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Merge remote-tracking branch 'upstream/master' into t5-clean-3

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This commit is contained in:
Stanisław Szymczyk 2024-06-27 09:29:26 +02:00
commit 7293243d4f
362 changed files with 2733 additions and 2068 deletions
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2
.devops/full-cuda.Dockerfile
View File

@ -27,7 +27,7 @@ COPY . .
# Set nvcc architecture
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable CUDA
ENV LLAMA_CUDA=1
ENV GGML_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1

2
.devops/full-rocm.Dockerfile
View File

@ -36,7 +36,7 @@ COPY . .
# Set nvcc architecture
ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
ENV LLAMA_HIPBLAS=1
ENV GGML_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++

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

@ -21,7 +21,7 @@ COPY . .
# Set nvcc architecture
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable CUDA
ENV LLAMA_CUDA=1
ENV GGML_CUDA=1
RUN make -j$(nproc) llama-cli

10
.devops/llama-cli-intel.Dockerfile
View File

@ -2,7 +2,7 @@ ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
ARG LLAMA_SYCL_F16=OFF
ARG GGML_SYCL_F16=OFF
RUN apt-get update && \
apt-get install -y git
@ -10,11 +10,11 @@ WORKDIR /app
COPY . .
RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
echo "LLAMA_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
echo "GGML_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
fi && \
cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
cmake --build build --config Release --target llama-cli
FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime

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

@ -36,7 +36,7 @@ COPY . .
# Set nvcc architecture
ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
ENV LLAMA_HIPBLAS=1
ENV GGML_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++

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

@ -14,7 +14,7 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
# Build it
WORKDIR /app
COPY . .
RUN cmake -B build -DLLAMA_VULKAN=1 && \
RUN cmake -B build -DGGML_VULKAN=1 && \
cmake --build build --config Release --target llama-cli
# Clean up

84
.devops/llama-cpp-clblast.srpm.spec
View File

@ -1,84 +0,0 @@
# SRPM for building from source and packaging an RPM for RPM-based distros.
# 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
# Notes for llama.cpp:
# 1. Tags are currently based on hash - which will not sort asciibetically.
# We need to declare standard versioning if people want to sort latest releases.
# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
# It is up to the user to install the correct vendor-specific support.
Name: llama.cpp-clblast
Version: %( date "+%%Y%%m%%d" )
Release: 1%{?dist}
Summary: OpenCL Inference of LLaMA model in C/C++
License: MIT
Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
BuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel
Requires: clblast
URL: https://github.com/ggerganov/llama.cpp
%define debug_package %{nil}
%define source_date_epoch_from_changelog 0
%description
CPU inference for Meta's Lllama2 models using default options.
%prep
%setup -n llama.cpp-master
%build
make -j LLAMA_CLBLAST=1
%install
mkdir -p %{buildroot}%{_bindir}/
cp -p llama-cli %{buildroot}%{_bindir}/llama-clblast-cli
cp -p llama-server %{buildroot}%{_bindir}/llama-clblast-server
cp -p llama-simple %{buildroot}%{_bindir}/llama-clblast-simple
mkdir -p %{buildroot}/usr/lib/systemd/system
%{__cat} <<EOF > %{buildroot}/usr/lib/systemd/system/llamaclblast.service
[Unit]
Description=Llama.cpp server, CPU only (no GPU support in this build).
After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
[Service]
Type=simple
EnvironmentFile=/etc/sysconfig/llama
ExecStart=/usr/bin/llama-clblast-server $LLAMA_ARGS
ExecReload=/bin/kill -s HUP $MAINPID
Restart=never
[Install]
WantedBy=default.target
EOF
mkdir -p %{buildroot}/etc/sysconfig
%{__cat} <<EOF > %{buildroot}/etc/sysconfig/llama
LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
EOF
%clean
rm -rf %{buildroot}
rm -rf %{_builddir}/*
%files
%{_bindir}/llama-clblast-cli
%{_bindir}/llama-clblast-server
%{_bindir}/llama-clblast-simple
/usr/lib/systemd/system/llamaclblast.service
%config /etc/sysconfig/llama
%pre
%post
%preun
%postun
%changelog

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

@ -32,7 +32,7 @@ CPU inference for Meta's Lllama2 models using default options.
%setup -n llama.cpp-master
%build
make -j LLAMA_CUDA=1
make -j GGML_CUDA=1
%install
mkdir -p %{buildroot}%{_bindir}/

2
.devops/llama-server-cuda.Dockerfile
View File

@ -21,7 +21,7 @@ COPY . .
# Set nvcc architecture
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable CUDA
ENV LLAMA_CUDA=1
ENV GGML_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1

10
.devops/llama-server-intel.Dockerfile
View File

@ -2,7 +2,7 @@ ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
ARG LLAMA_SYCL_F16=OFF
ARG GGML_SYCL_F16=OFF
RUN apt-get update && \
apt-get install -y git libcurl4-openssl-dev
@ -10,11 +10,11 @@ WORKDIR /app
COPY . .
RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
echo "LLAMA_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
echo "GGML_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
fi && \
cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake --build build --config Release --target llama-server
FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime

2
.devops/llama-server-rocm.Dockerfile
View File

@ -36,7 +36,7 @@ COPY . .
# Set nvcc architecture
ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
ENV LLAMA_HIPBLAS=1
ENV GGML_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++

2
.devops/llama-server-vulkan.Dockerfile
View File

@ -14,7 +14,7 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
# Build it
WORKDIR /app
COPY . .
RUN cmake -B build -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
RUN cmake -B build -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
cmake --build build --config Release --target llama-server
# Clean up

24
.devops/nix/package.nix
View File

@ -160,9 +160,9 @@ effectiveStdenv.mkDerivation (
};
postPatch = ''
substituteInPlace ./ggml-metal.m \
substituteInPlace ./ggml/src/ggml-metal.m \
--replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
substituteInPlace ./ggml-metal.m \
substituteInPlace ./ggml/src/ggml-metal.m \
--replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
'';
@ -205,17 +205,17 @@ effectiveStdenv.mkDerivation (
cmakeFlags =
[
(cmakeBool "LLAMA_NATIVE" false)
(cmakeBool "LLAMA_BUILD_SERVER" true)
(cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
(cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
(cmakeBool "LLAMA_BLAS" useBlas)
(cmakeBool "LLAMA_CLBLAST" useOpenCL)
(cmakeBool "LLAMA_CUDA" useCuda)
(cmakeBool "LLAMA_HIPBLAS" useRocm)
(cmakeBool "LLAMA_METAL" useMetalKit)
(cmakeBool "LLAMA_VULKAN" useVulkan)
(cmakeBool "LLAMA_STATIC" enableStatic)
(cmakeBool "GGML_NATIVE" false)
(cmakeBool "GGML_BLAS" useBlas)
(cmakeBool "GGML_CLBLAST" useOpenCL)
(cmakeBool "GGML_CUDA" useCuda)
(cmakeBool "GGML_HIPBLAS" useRocm)
(cmakeBool "GGML_METAL" useMetalKit)
(cmakeBool "GGML_VULKAN" useVulkan)
(cmakeBool "GGML_STATIC" enableStatic)
]
++ optionals useCuda [
(
@ -231,7 +231,7 @@ effectiveStdenv.mkDerivation (
]
++ optionals useMetalKit [
(lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
(cmakeBool "LLAMA_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
(cmakeBool "GGML_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
];
# Environment variables needed for ROCm
@ -244,7 +244,7 @@ effectiveStdenv.mkDerivation (
# if they haven't been added yet.
postInstall = ''
mkdir -p $out/include
cp $src/llama.h $out/include/
cp $src/include/llama.h $out/include/
'';
# Define the shells here, but don't add in the inputsFrom to avoid recursion.

28
.github/labeler.yml vendored
View File

@ -2,31 +2,31 @@
Kompute:
- changed-files:
- any-glob-to-any-file:
- ggml-kompute.h
- ggml-kompute.cpp
- ggml/include/ggml-kompute.h
- ggml/src/ggml-kompute.cpp
- README-kompute.md
Apple Metal:
- changed-files:
- any-glob-to-any-file:
- ggml-metal.h
- ggml-metal.cpp
- ggml/include/ggml-metal.h
- ggml/src/ggml-metal.cpp
- README-metal.md
SYCL:
- changed-files:
- any-glob-to-any-file:
- ggml-sycl.h
- ggml-sycl.cpp
- ggml/include/ggml-sycl.h
- ggml/src/ggml-sycl.cpp
- README-sycl.md
Nvidia GPU:
- changed-files:
- any-glob-to-any-file:
- ggml-cuda.h
- ggml-cuda/**
- ggml/include/ggml-cuda.h
- ggml/src/ggml-cuda/**
Vulkan:
- changed-files:
- any-glob-to-any-file:
- ggml_vk_generate_shaders.py
- ggml-vulkan*
- ggml/ggml_vk_generate_shaders.py
- ggml/src/ggml-vulkan*
documentation:
- changed-files:
- any-glob-to-any-file:
@ -73,10 +73,10 @@ server:
ggml:
- changed-files:
- any-glob-to-any-file:
- ggml.c
- ggml.h
- ggml-*.c
- ggml-*.h
- ggml/include/ggml*.h
- ggml/src/ggml*.c
- ggml/src/ggml*.cpp
- ggml/src/ggml*.h
- ggml-cuda/**
nix:
- changed-files:

