
"The model's vocab size is set to -1 in params.json. Please update it manually."
+ (f" Maybe {vocab.vocab_size}?" if isinstance(vocab, Vocab) else ""),
)
if isinstance(vocab, NoVocab):
if not isinstance(vocab, Vocab):
return # model has no vocab
# Check for a vocab size mismatch
@ -979,11 +1046,11 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
if vocab.vocab_size < params.n_vocab:
msg += " Add the --pad-vocab option and try again."
raise Exception(msg)
raise ValueError(msg)
class OutputFile:
def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE):
self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
def add_meta_arch(self, params: Params) -> None:
@ -1034,8 +1101,6 @@ class OutputFile:
self.gguf.add_file_type(params.ftype)
def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
assert not isinstance(vocab, NoVocab)
tokens = []
scores = []
toktypes = []
@ -1135,7 +1200,7 @@ class OutputFile:
@staticmethod
def write_all(
fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab,
fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab,
concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
pad_vocab: bool = False,
) -> None:
@ -1145,11 +1210,11 @@ class OutputFile:
# meta data
of.add_meta_arch(params)
if isinstance(vocab, NoVocab):
of.gguf.add_tokenizer_model(vocab.tokenizer_model)
else:
if isinstance(vocab, Vocab):
of.add_meta_vocab(vocab)
of.add_meta_special_vocab(svocab)
else: # NoVocab
of.gguf.add_tokenizer_model(vocab.tokenizer_model)
# tensor info
for name, lazy_tensor in model.items():
@ -1176,7 +1241,7 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT
name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
raise Exception(f"Unexpected combination of types: {name_to_type}")
raise ValueError(f"Unexpected combination of types: {name_to_type}")
def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
@ -1186,10 +1251,26 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel:
tmap = gguf.TensorNameMap(ARCH, params.n_layer)
should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
should_skip = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
tmp = model
# merge experts into one tensor
if params.n_experts and params.n_experts > 0:
for i_l in range(params.n_layer):
for w in range(1, 4):
experts = []
for e in range(params.n_experts):
if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model:
experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"])
del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]
elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model:
experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"])
del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]
else:
raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight")
tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts)
# HF models permut or pack some of the tensors, so we need to undo that
for i in itertools.count():
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
@ -1213,8 +1294,7 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->
if skip_unknown:
print(f"Unexpected tensor name: {name} - skipping")
continue
else:
raise Exception(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
if tensor_type in should_skip:
print(f"skipping tensor {name_new}")
@ -1231,7 +1311,7 @@ def nth_multifile_path(path: Path, n: int) -> Path | None:
the nth path in the model.
'''
# Support the following patterns:
patterns: list[tuple[str, str]] = [
patterns = [
# - x.00.pth, x.01.pth, etc.
(r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'),
# - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.
@ -1270,16 +1350,16 @@ def load_some_model(path: Path) -> ModelPlus:
# Be extra-friendly and accept either a file or a directory:
if path.is_dir():
# Check if it's a set of safetensors files first
globs = ["model-00001-of-*.safetensors", "model.safetensors"]
globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"]
files = [file for glob in globs for file in path.glob(glob)]
if not files:
# Try the PyTorch patterns too, with lower priority
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
files = [file for glob in globs for file in path.glob(glob)]
if not files:
raise Exception(f"Can't find model in directory {path}")
raise FileNotFoundError(f"Can't find model in directory {path}")
if len(files) > 1:
raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}")
raise ValueError(f"Found multiple models in {path}, not sure which to pick: {files}")
path = files[0]
paths = find_multifile_paths(path)
@ -1293,36 +1373,14 @@ def load_some_model(path: Path) -> ModelPlus:
class VocabFactory:
_FILES = {"spm": "tokenizer.model", "bpe": "vocab.json", "hfft": "tokenizer.json"}
_VOCAB_CLASSES: list[type[Vocab]] = [SentencePieceVocab, BpeVocab, LlamaHfVocab]
def __init__(self, path: Path):
self.path = path
self.file_paths = self._detect_files()
print(f"Found vocab files: {self.file_paths}")
def _detect_files(self) -> dict[str, Path | None]:
def locate(file: str) -> Path | None:
if (path := self.path / file).exists():
return path
if (path := self.path.parent / file).exists():
return path
return None
return {vt: locate(f) for vt, f in self._FILES.items()}
def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]:
for vtype in vocab_types:
try:
path = self.file_paths[vtype]
except KeyError:
raise ValueError(f"Unsupported vocabulary type {vtype}") from None
if path is not None:
return vtype, path
raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}")
def _create_special_vocab(self, vocab: Vocab, model_parent_path: Path) -> gguf.SpecialVocab:
def _create_special_vocab(self, vocab: BaseVocab, model_parent_path: Path) -> gguf.SpecialVocab:
load_merges = vocab.name == "bpe"
n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
n_vocab = vocab.vocab_size if isinstance(vocab, Vocab) else None
return gguf.SpecialVocab(
model_parent_path,
load_merges=load_merges,
@ -1331,27 +1389,29 @@ class VocabFactory:
)
def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
vocab_type, path = self._select_file(vocab_types)
print(f"Loading vocab file {path!r}, type {vocab_type!r}")
vocab_classes: dict[str, type[Vocab]] = {cls.name: cls for cls in self._VOCAB_CLASSES}
selected_vocabs: dict[str, type[Vocab]] = {}
for vtype in vocab_types:
try:
selected_vocabs[vtype] = vocab_classes[vtype]
except KeyError:
raise ValueError(f"Unsupported vocabulary type {vtype}") from None
added_tokens_path = path.parent / "added_tokens.json"
if vocab_type == "bpe":
return BpeVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
if vocab_type == "spm":
return SentencePieceVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
if vocab_type == "hfft":
return HfVocab(
path.parent, added_tokens_path if added_tokens_path.exists() else None
)
raise ValueError(vocab_type)
for vtype, cls in selected_vocabs.items():
try:
vocab = cls(self.path)
break
except FileNotFoundError:
pass # ignore unavailable tokenizers
else:
raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}")
def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
vocab: Vocab
if len(vocab_types) == 1 and "no_vocab" in vocab_types:
print(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
return vocab
def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]:
vocab: BaseVocab
if vocab_types is None:
vocab = NoVocab()
else:
vocab = self._create_vocab_by_path(vocab_types)
@ -1408,10 +1468,8 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing")
args = parser.parse_args(args_in)
if args.no_vocab:
if args.vocab_only:
raise ValueError("no need to specify --vocab-only if using --no-vocab")
args.vocab_type = "no_vocab"
if args.no_vocab and args.vocab_only:
raise ValueError("--vocab-only does not make sense with --no-vocab")
if args.dump_single:
model_plus = lazy_load_file(args.model)
@ -1433,10 +1491,12 @@ def main(args_in: list[str] | None = None) -> None:
params = Params.load(model_plus)
if params.n_ctx == -1:
if args.ctx is None:
raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n"
"Please specify one with --ctx:\n"
" - LLaMA v1: --ctx 2048\n"
" - LLaMA v2: --ctx 4096\n")
msg = """\
The model doesn't have a context size, and you didn't specify one with --ctx
Please specify one with --ctx:
- LLaMA v1: --ctx 2048
- LLaMA v2: --ctx 4096"""
parser.error(textwrap.dedent(msg))
params.n_ctx = args.ctx
if args.outtype:
@ -1451,9 +1511,11 @@ def main(args_in: list[str] | None = None) -> None:
model_parent_path = model_plus.paths[0].parent
vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
vocab_factory = VocabFactory(vocab_path)
vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type.split(","), model_parent_path)
vocab_types = None if args.no_vocab else args.vocab_type.split(",")
vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path)
if args.vocab_only:
assert isinstance(vocab, Vocab)
if not args.outfile:
raise ValueError("need --outfile if using --vocab-only")
outfile = args.outfile

119
docs/HOWTO-add-model.md Normal file
View file

@ -0,0 +1,119 @@
## Add a new model architecture to `llama.cpp`
Adding a model requires few steps:
1. Convert the model to GGUF
2. Define the model architecture in `llama.cpp`
3. Build the GGML graph implementation
After following these steps, you can open PR.
Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
- [main](../examples/main)
- [imatrix](../examples/imatrix)
- [quantize](../examples/quantize)
- [server](../examples/server)
### 1. Convert the model to GGUF
This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py).
The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
The required steps to implement for an HF model are:
1. Define the model `Model.register` annotation in a new `Model` subclass, example:
```python
@Model.register("MyModelForCausalLM")
class MyModel(Model):
model_arch = gguf.MODEL_ARCH.GROK
```
2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py)
Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`.
Example for `falcon` model:
```python
MODEL_ARCH.FALCON: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_NORM_2,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
]
```
3. Map the original tensor names to the standardize equivalent in GGUF
As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.
Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file.
If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it.
Example for the normalization tensor in attention layers:
```python
block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
# Attention norm
MODEL_TENSOR.ATTN_NORM: (
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
"transformer.blocks.{bid}.norm_1", # mpt
...
)
}
```
`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF.
Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:
- `Model#set_gguf_parameters`
- `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.
### 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`
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`
NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions.
### 3. Build the GGML graph implementation
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 to existing implementation 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 of missing backend operations can be added in another PR.
Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback).
## GGUF specification
https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
## Resources
- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268
- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009
- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283
- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406
- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423
- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204
- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491
- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515
- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948

1
examples/CMakeLists.txt
View file

@ -19,6 +19,7 @@ else()
add_subdirectory(benchmark)
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(embedding)
add_subdirectory(eval-callback)
add_subdirectory(finetune)
add_subdirectory(gritlm)
add_subdirectory(gguf-split)

8
examples/batched-bench/README.md
View file

@ -10,16 +10,16 @@ There are 2 modes of operation:
- `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
```bash
./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
./batched-bench MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99
./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 2048 512 0 99
# LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99
./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 2048 512 1 99
# custom set of batches
./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32
./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 512 512 0 999 0 128,256,512 128,256 1,2,4,8,16,32
```
## Sample results

29
examples/batched-bench/batched-bench.cpp
View file

@ -32,13 +32,15 @@ int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
printf(" <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
printf(" example: %s ggml-model-f16.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
printf(" example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
return 1 ;
}
int n_kv_max = 2048;
int n_batch = 2048;
int n_ubatch = 512;
int is_pp_shared = 0;
int n_gpu_layers = 0;
@ -56,23 +58,31 @@ int main(int argc, char ** argv) {
}
if (argc >= 4) {
is_pp_shared = std::atoi(argv[3]);
n_batch = std::atoi(argv[3]);
}
if (argc >= 5) {
n_gpu_layers = std::atoi(argv[4]);
n_ubatch = std::atoi(argv[4]);
}
if (argc >= 6) {
n_pp = parse_list(argv[5]);
is_pp_shared = std::atoi(argv[5]);
}
if (argc >= 7) {
n_tg = parse_list(argv[6]);
n_gpu_layers = std::atoi(argv[6]);
}
if (argc >= 8) {
n_pl = parse_list(argv[7]);
n_pp = parse_list(argv[7]);
}
if (argc >= 9) {
n_tg = parse_list(argv[8]);
}
if (argc >= 10) {
n_pl = parse_list(argv[9]);
}
// init LLM
@ -100,7 +110,8 @@ int main(int argc, char ** argv) {
ctx_params.seed = 1234;
ctx_params.n_ctx = n_kv_max;
ctx_params.n_batch = 512;
ctx_params.n_batch = n_batch;
ctx_params.n_ubatch = n_ubatch;
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
@ -158,7 +169,7 @@ int main(int argc, char ** argv) {
}
LOG_TEE("\n");
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("\n");
LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");

6
examples/embedding/embedding.cpp
View file

@ -123,10 +123,10 @@ int main(int argc, char ** argv) {
inputs.push_back(inp);
}
// add eos if not present
// add SEP if not present
for (auto & inp : inputs) {
if (inp.empty() || inp.back() != llama_token_eos(model)) {
inp.push_back(llama_token_eos(model));
if (inp.empty() || inp.back() != llama_token_sep(model)) {
inp.push_back(llama_token_sep(model));
}
}

9
examples/eval-callback/CMakeLists.txt Normal file
View file

@ -0,0 +1,9 @@
set(TARGET eval-callback)
add_executable(${TARGET} eval-callback.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
set(TEST_TARGET test-eval-callback)
add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl)

95
examples/eval-callback/README.md Normal file
View file

@ -0,0 +1,95 @@
# llama.cpp/examples/eval-callback
A simple example which demonstrates how to use callback during the inference.
It simply prints to the console all operations and tensor data.
Usage:
```shell
eval-callback \
--hf-repo ggml-org/models \
--hf-file phi-2/ggml-model-q4_0.gguf \
--model phi-2-q4_0.gguf \
--prompt hello \
--seed 42 \
-ngl 33
```
Will print:
```shell
llm_load_tensors: offloaded 33/33 layers to GPU
...
llama_new_context_with_model: n_ctx = 512
...
llama_new_context_with_model: CUDA0 compute buffer size = 105.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 6.01 MiB
llama_new_context_with_model: graph nodes = 1225
llama_new_context_with_model: graph splits = 2
ggml_debug: inp_embd = (f32) GET_ROWS(token_embd.weight{2560, 51200, 1, 1}, inp_tokens{1, 1, 1, 1}}) = {2560, 1, 1, 1}
[
[
[ -0.0181, 0.0272, 0.0272, ...],
],
]
ggml_debug: norm-0 = (f32) NORM(CUDA0#inp_embd#0{2560, 1, 1, 1}, }) = {2560, 1, 1, 1}
[
[
[ -0.6989, 1.0636, 1.0636, ...],
],
]
ggml_debug: norm_w-0 = (f32) MUL(norm-0{2560, 1, 1, 1}, blk.0.attn_norm.weight{2560, 1, 1, 1}}) = {2560, 1, 1, 1}
[
[
[ -0.1800, 0.2817, 0.2632, ...],
],
]
ggml_debug: attn_norm-0 = (f32) ADD(norm_w-0{2560, 1, 1, 1}, blk.0.attn_norm.bias{2560, 1, 1, 1}}) = {2560, 1, 1, 1}
[
[
[ -0.1863, 0.2970, 0.2604, ...],
],
]
ggml_debug: wqkv-0 = (f32) MUL_MAT(blk.0.attn_qkv.weight{2560, 7680, 1, 1}, attn_norm-0{2560, 1, 1, 1}}) = {7680, 1, 1, 1}
[
[
[ -1.1238, 1.2876, -1.8086, ...],
],
]
ggml_debug: bqkv-0 = (f32) ADD(wqkv-0{7680, 1, 1, 1}, blk.0.attn_qkv.bias{7680, 1, 1, 1}}) = {7680, 1, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
],
]
ggml_debug: bqkv-0 (view) = (f32) VIEW(bqkv-0{7680, 1, 1, 1}, }) = {2560, 1, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
],
]
ggml_debug: Qcur-0 = (f32) CONT(bqkv-0 (view){2560, 1, 1, 1}, }) = {2560, 1, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
],
]
ggml_debug: Qcur-0 (reshaped) = (f32) RESHAPE(Qcur-0{2560, 1, 1, 1}, }) = {80, 32, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
[ -0.3608, 0.5076, -1.8866, ...],
[ 1.7643, 0.0273, -2.1065, ...],
...
],
]
ggml_debug: Qcur-0 = (f32) ROPE(Qcur-0 (reshaped){80, 32, 1, 1}, CUDA0#inp_pos#0{1, 1, 1, 1}}) = {80, 32, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
[ -0.3608, 0.5076, -1.8866, ...],
[ 1.7643, 0.0273, -2.1065, ...],
...
],
]
```