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

@ -109,7 +109,7 @@ jobs:
run: |
set -eux
cmake -B build \
-DLLAMA_NATIVE=OFF \
-DGGML_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \
-DLLAMA_CUBLAS=ON \

78
.github/workflows/build.yml vendored
View File

@ -10,10 +10,10 @@ on:
push:
branches:
- master
paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
pull_request:
types: [opened, synchronize, reopened]
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m']
paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@ -47,7 +47,7 @@ jobs:
sysctl -a
mkdir build
cd build
cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON ..
cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON ..
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -105,7 +105,7 @@ jobs:
sysctl -a
# Metal is disabled due to intermittent failures with Github runners not having a GPU:
# https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313
cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF -DLLAMA_CURL=ON
cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF -DLLAMA_CURL=ON
cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -305,7 +305,7 @@ jobs:
run: |
mkdir build
cd build
cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DLLAMA_OPENMP=OFF
cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DGGML_OPENMP=OFF
cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
- name: Test
@ -335,7 +335,7 @@ jobs:
run: |
mkdir build
cd build
cmake -DLLAMA_RPC=ON ..
cmake -DGGML_RPC=ON ..
cmake --build . --config Release -j $(nproc)
- name: Test
@ -363,7 +363,7 @@ jobs:
run: |
mkdir build
cd build
cmake -DLLAMA_VULKAN=ON ..
cmake -DGGML_VULKAN=ON ..
cmake --build . --config Release -j $(nproc)
ubuntu-22-cmake-hip:
@ -384,13 +384,13 @@ jobs:
- name: Build with native CMake HIP support
id: cmake_build
run: |
cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DLLAMA_HIPBLAS=ON
cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIPBLAS=ON
cmake --build build --config Release -j $(nproc)
- name: Build with legacy HIP support
id: cmake_build_legacy_hip
run: |
cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DLLAMA_HIPBLAS=ON
cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIPBLAS=ON
cmake --build build2 --config Release -j $(nproc)
ubuntu-22-cmake-sycl:
@ -431,7 +431,7 @@ jobs:
source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
cmake --build . --config Release -j $(nproc)
ubuntu-22-cmake-sycl-fp16:
@ -472,10 +472,10 @@ jobs:
source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON ..
cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON ..
cmake --build . --config Release -j $(nproc)
# TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# TODO: build with GGML_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124
macOS-latest-make:
@ -497,15 +497,15 @@ jobs:
env:
LLAMA_FATAL_WARNINGS: 1
run: |
LLAMA_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
GGML_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
- name: Test
id: make_test
run: |
LLAMA_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make test -j $(sysctl -n hw.logicalcpu)
GGML_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu)
GGML_NO_METAL=1 make test -j $(sysctl -n hw.logicalcpu)
# TODO: build with LLAMA_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# TODO: build with GGML_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584
# would be great if we fix these
@ -529,7 +529,7 @@ jobs:
sysctl -a
mkdir build
cd build
cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF ..
cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF ..
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -559,13 +559,14 @@ jobs:
mkdir build
cd build
cmake -G Xcode .. \
-DLLAMA_METAL_EMBED_LIBRARY=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
-DLLAMA_BUILD_SERVER=OFF \
-DCMAKE_SYSTEM_NAME=iOS \
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
macOS-latest-cmake-tvos:
runs-on: macos-latest
@ -588,13 +589,14 @@ jobs:
mkdir build
cd build
cmake -G Xcode .. \
-DLLAMA_METAL_EMBED_LIBRARY=ON \
-DGGML_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
-DLLAMA_BUILD_SERVER=OFF \
-DCMAKE_SYSTEM_NAME=tvOS \
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
-DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
macOS-latest-swift:
runs-on: macos-latest
@ -662,7 +664,7 @@ jobs:
- name: Build using make w/ OpenBLAS
shell: msys2 {0}
run: |
make LLAMA_OPENBLAS=1 -j $(nproc)
make GGML_OPENBLAS=1 -j $(nproc)
- name: Build using CMake
shell: msys2 {0}
@ -678,7 +680,7 @@ jobs:
- name: Build using CMake w/ OpenBLAS
shell: msys2 {0}
run: |
cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
cmake --build build --config ${{ matrix.build }} -j $(nproc)
windows-latest-cmake:
@ -693,25 +695,25 @@ jobs:
matrix:
include:
- build: 'rpc-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_RPC=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=ON'
- build: 'noavx-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DBUILD_SHARED_LIBS=ON'
- build: 'avx2-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
- build: 'avx-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_AVX2=OFF -DBUILD_SHARED_LIBS=ON'
- build: 'avx512-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_AVX512=ON -DBUILD_SHARED_LIBS=ON'
- build: 'openblas-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_BLAS=ON -DBUILD_SHARED_LIBS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
- build: 'kompute-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
- build: 'vulkan-x64'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
defines: '-DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
- build: 'llvm-arm64'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
- build: 'msvc-arm64'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
steps:
- name: Clone
@ -724,7 +726,7 @@ jobs:
id: clone_kompute
if: ${{ matrix.build == 'kompute-x64' }}
run: |
git submodule update --init kompute
git submodule update --init ggml/src/kompute
- name: Download OpenBLAS
id: get_openblas
@ -854,7 +856,7 @@ jobs:
run: |
mkdir build
cd build
cmake .. -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUDA=ON -DBUILD_SHARED_LIBS=ON
cmake .. -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON
cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS}
- name: Determine tag name
@ -987,7 +989,7 @@ jobs:
run: |
$env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
$env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DLLAMA_HIPBLAS=ON
cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIPBLAS=ON
cmake --build build --config Release
ios-xcode-build:

1
.github/workflows/docker.yml vendored
View File

@ -14,6 +14,7 @@ on:
push:
branches:
- master
paths: ['.github/workflows/docker.yml', '.devops/*.Dockerfile', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}

6
.github/workflows/server.yml vendored
View File

@ -92,12 +92,12 @@ jobs:
if: ${{ matrix.sanitizer == 'THREAD' }}
run: |
cmake -B build \
-DLLAMA_NATIVE=OFF \
-DGGML_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-DLLAMA_OPENMP=OFF ;
-DGGML_OPENMP=OFF ;
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
- name: Build
@ -105,7 +105,7 @@ jobs:
if: ${{ matrix.sanitizer != 'THREAD' }}
run: |
cmake -B build \
-DLLAMA_NATIVE=OFF \
-DGGML_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \

1
.gitignore vendored
View File

@ -56,6 +56,7 @@ CMakeSettings.json
compile_commands.json
ggml-metal-embed.metal
llama-batched-swift
/rpc-server
out/
tmp/

2
.gitmodules vendored
View File

@ -1,3 +1,3 @@
[submodule "kompute"]
path = kompute
path = ggml/src/kompute
url = https://github.com/nomic-ai/kompute.git