195
examples/eval-callback/eval-callback.cpp Normal file
View file

@ -0,0 +1,195 @@
#include "common.h"
#include "llama.h"
#include "ggml.h"
#include <cstdio>
#include <random>
#include <string>
#include <tuple>
#include <vector>
/**
* This the arbitrary data which will be passed to each callback.
* Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor.
*/
struct callback_data {
std::vector<uint8_t> data;
};
static std::string ggml_ne_string(const ggml_tensor * t) {
std::string str;
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
str += std::to_string(t->ne[i]);
if (i + 1 < GGML_MAX_DIMS) {
str += ", ";
}
}
return str;
}
static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
GGML_ASSERT(n > 0);
float sum = 0;
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
printf(" [\n");
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
if (i2 == n && ne[2] > 2*n) {
printf(" ..., \n");
i2 = ne[2] - n;
}
printf(" [\n");
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
if (i1 == n && ne[1] > 2*n) {
printf(" ..., \n");
i1 = ne[1] - n;
}
printf(" [");
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
if (i0 == n && ne[0] > 2*n) {
printf("..., ");
i0 = ne[0] - n;
}
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
float v;
if (type == GGML_TYPE_F16) {
v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i);
} else if (type == GGML_TYPE_F32) {
v = *(float *) data + i;
} else if (type == GGML_TYPE_I32) {
v = (float) *(int32_t *) data + i;
} else if (type == GGML_TYPE_I16) {
v = (float) *(int16_t *) data + i;
} else if (type == GGML_TYPE_I8) {
v = (float) *(int8_t *) data + i;
} else {
GGML_ASSERT(false);
}
printf("%12.4f", v);
sum += v;
if (i0 < ne[0] - 1) printf(", ");
}
printf("],\n");
}
printf(" ],\n");
}
printf(" ]\n");
printf(" sum = %f\n", sum);
}
}
/**
* GGML operations callback during the graph execution.
*
* @param t current tensor
* @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
* if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
* see ggml_backend_sched_eval_callback
* @param user_data user data to pass at each call back
* @return true to receive data or continue the graph, false otherwise
*/
static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
auto * cb_data = (callback_data *) user_data;
const struct ggml_tensor * src0 = t->src[0];
const struct ggml_tensor * src1 = t->src[1];
if (ask) {
return true; // Always retrieve data
}
char src1_str[128] = {0};
if (src1) {
sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
}
printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
t->name, ggml_type_name(t->type), ggml_op_desc(t),
src0->name, ggml_ne_string(src0).c_str(),
src1 ? src1_str : "",
ggml_ne_string(t).c_str());
// copy the data from the GPU memory if needed
const bool is_host = ggml_backend_buffer_is_host(t->buffer);
if (!is_host) {
auto n_bytes = ggml_nbytes(t);
cb_data->data.resize(n_bytes);
ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
}
if (!ggml_is_quantized(t->type)) {
uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
}
return true;
}
static bool run(llama_context * ctx, const gpt_params & params) {
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
return true;
}
int main(int argc, char ** argv) {
callback_data cb_data;
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
print_build_info();
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_backend_init();
llama_numa_init(params.numa);
// pass the callback to the backend scheduler
// it will be executed for each node during the graph computation
params.cb_eval = ggml_debug;
params.cb_eval_user_data = &cb_data;
params.warmup = false;
// init
llama_model * model;
llama_context * ctx;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == nullptr || ctx == nullptr) {
fprintf(stderr, "%s : failed to init\n", __func__);
return 1;
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "%s\n", get_system_info(params).c_str());
}
bool OK = run(ctx, params);
if (!OK) {
return 1;
}
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

5
examples/gbnf-validator/CMakeLists.txt Normal file
View file

@ -0,0 +1,5 @@
set(TARGET gbnf-validator)
add_executable(${TARGET} gbnf-validator.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common grammar-parser llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

132
examples/gbnf-validator/gbnf-validator.cpp Normal file
View file

@ -0,0 +1,132 @@
#define LLAMA_API_INTERNAL
#include "grammar-parser.h"
#include "ggml.h"
#include "llama.h"
#include "unicode.h"
#include <cstdio>
#include <cstdlib>
#include <string>
#include <vector>
static bool llama_sample_grammar_string(struct llama_grammar * grammar, const std::string & input_str, size_t & error_pos, std::string & error_msg) {
auto decoded = decode_utf8(input_str, {});
const auto & code_points = decoded.first;
size_t pos = 0;
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
auto prev_stacks = grammar->stacks;
llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
if (grammar->stacks.empty()) {
error_pos = pos;
error_msg = "Unexpected character '" + unicode_cpt_to_utf8(*it) + "'";
grammar->stacks = prev_stacks;
return false;
}
++pos;
}
for (const auto & stack : grammar->stacks) {
if (stack.empty()) {
return true;
}
}
error_pos = pos;
error_msg = "Unexpected end of input";
return false;
}
static void print_error_message(const std::string & input_str, size_t error_pos, const std::string & error_msg) {
fprintf(stdout, "Input string is invalid according to the grammar.\n");
fprintf(stdout, "Error: %s at position %zu\n", error_msg.c_str(), error_pos);
fprintf(stdout, "\n");
fprintf(stdout, "Input string:\n");
fprintf(stdout, "%s", input_str.substr(0, error_pos).c_str());
if (error_pos < input_str.size()) {
fprintf(stdout, "\033[1;31m%c", input_str[error_pos]);
if (error_pos+1 < input_str.size()) {
fprintf(stdout, "\033[0;31m%s", input_str.substr(error_pos+1).c_str());
}
fprintf(stdout, "\033[0m\n");
}
}
int main(int argc, char** argv) {
if (argc != 3) {
fprintf(stdout, "Usage: %s <grammar_filename> <input_filename>\n", argv[0]);
return 1;
}
const std::string grammar_filename = argv[1];
const std::string input_filename = argv[2];
// Read the GBNF grammar file
FILE* grammar_file = fopen(grammar_filename.c_str(), "r");
if (!grammar_file) {
fprintf(stdout, "Failed to open grammar file: %s\n", grammar_filename.c_str());
return 1;
}
fseek(grammar_file, 0, SEEK_END);
size_t grammar_size = ftell(grammar_file);
fseek(grammar_file, 0, SEEK_SET);
std::string grammar_str(grammar_size, ' ');
fread(&grammar_str[0], 1, grammar_size, grammar_file);
fclose(grammar_file);
// Parse the GBNF grammar
auto parsed_grammar = grammar_parser::parse(grammar_str.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
fprintf(stdout, "%s: failed to parse grammar\n", __func__);
return 1;
}
// Ensure that there is a "root" node.
if (parsed_grammar.symbol_ids.find("root") == parsed_grammar.symbol_ids.end()) {
fprintf(stdout, "%s: grammar does not contain a 'root' symbol\n", __func__);
return 1;
}
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
// Create the LLAMA grammar
auto grammar = llama_grammar_init(
grammar_rules.data(),
grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
// Read the input file
FILE* input_file = fopen(input_filename.c_str(), "r");
if (!input_file) {
fprintf(stdout, "Failed to open input file: %s\n", input_filename.c_str());
return 1;
}
fseek(input_file, 0, SEEK_END);
size_t input_size = ftell(input_file);
fseek(input_file, 0, SEEK_SET);
std::string input_str(input_size, ' ');
fread(&input_str[0], 1, input_size, input_file);
fclose(input_file);
// Validate the input string against the grammar
size_t error_pos;
std::string error_msg;
bool is_valid = llama_sample_grammar_string(grammar, input_str, error_pos, error_msg);
if (is_valid) {
fprintf(stdout, "Input string is valid according to the grammar.\n");
} else {
print_error_message(input_str, error_pos, error_msg);
}
// Clean up
llama_grammar_free(grammar);
return 0;
}

1
examples/gguf-split/README.md
View file

@ -5,5 +5,6 @@ CLI to split / merge GGUF files.
**Command line options:**
- `--split`: split GGUF to multiple GGUF, default operation.
- `--split-max-size`: max size per split in `M` or `G`, f.ex. `500M` or `2G`.
- `--split-max-tensors`: maximum tensors in each split: default(128)
- `--merge`: merge multiple GGUF to a single GGUF.

297
examples/gguf-split/gguf-split.cpp
View file

@ -28,9 +28,11 @@ enum split_operation : uint8_t {
struct split_params {
split_operation operation = SPLIT_OP_SPLIT;
size_t n_bytes_split = 0;
int n_split_tensors = 128;
std::string input;
std::string output;
bool dry_run = false;
};
static void split_print_usage(const char * executable) {
@ -41,15 +43,36 @@ static void split_print_usage(const char * executable) {
printf("Apply a GGUF operation on IN to OUT.");
printf("\n");
printf("options:\n");
printf(" -h, --help show this help message and exit\n");
printf(" --version show version and build info\n");
printf(" --split split GGUF to multiple GGUF (default)\n");
printf(" --split-max-tensors max tensors in each split: default(%d)\n", default_params.n_split_tensors);
printf(" --merge merge multiple GGUF to a single GGUF\n");
printf(" -h, --help show this help message and exit\n");
printf(" --version show version and build info\n");
printf(" --split split GGUF to multiple GGUF (enabled by default)\n");
printf(" --merge merge multiple GGUF to a single GGUF\n");
printf(" --split-max-tensors max tensors in each split (default: %d)\n", default_params.n_split_tensors);
printf(" --split-max-size N(M|G) max size per split\n");
printf(" --dry-run only print out a split plan and exit, without writing any new files\n");
printf("\n");
}
static bool split_params_parse_ex(int argc, const char ** argv, split_params & params) {
// return convert string, for example "128M" or "4G" to number of bytes
static size_t split_str_to_n_bytes(std::string str) {
size_t n_bytes = 0;
int n;
if (str.back() == 'M') {
sscanf(str.c_str(), "%d", &n);
n_bytes = (size_t)n * 1024 * 1024; // megabytes
} else if (str.back() == 'G') {
sscanf(str.c_str(), "%d", &n);
n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes
} else {
throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
}
if (n <= 0) {
throw std::invalid_argument("error: size must be a positive value");
}
return n_bytes;
}
static void split_params_parse_ex(int argc, const char ** argv, split_params & params) {
std::string arg;
const std::string arg_prefix = "--";
bool invalid_param = false;
@ -62,6 +85,8 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
}
bool arg_found = false;
bool is_op_set = false;
bool is_mode_set = false;
if (arg == "-h" || arg == "--help") {
split_print_usage(argv[0]);
exit(0);
@ -71,23 +96,46 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
exit(0);
}
if (arg == "--dry-run") {
arg_found = true;
params.dry_run = true;
}
if (is_op_set) {
throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
}
if (arg == "--merge") {
arg_found = true;
is_op_set = true;
params.operation = SPLIT_OP_MERGE;
}
if (arg == "--split") {
arg_found = true;
is_op_set = true;
params.operation = SPLIT_OP_SPLIT;
}
if (is_mode_set) {
throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both");
}
if (arg == "--split-max-tensors") {
if (++arg_idx >= argc) {
invalid_param = true;
break;
}
arg_found = true;
is_mode_set = true;
params.n_split_tensors = atoi(argv[arg_idx]);
}
if (arg == "--split-max-size") {
if (++arg_idx >= argc) {
invalid_param = true;
break;
}
arg_found = true;
is_mode_set = true;
params.n_bytes_split = split_str_to_n_bytes(argv[arg_idx]);
}
if (!arg_found) {
throw std::invalid_argument("error: unknown argument: " + arg);
@ -99,24 +147,17 @@ static bool split_params_parse_ex(int argc, const char ** argv, split_params & p
}
if (argc - arg_idx < 2) {
printf("%s: bad arguments\n", argv[0]);
split_print_usage(argv[0]);
return false;
throw std::invalid_argument("error: bad arguments");
}
params.input = argv[arg_idx++];
params.output = argv[arg_idx++];
return true;
}
static bool split_params_parse(int argc, const char ** argv, split_params & params) {
bool result = true;
try {
if (!split_params_parse_ex(argc, argv, params)) {
split_print_usage(argv[0]);
exit(EXIT_FAILURE);
}
split_params_parse_ex(argc, argv, params);
}
catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
@ -140,15 +181,11 @@ struct split_strategy {
struct ggml_context * ctx_meta = NULL;
const int n_tensors;
const int n_split;
int i_split = 0;
// one ctx_out per one output file
std::vector<struct gguf_context *> ctx_outs;
int i_tensor = 0;
std::vector<uint8_t> read_data;
struct gguf_context * ctx_out;
std::ofstream fout;
// temporary buffer for reading in tensor data
std::vector<uint8_t> read_buf;
split_strategy(const split_params & params,
std::ifstream & f_input,
@ -158,79 +195,141 @@ struct split_strategy {
f_input(f_input),
ctx_gguf(ctx_gguf),
ctx_meta(ctx_meta),
n_tensors(gguf_get_n_tensors(ctx_gguf)),
n_split(std::ceil(1. * n_tensors / params.n_split_tensors)) {
n_tensors(gguf_get_n_tensors(ctx_gguf)) {
// because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits
int i_split = -1;
struct gguf_context * ctx_out = NULL;
auto new_ctx_out = [&]() {
i_split++;
if (ctx_out != NULL) {
if (gguf_get_n_tensors(ctx_out) == 0) {
fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n");
exit(EXIT_FAILURE);
}
ctx_outs.push_back(ctx_out);
}
ctx_out = gguf_init_empty();
// Save all metadata in first split only
if (i_split == 0) {
gguf_set_kv(ctx_out, ctx_gguf);
}
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, 0); // placeholder
gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
};
// initialize ctx_out for the first split
new_ctx_out();
// process tensors one by one
size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata)
for (int i = 0; i < n_tensors; ++i) {
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
// calculate the "imaginary" size = the current size + next tensor size
size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT);
size_t next_tensors_size = curr_tensors_size + n_bytes;
if (should_split(i, next_tensors_size)) {
new_ctx_out();
curr_tensors_size = n_bytes;
} else {
curr_tensors_size = next_tensors_size;
}
gguf_add_tensor(ctx_out, t);
}
bool should_split() const {
return i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
// push the last ctx_out
ctx_outs.push_back(ctx_out);
// set the correct n_split for all ctx_out
for (auto & ctx : ctx_outs) {
gguf_set_val_u16(ctx, LLM_KV_SPLIT_COUNT, ctx_outs.size());
}
}
void split_start() {
ctx_out = gguf_init_empty();
// Save all metadata in first split only
if (i_split == 0) {
gguf_set_kv(ctx_out, ctx_gguf);
~split_strategy() {
for (auto & ctx_out : ctx_outs) {
gguf_free(ctx_out);
}
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, n_split);
gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
// populate the original tensors, so we get an initial metadata
for (int i = i_split * params.n_split_tensors; i < n_tensors && i < (i_split + 1) * params.n_split_tensors; ++i) {
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
gguf_add_tensor(ctx_out, meta);
}
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
fprintf(stderr, "%s: %s ...", __func__, split_path);
fout = std::ofstream(split_path, std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
auto meta_size = gguf_get_meta_size(ctx_out);
// placeholder for the meta data
::zeros(fout, meta_size);
i_split++;
}
void next_tensor() {
const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
auto n_bytes = ggml_nbytes(t);
if (read_data.size() < n_bytes) {
read_data.resize(n_bytes);
bool should_split(int i_tensor, size_t next_size) {
if (params.n_bytes_split > 0) {
// split by max size per file
return next_size > params.n_bytes_split;
} else {
// split by number of tensors per file
return i_tensor > 0 && i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
}
auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
f_input.seekg(offset);
f_input.read((char *)read_data.data(), n_bytes);
t->data = read_data.data();
// write tensor data + padding
fout.write((const char *)t->data, n_bytes);
zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
i_tensor++;
}
void split_end() {
// go back to beginning of file and write the updated metadata
fout.seekp(0);
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
gguf_get_meta_data(ctx_out, data.data());
fout.write((const char *)data.data(), data.size());
void print_info() {
printf("n_split: %ld\n", ctx_outs.size());
int i_split = 0;
for (auto & ctx_out : ctx_outs) {
// re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
size_t total_size = gguf_get_meta_size(ctx_out);
for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
total_size += ggml_nbytes(t);
}
total_size = total_size / 1024 / 1024; // convert to megabytes
printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
i_split++;
}
}
fout.close();
gguf_free(ctx_out);
void write() {
int i_split = 0;
int n_split = ctx_outs.size();
for (auto & ctx_out : ctx_outs) {
// construct file path
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
fprintf(stderr, "\033[3Ddone\n");
// open the output file
printf("Writing file %s ... ", split_path);
fflush(stdout);
std::ofstream fout = std::ofstream(split_path, std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
// write metadata
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
gguf_get_meta_data(ctx_out, data.data());
fout.write((const char *)data.data(), data.size());
// write tensors
for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
// read tensor meta and prepare buffer
const char * t_name = gguf_get_tensor_name(ctx_out, i);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
auto n_bytes = ggml_nbytes(t);
read_buf.resize(n_bytes);
// calculate offset
auto i_tensor_in = gguf_find_tensor(ctx_gguf, t_name); // idx of tensor in the input file
auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
// copy tensor from input to output file
copy_file_to_file(f_input, fout, offset, n_bytes);
zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
}
printf("done\n");
// close the file
fout.close();
i_split++;
}
}
void copy_file_to_file(std::ifstream & f_in, std::ofstream & f_out, const size_t in_offset, const size_t len) {
// TODO: detect OS and use copy_file_range() here for better performance
if (read_buf.size() < len) {
read_buf.resize(len);
}
f_in.seekg(in_offset);
f_in.read((char *)read_buf.data(), len);
f_out.write((const char *)read_buf.data(), len);
}
};
@ -254,32 +353,22 @@ static void gguf_split(const split_params & split_params) {
exit(EXIT_FAILURE);
}
// prepare the strategy
split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
int n_split = strategy.ctx_outs.size();
strategy.print_info();
char first_split_path[PATH_MAX] = {0};
llama_split_path(first_split_path, sizeof(first_split_path),
split_params.output.c_str(), strategy.i_split, strategy.n_split);
fprintf(stderr, "%s: %s -> %s (%d tensors per file)\n",
__func__, split_params.input.c_str(),
first_split_path,
split_params.n_split_tensors);
strategy.split_start();
while (strategy.i_tensor < strategy.n_tensors) {
strategy.next_tensor();
if (strategy.should_split()) {
strategy.split_end();
strategy.split_start();
}
if (!split_params.dry_run) {
// write all output splits
strategy.write();
}
strategy.split_end();
// done, clean up
gguf_free(ctx_gguf);
f_input.close();
fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n",
__func__, strategy.n_split, strategy.n_tensors);
__func__, n_split, strategy.n_tensors);
}
static void gguf_merge(const split_params & split_params) {
@ -448,10 +537,6 @@ static void gguf_merge(const split_params & split_params) {
}
int main(int argc, const char ** argv) {
if (argc < 3) {
split_print_usage(argv[0]);
}
split_params params;
split_params_parse(argc, argv, params);

89
examples/gguf-split/tests.sh Normal file
View file

@ -0,0 +1,89 @@
#!/bin/bash
set -eu
if [ $# -lt 1 ]
then
echo "usage: $0 path_to_build_binary [path_to_temp_folder]"
echo "example: $0 ../../build/bin ../../tmp"
exit 1
fi
if [ $# -gt 1 ]
then
TMP_DIR=$2
else
TMP_DIR=/tmp
fi
set -x
SPLIT=$1/gguf-split
MAIN=$1/main
WORK_PATH=$TMP_DIR/gguf-split
CUR_DIR=$(pwd)
mkdir -p "$WORK_PATH"
# Clean up in case of previously failed test
rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf
# 1. Get a model
(
cd $WORK_PATH
"$CUR_DIR"/../../scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
)
echo PASS
# 2. Split with max tensors strategy
$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split
echo PASS
echo
# 2b. Test the sharded model is loading properly
$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32
echo PASS
echo
# 3. Merge
$SPLIT --merge $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-merge.gguf
echo PASS
echo
# 3b. Test the merged model is loading properly
$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32
echo PASS
echo
# 4. Split with no tensor in metadata
#$SPLIT --split-max-tensors 32 --no-tensor-in-metadata $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors
#echo PASS
#echo
# 4b. Test the sharded model is loading properly
#$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf --random-prompt --n-predict 32
#echo PASS
#echo
# 5. Merge
#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf
#echo PASS
#echo
# 5b. Test the merged model is loading properly
#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32
#echo PASS
#echo
# 6. Split with size strategy
$SPLIT --split-max-size 2G $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-2G
echo PASS
echo
# 6b. Test the sharded model is loading properly
$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32
echo PASS
echo
# Clean up
rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf

15
examples/gguf/gguf.cpp
View file

@ -142,7 +142,7 @@ static bool gguf_ex_read_0(const std::string & fname) {
}
// read and create ggml_context containing the tensors and their data
static bool gguf_ex_read_1(const std::string & fname) {
static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
struct ggml_context * ctx_data = NULL;
struct gguf_init_params params = {
@ -206,7 +206,7 @@ static bool gguf_ex_read_1(const std::string & fname) {
printf("\n\n");
// check data
{
if (check_data) {
const float * data = (const float *) cur->data;
for (int j = 0; j < ggml_nelements(cur); ++j) {
if (data[j] != 100 + i) {
@ -229,9 +229,16 @@ static bool gguf_ex_read_1(const std::string & fname) {
int main(int argc, char ** argv) {
if (argc < 3) {
printf("usage: %s data.gguf r|w\n", argv[0]);
printf("usage: %s data.gguf r|w [n]\n", argv[0]);
printf("r: read data.gguf file\n");
printf("w: write data.gguf file\n");
printf("n: no check of tensor data\n");
return -1;
}
bool check_data = true;
if (argc == 4) {
check_data = false;
}
const std::string fname(argv[1]);
const std::string mode (argv[2]);
@ -242,7 +249,7 @@ int main(int argc, char ** argv) {
GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file");
} else if (mode == "r") {
GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file");
GGML_ASSERT(gguf_ex_read_1(fname) && "failed to read gguf file");
GGML_ASSERT(gguf_ex_read_1(fname, check_data) && "failed to read gguf file");
}
return 0;

4
examples/gritlm/README.md
View file

@ -21,12 +21,12 @@ not have to be performed at all.
### Running the example
Download a Grit model:
```console
$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf
$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --outdir models
```
Run the example using the downloaded model:
```console
$ ./gritlm -m gritlm-7b_q4_1.gguf
$ ./gritlm -m models/gritlm-7b_q4_1.gguf
Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605
Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103

68
examples/imatrix/imatrix.cpp
View file

@ -98,35 +98,36 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
const float * data = is_host ? (const float *) src1->data : m_src1_data.data();
// this has been adapted to the new format of storing merged experts in a single 3d tensor
// ref: https://github.com/ggerganov/llama.cpp/pull/6387
if (t->op == GGML_OP_MUL_MAT_ID) {
const int idx = ((int32_t *) t->op_params)[0];
const int n_as = ((int32_t *) t->op_params)[1];
const ggml_tensor * ids = t->src[2];
const int n_as = src0->ne[2];
// the top-k selected expert ids are stored in the src0 tensor
// for simplicity, always copy src0 to host, because it is small
// take into account that src0 is not contiguous!
GGML_ASSERT(src0->ne[1] == src1->ne[1]);
GGML_ASSERT(n_as*ggml_nrows(src0)*sizeof(int) == GGML_PAD(ggml_nbytes(src0), n_as*sizeof(int)));
m_ids.resize(ggml_nbytes(src0)/sizeof(int));
ggml_backend_tensor_get(src0, m_ids.data(), 0, ggml_nbytes(src0));
// the top-k selected expert ids are stored in the ids tensor
// for simplicity, always copy ids to host, because it is small
GGML_ASSERT(ids->ne[1] == src1->ne[1]);
m_ids.resize(ggml_nbytes(ids)/sizeof(int));
ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));
auto & e = m_stats[wname];
++e.ncall;
// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
// using the following line, we can correct for that if needed by replacing the line above with:
//if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
// loop over all possible experts, regardless if they are used or not in the batch
// this is necessary to guarantee equal number of "ncall" for each tensor
for (int ex = 0; ex < n_as; ++ex) {
src0 = t->src[2 + ex];
wname = filter_tensor_name(src0->name);
auto& e = m_stats[wname];
size_t e_start = ex*src1->ne[0];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
e.values.resize(src1->ne[0]*n_as, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
exit(1); //GGML_ASSERT(false);
}
// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
// using the following line, we can correct for that if needed
//if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
++e.ncall;
if (m_params.verbosity > 1) {
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
}
@ -136,7 +137,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
if (excur != ex) continue;
const float * x = data + row * src1->ne[0];
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j];
e.values[e_start + j] += x[j]*x[j];
}
}
if (e.ncall > m_last_call) {
@ -346,12 +347,13 @@ static void process_logits(
static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
const int n_ctx = llama_n_ctx(ctx);
auto tim1 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
auto tim2 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -593,24 +595,18 @@ int main(int argc, char ** argv) {
llama_backend_init();
llama_numa_init(params.numa);
llama_model_params mparams = llama_model_params_from_gpt_params(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return 1;
}
llama_context_params cparams = llama_context_params_from_gpt_params(params);
// pass the callback to the backend scheduler
// it will be executed for each node during the graph computation
cparams.cb_eval = ik_collect_imatrix;
cparams.cb_eval_user_data = NULL;
params.cb_eval = ik_collect_imatrix;
params.cb_eval_user_data = NULL;
params.warmup = false;
llama_context * ctx = llama_new_context_with_model(model, cparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: unable to create context\n", __func__);
// init
llama_model * model;
llama_context * ctx;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == nullptr || ctx == nullptr) {
fprintf(stderr, "%s : failed to init\n", __func__);
return 1;
}

5
examples/infill/README.md
View file

@ -36,6 +36,11 @@ The `infill` program offers a seamless way to interact with LLaMA models, allowi
### Example
Download a model that supports infill, for example CodeLlama:
```console
scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.gguf --outdir models
```
```bash
./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n "
```

5
examples/infill/infill.cpp
View file

@ -239,6 +239,7 @@ int main(int argc, char ** argv) {
LOG_TEE("%s\n", get_system_info(params).c_str());
}
const bool add_bos = llama_should_add_bos_token(model);
GGML_ASSERT(llama_add_eos_token(model) != 1);
LOG("add_bos: %d\n", add_bos);
bool suff_rm_leading_spc = params.escape;
@ -279,10 +280,10 @@ int main(int argc, char ** argv) {
if (ctx_guidance) {
LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos);
guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true);
LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true);
LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
original_prompt_len = original_inp.size();

210
examples/json-schema-to-grammar.py → examples/json_schema_to_grammar.py
View file

@ -6,37 +6,94 @@ import re
import sys
from typing import Any, Dict, List, Set, Tuple, Union
def _build_repetition(item_rule, min_items, max_items, separator_rule=None, item_rule_is_literal=False):
if not separator_rule:
if min_items == 0 and max_items == 1:
return f'{item_rule}?'
elif min_items == 1 and max_items is None:
return f'{item_rule}+'
result = ''
if min_items > 0:
if item_rule_is_literal and separator_rule is None:
result = '"' + (item_rule[1:-1] * min_items) + '"'
else:
result = (f' {separator_rule} ' if separator_rule else ' ').join([item_rule] * min_items)
def opt_repetitions(up_to_n, prefix_with_sep=False):
'''
- n=4, no sep: '(a (a (a (a)?)?)?)?'
- n=4, sep=',', prefix: '("," a ("," a ("," a ("," a)?)?)?)?'
- n=4, sep=',', no prefix: '(a ("," a ("," a ("," a)?)?)?)?'
'''
content = f'{separator_rule} {item_rule}' if prefix_with_sep and separator_rule else item_rule
if up_to_n == 0:
return ''
elif up_to_n == 1:
return f'({content})?'
elif separator_rule and not prefix_with_sep:
return f'({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?'
else:
return (f'({content} ' * up_to_n).rstrip() + (')?' * up_to_n)
if min_items > 0 and max_items != min_items:
result += ' '
if max_items is not None:
result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0)
else:
item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})'
if min_items == 0 and separator_rule:
result = f'({item_rule} {item_operator}*)?'
else:
result += f'{item_operator}*'
return result
class BuiltinRule:
def __init__(self, content: str, deps: list = None):
self.content = content
self.deps = deps or []
_up_to_15_digits = _build_repetition('[0-9]', 0, 15)
# whitespace is constrained to a single space char to prevent model "running away" in
# whitespace. Also maybe improves generation quality?
SPACE_RULE = '" "?'
PRIMITIVE_RULES = {
'boolean': '("true" | "false") space',
'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space',
'value' : 'object | array | string | number | boolean',
'object' : '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space',
'array' : '"[" space ( value ("," space value)* )? "]" space',
'uuid' : '"\\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + ' "\\"" space',
'string': r''' "\"" (
[^"\\] |
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
)* "\"" space''',
'null': '"null" space',
'boolean' : BuiltinRule('("true" | "false") space', []),
'decimal-part' : BuiltinRule('[0-9] ' + _up_to_15_digits, []),
'integral-part': BuiltinRule('[0-9] | [1-9] ' + _up_to_15_digits, []),
'number' : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
'integer' : BuiltinRule('("-"? integral-part) space', ['integral-part']),
'value' : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
'object' : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
'array' : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
'uuid' : BuiltinRule(r'"\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + r' "\"" space', []),
'char' : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', []),
'string' : BuiltinRule(r'"\"" char* "\"" space', ['char']),
'null' : BuiltinRule('"null" space', []),
}
OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value']
# TODO: support "uri", "email" string formats
DATE_RULES = {
'date' : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )',
'time' : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )',
'date-time': 'date "T" time',
'date-string': '"\\"" date "\\"" space',
'time-string': '"\\"" time "\\"" space',
'date-time-string': '"\\"" date-time "\\"" space',
STRING_FORMAT_RULES = {
'date' : BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
'time' : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
'date-time' : BuiltinRule('date "T" time', ['date', 'time']),
'date-string' : BuiltinRule('"\\"" date "\\"" space', ['date']),
'time-string' : BuiltinRule('"\\"" time "\\"" space', ['time']),
'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
}
RESERVED_NAMES = set(["root", *PRIMITIVE_RULES.keys(), *DATE_RULES.keys()])
DOTALL = '[\\U00000000-\\U0010FFFF]'
DOT = '[^\\x0A\\x0D]'
RESERVED_NAMES = set(["root", "dot", *PRIMITIVE_RULES.keys(), *STRING_FORMAT_RULES.keys()])
INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')
GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
@ -46,8 +103,6 @@ GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']'
NON_LITERAL_SET = set('|.()[]{}*+?')
ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('[]()|{}*+?')
DATE_PATTERN = '[0-9]{4}-(0[1-9]|1[0-2])-([0-2][0-9]|3[0-1])'
TIME_PATTERN = '([01][0-9]|2[0-3])(:[0-5][0-9]){2}(\\.[0-9]{1,3})?(Z|[+-](([01][0-9]|2[0-3]):[0-5][0-9]))' # Cap millisecond precision w/ 3 digits
class SchemaConverter:
def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern):
@ -55,7 +110,9 @@ class SchemaConverter:
self._allow_fetch = allow_fetch
self._dotall = dotall
self._raw_pattern = raw_pattern
self._rules = {'space': SPACE_RULE}
self._rules = {
'space': SPACE_RULE,
}
self._refs = {}
self._refs_being_resolved = set()
@ -65,6 +122,29 @@ class SchemaConverter:
)
return f'"{escaped}"'
def not_literal(self, literal: str, dotall: bool = True, maybe_escaped_underscores = False) -> str:
'''
not_literal('a') -> '[^a]'
not_literal('abc') -> '([^a] | "a" ([^b] | "b" ([^c])?)?)?'
'''
assert len(literal) > 0, 'Empty literal not supported'
def recurse(i: int):
c = literal[i]
if maybe_escaped_underscores and c == '_':
yield f'[^{c}\\\\]'
yield ' | '
yield f'"\\\\"? "{c}"'
else:
yield f'[^{c}]'
if i < len(literal) - 1:
yield ' | '
yield self._format_literal(c)
yield ' ('
yield from recurse(i + 1)
yield ')?'
return ''.join(('(', *recurse(0), ')'))
def _add_rule(self, name, rule):
esc_name = INVALID_RULE_CHARS_RE.sub('-', name)
if esc_name not in self._rules or self._rules[esc_name] == rule:
@ -169,10 +249,10 @@ class SchemaConverter:
def get_dot():
if self._dotall:
rule = '[\\U00000000-\\U0010FFFF]'
rule = DOTALL
else:
# Accept any character... except \n and \r line break chars (\x0A and \xOD)
rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]'
rule = DOT
return self._add_rule(f'dot', rule)
def join_seq():
@ -246,26 +326,14 @@ class SchemaConverter:
(sub, sub_is_literal) = seq[-1]
if min_times == 0 and max_times is None:
seq[-1] = (f'{sub}*', False)
elif min_times == 0 and max_times == 1:
seq[-1] = (f'{sub}?', False)
elif min_times == 1 and max_times is None:
seq[-1] = (f'{sub}+', False)
else:
if not sub_is_literal:
id = sub_rule_ids.get(sub)
if id is None:
id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub)
sub_rule_ids[sub] = id
sub = id
if not sub_is_literal:
id = sub_rule_ids.get(sub)
if id is None:
id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub)
sub_rule_ids[sub] = id
sub = id
seq[-1] = (
' '.join(
([f'"{sub[1:-1] * min_times}"'] if sub_is_literal else [sub] * min_times) +
([f'{sub}?'] * (max_times - min_times) if max_times is not None else [f'{sub}*'])),
False
)
seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times, item_rule_is_literal=sub_is_literal), False)
else:
literal = ''
while i < length:
@ -373,49 +441,47 @@ class SchemaConverter:
' "]" space')
else:
item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item')
list_item_operator = f'( "," space {item_rule_name} )'
successive_items = ""
min_items = schema.get("minItems", 0)
max_items = schema.get("maxItems")
if min_items > 0:
successive_items = list_item_operator * (min_items - 1)
min_items -= 1
if max_items is not None and max_items > min_items:
successive_items += (list_item_operator + "?") * (max_items - min_items - 1)
else:
successive_items += list_item_operator + "*"
if min_items == 0:
rule = f'"[" space ( {item_rule_name} {successive_items} )? "]" space'
else:
rule = f'"[" space {item_rule_name} {successive_items} "]" space'
return self._add_rule(rule_name, rule)
return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space')
elif schema_type in (None, 'string') and 'pattern' in schema:
return self._visit_pattern(schema['pattern'], rule_name)
elif schema_type in (None, 'string') and re.match(r'^uuid[1-5]?$', schema_format or ''):
return self._add_rule(
return self._add_primitive(
'root' if rule_name == 'root' else schema_format,
PRIMITIVE_RULES['uuid']
)
elif schema_type in (None, 'string') and schema_format in DATE_RULES:
for t, r in DATE_RULES.items():
self._add_rule(t, r)
return schema_format + '-string'
elif schema_type in (None, 'string') and f'{schema_format}-string' in STRING_FORMAT_RULES:
prim_name = f'{schema_format}-string'
return self._add_rule(rule_name, self._add_primitive(prim_name, STRING_FORMAT_RULES[prim_name]))
elif schema_type == 'string' and ('minLength' in schema or 'maxLength' in schema):
char_rule = self._add_primitive('char', PRIMITIVE_RULES['char'])
min_len = schema.get('minLength', 0)
max_len = schema.get('maxLength')
return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space')
elif (schema_type == 'object') or (len(schema) == 0):
for n in OBJECT_RULE_NAMES:
self._add_rule(n, PRIMITIVE_RULES[n])
return self._add_rule(rule_name, 'object')
return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object']))
else:
assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}'
# TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
return self._add_rule(
'root' if rule_name == 'root' else schema_type,
PRIMITIVE_RULES[schema_type]
)
return self._add_primitive('root' if rule_name == 'root' else schema_type, PRIMITIVE_RULES[schema_type])
def _add_primitive(self, name: str, rule: BuiltinRule):
n = self._add_rule(name, rule.content)
for dep in rule.deps:
dep_rule = PRIMITIVE_RULES.get(dep) or STRING_FORMAT_RULES.get(dep)
assert dep_rule, f'Rule {dep} not known'
if dep not in self._rules:
self._add_primitive(dep, dep_rule)
return n
def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Union[bool, Any]):
prop_order = self._prop_order
@ -437,7 +503,7 @@ class SchemaConverter:
value_rule = self.visit({} if additional_properties == True else additional_properties, f'{sub_name}-value')
prop_kv_rule_names["*"] = self._add_rule(
f'{sub_name}-kv',
self._add_rule('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}'
self._add_primitive('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}'
)
optional_props.append("*")

200
examples/llava/MobileVLM-README.md
View file

@ -6,7 +6,7 @@ for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com
The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.
Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using MobileVLM as an example, the different conversion step will be shown.
Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown.
## Usage
Build with cmake or run `make llava-cli` to build it.
@ -22,7 +22,7 @@ After building, run: `./llava-cli` to see the usage. For example:
## Model conversion
- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
1. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
```sh
git clone https://huggingface.co/mtgv/MobileVLM-1.7B
@ -36,7 +36,7 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
```
3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** the arg is `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
```sh
python ./examples/llava/convert-image-encoder-to-gguf \
@ -78,7 +78,7 @@ cd examples/llava/android/build_64
### run on Android
refer to `android/adb_run.sh`, modify resources' `name` and `path`
## some result on Android with `Snapdragon 888` chip
## Some result on Android with `Snapdragon 888` chip
### case 1
**input**
```sh
@ -109,7 +109,6 @@ llama_print_timings: total time = 34731.93 ms
--image /data/local/tmp/cat.jpeg \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
```
**output**
```sh
encode_image_with_clip: image encoded in 21149.51 ms by CLIP ( 146.87 ms per image patch)
@ -121,12 +120,82 @@ llama_print_timings: eval time = 1279.03 ms / 18 runs ( 71.06 m
llama_print_timings: total time = 34570.79 ms
```
## Some result on Android with `Snapdragon 778G` chip
### MobileVLM-1.7B case
#### llava-cli release-b2005
**input**
```sh
/data/local/tmp/llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-t 4 \
--image /data/local/tmp/many_llamas.jpeg \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:"
```
**output**
```sh
encode_image_with_clip: image encoded in 18728.52 ms by CLIP ( 130.06 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
A group of llamas are standing in a green pasture.
llama_print_timings: load time = 20357.33 ms
llama_print_timings: sample time = 2.96 ms / 14 runs ( 0.21 ms per token, 4734.53 tokens per second)
llama_print_timings: prompt eval time = 8119.49 ms / 191 tokens ( 42.51 ms per token, 23.52 tokens per second)
llama_print_timings: eval time = 1005.75 ms / 14 runs ( 71.84 ms per token, 13.92 tokens per second)
llama_print_timings: total time = 28038.34 ms / 205 tokens
```
#### llava-cli latest-version
**input**
Just the same as above.
**output**(seems to be much slower)
```sh
encode_image_with_clip: image embedding created: 144 tokens
encode_image_with_clip: image encoded in 288268.88 ms by CLIP ( 2001.87 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
It is a group of sheep standing together in a grass field.
llama_print_timings: load time = 818120.91 ms
llama_print_timings: sample time = 3.44 ms / 14 runs ( 0.25 ms per token, 4067.40 tokens per second)
llama_print_timings: prompt eval time = 529274.69 ms / 191 tokens ( 2771.07 ms per token, 0.36 tokens per second)
llama_print_timings: eval time = 43894.02 ms / 13 runs ( 3376.46 ms per token, 0.30 tokens per second)
llama_print_timings: total time = 865441.76 ms / 204 tokens
```
### MobileVLM_V2-1.7B case
#### llava-cli release-2005b
**input**
Just the same as above.
**output**
```sh
encode_image_with_clip: image encoded in 20609.61 ms by CLIP ( 143.12 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
This image captures a lively scene of 20 llamas in motion on an expansive, grassy field. The llama is scattered across the landscape with some standing and others sitting down as if taking rest or observing their surroundings from different vantage points within this verdant setting.
The background offers glimpses into a picturesque town nestled amidst hills under an overcast sky, adding depth to the scene while also emphasizing that distance between these llama and human-made structures like houses or roads in which they roam freely without any barriers around them. The image is framed by text at both right angles on white backgrounds against a contrasting blue backdrop with green foliage, further drawing attention to the llamas amidst their natural habitat while also inviting viewers into this picturesque landscape within town limits of Alta Llama
llama_print_timings: load time = 22406.77 ms
llama_print_timings: sample time = 49.26 ms / 186 runs ( 0.26 ms per token, 3776.27 tokens per second)
llama_print_timings: prompt eval time = 9044.54 ms / 191 tokens ( 47.35 ms per token, 21.12 tokens per second)
llama_print_timings: eval time = 14497.49 ms / 186 runs ( 77.94 ms per token, 12.83 tokens per second)
llama_print_timings: total time = 44411.01 ms / 377 tokens
```
## Orin compile and run
### compile
```sh
make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
```
### run on Orin
### case 1
**input**
@ -175,8 +244,121 @@ llama_print_timings: eval time = 166.65 ms / 11 runs ( 15.15 m
llama_print_timings: total time = 1365.47 ms / 243 tokens
```
## Minor shortcomings
The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quickly, we uniformly modified `clip_n_patches` function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost.
## Running on Intel(R) Core(TM) i7-10750H
### Operating system
Ubuntu22.04
### compile
```sh
make -j32
```
### MobileVLM-1.7B case
**input**
```sh
-m /path/to/ggml-model-q4_k.gguf \
--mmproj /path/to/mmproj-model-f16.gguf \
--image /path/to/many_llamas.jpeg
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
```
**output**
```sh
encode_image_with_clip: image embedding created: 144 tokens
encode_image_with_clip: image encoded in 2730.94 ms by CLIP ( 18.96 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that?ASSISTANT:
A group of llamas are walking together in a field.
llama_print_timings: load time = 5506.60 ms
llama_print_timings: sample time = 0.44 ms / 13 runs ( 0.03 ms per token, 29545.45 tokens per second)
llama_print_timings: prompt eval time = 2031.58 ms / 190 tokens ( 10.69 ms per token, 93.52 tokens per second)
llama_print_timings: eval time = 438.92 ms / 12 runs ( 36.58 ms per token, 27.34 tokens per second)
llama_print_timings: total time = 5990.25 ms / 202 tokens
```
### MobileVLM_V2-1.7B case
**input**
Just the same as above.
**ouput**
```sh
encode_image_with_clip: image embedding created: 144 tokens
encode_image_with_clip: image encoded in 3223.89 ms by CLIP ( 22.39 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that?ASSISTANT:
The image captures a tranquil scene in a park, where a group of approximately 20 llamas are gathered. The llamas, a mix of white and black, are standing in a line, their black and white patterns contrasting with the lush green grass of the park. The lamas are arranged in a line, suggesting a social order.
The park itself is lush and green, with trees dotting the landscape in the background. A sign reading "Llamas Tico Ana" is also visible in the image, possibly indicating the location or the breed of the llamas. The image seems to be taken from a distance, providing a wide view of the scene and the surrounding environment.
The llamas' positions relative to each other, the sign, and the trees create a harmonious composition. The image does not contain any discernible text. The overall scene is one of peace and natural beauty, with the llamas in their natural habitat, surrounded by the vibrant colors and lush greenery of the park.
llama_print_timings: load time = 6642.61 ms
llama_print_timings: sample time = 8.15 ms / 223 runs ( 0.04 ms per token, 27358.61 tokens per second)
llama_print_timings: prompt eval time = 2475.07 ms / 190 tokens ( 13.03 ms per token, 76.77 tokens per second)
llama_print_timings: eval time = 8760.60 ms / 222 runs ( 39.46 ms per token, 25.34 tokens per second)
llama_print_timings: total time = 15513.95 ms / 412 tokens
```
## Run on Intel(R) Core(TM) Ultra7 115H
### operation system
Windows11
### comiple
```sh
make -j32
```
### MobileVLM-1.7B case
**input**
```sh
-m /path/to/ggml-model-q4_k.gguf \
--mmproj /path/to/tmp/mmproj-model-f16.gguf \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
```
**output**
```sh
encode_image_with_clip: image encoded in 4902.81 ms by CLIP ( 34.05 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
The image features a group of brown and white llamas standing in a grassy field.
llama_print_timings: load time = 7441.06 ms
llama_print_timings: sample time = 0.72 ms / 19 runs ( 0.04 ms per token, 26279.39 tokens per second)
llama_print_timings: prompt eval time = 2090.71 ms / 191 tokens ( 10.95 ms per token, 91.36 tokens per second)
llama_print_timings: eval time = 512.35 ms / 18 runs ( 28.46 ms per token, 35.13 tokens per second)
llama_print_timings: total time = 7987.23 ms / 209 tokens
```
### MobileVLM_V2-1.7B case
**input**
Just the same as above.
**output**
```sh
encode_image_with_clip: image encoded in 4682.44 ms by CLIP ( 32.52 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
This image captures a lively scene of a group of 14 llamas in a grassy field. The llamas, with their distinctive black and white coats, are standing and walking in a line, seemingly engaged in a social activity. One
of them, possibly the first in the line, has its back turned, perhaps observing something in the distance.
The llama in the front of the line stands out due to its black and white coloring, which is quite unusual for llama patterns. The llama in the front also seems to be more aware of its surroundings, as it faces the camera, giving a sense of engagement with the viewer.
The image is taken from the side of the llama, providing a clear view of the llama in the front and its companions. The lameness in the llama in
front is not visible, indicating that it might not be the main focus of the photo.
The background of the image features a grassy field, with a fence and a tree visible in the distance. The tree appears to be bare, suggesting that it might be during a time of year when most trees are dormant or have shed their leaves.
llama_print_timings: load time = 7015.35 ms
llama_print_timings: sample time = 10.61 ms / 256 runs ( 0.04 ms per token, 24119.09 tokens per second)
llama_print_timings: prompt eval time = 2052.45 ms / 191 tokens ( 10.75 ms per token, 93.06 tokens per second)
llama_print_timings: eval time = 7259.43 ms / 255 runs ( 28.47 ms per token, 35.13 tokens per second)
llama_print_timings: total time = 14371.19 ms / 446 tokens
```
## TODO
@ -191,5 +373,5 @@ The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quic
## contributor
```sh
zhangjidong05, yangyang260, huyiming03, chenxiaotao03
zhangjidong05, yangyang260, huyiming03, chenxiaotao03, ZiangWu-77
```

2
examples/llava/README.md
View file

@ -24,7 +24,7 @@ After building, run: `./llava-cli` to see the usage. For example:
## LLaVA 1.5
- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
1. Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
```sh
git clone https://huggingface.co/liuhaotian/llava-v1.5-7b