129
AUTHORS
View File

@ -1,8 +1,9 @@
# date: Tue Apr 9 09:17:14 EEST 2024
# date: Wed Jun 26 19:36:34 EEST 2024
# this file is auto-generated by scripts/gen-authors.sh
0cc4m <picard12@live.de>
0xspringtime <110655352+0xspringtime@users.noreply.github.com>
20kdc <asdd2808@gmail.com>
2f38b454 <dxf@protonmail.com>
3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com>
44670 <44670@users.noreply.github.com>
@ -11,14 +12,18 @@ AT <manyoso@users.noreply.github.com>
Aarni Koskela <akx@iki.fi>
Aaron Miller <apage43@ninjawhale.com>
Aaryaman Vasishta <aaryaman.vasishta@amd.com>
Abheek Gulati <abheekg@hotmail.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>
Ahmet Zeer <ahmed.zeer@std.yildiz.edu.tr>
AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
Aisuko <urakiny@gmail.com>
Akarshan Biswas <akarshanbiswas@fedoraproject.org>
Albert Jin <albert.jin@gmail.com>
Alberto <57916483+albbus-stack@users.noreply.github.com>
Alex <awhill19@icloud.com>
Alex Azarov <alex@azarov.by>
@ -35,19 +40,24 @@ Ali Nehzat <ali.nehzat@thanks.dev>
Ali Tariq <ali.tariq@10xengineers.ai>
Alon <alonfaraj@gmail.com>
AlpinDale <52078762+AlpinDale@users.noreply.github.com>
Amir <amir_zia@outlook.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 Downing <andrew2085@gmail.com>
Andrew Duffy <a10y@users.noreply.github.com>
Andrew Godfrey <AndrewGodfrey@users.noreply.github.com>
Andy Tai <andy-tai@users.noreply.github.com>
Arik Poznanski <arikpoz@users.noreply.github.com>
Artem <guinmoon@gmail.com>
Artem Zinnatullin <ceo@abstractny.gay>
Artyom Lebedev <vagran.ast@gmail.com>
Asbjørn Olling <asbjornolling@gmail.com>
Ásgeir Bjarni Ingvarsson <asgeir@fundinn.org>
Ashish <1856117+ashishdatta@users.noreply.github.com>
Ashok Gelal <401055+ashokgelal@users.noreply.github.com>
Ashraful Islam <ashraful.meche@gmail.com>
Atsushi Tatsuma <yoshoku@outlook.com>
@ -57,35 +67,46 @@ BADR <contact@pythops.com>
Bach Le <bach@bullno1.com>
Bailey Chittle <39804642+bachittle@users.noreply.github.com>
BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com>
Bartowski <ckealty1182@gmail.com>
Behnam M <58621210+ibehnam@users.noreply.github.com>
Ben Ashbaugh <ben.ashbaugh@intel.com>
Ben Garney <bengarney@users.noreply.github.com>
Ben Siraphob <bensiraphob@gmail.com>
Ben Williams <ben@719ben.com>
Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com>
Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com>
Bernat Vadell <hounter.caza@gmail.com>
Bingan <70050083+binganao@users.noreply.github.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>
Bryan Honof <bryanhonof@gmail.com>
CJ Pais <cj@cjpais.com>
CRD716 <crd716@gmail.com>
Calvin Laurenson <calvin@laurenson.dev>
Cameron <csteele@steelecameron.com>
Cameron Kaiser <classilla@users.noreply.github.com>
Carolinabanana <140120812+Carolinabanana@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>
Chao Jiang <jc19chaoj@zoho.com>
Cheng Shao <terrorjack@type.dance>
Chris Elrod <elrodc@gmail.com>
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>
Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com>
Clark Saben <76020733+csaben@users.noreply.github.com>
Clint Herron <hanclinto@gmail.com>
CrispStrobe <154636388+CrispStrobe@users.noreply.github.com>
Cuong Trinh Manh <nguoithichkhampha@gmail.com>
DAN™ <dranger003@gmail.com>
Damian Stewart <d@damianstewart.com>
@ -95,8 +116,12 @@ 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>
Daniele <57776841+daniandtheweb@users.noreply.github.com>
DannyDaemonic <DannyDaemonic@gmail.com>
Dat Quoc Nguyen <2412555+datquocnguyen@users.noreply.github.com>
Dave <dave-fl@users.noreply.github.com>
Dave Airlie <airlied@gmail.com>
Dave Airlie <airlied@redhat.com>
Dave Della Costa <ddellacosta+github@gmail.com>
David Friehs <david@friehs.info>
David Kennedy <dakennedyd@gmail.com>
@ -104,10 +129,13 @@ David Pflug <david@pflug.email>
David Renshaw <dwrenshaw@gmail.com>
David Sommers <12738+databyte@users.noreply.github.com>
David Yang <davidyang6us@gmail.com>
Dawid Potocki <github@dawidpotocki.com>
Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com>
Dean <Dean.Sinaean@gmail.com>
Deins <deinsegle@gmail.com>
Deven Mistry <31466137+deven367@users.noreply.github.com>
Didzis Gosko <didzis@users.noreply.github.com>
Djip007 <djip.perois@free.fr>
Don Mahurin <dmahurin@users.noreply.github.com>
DooWoong Lee (David) <manics99@naver.com>
Doomsdayrs <38189170+Doomsdayrs@users.noreply.github.com>
@ -116,8 +144,11 @@ 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>
Eddie-Wang <wangjinheng1120@163.com>
Edward Taylor <edeetee@gmail.com>
Elaine <elaine.zosa@gmail.com>
Elbios <141279586+Elbios@users.noreply.github.com>
Elton Kola <eltonkola@gmail.com>
Engininja2 <139037756+Engininja2@users.noreply.github.com>
Equim <sayaka@ekyu.moe>
Eric Sommerlade <es0m@users.noreply.github.com>
@ -143,37 +174,47 @@ 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>
Frank Mai <thxcode0824@gmail.com>
FrankHB <frankhb1989@gmail.com>
Fred Douglas <43351173+fredlas@users.noreply.github.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>
Gavin Zhao <gavinzhaojw@protonmail.com>
Genkagaku.GPT <hlhr202@163.com>
Georgi Gerganov <ggerganov@gmail.com>
Gilad S <giladgd@users.noreply.github.com>
Giuseppe Scrivano <giuseppe@scrivano.org>
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>
Haggai Nuchi <h.nuchi@gmail.com>
Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
Hamdoud Hakem <90524568+hamdoudhakem@users.noreply.github.com>
HanishKVC <hanishkvc@gmail.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>
HatsuneMikuUwU33 <173229399+HatsuneMikuUwU33@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>
Hong Bo PENG <penghb@cn.ibm.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>
Hugo Roussel <hugo.rous@gmail.com>
Ian Bull <irbull@eclipsesource.com>
Ian Bull <irbull@gmail.com>
Ian Scrivener <github@zilogy.asia>
@ -190,8 +231,10 @@ 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>
Jaemin Son <woalsdnd@gmail.com>
Jag Chadha <jagtesh@gmail.com>
Jakub N <jakubniemczyk97@gmail.com>
James A Capozzoli <157492257+jac-jim@users.noreply.github.com>
James Reynolds <magnusviri@users.noreply.github.com>
Jan Boon <jan.boon@kaetemi.be>
Jan Boon <kaetemi@gmail.com>
@ -205,12 +248,17 @@ Jean-Michaël Celerier <jeanmichael.celerier+github@gmail.com>
Jed Fox <git@jedfox.com>
Jeffrey Quesnelle <emozilla@nousresearch.com>
Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
Jeximo <jeximo@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>
Jiří Sejkora <Sejseloid@gmail.com>
Joan Fontanals <jfontanalsmartinez@gmail.com>
Joan Fontanals <joan.fontanals.martinez@jina.ai>
Johan <JohanAR@users.noreply.github.com>
Johannes Gäßler <johannesg@5d6.de>
Johannes Rudolph <johannes.rudolph@gmail.com>
John <78893154+cmp-nct@users.noreply.github.com>
@ -221,15 +269,19 @@ 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>
Josh Ramer <josh.ramer@icloud.com>
Joyce <joycebrum@google.com>
Juan Calderon-Perez <835733+gaby@users.noreply.github.com>
Judd <foldl@users.noreply.github.com>
Julius Arkenberg <arki05@users.noreply.github.com>
Jun Jie <71215065+junnjiee16@users.noreply.github.com>
Junyang Lin <justinlin930319@hotmail.com>
Juraj Bednar <juraj@bednar.io>
Justin Parker <jparkerweb@gmail.com>
Justin Suess <justin.suess@westpoint.edu>
Justina Cho <justcho5@gmail.com>
Justine Tunney <jtunney@gmail.com>
Justine Tunney <jtunney@mozilla.com>
Juuso Alasuutari <juuso.alasuutari@gmail.com>
KASR <karim.asrih@gmail.com>
Kamil Tomšík <info@tomsik.cz>
@ -242,6 +294,7 @@ 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 Gibbons <bakkot@gmail.com>
Kevin Ji <1146876+kevinji@users.noreply.github.com>
Kevin Kwok <antimatter15@gmail.com>
Kevin Lo <kevlo@kevlo.org>
@ -257,6 +310,7 @@ Laura <Tijntje_7@msn.com>
Lee <44310445+lx200916@users.noreply.github.com>
Lee Drake <b.lee.drake@gmail.com>
Leng Yue <lengyue@lengyue.me>
Leon Knauer <git@leonknauer.com>
LeonEricsson <70749762+LeonEricsson@users.noreply.github.com>
Leonardo Neumann <leonardo@neumann.dev.br>
Li Tan <tanliboy@gmail.com>
@ -265,20 +319,26 @@ LoganDark <github@logandark.mozmail.com>
LostRuins <39025047+LostRuins@users.noreply.github.com>
Luciano <lucianostrika44@gmail.com>
Luo Tian <lt@basecity.com>
Lyle Dean <dean@lyle.dev>
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>
Manuel <44313466+makuche@users.noreply.github.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>
Markus Tavenrath <mtavenrath@users.noreply.github.com>
Martin Delille <martin@delille.org>
Martin Krasser <krasserm@googlemail.com>
Martin Schwaighofer <mschwaig@users.noreply.github.com>
Marvin Gießing <marvin.giessing@gmail.com>
Masaya, Kato <62578291+msy-kato@users.noreply.github.com>
MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com>
Mateusz Charytoniuk <mateusz.charytoniuk@protonmail.com>
Matheus C. França <matheus-catarino@hotmail.com>
Matheus Gabriel Alves Silva <matheusgasource@gmail.com>
@ -287,8 +347,11 @@ 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>
Mattheus Chediak <shammcity00@gmail.com>
Matthew Tejo <matthew.tejo@gmail.com>
Matvey Soloviev <blackhole89@gmail.com>
Max Krasnyansky <max.krasnyansky@gmail.com>
Max Krasnyansky <quic_maxk@quicinc.com>
Maxime <672982+maximegmd@users.noreply.github.com>
Maximilian Winter <maximilian.winter.91@gmail.com>
Meng Zhang <meng@tabbyml.com>
@ -300,32 +363,41 @@ Michael Kesper <mkesper@schokokeks.org>
Michael Klimenko <mklimenko29@gmail.com>
Michael Podvitskiy <podvitskiymichael@gmail.com>
Michael Potter <NanoTekGuy@Gmail.com>
Michael de Gans <michael.john.degans@gmail.com>
Michaël de Vries <vriesdemichael@gmail.com>
Mihai <mihai.chirculescu@yahoo.com>
Mike <ytianhui2004@gmail.com>
Mikko Juola <mikjuo@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>
Mohammadreza Hendiani <mohammad.r.hendiani@gmail.com>
Murilo Santana <mvrilo@gmail.com>
Musab Gultekin <musabgultekin@users.noreply.github.com>
Nam D. Tran <42194884+namtranase@users.noreply.github.com>
Nathan Epstein <nate2@umbc.edu>
NawafAlansari <72708095+NawafAlansari@users.noreply.github.com>
Nebula <infinitewormhole@gmail.com>
Neo Zhang <14088817+arthw@users.noreply.github.com>