5
examples/llava/clip.cpp
View file

@ -835,9 +835,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
// weight ne = [3, 3, 2048, 1]
struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
peg_0 = ggml_add(ctx0, peg_0, mlp_2);
peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
peg_0 = ggml_add(ctx0, peg_0, mlp_2);
peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
embeddings = peg_0;
}
@ -1755,7 +1756,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
n_patches /= 4;
}

3
examples/llava/llava-cli.cpp
View file

@ -146,7 +146,6 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
int n_past = 0;
const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama));
std::string system_prompt, user_prompt;
size_t image_pos = prompt.find("<image>");
@ -180,7 +179,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
}
}
eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos);
eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, true);
llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);

5
examples/lookahead/lookahead.cpp
View file

@ -64,13 +64,10 @@ int main(int argc, char ** argv) {
std::tie(model, ctx) = llama_init_from_gpt_params(params);
// Tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
std::vector<llama_token> all;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
inp = ::llama_tokenize(ctx, params.prompt, true, true);
all = inp;
const int max_context_size = llama_n_ctx(ctx);

4
examples/lookup/lookup-create.cpp
View file

@ -28,10 +28,8 @@ int main(int argc, char ** argv){
GGML_ASSERT(model != nullptr);
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
inp = ::llama_tokenize(ctx, params.prompt, true, true);
fprintf(stderr, "%s: tokenization done\n", __func__);

5
examples/lookup/lookup-stats.cpp
View file

@ -34,11 +34,8 @@ int main(int argc, char ** argv){
GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
inp = ::llama_tokenize(ctx, params.prompt, true, true);
llama_ngram_cache ngram_cache_context;
llama_ngram_cache ngram_cache_dynamic;

5
examples/lookup/lookup.cpp
View file

@ -42,11 +42,8 @@ int main(int argc, char ** argv){
GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
inp = ::llama_tokenize(ctx, params.prompt, true, true);
llama_ngram_cache ngram_cache_context;
llama_ngram_cache ngram_cache_dynamic;

6
examples/main/README.md
View file

@ -304,13 +304,15 @@ These options help improve the performance and memory usage of the LLaMA models.
- `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation.
### Grammars
### Grammars & JSON schemas
- `--grammar GRAMMAR`, `--grammar-file FILE`: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the [GBNF guide](../../grammars/README.md) for details on the syntax.
- `--json-schema SCHEMA`: Specify a [JSON schema](https://json-schema.org/) to constrain model output to (e.g. `{}` for any JSON object, or `{"items": {"type": "string", "minLength": 10, "maxLength": 100}, "minItems": 10}` for a JSON array of strings with size constraints). If a schema uses external `$ref`s, you should use `--grammar "$( python examples/json_schema_to_grammar.py myschema.json )"` instead.
### Quantization
For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-data--run).
For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize).
## Additional Options

19
examples/main/main.cpp
View file

@ -235,7 +235,7 @@ int main(int argc, char ** argv) {
// The file exists and is not empty
session_tokens.resize(n_ctx);
size_t n_token_count_out = 0;
if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
if (!llama_state_load_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
return 1;
}
@ -246,6 +246,7 @@ int main(int argc, char ** argv) {
}
const bool add_bos = llama_should_add_bos_token(model);
GGML_ASSERT(llama_add_eos_token(model) != 1);
LOG("add_bos: %d\n", add_bos);
std::vector<llama_token> embd_inp;
@ -255,7 +256,7 @@ int main(int argc, char ** argv) {
if (params.chatml) {
params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
}
embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
embd_inp = ::llama_tokenize(ctx, params.prompt, true, true);
} else {
LOG("use session tokens\n");
embd_inp = session_tokens;
@ -277,10 +278,10 @@ int main(int argc, char ** argv) {
if (ctx_guidance) {
LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true, true);
LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true, true);
LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
original_prompt_len = original_inp.size();
@ -339,14 +340,14 @@ int main(int argc, char ** argv) {
}
// prefix & suffix for instruct mode
const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true);
const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true, true);
const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true);
LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
// chatml prefix & suffix
const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", true, true);
const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
@ -693,7 +694,7 @@ int main(int argc, char ** argv) {
// optionally save the session on first sample (for faster prompt loading next time)
if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
need_to_save_session = false;
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
LOG("saved session to %s\n", path_session.c_str());
}
@ -935,7 +936,7 @@ int main(int argc, char ** argv) {
if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
}
llama_print_timings(ctx);

31
examples/perplexity/README.md
View file

@ -3,19 +3,18 @@
TODO
## Llama 2 70B Scorechart
Quantization | Model size (GiB) | Perplexity | Delta to fp16
-- | -- | -- | --
Q4_0 | 36.20 | 3.5550 | 3.61%
Q4_1 | 40.20 | 3.5125 | 2.37%
Q5_0 | 44.20 | 3.4744 | 1.26%
Q2_K | 27.27 | 3.7339 | 8.82%
Q3_K_S | 27.86 | 3.7019 | 7.89%
Q3_K_M | 30.83 | 3.5932 | 4.72%
Q3_K_L | 33.67 | 3.5617 | 3.80%
Q4_K_S | 36.39 | 3.4852 | 1.57%
Q4_K_M | 38.54 | 3.4725 | 1.20%
Q5_K_S | 44.20 | 3.4483 | 0.50%
Q5_K_M | 45.41 | 3.4451 | 0.40%
Q6_K | 52.70 | 3.4367 | 0.16%
fp16 | 128.5 | 3.4313 | -
| Quantization | Model size (GiB) | Perplexity | Delta to fp16 |
|--------------|------------------|------------|---------------|
| Q4_0 | 36.20 | 3.5550 | 3.61% |
| Q4_1 | 40.20 | 3.5125 | 2.37% |
| Q5_0 | 44.20 | 3.4744 | 1.26% |
| Q2_K | 27.27 | 3.7339 | 8.82% |
| Q3_K_S | 27.86 | 3.7019 | 7.89% |
| Q3_K_M | 30.83 | 3.5932 | 4.72% |
| Q3_K_L | 33.67 | 3.5617 | 3.80% |
| Q4_K_S | 36.39 | 3.4852 | 1.57% |
| Q4_K_M | 38.54 | 3.4725 | 1.20% |
| Q5_K_S | 44.20 | 3.4483 | 0.50% |
| Q5_K_M | 45.41 | 3.4451 | 0.40% |
| Q6_K | 52.70 | 3.4367 | 0.16% |
| fp16 | 128.5 | 3.4313 | - |

52
examples/perplexity/perplexity.cpp
View file

@ -315,10 +315,11 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
// BOS tokens will be added for each chunk before eval
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
const int n_ctx = llama_n_ctx(ctx);
@ -454,6 +455,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
// BOS tokens will be added for each chunk before eval
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
std::ofstream logits_stream;
if (!params.logits_file.empty()) {
@ -470,7 +472,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
auto tim1 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
auto tim2 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -771,9 +773,6 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;
fprintf(stderr, "================================= is_spm = %d\n", is_spm);
// This is needed as usual for LLaMA models
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
// The tasks should be randomized so the score stabilizes quickly.
bool randomize_tasks = true;
@ -818,7 +817,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
for (size_t j = 0; j < 4; j++) {
hs_cur.ending[j] = prompt_lines[idx*6+2+j];
hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], add_bos);
hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
}
// determine the common prefix of the endings
@ -837,7 +836,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
hs_cur.seq_tokens[2].size() - hs_cur.common_prefix +
hs_cur.seq_tokens[3].size() - hs_cur.common_prefix;
//GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, add_bos).size());
//GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, true).size());
// Delete the selected random example from the prompt
if (randomize_tasks) {
@ -1110,12 +1109,9 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
fprintf(stderr, "%s : tokenizing selected tasks\n", __func__);
// This is needed as usual for LLaMA models
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
for (auto & task : data) {
task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, add_bos);
task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, add_bos);
task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, true);
task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, true);
task.common_prefix = 0;
for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
@ -1130,8 +1126,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
task.seq_tokens[0].size() - task.common_prefix +
task.seq_tokens[1].size() - task.common_prefix;
task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos).size();
task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos).size();
task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], true).size();
task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], true).size();
}
fprintf(stderr, "%s : calculating winogrande score over selected tasks.\n", __func__);
@ -1322,7 +1318,7 @@ struct multiple_choice_task {
std::vector<float> log_probs;
};
static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, multiple_choice_task& task, bool log_error) {
static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choice_task& task, bool log_error) {
if (task.question.empty() || task.mc1.answers.empty()) {
if (log_error) {
printf("%s: found bad task with empty question and/or answers\n", __func__);
@ -1337,7 +1333,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos,
}
return false;
}
task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, add_bos));
task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, true));
}
auto min_len = task.seq_tokens.front().size();
for (auto& seq : task.seq_tokens) {
@ -1436,9 +1432,6 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
n_task = params.multiple_choice_tasks;
}
// This is needed as usual for LLaMA models
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
printf("%s: preparing task data", __func__);
fflush(stdout);
if (n_task > 500) {
@ -1446,7 +1439,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
fflush(stdout);
std::atomic<int> counter(0);
std::atomic<int> n_bad(0);
auto prepare = [&counter, &n_bad, &tasks, ctx, add_bos] () {
auto prepare = [&counter, &n_bad, &tasks, ctx] () {
int num_tasks = tasks.size();
int n_bad_local = 0;
while (true) {
@ -1457,7 +1450,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
}
int last = std::min(first + K_TOKEN_CHUNK, num_tasks);
for (int i = first; i < last; ++i) {
if (!multiple_choice_prepare_one_task(ctx, add_bos, tasks[i], false)) ++n_bad_local;
if (!multiple_choice_prepare_one_task(ctx, tasks[i], false)) ++n_bad_local;
}
}
};
@ -1479,7 +1472,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
int i_task = 0;
for (auto& task : tasks) {
++i_task;
if (!multiple_choice_prepare_one_task(ctx, add_bos, task, true)) {
if (!multiple_choice_prepare_one_task(ctx, task, true)) {
return;
}
if (i_task%n_dot == 0) {
@ -1715,6 +1708,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
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_should_add_bos_token(llama_get_model(ctx));
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
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);
@ -1858,12 +1852,20 @@ int main(int argc, char ** argv) {
const int32_t n_ctx = params.n_ctx;
if (n_ctx <= 0) {
fprintf(stderr, "%s: perplexity tool requires '--ctx-size' > 0\n", __func__);
return 1;
}
const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence;
if (ppl) {
int n_seq = std::max(1, params.n_batch / n_ctx);
int32_t n_kv = n_seq * n_ctx;
const int32_t n_seq = std::max(1, params.n_batch / n_ctx);
const int32_t n_kv = n_seq * n_ctx;
params.n_parallel = n_seq;
params.n_ctx = n_kv;
params.n_ctx = n_kv;
params.n_batch = std::min(params.n_batch, n_kv);
} else {
params.n_batch = std::min(params.n_batch, params.n_ctx);

22
examples/quantize/README.md
View file

@ -4,17 +4,17 @@ TODO
## Llama 2 7B
Quantization | Bits per Weight (BPW)
-- | --
Q2_K | 3.35
Q3_K_S | 3.50
Q3_K_M | 3.91
Q3_K_L | 4.27
Q4_K_S | 4.58
Q4_K_M | 4.84
Q5_K_S | 5.52
Q5_K_M | 5.68
Q6_K | 6.56
| Quantization | Bits per Weight (BPW) |
|--------------|-----------------------|
| Q2_K | 3.35 |
| Q3_K_S | 3.50 |
| Q3_K_M | 3.91 |
| Q3_K_L | 4.27 |
| Q4_K_S | 4.58 |
| Q4_K_M | 4.84 |
| Q5_K_S | 5.52 |
| Q5_K_M | 5.68 |
| Q6_K | 6.56 |
## Llama 2 13B
Quantization | Bits per Weight (BPW)

16
examples/quantize/quantize.cpp
View file

@ -116,13 +116,13 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map<st
std::ifstream in(imatrix_file.c_str(), std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
return;
exit(1);
}
int n_entries;
in.read((char *)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
return;
exit(1);
}
for (int i = 0; i < n_entries; ++i) {
int len; in.read((char *)&len, sizeof(len));
@ -130,11 +130,11 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map<st
in.read((char *)name_as_vec.data(), len);
if (in.fail()) {
printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
return;
exit(1);
}
name_as_vec[len] = 0;
std::string name{name_as_vec.data()};
auto & e = imatrix_data[std::move(name)];
auto & e = imatrix_data[name];
int ncall;
in.read((char *)&ncall, sizeof(ncall));
int nval;
@ -142,18 +142,22 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map<st
if (in.fail() || nval < 1) {
printf("%s: failed reading number of values for entry %d\n", __func__, i);
imatrix_data = {};
return;
exit(1);
}
e.resize(nval);
in.read((char *)e.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n", __func__, i);
imatrix_data = {};
return;
exit(1);
}
if (ncall > 0) {
for (auto& v : e) v /= ncall;
}
if (getenv("LLAMA_TRACE")) {
printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
}
}
printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
}

2
examples/regex-to-grammar.py
View file

@ -8,7 +8,7 @@ print(subprocess.check_output(
"python",
os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"json-schema-to-grammar.py"),
"json_schema_to_grammar.py"),
*rest,
"-",
"--raw-pattern",

101
examples/save-load-state/save-load-state.cpp
View file

@ -24,6 +24,7 @@ int main(int argc, char ** argv) {
std::string result0;
std::string result1;
std::string result2;
// init
llama_model * model;
@ -44,8 +45,8 @@ int main(int argc, char ** argv) {
// save state (rng, logits, embedding and kv_cache) to file
{
std::vector<uint8_t> state_mem(llama_get_state_size(ctx));
const size_t written = llama_copy_state_data(ctx, state_mem.data());
std::vector<uint8_t> state_mem(llama_state_get_size(ctx));
const size_t written = llama_state_get_data(ctx, state_mem.data());
FILE *fp_write = fopen("dump_state.bin", "wb");
fwrite(state_mem.data(), 1, written, fp_write);
@ -97,13 +98,13 @@ int main(int argc, char ** argv) {
// load state (rng, logits, embedding and kv_cache) from file
{
std::vector<uint8_t> state_mem(llama_get_state_size(ctx2));
std::vector<uint8_t> state_mem(llama_state_get_size(ctx2));
FILE * fp_read = fopen("dump_state.bin", "rb");
const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
fclose(fp_read);
if (read != llama_set_state_data(ctx2, state_mem.data())) {
if (read != llama_state_set_data(ctx2, state_mem.data())) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
llama_free(ctx2);
llama_free_model(model);
@ -141,16 +142,104 @@ int main(int argc, char ** argv) {
n_past += 1;
}
printf("\n");
printf("\n\n");
llama_free(ctx2);
llama_free_model(model);
if (result0 != result1) {
fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
return 1;
}
// make new context
auto* ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
printf("\nsingle seq run: %s", params.prompt.c_str());
// load state (rng, logits, embedding and kv_cache) from file
{
std::vector<uint8_t> state_mem(llama_state_get_size(ctx3));
FILE * fp_read = fopen("dump_state.bin", "rb");
const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
fclose(fp_read);
if (read != llama_state_set_data(ctx3, state_mem.data())) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
}
// restore state (last tokens)
n_past = n_past_saved;
// save seq 0 and load into seq 1
{
// save kv of seq 0
std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx3, 0));
const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), 0);
if (ncopy != seq_store.size()) {
fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
// erase whole kv
llama_kv_cache_clear(ctx3);
fprintf(stderr, "%s : kv cache cleared\n", __func__);
// restore kv into seq 1
const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), 1);
if (nset != seq_store.size()) {
fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
}
// third run with seq 1 instead of 0
for (auto i = 0; i < params.n_predict; i++) {
auto * logits = llama_get_logits(ctx3);
auto n_vocab = llama_n_vocab(model);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx3, &candidates_p);
auto next_token_str = llama_token_to_piece(ctx3, next_token);
printf("%s", next_token_str.c_str());
result2 += next_token_str;
if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx3);
llama_free_model(model);
return 1;
}
n_past += 1;
}
printf("\n");
llama_free(ctx3);
llama_free_model(model);
if (result0 != result2) {
fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
return 1;
}
fprintf(stderr, "\n%s : success\n", __func__);
return 0;

2
examples/server/CMakeLists.txt
View file

@ -11,7 +11,7 @@ install(TARGETS ${TARGET} RUNTIME)
target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
)
target_link_libraries(${TARGET} PRIVATE common json-schema-to-grammar ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
if (LLAMA_SERVER_SSL)
find_package(OpenSSL REQUIRED)
target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto)