Neo Zhang <zhang.jianyu@outlook.com>
Neo Zhang Jianyu <jianyu.zhang@intel.com>
Neuman Vong <neuman.vong@gmail.com>
Nexesenex <124105151+Nexesenex@users.noreply.github.com>
Niall Coates <1349685+Niall-@users.noreply.github.com>
Nicolai Weitkemper <kontakt@nicolaiweitkemper.de>
Nicolás Pérez <nicolas_perez@brown.edu>
Nigel Bosch <pnigelb@gmail.com>
Niklas Korz <niklas@niklaskorz.de>
Nikolas <127742645+nneubacher@users.noreply.github.com>
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>
Patrice Ferlet <metal3d@gmail.com>
Paul Tsochantaris <ptsochantaris@icloud.com>
Pavol Rusnak <pavol@rusnak.io>
Pedro Cuenca <pedro@huggingface.co>
@ -343,9 +415,14 @@ 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>
Raj Hammeer Singh Hada <hammeerraj@gmail.com>
Ralph Soika <ralph.soika@imixs.com>
Rand Xie <randxiexyy29@gmail.com>
Randall Fitzgerald <randall@dasaku.net>
Reinforce-II <fate@eastal.com>
Ren Xuancheng <jklj077@users.noreply.github.com>
Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com>
RhinoDevel <RhinoDevel@users.noreply.github.com>
Riceball LEE <snowyu.lee@gmail.com>
Richard Kiss <him@richardkiss.com>
Richard Roberson <richardr1126@gmail.com>
@ -373,6 +450,7 @@ Rowan Hart <rowanbhart@gmail.com>
Rune <43761327+Rune-AI@users.noreply.github.com>
Ryan Landay <rlanday@gmail.com>
Ryder Wishart <ryderwishart@gmail.com>
Ryuei <louixs@users.noreply.github.com>
Rőczey Barnabás <31726601+An0nie@users.noreply.github.com>
SakuraUmi <yukinon244@gmail.com>
Salvador E. Tropea <stropea@inti.gob.ar>
@ -386,6 +464,7 @@ SebastianApel <13675545+SebastianApel@users.noreply.github.com>
Senemu <10880819+Senemu@users.noreply.github.com>
Sergey Alirzaev <zl29ah@gmail.com>
Sergio López <slp@sinrega.org>
Sertaç Özercan <852750+sozercan@users.noreply.github.com>
SeungWon Jeong <65549245+redlion0929@users.noreply.github.com>
ShadovvBeast <ShadovvBeast@gmail.com>
Shakhar Dasgupta <shakhardasgupta@gmail.com>
@ -394,6 +473,7 @@ Shijie <821898965@qq.com>
Shintarou Okada <kokuzen@gmail.com>
Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com>
Shouzheng Liu <lshzh.hi@gmail.com>
Shuichi Tsutsumi <shuichi0526@gmail.com>
Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Simon Willison <swillison@gmail.com>
Siwen Yu <yusiwen@gmail.com>
@ -405,11 +485,14 @@ 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>
Srihari-mcw <96763064+Srihari-mcw@users.noreply.github.com>
Srinivas Billa <nivibilla@gmail.com>
Stefan Sydow <stefan@sydow.email>
Steffen Röcker <sroecker@gmail.com>
Stephan Walter <stephan@walter.name>
Stephen Nichols <snichols@users.noreply.github.com>
Steve Grubb <ausearch.1@gmail.com>
Steven Prichard <spprichard20@gmail.com>
Steven Roussey <sroussey@gmail.com>
Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com>
@ -434,16 +517,19 @@ 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 Druyen <tristan@vault81.mozmail.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>
Ulrich Drepper <drepper@gmail.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 Nogueira <felladrin@gmail.com>
Victor Z. Peng <ziliangdotme@gmail.com>
Vlad <spitfireage@gmail.com>
Vladimir <bogdad@gmail.com>
@ -455,7 +541,9 @@ Weird Constructor <weirdconstructor@gmail.com>
Welby Seely <welbyseely@gmail.com>
Wentai Zhang <rchardx@gmail.com>
WillCorticesAI <150854901+WillCorticesAI@users.noreply.github.com>
William Tambellini <william.tambellini@gmail.com>
Willy Tarreau <w@1wt.eu>
Wouter <9594229+DifferentialityDevelopment@users.noreply.github.com>
Wu Jian Ping <wujjpp@hotmail.com>
Wu Jian Ping <wujp@greatld.com>
Xiake Sun <xiake.sun@intel.com>
@ -466,6 +554,8 @@ 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>
Yaroslav <yaroslav.yashin@me.com>
Yazan Agha-Schrader <mountaiin@icloud.com>
Yiming Cui <conandiy@vip.qq.com>
Yishuo Wang <MeouSker77@outlook.com>
Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com>
@ -477,6 +567,7 @@ Zane Shannon <z@zcs.me>
Zay <95888118+isaiahbjork@users.noreply.github.com>
Zenix <zenixls2@gmail.com>
Zhang Peiyuan <a1286225768@gmail.com>
Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com>
ZhouYuChen <zhouyuchen@naver.com>
Ziad Ben Hadj-Alouane <zied.benhadjalouane@gmail.com>
Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com>
@ -484,14 +575,18 @@ 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>
agray3 <agray3@users.noreply.github.com>
akawrykow <142945436+akawrykow@users.noreply.github.com>
alexpinel <93524949+alexpinel@users.noreply.github.com>
alonfaraj <alonfaraj@gmail.com>
alwqx <kenan3015@gmail.com>
amd-lalithnc <lalithnc@amd.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>
arch-btw <57669023+arch-btw@users.noreply.github.com>
arcrank <arcrank@gmail.com>
arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com>
at8u <129688334+at8u@users.noreply.github.com>
@ -514,13 +609,17 @@ cocktailpeanut <121128867+cocktailpeanut@users.noreply.github.com>
coezbek <c.oezbek@gmail.com>
comex <comexk@gmail.com>
compilade <113953597+compilade@users.noreply.github.com>
compilade <git@compilade.net>
cpumaxx <163466046+cpumaxx@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>
ddh0 <dylanhalladay02@icloud.com>
ddpasa <112642920+ddpasa@users.noreply.github.com>
deepdiffuser <112834445+deepdiffuser@users.noreply.github.com>
divinity76 <divinity76@gmail.com>
dm4 <sunrisedm4@gmail.com>
dotpy314 <33351922+dotpy314@users.noreply.github.com>
drbh <david.richard.holtz@gmail.com>
ds5t5 <145942675+ds5t5@users.noreply.github.com>
@ -529,6 +628,7 @@ eastriver <lee@eastriver.dev>
ebraminio <ebraminio@gmail.com>
eiery <19350831+eiery@users.noreply.github.com>
eric8607242 <e0928021388@gmail.com>
fairydreaming <166155368+fairydreaming@users.noreply.github.com>
fraxy-v <65565042+fraxy-v@users.noreply.github.com>
github-actions[bot] <github-actions[bot]@users.noreply.github.com>
gliptic <gliptic@users.noreply.github.com>
@ -539,6 +639,7 @@ 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>
hopkins385 <98618192+hopkins385@users.noreply.github.com>
howlger <eclipse@voormann.de>
howlger <github@voormann.de>
hutli <6594598+hutli@users.noreply.github.com>
@ -549,14 +650,22 @@ hydai <z54981220@gmail.com>
iSma <ismail.senhaji@gmail.com>
iacore <74560659+iacore@users.noreply.github.com>
igarnier <igarnier@protonmail.com>
intelmatt <61025942+intelmatt@users.noreply.github.com>
iohub <rickyang.pro@gmail.com>
jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com>
jaime-m-p <167997752+jaime-m-p@users.noreply.github.com>
jameswu2014 <545426914@qq.com>
jiez <373447296@qq.com>
jneem <joeneeman@gmail.com>
joecryptotoo <80373433+joecryptotoo@users.noreply.github.com>
johnson442 <56517414+johnson442@users.noreply.github.com>
jojorne <jojorne@users.noreply.github.com>
jon-chuang <9093549+jon-chuang@users.noreply.github.com>
jp-x-g <jpxg-dev@protonmail.com>
jukofyork <69222624+jukofyork@users.noreply.github.com>
junchao-loongson <68935141+junchao-loongson@users.noreply.github.com>
jwj7140 <32943891+jwj7140@users.noreply.github.com>
k.h.lai <adrian.k.h.lai@outlook.com>
kaizau <kaizau@users.noreply.github.com>
kalomaze <66376113+kalomaze@users.noreply.github.com>
kang <tpdns9032100@gmail.com>
@ -575,11 +684,15 @@ ldwang <ftgreat@163.com>
le.chang <cljs118@126.com>
leejet <leejet714@gmail.com>
limitedAtonement <limitedAtonement@users.noreply.github.com>
liuwei-git <14815172+liuwei-git@users.noreply.github.com>
lon <114724657+longregen@users.noreply.github.com>
loonerin <132926317+loonerin@users.noreply.github.com>
luoyu-intel <yu.luo@intel.com>
m3ndax <adrian.goessl@outlook.com>
maddes8cht <55592906+maddes8cht@users.noreply.github.com>
makomk <makosoft@googlemail.com>
manikbhandari <mbbhandarimanik2@gmail.com>
maor-ps <154728172+maor-ps@users.noreply.github.com>
mdrokz <mohammadmunshi@gmail.com>
mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com>
minarchist <minarchist@users.noreply.github.com>
@ -593,15 +706,19 @@ 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>
nickp27 <nb.porter@gmail.com>
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>
omahs <73983677+omahs@users.noreply.github.com>
oobabooga <112222186+oobabooga@users.noreply.github.com>
opparco <parco.opaai@gmail.com>
ostix360 <55257054+ostix360@users.noreply.github.com>
pengxin99 <pengxin.yuan@intel.com>
perserk <perserk@gmail.com>
pmysl <piotr.myslinski@outlook.com>
postmasters <namnguyen@google.com>
pudepiedj <pudepiedj@gmail.com>
qingfengfenga <41416092+qingfengfenga@users.noreply.github.com>
@ -614,16 +731,19 @@ rhuddleston <ryan.huddleston@percona.com>
rimoliga <53384203+rimoliga@users.noreply.github.com>
runfuture <runfuture@users.noreply.github.com>
sandyiscool <sandyiscool@gmail.com>
sasha0552 <admin@sasha0552.org>
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>
sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com>
slaren <2141330+slaren@users.noreply.github.com>
slaren <slarengh@gmail.com>
snadampal <87143774+snadampal@users.noreply.github.com>
staviq <staviq@gmail.com>
stduhpf <stephduh@live.fr>
strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com>
swittk <switt1995@gmail.com>
takov751 <40316768+takov751@users.noreply.github.com>
tarcey <cey.tarik@gmail.com>
@ -636,12 +756,16 @@ uint256_t <konndennsa@gmail.com>
uint256_t <maekawatoshiki1017@gmail.com>
unbounded <haakon@likedan.net>
valiray <133289098+valiray@users.noreply.github.com>
vik <vikhyatk@gmail.com>
viric <viric@viric.name>
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>
woachk <24752637+woachk@users.noreply.github.com>
wonjun Jang <strutive07@gmail.com>
woodx <124784234+woodx9@users.noreply.github.com>
wzy <32936898+Freed-Wu@users.noreply.github.com>
xaedes <xaedes@gmail.com>
xaedes <xaedes@googlemail.com>
@ -649,7 +773,10 @@ xloem <0xloem@gmail.com>
yangli2 <yangli2@gmail.com>
yuiseki <yuiseki@gmail.com>
zakkor <edward.partenie@gmail.com>
zhangkaihuo <zhangkaihuo@gmail.com>
zhouwg <6889919+zhouwg@users.noreply.github.com>
zhouwg <zhouwg2000@gmail.com>
zrm <trustiosity.zrm@gmail.com>
Ștefan-Gabriel Muscalu <legraphista@users.noreply.github.com>
源文雨 <41315874+fumiama@users.noreply.github.com>
Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>

1353
CMakeLists.txt
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4
CMakePresets.json
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@ -19,14 +19,14 @@
"cacheVariables": {
"CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
"CMAKE_CXX_COMPILER": "icx",
"LLAMA_SYCL": "ON",
"GGML_SYCL": "ON",
"CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
}
},
{ "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } },
{ "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
{ "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
{ "name": "static", "hidden": true, "cacheVariables": { "LLAMA_STATIC": "ON" } },
{ "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
{
"name": "arm64-windows-msvc", "hidden": true,

1033
Makefile
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21
Package.swift
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@ -3,14 +3,13 @@
import PackageDescription
var sources = [
"ggml.c",
"sgemm.cpp",
"llama.cpp",
"unicode.cpp",
"unicode-data.cpp",
"ggml-alloc.c",
"ggml-backend.c",
"ggml-quants.c",
"src/llama.cpp",
"src/unicode.cpp",
"src/unicode-data.cpp",
"ggml/src/ggml.c",
"ggml/src/ggml-alloc.c",
"ggml/src/ggml-backend.c",
"ggml/src/ggml-quants.c",
]
var resources: [Resource] = []
@ -26,8 +25,8 @@ var cSettings: [CSetting] = [
]
#if canImport(Darwin)
sources.append("ggml-metal.m")
resources.append(.process("ggml-metal.metal"))
sources.append("ggml/src/ggml-metal.m")
resources.append(.process("ggml/src/ggml-metal.metal"))
linkerSettings.append(.linkedFramework("Accelerate"))
cSettings.append(
contentsOf: [
@ -63,8 +62,6 @@ let package = Package(
"models",
"tests",
"CMakeLists.txt",
"ggml-cuda.cu",
"ggml-cuda.h",
"Makefile"
],
sources: sources,

24
README-sycl.md
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@ -115,12 +115,12 @@ The docker build option is currently limited to *intel GPU* targets.
### Build image
```sh
# Using FP16
docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile .
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile .
```
*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.
To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="GGML_SYCL_F16=ON"` argument from the previous command.
You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
@ -244,10 +244,10 @@ source /opt/intel/oneapi/setvars.sh
# Build LLAMA with MKL BLAS acceleration for intel GPU
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 2: Use FP16
cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
# build all binary
cmake --build build --config Release -j -v
@ -264,10 +264,10 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
# Build LLAMA with Nvidia BLAS acceleration through SYCL
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 2: Use FP16
cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
# build all binary
cmake --build build --config Release -j -v
@ -422,10 +422,10 @@ On the oneAPI command line window, step into the llama.cpp main directory and ru
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -G "Ninja" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
# Option 2: Or FP16
cmake -B build -G "Ninja" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
cmake --build build --config Release -j
```
@ -440,7 +440,7 @@ Or, use CMake presets to build:
cmake --preset x64-windows-sycl-release
cmake --build build-x64-windows-sycl-release -j --target llama-cli
cmake -DLLAMA_SYCL_F16=ON --preset x64-windows-sycl-release
cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release
cmake --build build-x64-windows-sycl-release -j --target llama-cli
cmake --preset x64-windows-sycl-debug
@ -544,9 +544,9 @@ use 1 SYCL GPUs: [0] with Max compute units:512
| 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. |
| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. |
| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. |
| GGML_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. |

59
README.md
View File

@ -15,6 +15,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Recent API changes
- [2024 Jun 26] The source code and CMake build scripts have been restructured https://github.com/ggerganov/llama.cpp/pull/8006
- [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807
- [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
@ -415,7 +416,7 @@ Flox follows the nixpkgs build of llama.cpp.
### Metal Build
On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or the `LLAMA_METAL=OFF` cmake option.
To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option.
When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
argument.
@ -435,7 +436,7 @@ Building the program with BLAS support may lead to some performance improvements
- Using `make`:
- On Linux:
```bash
make LLAMA_OPENBLAS=1
make GGML_OPENBLAS=1
```
- On Windows:
@ -450,13 +451,13 @@ Building the program with BLAS support may lead to some performance improvements
8. From here you can run:
```bash
make LLAMA_OPENBLAS=1
make GGML_OPENBLAS=1
```
- Using `CMake` on Linux:
```bash
cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
cmake --build build --config Release
```
@ -475,10 +476,10 @@ Building the program with BLAS support may lead to some performance improvements
Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md).
- Using manual oneAPI installation:
By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
```bash
source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation
cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON
cmake --build build --config Release
```
@ -495,28 +496,28 @@ Building the program with BLAS support may lead to some performance improvements
- Using `make`:
```bash
make LLAMA_CUDA=1
make GGML_CUDA=1
```
- Using `CMake`:
```bash
cmake -B build -DLLAMA_CUDA=ON
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
```
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. 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_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. |
| LLAMA_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models |
| 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. |
| LLAMA_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. |
|-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| GGML_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. |
| GGML_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. |
| GGML_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. |
| GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. |
| GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models |
| GGML_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. |
| GGML_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. |
| GGML_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. |
| GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. |
- #### hipBLAS
@ -526,15 +527,15 @@ Building the program with BLAS support may lead to some performance improvements
- Using `make`:
```bash
make LLAMA_HIPBLAS=1
make GGML_HIPBLAS=1
```
- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
```bash
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
&& cmake --build build --config Release -- -j 16
```
On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON`.
On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
Note that if you get the following error:
@ -548,19 +549,19 @@ Building the program with BLAS support may lead to some performance improvements
```bash
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
&& cmake --build build -- -j 16
```
- Using `make` (example for target gfx1030, build with 16 CPU threads):
```bash
make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
make -j16 GGML_HIPBLAS=1 GGML_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):
```bash
set PATH=%HIP_PATH%\bin;%PATH%
cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
cmake --build 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)
@ -572,10 +573,10 @@ Building the program with BLAS support may lead to some performance improvements
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. |
|------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| GGML_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. |
| GGML_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. |
| GGML_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. |
- #### Vulkan
@ -613,7 +614,7 @@ Building the program with BLAS support may lead to some performance improvements
Then, build llama.cpp using the cmake command below:
```bash
cmake -B build -DLLAMA_VULKAN=1
cmake -B build -DGGML_VULKAN=1
cmake --build build --config Release
# Test the output binary (with "-ngl 33" to offload all layers to GPU)
./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4