211
examples/server/README.md
View file

@ -11,57 +11,57 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
* Continuous batching
* Multimodal (wip)
* Monitoring endpoints
* Schema-constrained JSON response format
The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216).
**Command line options:**
- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching, this parameter is used only if one token is to be processed on CPU backend.
- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend.
- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. Not used if model layers are offloaded to GPU.
- `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`)
- `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (default: unused).
- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository (default: unused).
- `-hff FILE, --hf-file FILE`: Hugging Face model file (default: unused).
- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused
- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository. Default: unused
- `-hff FILE, --hf-file FILE`: Hugging Face model file. Default: unused
- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is `512`, but LLaMA models were built with a context of `2048`, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of `4096`.
- `-ngl N`, `--n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`.
- `-ub N`, `--ubatch-size N`: physical maximum batch size. Default: `512`.
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
- `-mg i, --main-gpu i`: When using multiple GPUs, this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default, GPU `0` is used.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs, this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default, the data is split in proportion to VRAM, but this may not be optimal for performance.
- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`
- `-ub N`, `--ubatch-size N`: Physical maximum batch size. Default: `512`
- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
- `--numa STRATEGY`: Attempt one of the below optimization strategies that help on some NUMA systems
- `--numa STRATEGY`: Attempt one of the below optimization strategies that may help on some NUMA systems
- `--numa distribute`: Spread execution evenly over all nodes
- `--numa isolate`: Only spawn threads on CPUs on the node that execution started on
- `--numa numactl`: Use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggerganov/llama.cpp/issues/1437
- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system
page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437
- `--numa`: Attempt optimizations that help on some NUMA systems.
- `--numa`: Attempt optimizations that may help on some NUMA systems.
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
- `--port`: Set the port to listen. Default: `8080`.
- `--path`: path from which to serve static files (default: disabled)
- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s.
- `--embedding`: Enable embedding extraction, Default: disabled.
- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1)
- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)
- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`
- `--port`: Set the port to listen. Default: `8080`
- `--path`: Path from which to serve static files. Default: disabled
- `--api-key`: Set an api key for request authorization. By default, the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
- `--api-key-file`: Path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`s.
- `--embedding`: Enable embedding extraction. Default: disabled
- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`
- `-cb`, `--cont-batching`: Enable continuous batching (a.k.a dynamic batching). Default: disabled
- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots). This is useful for chat applications. [See more](#change-system-prompt-on-runtime)
- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`
- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`
- `-n N, --n-predict N`: Set the maximum tokens to predict (default: -1)
- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend. Used together with group attention width `--grp-attn-w`. Default: `1`, which is disabled.
- `--grp-attn-w`: Set the group attention width to extend context size through self-extend. Used together with group attention factor `--grp-attn-n`. Default: `512`
- `-n N, --n-predict N`: Set the maximum tokens to predict. Default: `-1`
- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
- `--metrics`: enable prometheus `/metrics` compatible endpoint (default: disabled)
- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
- `--log-disable`: Output logs to stdout only, not to `llama.log`. default: enabled.
- `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json)
- `--metrics`: enable prometheus `/metrics` compatible endpoint. Default: disabled
- `--slot-save-path PATH`: Specifies the path where the state of slots (the prompt cache) can be stored. If not provided, the slot management endpoints will be disabled.
- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name. Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
- `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled
- `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json`
**If compiled with `LLAMA_SERVER_SSL=ON`**
- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
@ -69,7 +69,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
## Build
server is build alongside everything else from the root of the project
`server` is built alongside everything else from the root of the project
- Using `make`:
@ -85,7 +85,7 @@ server is build alongside everything else from the root of the project
## Build with SSL
server can also be built with SSL support using OpenSSL 3
`server` can also be built with SSL support using OpenSSL 3
- Using `make`:
@ -135,7 +135,7 @@ docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/
## Testing with CURL
Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.
Using [curl](https://curl.se/). On Windows, `curl.exe` should be available in the base OS.
```sh
curl --request POST \
@ -159,7 +159,7 @@ mkdir llama-client
cd llama-client
```
Create a index.js file and put inside this:
Create a index.js file and put this inside:
```javascript
const prompt = `Building a website can be done in 10 simple steps:`;
@ -190,8 +190,8 @@ node index.js
- 503 -> `{"status": "loading model"}` if the model is still being loaded.
- 500 -> `{"status": "error"}` if the model failed to load.
- 200 -> `{"status": "ok", "slots_idle": 1, "slots_processing": 2 }` if the model is successfully loaded and the server is ready for further requests mentioned below.
- 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available.
- 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slot are currently available.
- 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slots are currently available.
- 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slots are currently available.
If the query parameter `include_slots` is passed, `slots` field will contain internal slots data except if `--slots-endpoint-disable` is set.
@ -205,75 +205,77 @@ node index.js
- The model's `tokenizer.ggml.add_bos_token` metadata is `true`
- The system prompt is empty
`temperature`: Adjust the randomness of the generated text (default: 0.8).
`temperature`: Adjust the randomness of the generated text. Default: `0.8`
`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` (default: 0.0, 0.0 = disabled).
`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled.
`dynatemp_exponent`: Dynamic temperature exponent (default: 1.0).
`dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0`
`top_k`: Limit the next token selection to the K most probable tokens (default: 40).
`top_k`: Limit the next token selection to the K most probable tokens. Default: `40`
`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).
`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95`
`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05).
`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05`
`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity).
`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
`n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.
By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt.
By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
`stop`: Specify a JSON array of stopping strings.
These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
`tfs_z`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
`tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled.
`typical_p`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.
`repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1.1).
`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`
`repeat_last_n`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
`repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
`penalize_nl`: Penalize newline tokens when applying the repeat penalty (default: true).
`penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
`presence_penalty`: Repeat alpha presence penalty (default: 0.0, 0.0 = disabled).
`presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
`frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);
`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
`penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens (default: `null` = use the original `prompt`).
`penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens. Default: `null`, which is to use the original `prompt`.
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
`mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).
`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
`mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
`mirostat_eta`: Set the Mirostat learning rate, parameter eta. Default: `0.1`
`grammar`: Set grammar for grammar-based sampling (default: no grammar)
`grammar`: Set grammar for grammar-based sampling. Default: no grammar
`seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
`json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features. Default: no JSON schema.
`ignore_eos`: Ignore end of stream token and continue generating (default: false).
`seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed.
`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. (default: []).
`ignore_eos`: Ignore end of stream token and continue generating. Default: `false`
`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)
`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]`
`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum (default: 0)
`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token. Default: `0`
`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`
`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1`
`cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. (default: false)
`cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. Default: `false`
`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. (default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values)
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
### Result JSON
- Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
- Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion.
- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
@ -287,7 +289,7 @@ node index.js
},
{
"prob": float,
"tok_str": "<second most likely tonen>"
"tok_str": "<second most likely token>"
},
...
]
@ -357,14 +359,16 @@ Notice that each `probs` is an array of length `n_probs`.
- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only model with [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, ChatML template will be used.
*Options:*
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such are `mirostat` are supported.
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}`), similar to other OpenAI-inspired API providers.
*Examples:*
@ -514,16 +518,67 @@ Available metrics:
- `llamacpp:tokens_predicted_total`: Number of generation tokens processed.
- `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s.
- `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s.
- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. 1 means 100 percent usage.
- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. `1` means 100 percent usage.
- `llamacpp:kv_cache_tokens`: KV-cache tokens.
- `llamacpp:requests_processing`: Number of request processing.
- `llamacpp:requests_deferred`: Number of request deferred.
- `llamacpp:requests_processing`: Number of requests processing.
- `llamacpp:requests_deferred`: Number of requests deferred.
- **POST** `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.
*Options:*
`filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter.
### Result JSON
```json
{
"id_slot": 0,
"filename": "slot_save_file.bin",
"n_saved": 1745,
"n_written": 14309796,
"timings": {
"save_ms": 49.865
}
}
```
- **POST** `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file.
*Options:*
`filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter.
### Result JSON
```json
{
"id_slot": 0,
"filename": "slot_save_file.bin",
"n_restored": 1745,
"n_read": 14309796,
"timings": {
"restore_ms": 42.937
}
}
```
- **POST** `/slots/{id_slot}?action=erase`: Erase the prompt cache of the specified slot.
### Result JSON
```json
{
"id_slot": 0,
"n_erased": 1745
}
```
## More examples
### Change system prompt on runtime
To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it.
To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt`. This only needs to be used once.
`prompt`: Specify a context that you want all connecting clients to respect.
@ -562,11 +617,11 @@ bash chat.sh
### OAI-like API
The HTTP server supports OAI-like API: https://github.com/openai/openai-openapi
The HTTP `server` supports an OAI-like API: https://github.com/openai/openai-openapi
### API errors
Server returns error in the same format as OAI: https://github.com/openai/openai-openapi
`server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
Example of an error:

42
examples/server/bench/README.md
View file

@ -2,13 +2,15 @@
Benchmark is using [k6](https://k6.io/).
##### Install k6
##### Install k6 and sse extension
Follow instruction from: https://k6.io/docs/get-started/installation/
SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension.
Example for ubuntu:
Example:
```shell
snap install k6
go install go.k6.io/xk6/cmd/xk6@latest
xk6 build master \
--with github.com/phymbert/xk6-sse
```
#### Download a dataset
@ -46,7 +48,7 @@ server --host localhost --port 8080 \
For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
```shell
k6 run script.js --duration 10m --iterations 500 --vus 8
./k6 run script.js --duration 10m --iterations 500 --vus 8
```
The benchmark values can be overridden with:
@ -86,3 +88,33 @@ K6 metrics might be compared against [server metrics](../README.md), with:
```shell
curl http://localhost:8080/metrics
```
### Using the CI python script
The `bench.py` script does several steps:
- start the server
- define good variable for k6
- run k6 script
- extract metrics from prometheus
It aims to be used in the CI, but you can run it manually:
```shell
LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/server python bench.py \
--runner-label local \
--name local \
--branch `git rev-parse --abbrev-ref HEAD` \
--commit `git rev-parse HEAD` \
--scenario script.js \
--duration 5m \
--hf-repo ggml-org/models \
--hf-file phi-2/ggml-model-q4_0.gguf \
--model-path-prefix models \
--parallel 4 \
-ngl 33 \
--batch-size 2048 \
--ubatch-size 256 \
--ctx-size 4096 \
--n-prompts 200 \
--max-prompt-tokens 256 \
--max-tokens 256
```

21
examples/server/bench/bench.py
View file

@ -16,6 +16,7 @@ import matplotlib
import matplotlib.dates
import matplotlib.pyplot as plt
import requests
from statistics import mean
def main(args_in: list[str] | None = None) -> None:
@ -75,7 +76,6 @@ def main(args_in: list[str] | None = None) -> None:
data['metrics'][metric_name][metric_metric]=value
github_env.write(
f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n")
token_seconds = data['metrics']['llamacpp_tokens_second']['avg']
iterations = data['root_group']['checks']['success completion']['passes']
except Exception:
@ -109,6 +109,7 @@ def main(args_in: list[str] | None = None) -> None:
# Prometheus
end_time = time.time()
prometheus_metrics = {}
if is_server_listening("0.0.0.0", 9090):
metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds',
'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred']
@ -127,6 +128,7 @@ def main(args_in: list[str] | None = None) -> None:
values = metric_data['data']['result'][0]['values']
timestamps, metric_values = zip(*values)
metric_values = [float(value) for value in metric_values]
prometheus_metrics[metric] = metric_values
timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
plt.figure(figsize=(16, 10), dpi=80)
plt.plot(timestamps_dt, metric_values, label=metric)
@ -176,17 +178,20 @@ xychart-beta
# 140 chars max for commit status description
bench_results = {
"i": iterations,
"req": {
"p90": data['metrics']["http_req_duration"]["p(90)"],
"avg": data['metrics']["http_req_duration"]["avg"],
"p95": round(data['metrics']["http_req_duration"]["p(95)"], 2),
"avg": round(data['metrics']["http_req_duration"]["avg"], 2),
},
"pp": {
"p90": data['metrics']["llamacpp_prompt_tokens"]["p(90)"],
"avg": data['metrics']["llamacpp_prompt_tokens"]["avg"],
"p95": round(data['metrics']["llamacpp_prompt_processing_second"]["p(95)"], 2),
"avg": round(data['metrics']["llamacpp_prompt_processing_second"]["avg"], 2),
"0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2),
},
"tg": {
"p90": data['metrics']["llamacpp_tokens_second"]["p(90)"],
"avg": data['metrics']["llamacpp_tokens_second"]["avg"],
"p95": round(data['metrics']["llamacpp_tokens_second"]["p(95)"], 2),
"avg": round(data['metrics']["llamacpp_tokens_second"]["avg"], 2),
"0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2),
},
}
with open("results.github.env", 'a') as github_env:
@ -200,7 +205,7 @@ xychart-beta
def start_benchmark(args):
k6_path = 'k6'
k6_path = './k6'
if 'BENCH_K6_BIN_PATH' in os.environ:
k6_path = os.environ['BENCH_K6_BIN_PATH']
k6_args = [

69
examples/server/bench/script.js
View file

@ -1,4 +1,4 @@
import http from 'k6/http'
import sse from 'k6/x/sse'
import {check, sleep} from 'k6'
import {SharedArray} from 'k6/data'
import {Counter, Rate, Trend} from 'k6/metrics'
@ -53,7 +53,9 @@ const data = new SharedArray('conversations', function () {
const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
const llamacpp_prompt_processing_second = new Trend('llamacpp_prompt_processing_second')
const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
@ -86,35 +88,62 @@ export default function () {
}
],
"model": model,
"stream": false,
"stream": true,
"seed": 42,
"max_tokens": max_tokens
}
const body = JSON.stringify(payload)
const params = {method: 'POST', body: JSON.stringify(payload)};
let res = http.post(`${server_url}/chat/completions`, body, {
headers: {'Content-Type': 'application/json'},
timeout: '300s'
const startTime = new Date()
let promptEvalEndTime = null
let prompt_tokens = 0
let completions_tokens = 0
let finish_reason = null
const res = sse.open(`${server_url}/chat/completions`, params, function (client) {
client.on('event', function (event) {
if (promptEvalEndTime == null) {
promptEvalEndTime = new Date()
}
let chunk = JSON.parse(event.data)
let choice = chunk.choices[0]
if (choice.finish_reason) {
finish_reason = choice.finish_reason
}
if (chunk.usage) {
prompt_tokens = chunk.usage.prompt_tokens
llamacpp_prompt_tokens.add(prompt_tokens)
llamacpp_prompt_tokens_total_counter.add(prompt_tokens)
completions_tokens = chunk.usage.completion_tokens
llamacpp_completion_tokens.add(completions_tokens)
llamacpp_completion_tokens_total_counter.add(completions_tokens)
}
})
client.on('error', function (e) {
console.log('An unexpected error occurred: ', e.error());
throw e;
})
})
check(res, {'success completion': (r) => r.status === 200})
if (res.status === 200) {
const completions = res.json()
const endTime = new Date()
llamacpp_prompt_tokens.add(completions.usage.prompt_tokens)
llamacpp_prompt_tokens_total_counter.add(completions.usage.prompt_tokens)
llamacpp_completion_tokens.add(completions.usage.completion_tokens)
llamacpp_completion_tokens_total_counter.add(completions.usage.completion_tokens)
llamacpp_completions_truncated_rate.add(completions.choices[0].finish_reason === 'length')
llamacpp_completions_stop_rate.add(completions.choices[0].finish_reason === 'stop')
llamacpp_tokens_second.add(completions.usage.total_tokens / res.timings.duration * 1.e3)
} else {
console.error(`response: ${res.body} request=${payload}`)
const promptEvalTime = promptEvalEndTime - startTime
if (promptEvalTime > 0) {
llamacpp_prompt_processing_second.add(prompt_tokens / (promptEvalEndTime - startTime) * 1.e3)
}
const completion_time = endTime - promptEvalEndTime
if (completions_tokens > 0 && completion_time > 0) {
llamacpp_tokens_second.add(completions_tokens / completion_time * 1.e3)
}
llamacpp_completions_truncated_rate.add(finish_reason === 'length')
llamacpp_completions_stop_rate.add(finish_reason === 'stop')
sleep(0.3)
}

4774
examples/server/index.html.hpp
View file

File diff suppressed because it is too large Load diff

3364
examples/server/json-schema-to-grammar.mjs.hpp
View file

File diff suppressed because it is too large Load diff

18
examples/server/public/index.html
View file

@ -222,6 +222,7 @@
temperature: 0.7,
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.18, // 1.0 = disabled
penalize_nl: false,
top_k: 40, // <= 0 to use vocab size
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
@ -405,7 +406,7 @@
throw new Error("already running");
}
controller.value = new AbortController();
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: document.baseURI.replace(/\/+$/, '') })) {
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) {
const data = chunk.data;
if (data.stop) {
@ -627,6 +628,7 @@