10
ci/run.sh
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@ -36,11 +36,11 @@ SRC=`pwd`
CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON"
if [ ! -z ${GG_BUILD_METAL} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON"
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
fi
if [ ! -z ${GG_BUILD_CUDA} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUDA=1"
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=1"
fi
if [ ! -z ${GG_BUILD_SYCL} ]; then
@ -50,7 +50,7 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then
exit 1
fi
CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON"
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
fi
## helpers
@ -284,7 +284,7 @@ function gg_run_open_llama_7b_v2 {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../examples/convert-legacy-llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
@ -550,7 +550,7 @@ function gg_run_pythia_2_8b {
set -e
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf

0
scripts/build-info.cmake → cmake/build-info.cmake
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22
cmake/git-vars.cmake Normal file
View File

@ -0,0 +1,22 @@
find_package(Git)
# the commit's SHA1
execute_process(COMMAND
"${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_SHA1
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
# the date of the commit
execute_process(COMMAND
"${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_DATE
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
# the subject of the commit
execute_process(COMMAND
"${GIT_EXECUTABLE}" log -1 --format=%s
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)

24
scripts/LlamaConfig.cmake.in → cmake/llama-config.cmake.in
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@ -2,11 +2,12 @@ set(LLAMA_VERSION @LLAMA_INSTALL_VERSION@)
set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
set(LLAMA_BLAS @LLAMA_BLAS@)
set(LLAMA_CUDA @LLAMA_CUDA@)
set(LLAMA_METAL @LLAMA_METAL@)
set(LLAMA_HIPBLAS @LLAMA_HIPBLAS@)
set(LLAMA_ACCELERATE @LLAMA_ACCELERATE@)
set(GGML_BLAS @GGML_BLAS@)
set(GGML_CUDA @GGML_CUDA@)
set(GGML_METAL @GGML_METAL@)
set(GGML_HIPBLAS @GGML_HIPBLAS@)
set(GGML_ACCELERATE @GGML_ACCELERATE@)
@PACKAGE_INIT@
@ -17,25 +18,26 @@ set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
# Ensure transient dependencies satisfied
find_package(Threads REQUIRED)
if (APPLE AND LLAMA_ACCELERATE)
if (APPLE AND GGML_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
endif()
if (LLAMA_BLAS)
if (GGML_BLAS)
find_package(BLAS REQUIRED)
endif()
if (LLAMA_CUDA)
if (GGML_CUDA)
find_package(CUDAToolkit REQUIRED)
endif()
if (LLAMA_METAL)
if (GGML_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
endif()
if (LLAMA_HIPBLAS)
if (GGML_HIPBLAS)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
@ -47,7 +49,9 @@ find_library(llama_LIBRARY llama
set(_llama_link_deps "Threads::Threads" "@LLAMA_EXTRA_LIBS@")
set(_llama_transient_defines "@LLAMA_TRANSIENT_DEFINES@")
add_library(llama UNKNOWN IMPORTED)
set_target_properties(llama
PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"

3
common/CMakeLists.txt
View File

@ -1,5 +1,6 @@
# common
find_package(Threads REQUIRED)
# Build info header
#
@ -36,7 +37,7 @@ add_custom_command(
COMMENT "Generating build details from Git"
COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
-DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
VERBATIM

2
scripts/gen-build-info-cpp.cmake → common/cmake/build-info-gen-cpp.cmake
View File

@ -1,4 +1,4 @@
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")

6
docs/BLIS.md
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@ -30,8 +30,8 @@ We recommend using openmp since it's easier to modify the cores being used.
Makefile:
```bash
make LLAMA_BLIS=1 -j
# make LLAMA_BLIS=1 benchmark-matmult
make GGML_BLIS=1 -j
# make GGML_BLIS=1 llama-benchmark-matmult
```
CMake:
@ -39,7 +39,7 @@ CMake:
```bash
mkdir build
cd build
cmake -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=FLAME ..
cmake -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=FLAME ..
make -j
```

4
examples/CMakeLists.txt
View File

@ -39,13 +39,13 @@ else()
add_subdirectory(quantize-stats)
add_subdirectory(quantize)
add_subdirectory(retrieval)
if (LLAMA_RPC)
if (GGML_RPC)
add_subdirectory(rpc)
endif()
if (LLAMA_BUILD_SERVER)
add_subdirectory(server)
endif()
if (LLAMA_SYCL)
if (GGML_SYCL)
add_subdirectory(sycl)
endif()
add_subdirectory(save-load-state)

2
examples/imatrix/README.md
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@ -25,7 +25,7 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument
## Example
```bash
LLAMA_CUDA=1 make -j
GGML_CUDA=1 make -j
# generate importance matrix (imatrix.dat)
./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99

2
examples/llama.android/llama/src/main/cpp/llama-android.cpp
View File

@ -5,7 +5,7 @@
#include <string>
#include <unistd.h>
#include "llama.h"
#include "common/common.h"
#include "common.h"
// Write C++ code here.
//

2
examples/llava/MobileVLM-README.md
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@ -194,7 +194,7 @@ 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
make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 GGML_CUDA_F16=1 -j 32
```
### run on Orin
### case 1

26
examples/llava/clip.cpp
View File

@ -1121,20 +1121,20 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
if (n < 32)
hparams.image_grid_pinpoints[n] = 0;
} catch (std::runtime_error & e) {
} catch (std::runtime_error & /*e*/) {
hparams.image_grid_pinpoints[0]=0;
}
try {
int idx = get_key_idx(ctx, KEY_MM_PATCH_MERGE_TYPE);
strcpy(hparams.mm_patch_merge_type, gguf_get_val_str(ctx, idx));
} catch (std::runtime_error & e) {
} catch (std::runtime_error & /*e*/) {
strcpy(hparams.mm_patch_merge_type, "flat");
}
try {
hparams.image_crop_resolution = get_u32(ctx, KEY_IMAGE_CROP_RESOLUTION); // llava-1.6
} catch(const std::exception& e) {
} catch(const std::exception& /*e*/) {
hparams.image_crop_resolution = hparams.image_size;
}
@ -1173,7 +1173,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
try {
vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
new_clip->has_class_embedding = true;
} catch (const std::exception& e) {
} catch (const std::exception& /*e*/) {
new_clip->has_class_embedding = false;
}
@ -1181,7 +1181,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
new_clip->has_pre_norm = true;
} catch (std::exception & e) {
} catch (std::exception & /*e*/) {
new_clip->has_pre_norm = false;
}
@ -1189,21 +1189,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight"));
vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias"));
new_clip->has_post_norm = true;
} catch (std::exception & e) {
} catch (std::exception & /*e*/) {
new_clip->has_post_norm = false;
}
try {
vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS);
new_clip->has_patch_bias = true;
} catch (std::exception & e) {
} catch (std::exception & /*e*/) {
new_clip->has_patch_bias = false;
}
try {
vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
} catch(const std::exception& e) {
} catch(const std::exception& /*e*/) {
LOG_TEE("%s: failed to load vision model tensors\n", __func__);
}
@ -1215,26 +1215,26 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
// Yi-type llava
vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight"));
vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias"));
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
try {
// missing in Yi-type llava
vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
try {
// Yi-type llava
vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight"));
vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias"));
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
try {
// Yi-type llava
vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight"));
vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias"));
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
try {
vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
// LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
} catch (std::runtime_error & e) { }
} catch (std::runtime_error & /*e*/) { }
} else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
// MobileVLM projection
vision_model.mm_model_mlp_1_w = get_tensor(new_clip->ctx_data, format(TN_MVLM_PROJ_MLP, 1, "weight"));

8
examples/rpc/README.md
View File

@ -29,13 +29,13 @@ You can also run multiple `rpc-server` instances on the same host, each with a d
## Usage
On each host, build the corresponding backend with `cmake` and add `-DLLAMA_RPC=ON` to the build options.
On each host, build the corresponding backend with `cmake` and add `-DGGML_RPC=ON` to the build options.
For example, to build the CUDA backend with RPC support:
```bash
mkdir build-rpc-cuda
cd build-rpc-cuda
cmake .. -DLLAMA_CUDA=ON -DLLAMA_RPC=ON
cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON
cmake --build . --config Release
```
@ -58,12 +58,12 @@ $ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052
This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device.
On the main host build `llama.cpp` only with `-DLLAMA_RPC=ON`:
On the main host build `llama.cpp` only with `-DGGML_RPC=ON`:
```bash
mkdir build-rpc
cd build-rpc
cmake .. -DLLAMA_RPC=ON
cmake .. -DGGML_RPC=ON
cmake --build . --config Release
```