const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked }
const grammarJsonSchemaPropOrder = signal('')
const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value
@ -670,6 +672,15 @@
`
};
const BoolField = ({ label, name, value }) => {
return html`
<div>
<label for="${name}">${label}</label>
<input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} />
</div>
`
};
const userTemplateReset = (e) => {
e.preventDefault();
userTemplateResetToDefaultAndApply()
@ -769,6 +780,7 @@
${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
@ -1003,6 +1015,10 @@
}
function App(props) {
useEffect(() => {
const query = new URLSearchParams(location.search).get("q");
if (query) chat(query);
}, []);
return html`
<div class="mode-${session.value.type}">

200
examples/server/public/json-schema-to-grammar.mjs
View file

@ -1,33 +1,95 @@
// WARNING: This file was ported from json-schema-to-grammar.py, please fix bugs / add features there first.
// WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first.
const SPACE_RULE = '" "?';
function _buildRepetition(itemRule, minItems, maxItems, opts={}) {
const separatorRule = opts.separatorRule ?? '';
const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false
if (separatorRule === '') {
if (minItems === 0 && maxItems === 1) {
return `${itemRule}?`;
} else if (minItems === 1 && maxItems === undefined) {
return `${itemRule}+`;
}
}
let result = '';
if (minItems > 0) {
if (itemRuleIsLiteral && separatorRule === '') {
result = `"${itemRule.slice(1, -1).repeat(minItems)}"`;
} else {
result = Array.from({ length: minItems }, () => itemRule)
.join(separatorRule !== '' ? ` ${separatorRule} ` : ' ');
}
}
const optRepetitions = (upToN, prefixWithSep=false) => {
const content = separatorRule !== '' && prefixWithSep ? `${separatorRule} ${itemRule}` : itemRule;
if (upToN === 0) {
return '';
} else if (upToN === 1) {
return `(${content})?`;
} else if (separatorRule !== '' && !prefixWithSep) {
return `(${content} ${optRepetitions(upToN - 1, true)})?`;
} else {
return Array.from({ length: upToN }, () => `(${content}`).join(' ').trim() + Array.from({ length: upToN }, () => ')?').join('');
}
};
if (minItems > 0 && maxItems !== minItems) {
result += ' ';
}
if (maxItems !== undefined) {
result += optRepetitions(maxItems - minItems, minItems > 0);
} else {
const itemOperator = `(${separatorRule !== '' ? separatorRule + ' ' : ''}${itemRule})`;
if (minItems === 0 && separatorRule !== '') {
result = `(${itemRule} ${itemOperator}*)?`;
} else {
result += `${itemOperator}*`;
}
}
return result;
}
class BuiltinRule {
constructor(content, deps) {
this.content = content;
this.deps = deps || [];
}
}
const UP_TO_15_DIGITS = _buildRepetition('[0-9]', 0, 15);
const PRIMITIVE_RULES = {
boolean: '("true" | "false") space',
number: '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
integer: '("-"? ([0-9] | [1-9] [0-9]*)) space',
value: 'object | array | string | number | boolean',
object: '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space',
array: '"[" space ( value ("," space value)* )? "]" space',
uuid: '"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space',
string: ` "\\"" (
[^"\\\\] |
"\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
)* "\\"" space`,
null: '"null" space',
boolean : new BuiltinRule('("true" | "false") space', []),
'decimal-part' : new BuiltinRule('[0-9] ' + UP_TO_15_DIGITS, []),
'integral-part': new BuiltinRule('[0-9] | [1-9] ' + UP_TO_15_DIGITS, []),
number : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
integer : new BuiltinRule('("-"? integral-part) space', ['integral-part']),
value : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
object : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
array : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
uuid : new BuiltinRule('"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', []),
char : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])`, []),
string : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']),
null : new BuiltinRule('"null" space', []),
};
const OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value'];
// TODO: support "uri", "email" string formats
const DATE_RULES = {
'date' : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )',
'time' : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )',
'date-time': 'date "T" time',
'date-string': '"\\"" date "\\"" space',
'time-string': '"\\"" time "\\"" space',
'date-time-string': '"\\"" date-time "\\"" space',
};
const STRING_FORMAT_RULES = {
'date' : new BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
'time' : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
'date-time' : new BuiltinRule('date "T" time', ['date', 'time']),
'date-string' : new BuiltinRule('"\\"" date "\\"" space', ['date']),
'time-string' : new BuiltinRule('"\\"" time "\\"" space', ['time']),
'date-time-string': new BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
}
const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...DATE_RULES};
const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...STRING_FORMAT_RULES};
const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g;
const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g;
@ -158,7 +220,7 @@ export class SchemaConverter {
rule = '[\\U00000000-\\U0010FFFF]';
} else {
// Accept any character... except \n and \r line break chars (\x0A and \xOD)
rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]';
rule = '[^\\x0A\\x0D]';
}
return this._addRule('dot', rule);
};
@ -259,26 +321,19 @@ export class SchemaConverter {
let [sub, subIsLiteral] = seq[seq.length - 1];
if (minTimes === 0 && maxTimes === Infinity) {
seq[seq.length - 1] = [`${sub}*`, false];
} else if (minTimes === 0 && maxTimes === 1) {
seq[seq.length - 1] = [`${sub}?`, false];
} else if (minTimes === 1 && maxTimes === Infinity) {
seq[seq.length - 1] = [`${sub}+`, false];
} else {
if (!subIsLiteral) {
let id = subRuleIds[sub];
if (id === undefined) {
id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub);
subRuleIds[sub] = id;
}
sub = id;
if (!subIsLiteral) {
let id = subRuleIds[sub];
if (id === undefined) {
id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub);
subRuleIds[sub] = id;
}
const repeatedSub = Array.from({ length: minTimes }, () => subIsLiteral ? `"${sub.slice(1, -1).repeat(minTimes)}"` : sub);
const optionalSub = maxTimes !== undefined ? Array.from({ length: maxTimes - minTimes }, () => `${sub}?`) : [`${sub}*`];
seq[seq.length - 1] = [repeatedSub.concat(optionalSub).join(' '), false];
sub = id;
}
seq[seq.length - 1] = [
_buildRepetition(subIsLiteral ? `"${sub}"` : sub, minTimes, maxTimes, {itemRuleIsLiteral: subIsLiteral}),
false
];
} else {
let literal = '';
while (i < length) {
@ -394,49 +449,50 @@ export class SchemaConverter {
);
} else {
const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`);
const listItemOperator = `( "," space ${itemRuleName} )`;
let successiveItems = '';
let minItems = schema.minItems || 0;
const minItems = schema.minItems || 0;
const maxItems = schema.maxItems;
if (minItems > 0) {
successiveItems = listItemOperator.repeat(minItems - 1);
minItems--;
}
if (maxItems !== undefined && maxItems > minItems) {
successiveItems += `${listItemOperator}?`.repeat(maxItems - minItems - 1);
} else {
successiveItems += `${listItemOperator}*`;
}
const rule = minItems === 0
? `"[" space ( ${itemRuleName} ${successiveItems} )? "]" space`
: `"[" space ${itemRuleName} ${successiveItems} "]" space`;
return this._addRule(ruleName, rule);
return this._addRule(ruleName, '"[" space ' + _buildRepetition(itemRuleName, minItems, maxItems, {separatorRule: '"," space'}) + ' "]" space');
}
} else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) {
return this._visitPattern(schema.pattern, ruleName);
} else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.test(schema.format || '')) {
return this._addRule(
ruleName === 'root' ? 'root' : schemaFormat,
PRIMITIVE_RULES['uuid'])
} else if ((schemaType === undefined || schemaType === 'string') && schema.format in DATE_RULES) {
for (const [t, r] of Object.entries(DATE_RULES)) {
this._addRule(t, r);
}
return schemaFormat + '-string';
return this._addPrimitive(
ruleName === 'root' ? 'root' : schemaFormat,
PRIMITIVE_RULES['uuid']
);
} else if ((schemaType === undefined || schemaType === 'string') && `${schema.format}-string` in STRING_FORMAT_RULES) {
const primName = `${schema.format}-string`
return this._addRule(ruleName, this._addPrimitive(primName, STRING_FORMAT_RULES[primName]));
} else if (schemaType === 'string' && ('minLength' in schema || 'maxLength' in schema)) {
const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']);
const minLen = schema.minLength || 0;
const maxLen = schema.maxLength;
return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space');
} else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) {
for (const n of OBJECT_RULE_NAMES) {
this._addRule(n, PRIMITIVE_RULES[n]);
}
return this._addRule(ruleName, 'object');
return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object']));
} else {
if (!(schemaType in PRIMITIVE_RULES)) {
throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`);
}
// TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
return this._addRule(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]);
return this._addPrimitive(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]);
}
}
_addPrimitive(name, rule) {
let n = this._addRule(name, rule.content);
for (const dep of rule.deps) {
const depRule = PRIMITIVE_RULES[dep] || STRING_FORMAT_RULES[dep];
if (!depRule) {
throw new Error(`Rule ${dep} not known`);
}
if (!(dep in this._rules)) {
this._addPrimitive(dep, depRule);
}
}
return n;
}
_buildObjectRule(properties, required, name, additionalProperties) {
const propOrder = this._propOrder;
// sort by position in prop_order (if specified) then by original order
@ -462,7 +518,7 @@ export class SchemaConverter {
const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`);
propKvRuleNames['*'] = this._addRule(
`${subName}-kv`,
`${this._addRule('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`);
`${this._addPrimitive('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`);
optionalProps.push('*');
}

269
examples/server/server.cpp
View file

@ -61,7 +61,10 @@ enum server_task_type {
SERVER_TASK_TYPE_COMPLETION,
SERVER_TASK_TYPE_CANCEL,
SERVER_TASK_TYPE_NEXT_RESPONSE,
SERVER_TASK_TYPE_METRICS
SERVER_TASK_TYPE_METRICS,
SERVER_TASK_TYPE_SLOT_SAVE,
SERVER_TASK_TYPE_SLOT_RESTORE,
SERVER_TASK_TYPE_SLOT_ERASE,
};
struct server_task {
@ -128,6 +131,7 @@ struct server_params {
bool slots_endpoint = true;
bool metrics_endpoint = false;
std::string slot_save_path;
};
struct server_slot {
@ -685,6 +689,7 @@ struct server_context {
n_ctx = llama_n_ctx(ctx);
add_bos_token = llama_should_add_bos_token(model);
GGML_ASSERT(llama_add_eos_token(model) != 1);
return true;
}
@ -754,7 +759,7 @@ struct server_context {
metrics.init();
}
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const {
std::vector<llama_token> tokenize(const json & json_prompt, bool add_special) const {
// TODO: currently, we tokenize using special tokens by default
// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
// but it's better compared to completely ignoring ChatML and other chat templates
@ -772,7 +777,7 @@ struct server_context {
std::vector<llama_token> p;
if (first) {
p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
first = false;
} else {
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
@ -789,7 +794,7 @@ struct server_context {
}
} else {
auto s = json_prompt.template get<std::string>();
prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
}
return prompt_tokens;
@ -854,7 +859,7 @@ struct server_context {
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
// process "json_schema" and "grammar"
if (data.contains("json_schema") && data.contains("grammar")) {
if (data.contains("json_schema") && !data["json_schema"].is_null() && data.contains("grammar") && !data["grammar"].is_null()) {
send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST);
return false;
} else if (data.contains("json_schema") && !data.contains("grammar")) {
@ -1054,7 +1059,7 @@ struct server_context {
system_tokens.clear();
if (!system_prompt.empty()) {
system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
system_tokens = ::llama_tokenize(ctx, system_prompt, true);
llama_batch_clear(batch);
@ -1078,7 +1083,7 @@ struct server_context {
};
if (llama_decode(ctx, batch_view) != 0) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
LOG_ERROR("llama_decode() failed", {});
return;
}
}
@ -1276,7 +1281,11 @@ struct server_context {
}
void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
LOG_TEE("task %i - error: %s\n", id_task, error.c_str());
LOG_ERROR("task error", {
{"id_multi", id_multi},
{"id_task", id_task},
{"error", error},
});
server_task_result res;
res.id = id_task;
@ -1612,6 +1621,107 @@ struct server_context {
}
queue_results.send(res);
} break;
case SERVER_TASK_TYPE_SLOT_SAVE:
{
int id_slot = task.data["id_slot"];
server_slot * slot = get_slot(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
break;
}
const size_t token_count = slot->cache_tokens.size();
const int64_t t_start = ggml_time_us();
std::string filename = task.data["filename"];
std::string filepath = task.data["filepath"];
const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count);
const int64_t t_end = ggml_time_us();
const double t_save_ms = (t_end - t_start) / 1000.0;
server_task_result result;
result.id = task.id;
result.stop = true;
result.error = false;
result.data = json {
{ "id_slot", id_slot },
{ "filename", filename },
{ "n_saved", token_count }, // tokens saved
{ "n_written", nwrite }, // bytes written
{ "timings", {
{ "save_ms", t_save_ms }
} }
};
queue_results.send(result);
} break;
case SERVER_TASK_TYPE_SLOT_RESTORE:
{
int id_slot = task.data["id_slot"];
server_slot * slot = get_slot(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
break;
}
const int64_t t_start = ggml_time_us();
std::string filename = task.data["filename"];
std::string filepath = task.data["filepath"];
slot->cache_tokens.resize(slot->n_ctx);
size_t token_count = 0;
size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
if (nread == 0) {
slot->cache_tokens.resize(0);
send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
break;
}
slot->cache_tokens.resize(token_count);
const int64_t t_end = ggml_time_us();
const double t_restore_ms = (t_end - t_start) / 1000.0;
server_task_result result;
result.id = task.id;
result.stop = true;
result.error = false;
result.data = json {
{ "id_slot", id_slot },
{ "filename", filename },
{ "n_restored", token_count }, // tokens restored
{ "n_read", nread }, // bytes read
{ "timings", {
{ "restore_ms", t_restore_ms }
} }
};
queue_results.send(result);
} break;
case SERVER_TASK_TYPE_SLOT_ERASE:
{
int id_slot = task.data["id_slot"];
server_slot * slot = get_slot(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
break;
}
// Erase token cache
const size_t n_erased = slot->cache_tokens.size();
llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1);
slot->cache_tokens.clear();
server_task_result result;
result.id = task.id;
result.stop = true;
result.error = false;
result.data = json {
{ "id_slot", id_slot },
{ "n_erased", n_erased }
};
queue_results.send(result);
} break;
}
}
@ -1809,7 +1919,7 @@ struct server_context {
prefix_tokens.push_back(llama_token_middle(model));
prompt_tokens = prefix_tokens;
} else {
prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt
prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
}
slot.n_past = 0;
@ -2080,7 +2190,11 @@ struct server_context {
if (ret != 0) {
if (n_batch == 1 || ret < 0) {
// if you get here, it means the KV cache is full - try increasing it via the context size
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
LOG_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size", {
{"i", i},
{"n_batch", ret},
{"ret", ret},
});
for (auto & slot : slots) {
slot.state = SLOT_STATE_PROCESSING;
slot.command = SLOT_COMMAND_NONE;
@ -2090,12 +2204,16 @@ struct server_context {
break; // break loop of n_batch
}
LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
// retry with half the batch size to try to find a free slot in the KV cache
n_batch /= 2;
i -= n_batch;
LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation", {
{"i", i},
{"n_batch", n_batch},
{"ret", ret},
});
continue; // continue loop of n_batch
}
@ -2189,8 +2307,6 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch);
printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch);
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
if (llama_supports_mlock()) {
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
@ -2213,6 +2329,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n");
printf(" or for intermediate results and KV (with split-mode = row)\n");
printf(" -nkvo, --no-kv-offload\n");
printf(" disable KV offload\n");
}
printf(" -m FNAME, --model FNAME\n");
printf(" model path (default: %s)\n", params.model.c_str());
@ -2249,6 +2367,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
printf(" --log-disable disables logging to a file.\n");
printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n");
printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
printf(" --slot-save-path PATH path to save slot kv cache (default: disabled)\n");
printf("\n");
printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict);
printf(" --override-kv KEY=TYPE:VALUE\n");
@ -2498,6 +2617,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
"See main README.md for information on enabling GPU BLAS support",
{{"n_gpu_layers", params.n_gpu_layers}});
}
} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
params.no_kv_offload = true;
} else if (arg == "--split-mode" || arg == "-sm") {
if (++i >= argc) {
invalid_param = true;
@ -2655,6 +2776,16 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
sparams.slots_endpoint = false;
} else if (arg == "--metrics") {
sparams.metrics_endpoint = true;
} else if (arg == "--slot-save-path") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.slot_save_path = argv[i];
// if doesn't end with DIRECTORY_SEPARATOR, add it
if (!sparams.slot_save_path.empty() && sparams.slot_save_path[sparams.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
sparams.slot_save_path += DIRECTORY_SEPARATOR;
}
} else if (arg == "--chat-template") {
if (++i >= argc) {
invalid_param = true;
@ -3157,6 +3288,112 @@ int main(int argc, char ** argv) {
res.status = 200; // HTTP OK
};
const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
json request_data = json::parse(req.body);
std::string filename = request_data["filename"];
if (!validate_file_name(filename)) {
res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
return;
}
std::string filepath = sparams.slot_save_path + filename;
server_task task;
task.type = SERVER_TASK_TYPE_SLOT_SAVE;
task.data = {
{ "id_slot", id_slot },
{ "filename", filename },
{ "filepath", filepath }
};
const int id_task = ctx_server.queue_tasks.post(task);
ctx_server.queue_results.add_waiting_task_id(id_task);
server_task_result result = ctx_server.queue_results.recv(id_task);
ctx_server.queue_results.remove_waiting_task_id(id_task);
if (result.error) {
res_error(res, result.data);
} else {
res.set_content(result.data.dump(), "application/json");
}
};
const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
json request_data = json::parse(req.body);
std::string filename = request_data["filename"];
if (!validate_file_name(filename)) {
res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
return;
}
std::string filepath = sparams.slot_save_path + filename;
server_task task;
task.type = SERVER_TASK_TYPE_SLOT_RESTORE;
task.data = {
{ "id_slot", id_slot },
{ "filename", filename },
{ "filepath", filepath }
};
const int id_task = ctx_server.queue_tasks.post(task);
ctx_server.queue_results.add_waiting_task_id(id_task);
server_task_result result = ctx_server.queue_results.recv(id_task);
ctx_server.queue_results.remove_waiting_task_id(id_task);
if (result.error) {
res_error(res, result.data);
} else {
res.set_content(result.data.dump(), "application/json");
}
};
const auto handle_slots_erase = [&ctx_server, &res_error](const httplib::Request & /* req */, httplib::Response & res, int id_slot) {
server_task task;
task.type = SERVER_TASK_TYPE_SLOT_ERASE;
task.data = {
{ "id_slot", id_slot },
};
const int id_task = ctx_server.queue_tasks.post(task);
ctx_server.queue_results.add_waiting_task_id(id_task);
server_task_result result = ctx_server.queue_results.recv(id_task);
ctx_server.queue_results.remove_waiting_task_id(id_task);
if (result.error) {
res_error(res, result.data);
} else {
res.set_content(result.data.dump(), "application/json");
}
};
const auto handle_slots_action = [&res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
std::string id_slot_str = req.path_params.at("id_slot");
int id_slot;
try {
id_slot = std::stoi(id_slot_str);
} catch (const std::exception &) {
res_error(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST));
return;
}
std::string action = req.get_param_value("action");
if (action == "save") {
handle_slots_save(req, res, id_slot);
} else if (action == "restore") {
handle_slots_restore(req, res, id_slot);
} else if (action == "erase") {
handle_slots_erase(req, res, id_slot);
} else {
res_error(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST));
}
};
const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = {
@ -3519,6 +3756,10 @@ int main(int argc, char ** argv) {
svr->Post("/v1/embeddings", handle_embeddings);
svr->Post("/tokenize", handle_tokenize);
svr->Post("/detokenize", handle_detokenize);
if (!sparams.slot_save_path.empty()) {
// only enable slot endpoints if slot_save_path is set
svr->Post("/slots/:id_slot", handle_slots_action);
}
//
// Start the server

58
examples/server/tests/features/slotsave.feature Normal file
View file

@ -0,0 +1,58 @@
@llama.cpp
@slotsave
Feature: llama.cpp server slot management
Background: Server startup
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And prompt caching is enabled
And 2 slots
And . as slot save path
And 2048 KV cache size
And 42 as server seed
And 24 max tokens to predict
Then the server is starting
Then the server is healthy
Scenario: Save and Restore Slot
# First prompt in slot 1 should be fully processed
Given a user prompt "What is the capital of France?"
And using slot id 1
And a completion request with no api error
Then 24 tokens are predicted matching (Lily|cake)
And 22 prompt tokens are processed
When the slot 1 is saved with filename "slot1.bin"
Then the server responds with status code 200
# Since we have cache, this should only process the last tokens
Given a user prompt "What is the capital of Germany?"
And a completion request with no api error
Then 24 tokens are predicted matching (Thank|special)
And 7 prompt tokens are processed
# Loading the original cache into slot 0,
# we should only be processing 1 prompt token and get the same output
When the slot 0 is restored with filename "slot1.bin"
Then the server responds with status code 200
Given a user prompt "What is the capital of France?"
And using slot id 0
And a completion request with no api error
Then 24 tokens are predicted matching (Lily|cake)
And 1 prompt tokens are processed
# For verification that slot 1 was not corrupted during slot 0 load, same thing
Given a user prompt "What is the capital of Germany?"
And using slot id 1
And a completion request with no api error
Then 24 tokens are predicted matching (Thank|special)
And 1 prompt tokens are processed
Scenario: Erase Slot
Given a user prompt "What is the capital of France?"
And using slot id 1
And a completion request with no api error
Then 24 tokens are predicted matching (Lily|cake)
And 22 prompt tokens are processed
When the slot 1 is erased
Then the server responds with status code 200
Given a user prompt "What is the capital of France?"
And a completion request with no api error
Then 24 tokens are predicted matching (Lily|cake)
And 22 prompt tokens are processed

60
examples/server/tests/features/steps/steps.py
View file

@ -49,6 +49,9 @@ def step_server_config(context, server_fqdn, server_port):
context.n_predict = None
context.n_prompts = 0
context.n_server_predict = None
context.slot_save_path = None
context.id_slot = None
context.cache_prompt = None
context.n_slots = None
context.prompt_prefix = None
context.prompt_suffix = None
@ -119,6 +122,21 @@ def step_server_n_predict(context, n_predict):
context.n_server_predict = n_predict
@step('{slot_save_path} as slot save path')
def step_slot_save_path(context, slot_save_path):
context.slot_save_path = slot_save_path
@step('using slot id {id_slot:d}')
def step_id_slot(context, id_slot):
context.id_slot = id_slot
@step('prompt caching is enabled')
def step_enable_prompt_cache(context):
context.cache_prompt = True
@step('continuous batching')
def step_server_continuous_batching(context):
context.server_continuous_batching = True
@ -212,6 +230,8 @@ async def step_request_completion(context, api_error):
context.base_url,
debug=context.debug,
n_predict=context.n_predict,
cache_prompt=context.cache_prompt,
id_slot=context.id_slot,
seed=await completions_seed(context),
expect_api_error=expect_api_error,
user_api_key=context.user_api_key)
@ -711,12 +731,48 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
await asyncio.sleep(0.1)
@step('the slot {slot_id:d} is saved with filename "{filename}"')
@async_run_until_complete
async def step_save_slot(context, slot_id, filename):
async with aiohttp.ClientSession() as session:
async with session.post(f'{context.base_url}/slots/{slot_id}?action=save',
json={"filename": filename},
headers={"Content-Type": "application/json"}) as response:
context.response = response
@step('the slot {slot_id:d} is restored with filename "{filename}"')
@async_run_until_complete
async def step_restore_slot(context, slot_id, filename):
async with aiohttp.ClientSession() as session:
async with session.post(f'{context.base_url}/slots/{slot_id}?action=restore',
json={"filename": filename},
headers={"Content-Type": "application/json"}) as response:
context.response = response
@step('the slot {slot_id:d} is erased')
@async_run_until_complete
async def step_erase_slot(context, slot_id):
async with aiohttp.ClientSession() as session:
async with session.post(f'{context.base_url}/slots/{slot_id}?action=erase',
headers={"Content-Type": "application/json"}) as response:
context.response = response
@step('the server responds with status code {status_code:d}')
def step_server_responds_with_status_code(context, status_code):
assert context.response.status == status_code
async def request_completion(prompt,
base_url,
debug=False,
prompt_prefix=None,
prompt_suffix=None,
n_predict=None,
cache_prompt=False,
id_slot=None,
seed=None,
expect_api_error=None,
user_api_key=None):
@ -738,6 +794,8 @@ async def request_completion(prompt,
"prompt": prompt,
"input_suffix": prompt_suffix,
"n_predict": n_predict if n_predict is not None else -1,
"cache_prompt": cache_prompt,
"id_slot": id_slot,
"seed": seed if seed is not None else 42
},
headers=headers,
@ -1104,6 +1162,8 @@ def start_server_background(context):
server_args.extend(['--parallel', context.n_slots])
if context.n_server_predict:
server_args.extend(['--n-predict', context.n_server_predict])
if context.slot_save_path:
server_args.extend(['--slot-save-path', context.slot_save_path])
if context.server_api_key:
server_args.extend(['--api-key', context.server_api_key])
if context.n_ga:

26
examples/server/utils.hpp
View file

@ -49,12 +49,23 @@ extern bool server_log_json;
#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra);
template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value) {
// Fallback null to default value
return body.contains(key) && !body.at(key).is_null()
? body.value(key, default_value)
: default_value;
if (body.contains(key) && !body.at(key).is_null()){
try {
return body.value(key, default_value);
}
catch (nlohmann::json_abi_v3_11_3::detail::type_error const&){
std::string message = "Wrong type supplied for parameter '" + key + "'. Expected '" + typeid(default_value).name() + "', using default value.";
server_log("WARN", __func__, __LINE__, message.c_str(), body);
return default_value;
}
} else {
return default_value;
}
}
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) {
@ -556,6 +567,15 @@ static std::vector<json> format_partial_response_oaicompat(json result, const st
{"model", modelname},
{"object", "chat.completion.chunk"}
};
if (!finish_reason.empty()) {
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
ret.push_back({"usage", json {
{"completion_tokens", num_tokens_predicted},
{"prompt_tokens", num_prompt_tokens},
{"total_tokens", num_tokens_predicted + num_prompt_tokens}
}});
}
return std::vector<json>({ret});
}

36
examples/speculative/speculative.cpp
View file

@ -76,6 +76,28 @@ int main(int argc, char ** argv) {
params.n_threads_batch = params.n_threads_batch_draft;
std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
const bool vocab_type_tgt = llama_vocab_type(model_tgt);
LOG("vocab_type tgt: %d\n", vocab_type_tgt);
const bool vocab_type_dft = llama_vocab_type(model_dft);
LOG("vocab_type dft: %d\n", vocab_type_dft);
if (vocab_type_tgt != vocab_type_dft) {
fprintf(stderr, "%s: error: draft model vocab type must match target model to use speculation but ", __func__);
fprintf(stderr, "vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
return 1;
}
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)
) {
fprintf(stderr, "%s: error: draft model special tokens must match target model to use speculation\n", __func__);
return 1;
}
{
const int n_vocab_tgt = llama_n_vocab(model_tgt);
const int n_vocab_dft = llama_n_vocab(model_dft);
@ -105,20 +127,8 @@ int main(int argc, char ** argv) {
// Tokenize the prompt
const bool add_bos_tgt = llama_should_add_bos_token(model_tgt);
LOG("add_bos tgt: %d\n", add_bos_tgt);
const bool add_bos_dft = llama_should_add_bos_token(model_dft);
LOG("add_bos dft: %d\n", add_bos_dft);
if (add_bos_tgt != add_bos_dft) {
fprintf(stderr, "%s: error: draft model add_bos must match target model to use speculation but ", __func__);
fprintf(stderr, "add_bos_dft = %d while add_bos_tgt = %d\n", add_bos_dft, add_bos_tgt);
return 1;
}
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx_tgt, params.prompt, add_bos_tgt, true);
inp = ::llama_tokenize(ctx_tgt, params.prompt, true, true);
const int max_context_size = llama_n_ctx(ctx_tgt);
const int max_tokens_list_size = max_context_size - 4;

2
examples/sycl/build.sh
View file

@ -20,4 +20,4 @@ cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
#cmake --build . --config Release --target llama-bench
#build all binary
cmake --build . --config Release -v
cmake --build . --config Release -j -v

1
examples/sycl/run-llama2.sh
View file

@ -12,6 +12,7 @@ if [ $# -gt 0 ]; then
GGML_SYCL_SINGLE_GPU=1
else
GGML_SYCL_DEVICE=0
GGML_SYCL_SINGLE_GPU=0
fi
#export GGML_SYCL_DEBUG=1

4
examples/tokenize/tokenize.cpp
View file

@ -26,11 +26,9 @@ int main(int argc, char ** argv) {
llama_context_params ctx_params = llama_context_default_params();
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
const bool add_bos = llama_should_add_bos_token(model);
std::vector<llama_token> tokens;
tokens = ::llama_tokenize(model, prompt, add_bos, true);
tokens = ::llama_tokenize(model, prompt, true, true);
for (int i = 0; i < (int) tokens.size(); i++) {
if (printing_ids) {

4
examples/ts-type-to-grammar.sh
View file

@ -1,7 +1,7 @@
#!/bin/bash
#
# ./examples/ts-type-to-grammar.sh "{a:string,b:string,c?:string}"
# python examples/json-schema-to-grammar.py https://json.schemastore.org/tsconfig.json
# python examples/json_schema_to_grammar.py https://json.schemastore.org/tsconfig.json
#
set -euo pipefail
@ -25,4 +25,4 @@ npx ts-json-schema-generator --unstable --no-top-ref --path "$DTS_FILE" --type M
# https://github.com/YousefED/typescript-json-schema
# npx typescript-json-schema --defaultProps --required "$DTS_FILE" MyType | tee "$SCHEMA_FILE" >&2
./examples/json-schema-to-grammar.py "$SCHEMA_FILE"
./examples/json_schema_to_grammar.py "$SCHEMA_FILE"

18
flake.lock generated
View file

@ -5,11 +5,11 @@
"nixpkgs-lib": "nixpkgs-lib"
},
"locked": {
"lastModified": 1709336216,
"narHash": "sha256-Dt/wOWeW6Sqm11Yh+2+t0dfEWxoMxGBvv3JpIocFl9E=",
"lastModified": 1712014858,
"narHash": "sha256-sB4SWl2lX95bExY2gMFG5HIzvva5AVMJd4Igm+GpZNw=",
"owner": "hercules-ci",
"repo": "flake-parts",
"rev": "f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2",
"rev": "9126214d0a59633752a136528f5f3b9aa8565b7d",
"type": "github"
},
"original": {
@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1711163522,
"narHash": "sha256-YN/Ciidm+A0fmJPWlHBGvVkcarYWSC+s3NTPk/P+q3c=",
"lastModified": 1712791164,
"narHash": "sha256-3sbWO1mbpWsLepZGbWaMovSO7ndZeFqDSdX0hZ9nVyw=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "44d0940ea560dee511026a53f0e2e2cde489b4d4",
"rev": "1042fd8b148a9105f3c0aca3a6177fd1d9360ba5",
"type": "github"
},
"original": {
@ -37,11 +37,11 @@
"nixpkgs-lib": {
"locked": {
"dir": "lib",
"lastModified": 1709237383,
"narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=",
"lastModified": 1711703276,
"narHash": "sha256-iMUFArF0WCatKK6RzfUJknjem0H9m4KgorO/p3Dopkk=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8",
"rev": "d8fe5e6c92d0d190646fb9f1056741a229980089",
"type": "github"
},
"original": {

9
ggml-alloc.c
View file

@ -705,8 +705,13 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
struct ggml_tensor * leaf = graph->leafs[i];
struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
galloc->leaf_allocs[i].leaf.offset = hn->offset;
galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
if (leaf->view_src || leaf->data) {
galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
galloc->leaf_allocs[i].leaf.size_max = 0;
} else {
galloc->leaf_allocs[i].leaf.offset = hn->offset;
galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
}
}
// reallocate buffers if needed

2
ggml-backend.h
View file

@ -137,7 +137,7 @@ extern "C" {
/*
Example usage:
// operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be asigned
// operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned
// preferrably to run on the same backend as the buffer
ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);

5
ggml-common.h
View file

@ -447,10 +447,11 @@ static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_
#define GGML_COMMON_IMPL
#elif defined(GGML_COMMON_IMPL_SYCL)
#include <cstdint>
#define GGML_TABLE_BEGIN(type, name, size) static dpct::global_memory<const type, 1> name(sycl::range<1>(size), {
#define GGML_TABLE_END() });
#define GGML_TABLE_BEGIN(type, name, size) static const type name[size] = {
#define GGML_TABLE_END() };
#define GGML_COMMON_IMPL
#endif

226
ggml-cuda.cu
View file

@ -401,10 +401,8 @@ GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t
GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
if (tensor->view_src != NULL && tensor->view_offs == 0) {
if (tensor->view_src != NULL) {
assert(tensor->view_src->buffer->buft == buffer->buft);
tensor->backend = tensor->view_src->backend;
tensor->extra = tensor->view_src->extra;
return;
}
@ -1227,7 +1225,7 @@ static void ggml_cuda_op_mul_mat_cublas(
// the main device has a larger memory buffer to hold the results from all GPUs
// ldc == nrows of the matrix that cuBLAS writes into
int ldc = id == ctx.device ? ne0 : row_diff;
int64_t ldc = id == ctx.device ? ne0 : row_diff;
const int compute_capability = ggml_cuda_info().devices[id].cc;
@ -1379,8 +1377,8 @@ static void ggml_cuda_op_mul_mat(
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const int64_t nb2 = dst->nb[2];
const int64_t nb3 = dst->nb[3];
GGML_ASSERT(ggml_backend_buffer_is_cuda(dst->buffer));
GGML_ASSERT(ggml_backend_buffer_is_cuda(src1->buffer));
@ -1948,7 +1946,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
} else if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
// KQV single-batch
ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst);
} else if (!split && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
} else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || fp16_performance_good) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
// KQ + KQV multi-batch
ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
} else if (use_dequantize_mul_mat_vec) {
@ -1962,227 +1960,49 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
}
}
#if 0
template<typename ... Srcs>
static __global__ void k_compute_batched_ptrs_id(
const void ** ptrs_src, void ** ptrs_dst,
int ne12, int ne13,
int ne23,
int nb02, int nb03,
int nb12, int nb13,
int nb2, int nb3,
int r2, int r3,
ggml_type src0_type, half * src0_as_f16, int64_t src0_ne,
const half * src1_f16, half * dst_f16,
const int32_t * ids, const int id,
Srcs... src0s) {
int i = ids[id];
half * src0_f16;
const void * srcs_ar[] = { (const half *) src0s... };
if (src0_type == GGML_TYPE_F16) {
src0_f16 = (half *) srcs_ar[i];
} else {
src0_f16 = src0_as_f16;
if (threadIdx.x == 0 && threadIdx.y == 0) {
const to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(src0_type);
to_fp16(srcs_ar[i], src0_f16, src0_ne, cudaStreamFireAndForget);
}
}
int i13 = blockIdx.x * blockDim.x + threadIdx.x;
int i12 = blockIdx.y * blockDim.y + threadIdx.y;
if (i13 >= ne13 || i12 >= ne12) {
return;
}
int i03 = i13 / r3;
int i02 = i12 / r2;
ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02 + i03*nb03;
ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2;
ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2;
}
static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) {
const struct ggml_tensor * ids = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const struct ggml_tensor * src00 = dst->src[2];
const int id = dst->op_params[0];
GGML_ASSERT(!ggml_is_transposed(src00));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00);
const int64_t ne01 = src00->ne[1];
const int64_t ne02 = src00->ne[2];
const int64_t ne03 = src00->ne[3];
//const int64_t nb01 = src00->nb[1];
const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02);
const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03);
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int64_t ne12 = src1->ne[2];
const int64_t ne13 = src1->ne[3];
//const int64_t nb11 = src1->nb[1];
const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);
const int64_t ne1 = ggml_nelements(src1);
const int64_t ne = ggml_nelements(dst);
ggml_cuda_set_device(g_main_device);
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream));
//ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
//void * src0_ddq = src0_extra->data_device[g_main_device];
//half * src0_as_f16 = (half *) src0_ddq;
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
// convert src1 to fp16
const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
GGML_ASSERT(to_fp16_cuda != nullptr);
size_t src1_as = 0;
half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
size_t dst_as = 0;
half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);
// broadcast factors
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
const half alpha_f16 = 1.0f;
const half beta_f16 = 0.0f;
// use cublasGemmBatchedEx
const int ne23 = ne12*ne13;
const void ** ptrs_src = nullptr;
void ** ptrs_dst = nullptr;
size_t ptrs_src_s = 0;
size_t ptrs_dst_s = 0;
ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s);
ptrs_dst = ( void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s);
int64_t src0_ne = ggml_nelements(src00);
half * src0_as_f16 = nullptr;
size_t src0_as = 0;
if (src00->type != GGML_TYPE_F16) {
src0_as_f16 = (half *) ggml_cuda_pool_malloc(src0_ne * sizeof(half), &src0_as);
}
static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6");
dim3 block_dims(ne13, ne12);
k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>(
ptrs_src, ptrs_dst,
ne12, ne13,
ne23,
ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half),
nb12, nb13,
dst->nb[2], dst->nb[3],
r2, r3,
src00->type, src0_as_f16, src0_ne,
src1_as_f16, dst_f16,
(const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id,
dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr,
dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr,
dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr,
dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr
);
CUDA_CHECK(cudaGetLastError());
CUBLAS_CHECK(
cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, ne00,
(const void **) (ptrs_src + 1*ne23), CUDA_R_16F, ne10,
&beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
ne23,
CUBLAS_COMPUTE_16F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
if (src0_as != 0) {
ggml_cuda_pool_free(src0_as_f16, src0_as);
}
if (ptrs_src_s != 0) {
ggml_cuda_pool_free(ptrs_src, ptrs_src_s);
}
if (ptrs_dst_s != 0) {
ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s);
}
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
ggml_cuda_pool_free(src1_as_f16, src1_as);
ggml_cuda_pool_free(dst_f16, dst_as);
}
#endif
static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
#if 0
ggml_cuda_mul_mat_id_cublas(dst);
// TODO: mmq/mmv support
#endif
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
const ggml_tensor * ids = dst->src[2];
GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers");
cudaStream_t stream = ctx.stream();
const size_t nb11 = src1->nb[1];
const size_t nb1 = dst->nb[1];
const struct ggml_tensor * ids = src0;
const int32_t id = ((int32_t *) dst->op_params)[0];
const int32_t n_as = ((int32_t *) dst->op_params)[1];
const int32_t n_as = src0->ne[2];
std::vector<char> ids_host(ggml_nbytes(ids));
const char * ids_dev = (const char *) ids->data;
CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
CUDA_CHECK(cudaStreamSynchronize(stream));
ggml_tensor src0_row = *src0;
ggml_tensor src1_row = *src1;
ggml_tensor dst_row = *dst;
char * src0_original = (char *) src0->data;
char * src1_original = (char *) src1->data;
char * dst_original = (char *) dst->data;
src0_row.ne[2] = 1;
src0_row.ne[3] = 1;
src0_row.nb[3] = src0->nb[2];
if (src1->ne[1] == 1) {
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
GGML_ASSERT(row_id >= 0 && row_id < n_as);
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
src0_row.data = src0_original + row_id*src0->nb[2];
src1_row.data = src1_original + i01*src1->nb[1];
dst_row.data = dst_original + i01*dst->nb[1];
ggml_cuda_mul_mat(ctx, src0_row, &src1_row, &dst_row);
ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
}
} else {
ggml_cuda_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
@ -2192,8 +2012,6 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
dst_row.data = dst_contiguous.get();
for (int32_t row_id = 0; row_id < n_as; ++row_id) {
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
int64_t num_src1_rows = 0;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
@ -2213,6 +2031,8 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
continue;
}
src0_row.data = src0_original + row_id*src0->nb[2];
src1_row.ne[1] = num_src1_rows;
dst_row.ne[1] = num_src1_rows;
@ -2224,7 +2044,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
dst_row.nb[2] = num_src1_rows*nb1;
dst_row.nb[3] = num_src1_rows*nb1;
ggml_cuda_mul_mat(ctx, src0_row, &src1_row, &dst_row);
ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
num_src1_rows = 0;
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
@ -2389,7 +2209,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
fprintf(stderr, "%s: %s failed\n", __func__, ggml_op_desc(dst));
GGML_ASSERT(false);
CUDA_CHECK(err);
}
return true;
@ -2797,6 +2617,7 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size
return false;
}
#if CUDART_VERSION >= 11100
cudaError_t err = cudaHostRegister(buffer, size, cudaHostRegisterPortable | cudaHostRegisterReadOnly);
if (err != cudaSuccess) {
// clear the error
@ -2807,6 +2628,9 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size
return false;
}
return true;
#else
return false;
#endif
}
GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer) {

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