13
examples/server/CMakeLists.txt
View File

@ -1,7 +1,14 @@
set(TARGET llama-server)
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
if (MINGW)
# fix: https://github.com/ggerganov/llama.cpp/actions/runs/9651004652/job/26617901362?pr=8006
add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
endif()
set(TARGET_SRCS
server.cpp
utils.hpp
@ -24,6 +31,7 @@ set(PUBLIC_ASSETS
prompt-formats.js
json-schema-to-grammar.mjs
)
foreach(asset ${PUBLIC_ASSETS})
set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
@ -34,18 +42,23 @@ foreach(asset ${PUBLIC_ASSETS})
COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
)
endforeach()
add_executable(${TARGET} ${TARGET_SRCS})
install(TARGETS ${TARGET} RUNTIME)
target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
)
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)
target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT)
endif()
if (WIN32)
TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
endif()
target_compile_features(${TARGET} PRIVATE cxx_std_11)

4
examples/sycl/build.sh
View File

@ -8,10 +8,10 @@ cd build
source /opt/intel/oneapi/setvars.sh
#for FP16
#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference
#cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON # faster for long-prompt inference
#for FP32
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
#build example/main
#cmake --build . --config Release --target main

4
examples/sycl/win-build-sycl.bat
View File

@ -13,10 +13,10 @@ if %errorlevel% neq 0 goto ERROR
:: for FP16
:: faster for long-prompt inference
:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
:: cmake -G "MinGW Makefiles" .. -DGGML_SYCL=ON -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
:: for FP32
cmake -G "Ninja" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
cmake -G "Ninja" .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release
if %errorlevel% neq 0 goto ERROR
:: build example/main only
:: make main

240
ggml/CMakeLists.txt Normal file
View File

@ -0,0 +1,240 @@
cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories.
project("ggml" C CXX)
include(CheckIncludeFileCXX)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
endif()
if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
set(GGML_STANDALONE ON)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
# configure project version
# TODO
else()
set(GGML_STANDALONE OFF)
endif()
if (EMSCRIPTEN)
set(BUILD_SHARED_LIBS_DEFAULT OFF)
option(GGML_WASM_SINGLE_FILE "ggml: embed WASM inside the generated ggml.js" ON)
else()
if (MINGW)
set(BUILD_SHARED_LIBS_DEFAULT OFF)
else()
set(BUILD_SHARED_LIBS_DEFAULT ON)
endif()
endif()
option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
#
# option list
#
# TODO: mark all options as advanced when not GGML_STANDALONE
if (APPLE)
set(GGML_METAL_DEFAULT ON)
set(GGML_BLAS_DEFAULT ON)
set(GGML_BLAS_VENDOR_DEFAULT "Apple")
else()
set(GGML_METAL_DEFAULT OFF)
set(GGML_BLAS_DEFAULT OFF)
set(GGML_BLAS_VENDOR_DEFAULT "Generic")
endif()
# general
option(GGML_STATIC "ggml: static link libraries" OFF)
option(GGML_NATIVE "ggml: enable -march=native flag" ON)
option(GGML_LTO "ggml: enable link time optimization" OFF)
option(GGML_CCACHE "ggml: use ccache if available" ON)
# debug
option(GGML_ALL_WARNINGS "ggml: enable all compiler warnings" ON)
option(GGML_ALL_WARNINGS_3RD_PARTY "ggml: enable all compiler warnings in 3rd party libs" OFF)
option(GGML_GPROF "ggml: enable gprof" OFF)
# build
option(GGML_FATAL_WARNINGS "ggml: enable -Werror flag" OFF)
# sanitizers
option(GGML_SANITIZE_THREAD "ggml: enable thread sanitizer" OFF)
option(GGML_SANITIZE_ADDRESS "ggml: enable address sanitizer" OFF)
option(GGML_SANITIZE_UNDEFINED "ggml: enable undefined sanitizer" OFF)
# instruction set specific
if (GGML_NATIVE)
set(INS_ENB OFF)
else()
set(INS_ENB ON)
endif()
option(GGML_CPU_HBM "ggml: use memkind for CPU HBM" OFF)
option(GGML_AVX "ggml: enable AVX" ${INS_ENB})
option(GGML_AVX2 "ggml: enable AVX2" ${INS_ENB})
option(GGML_AVX512 "ggml: enable AVX512" OFF)
option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI" OFF)
option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI" OFF)
option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16" OFF)
option(GGML_FMA "ggml: enable FMA" ${INS_ENB})
if (NOT MSVC)
option(GGML_F16C "ggml: enable F16C" ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512
endif()
option(GGML_LASX "ggml: enable lasx" ON)
option(GGML_LSX "ggml: enable lsx" ON)
option(GGML_SVE "ggml: enable SVE" OFF)
if (WIN32)
set(GGML_WIN_VER "0x602" CACHE STRING "ggml: Windows Version")
endif()
# ggml core
set(GGML_SCHED_MAX_COPIES "4" CACHE STRING "ggml: max input copies for pipeline parallelism")
# 3rd party libs / backends
option(GGML_ACCELERATE "ggml: enable Accelerate framework" ON)
option(GGML_BLAS "ggml: use BLAS" ${GGML_BLAS_DEFAULT})
set(GGML_BLAS_VENDOR ${GGML_BLAS_VENDOR_DEFAULT} CACHE STRING
"ggml: BLAS library vendor")
option(GGML_LLAMAFILE "ggml: use ggml SGEMM" OFF)
option(GGML_CUDA "ggml: use CUDA" OFF)
option(GGML_CUDA_FORCE_DMMV "ggml: use dmmv instead of mmvq CUDA kernels" OFF)
option(GGML_CUDA_FORCE_MMQ "ggml: use mmq kernels instead of cuBLAS" OFF)
option(GGML_CUDA_FORCE_CUBLAS "ggml: always use cuBLAS instead of mmq kernels" OFF)
set (GGML_CUDA_DMMV_X "32" CACHE STRING "ggml: x stride for dmmv CUDA kernels")
set (GGML_CUDA_MMV_Y "1" CACHE STRING "ggml: y block size for mmv CUDA kernels")
option(GGML_CUDA_F16 "ggml: use 16 bit floats for some calculations" OFF)
set (GGML_CUDA_KQUANTS_ITER "2" CACHE STRING
"ggml: iters./thread per block for Q2_K/Q6_K")
set (GGML_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
"ggml: max. batch size for using peer access")
option(GGML_CUDA_NO_PEER_COPY "ggml: do not use peer to peer copies" OFF)
option(GGML_CUDA_NO_VMM "ggml: do not try to use CUDA VMM" OFF)
option(GGML_CUDA_FA_ALL_QUANTS "ggml: compile all quants for FlashAttention" OFF)
option(GGML_CUDA_USE_GRAPHS "ggml: use CUDA graphs (llama.cpp only)" OFF)
option(GGML_CURL "ggml: use libcurl to download model from an URL" OFF)
option(GGML_HIPBLAS "ggml: use hipBLAS" OFF)
option(GGML_HIP_UMA "ggml: use HIP unified memory architecture" OFF)
option(GGML_VULKAN "ggml: use Vulkan" OFF)
option(GGML_VULKAN_CHECK_RESULTS "ggml: run Vulkan op checks" OFF)
option(GGML_VULKAN_DEBUG "ggml: enable Vulkan debug output" OFF)
option(GGML_VULKAN_MEMORY_DEBUG "ggml: enable Vulkan memory debug output" OFF)
option(GGML_VULKAN_VALIDATE "ggml: enable Vulkan validation" OFF)
option(GGML_VULKAN_RUN_TESTS "ggml: run Vulkan tests" OFF)
option(GGML_KOMPUTE "ggml: use Kompute" OFF)
option(GGML_METAL "ggml: use Metal" ${GGML_METAL_DEFAULT})
option(GGML_METAL_NDEBUG "ggml: disable Metal debugging" OFF)
option(GGML_METAL_SHADER_DEBUG "ggml: compile Metal with -fno-fast-math" OFF)
option(GGML_METAL_EMBED_LIBRARY "ggml: embed Metal library" ${GGML_METAL})
set (GGML_METAL_MACOSX_VERSION_MIN "" CACHE STRING
"ggml: metal minimum macOS version")
set (GGML_METAL_STD "" CACHE STRING "ggml: metal standard version (-std flag)")
option(GGML_OPENMP "ggml: use OpenMP" ON)
option(GGML_RPC "ggml: use RPC" OFF)
option(GGML_SYCL "ggml: use SYCL" OFF)
option(GGML_SYCL_F16 "ggml: use 16 bit floats for sycl calculations" OFF)
set (GGML_SYCL_TARGET "INTEL" CACHE STRING
"ggml: sycl target device")
# extra artifacts
option(GGML_BUILD_TESTS "ggml: build tests" ${GGML_STANDALONE})
option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
#
# dependencies
#
set(CMAKE_C_STANDARD 11)
set(CMAKE_C_STANDARD_REQUIRED true)
if (GGML_SYCL)
set(CMAKE_CXX_STANDARD 17)
else()
set(CMAKE_CXX_STANDARD 11)
endif()
set(CMAKE_CXX_STANDARD_REQUIRED true)
set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
#
# build the library
#
add_subdirectory(src)
#
# tests and examples
#
if (GGML_BUILD_TESTS)
enable_testing()
add_subdirectory(tests)
endif ()
if (GGML_BUILD_EXAMPLES)
add_subdirectory(examples)
endif ()
#
# install
#
include(GNUInstallDirs)
include(CMakePackageConfigHelpers)
set(GGML_PUBLIC_HEADERS
include/ggml.h
include/ggml-alloc.h
include/ggml-backend.h
"${GGML_HEADERS_CUDA}"
"${GGML_HEADERS_METAL}"
"${GGML_HEADERS_EXTRA}")
set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
#if (GGML_METAL)
# set_target_properties(ggml PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/src/ggml-metal.metal")
#endif()
install(TARGETS ggml PUBLIC_HEADER)
if (BUILD_SHARED_LIBS)
install(TARGETS ggml LIBRARY)
endif()
if (GGML_METAL)
install(
FILES src/ggml-metal.metal
PERMISSIONS
OWNER_READ
OWNER_WRITE
GROUP_READ
WORLD_READ
DESTINATION ${CMAKE_INSTALL_BINDIR})
if (NOT GGML_METAL_EMBED_LIBRARY)
install(
FILES ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
DESTINATION ${CMAKE_INSTALL_BINDIR}
)
endif()
endif()
if (GGML_STANDALONE)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/ggml.pc.in
${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
@ONLY)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ggml.pc
DESTINATION share/pkgconfig)
endif()

12
cmake/FindSIMD.cmake → ggml/cmake/FindSIMD.cmake
View File

@ -79,22 +79,22 @@ endmacro()
# flags are for MSVC only!
check_sse("AVX" " ;/arch:AVX")
if (NOT ${AVX_FOUND})
set(LLAMA_AVX OFF)
set(GGML_AVX OFF)
else()
set(LLAMA_AVX ON)
set(GGML_AVX ON)
endif()
check_sse("AVX2" " ;/arch:AVX2")
check_sse("FMA" " ;/arch:AVX2")
if ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND}))
set(LLAMA_AVX2 OFF)
set(GGML_AVX2 OFF)
else()
set(LLAMA_AVX2 ON)
set(GGML_AVX2 ON)
endif()
check_sse("AVX512" " ;/arch:AVX512")
if (NOT ${AVX512_FOUND})
set(LLAMA_AVX512 OFF)
set(GGML_AVX512 OFF)
else()
set(LLAMA_AVX512 ON)
set(GGML_AVX512 ON)
endif()

0
ggml_vk_generate_shaders.py → ggml/ggml_vk_generate_shaders.py
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0
ggml-alloc.h → ggml/include/ggml-alloc.h
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0
ggml-backend.h → ggml/include/ggml-backend.h
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0
ggml-blas.h → ggml/include/ggml-blas.h
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0
ggml-cuda.h → ggml/include/ggml-cuda.h
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0
ggml-kompute.h → ggml/include/ggml-kompute.h
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0
ggml-metal.h → ggml/include/ggml-metal.h
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0
ggml-rpc.h → ggml/include/ggml-rpc.h
View File

4
ggml-sycl.h → ggml/include/ggml-sycl.h
View File

@ -8,7 +8,9 @@
#include "ggml.h"
#include "ggml-backend.h"
#include "ggml-sycl/presets.hpp"
#define GGML_SYCL_NAME "SYCL"
#define GGML_SYCL_MAX_DEVICES 48
#ifdef __cplusplus
extern "C" {

0
ggml-vulkan.h → ggml/include/ggml-vulkan.h
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0
ggml.h → ggml/include/ggml.h
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1174
ggml/src/CMakeLists.txt Normal file
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File diff suppressed because it is too large Load Diff

0
ggml-alloc.c → ggml/src/ggml-alloc.c
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0
ggml-backend-impl.h → ggml/src/ggml-backend-impl.h
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0
ggml-backend.c → ggml/src/ggml-backend.c
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0
ggml-blas.cpp → ggml/src/ggml-blas.cpp
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0
ggml-common.h → ggml/src/ggml-common.h
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0
ggml-cuda.cu → ggml/src/ggml-cuda.cu
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0
ggml-cuda/acc.cu → ggml/src/ggml-cuda/acc.cu
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0
ggml-cuda/acc.cuh → ggml/src/ggml-cuda/acc.cuh
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0
ggml-cuda/arange.cu → ggml/src/ggml-cuda/arange.cu
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0
ggml-cuda/arange.cuh → ggml/src/ggml-cuda/arange.cuh
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0
ggml-cuda/argsort.cu → ggml/src/ggml-cuda/argsort.cu
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0
ggml-cuda/argsort.cuh → ggml/src/ggml-cuda/argsort.cuh
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0
ggml-cuda/binbcast.cu → ggml/src/ggml-cuda/binbcast.cu
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0
ggml-cuda/binbcast.cuh → ggml/src/ggml-cuda/binbcast.cuh
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0
ggml-cuda/clamp.cu → ggml/src/ggml-cuda/clamp.cu
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0
ggml-cuda/clamp.cuh → ggml/src/ggml-cuda/clamp.cuh
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0
ggml-cuda/common.cuh → ggml/src/ggml-cuda/common.cuh
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0
ggml-cuda/concat.cu → ggml/src/ggml-cuda/concat.cu
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0
ggml-cuda/concat.cuh → ggml/src/ggml-cuda/concat.cuh
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0
ggml-cuda/convert.cu → ggml/src/ggml-cuda/convert.cu
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0
ggml-cuda/convert.cuh → ggml/src/ggml-cuda/convert.cuh
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0
ggml-cuda/cpy.cu → ggml/src/ggml-cuda/cpy.cu
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0
ggml-cuda/cpy.cuh → ggml/src/ggml-cuda/cpy.cuh
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0
ggml-cuda/dequantize.cuh → ggml/src/ggml-cuda/dequantize.cuh
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0
ggml-cuda/diagmask.cu → ggml/src/ggml-cuda/diagmask.cu
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0
ggml-cuda/diagmask.cuh → ggml/src/ggml-cuda/diagmask.cuh
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0
ggml-cuda/dmmv.cu → ggml/src/ggml-cuda/dmmv.cu
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0
ggml-cuda/dmmv.cuh → ggml/src/ggml-cuda/dmmv.cuh
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4
ggml-cuda/fattn-common.cuh → ggml/src/ggml-cuda/fattn-common.cuh
View File

@ -603,7 +603,7 @@ static void on_no_fattn_vec_case(const int D) {
if (D == 64) {
fprintf(stderr, "Unsupported KV type combination for head_size 64.\n");
fprintf(stderr, "By default only f16 KV cache is supported.\n");
fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for V cache quantization support.\n");
fprintf(stderr, "Compile with GGML_CUDA_FA_ALL_QUANTS for V cache quantization support.\n");
GGML_ASSERT(false);
} else if (D == 128) {
fprintf(stderr, "Unsupported KV type combination for head_size 128.\n");
@ -611,7 +611,7 @@ static void on_no_fattn_vec_case(const int D) {
fprintf(stderr, " - K == q4_0, V == q4_0, 4.50 BPV\n");
fprintf(stderr, " - K == q8_0, V == q8_0, 8.50 BPV\n");
fprintf(stderr, " - K == f16, V == f16, 16.00 BPV\n");
fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n");
fprintf(stderr, "Compile with GGML_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n");
GGML_ASSERT(false);
} else {
fprintf(stderr, "Unsupported KV type combination for head_size 256.\n");

0
ggml-cuda/fattn-tile-f16.cu → ggml/src/ggml-cuda/fattn-tile-f16.cu
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0
ggml-cuda/fattn-tile-f16.cuh → ggml/src/ggml-cuda/fattn-tile-f16.cuh
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0
ggml-cuda/fattn-tile-f32.cu → ggml/src/ggml-cuda/fattn-tile-f32.cu
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0
ggml-cuda/fattn-tile-f32.cuh → ggml/src/ggml-cuda/fattn-tile-f32.cuh
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0
ggml-cuda/fattn-vec-f16.cuh → ggml/src/ggml-cuda/fattn-vec-f16.cuh
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0
ggml-cuda/fattn-vec-f32.cuh → ggml/src/ggml-cuda/fattn-vec-f32.cuh
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0
ggml-cuda/fattn-wmma-f16.cuh → ggml/src/ggml-cuda/fattn-wmma-f16.cuh
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0
ggml-cuda/fattn.cu → ggml/src/ggml-cuda/fattn.cu
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0
ggml-cuda/fattn.cuh → ggml/src/ggml-cuda/fattn.cuh
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0
ggml-cuda/getrows.cu → ggml/src/ggml-cuda/getrows.cu
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0
ggml-cuda/getrows.cuh → ggml/src/ggml-cuda/getrows.cuh
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0
ggml-cuda/im2col.cu → ggml/src/ggml-cuda/im2col.cu
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0
ggml-cuda/im2col.cuh → ggml/src/ggml-cuda/im2col.cuh
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0
ggml-cuda/mma.cuh → ggml/src/ggml-cuda/mma.cuh
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