pick from master
This commit is contained in:
changhz
parent
47e41fa8ce
commit
96165b1201
19 changed files with 1509 additions and 682 deletions
28
.github/workflows/build.yml
vendored
28
.github/workflows/build.yml
vendored
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@ -10,10 +10,10 @@ on:
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push:
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branches:
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- master
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paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.c', '**/*.cpp']
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paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp']
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pull_request:
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types: [opened, synchronize, reopened]
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paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.c', '**/*.cpp']
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paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp']
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env:
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BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
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@ -151,21 +151,21 @@ jobs:
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env:
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OPENBLAS_VERSION: 0.3.23
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OPENCL_VERSION: 2023.04.17
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CLBLAST_VERSION: 1.5.3
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CLBLAST_VERSION: 1.6.0
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strategy:
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matrix:
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include:
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- build: 'avx2'
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defines: ''
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defines: '-DLLAMA_BUILD_SERVER=ON'
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- build: 'avx'
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defines: '-DLLAMA_AVX2=OFF'
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defines: '-DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF'
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- build: 'avx512'
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defines: '-DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'
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defines: '-DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'
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- build: 'clblast'
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defines: '-DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"'
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defines: '-DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"'
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- build: 'openblas'
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defines: '-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include"'
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defines: '-DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
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steps:
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- name: Clone
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@ -184,13 +184,13 @@ jobs:
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id: get_clblast
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if: ${{ matrix.build == 'clblast' }}
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run: |
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curl.exe -o $env:RUNNER_TEMP/clblast.zip -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-Windows-x64.zip"
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curl.exe -o $env:RUNNER_TEMP/clblast.7z -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z"
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curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L "https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE"
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mkdir $env:RUNNER_TEMP/clblast
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tar.exe -xvf $env:RUNNER_TEMP/clblast.zip -C $env:RUNNER_TEMP/clblast
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7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/clblast.7z
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rename-item $env:RUNNER_TEMP/CLBlast-${env:CLBLAST_VERSION}-windows-x64 clblast
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foreach ($f in (gci -Recurse -Path "$env:RUNNER_TEMP/clblast" -Filter '*.cmake')) {
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$txt = Get-Content -Path $f -Raw
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$txt.Replace('C:/dependencies/opencl/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8
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$txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8
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}
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- name: Download OpenBLAS
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@ -213,7 +213,6 @@ jobs:
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cd build
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cmake .. ${{ matrix.defines }}
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cmake --build . --config Release
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cp ../LICENSE ./bin/Release/llama.cpp.txt
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- name: Add clblast.dll
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id: add_clblast_dll
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@ -258,6 +257,7 @@ jobs:
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id: pack_artifacts
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if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
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run: |
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Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt
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7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\*
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- name: Upload artifacts
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@ -292,7 +292,7 @@ jobs:
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run: |
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mkdir build
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cd build
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cmake .. -DLLAMA_CUBLAS=ON
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cmake .. -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON
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cmake --build . --config Release
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- name: Get commit hash
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@ -66,8 +66,10 @@ endif()
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# 3rd party libs
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option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
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option(LLAMA_BLAS "llama: use BLAS" OFF)
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option(LLAMA_BLAS_VENDOR "llama: BLA_VENDOR from https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" Generic)
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set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
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option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
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set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
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set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
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@ -184,6 +186,8 @@ if (LLAMA_CUBLAS)
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set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
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add_compile_definitions(GGML_USE_CUBLAS)
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add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
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add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
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if (LLAMA_STATIC)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
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@ -201,7 +205,7 @@ if (LLAMA_CLBLAST)
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if (CLBlast_FOUND)
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message(STATUS "CLBlast found")
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set(GGML_OPENCL_SOURCES ggml-opencl.c ggml-opencl.h)
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set(GGML_OPENCL_SOURCES ggml-opencl.cpp ggml-opencl.h)
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add_compile_definitions(GGML_USE_CLBLAST)
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36
Makefile
36
Makefile
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@ -1,5 +1,11 @@
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# Define the default target now so that it is always the first target
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default: main quantize quantize-stats perplexity embedding vdot
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BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot
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ifdef LLAMA_BUILD_SERVER
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BUILD_TARGETS += server
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endif
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default: $(BUILD_TARGETS)
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ifndef UNAME_S
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UNAME_S := $(shell uname -s)
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@ -38,7 +44,11 @@ CFLAGS = -I. -O3 -std=c11 -fPIC
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CXXFLAGS = -I. -I./examples -O3 -std=c++11 -fPIC
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LDFLAGS =
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ifndef LLAMA_DEBUG
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ifdef LLAMA_DEBUG
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CFLAGS += -O0 -g
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CXXFLAGS += -O0 -g
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LDFLAGS += -g
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else
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CFLAGS += -DNDEBUG
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CXXFLAGS += -DNDEBUG
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endif
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@ -133,11 +143,22 @@ ifdef LLAMA_CUBLAS
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OBJS += ggml-cuda.o
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NVCC = nvcc
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NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
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ifdef LLAMA_CUDA_DMMV_X
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NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
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else
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NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
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endif # LLAMA_CUDA_DMMV_X
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ifdef LLAMA_CUDA_DMMV_Y
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NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y)
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else
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NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
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endif # LLAMA_CUDA_DMMV_Y
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ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
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$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
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endif
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endif # LLAMA_CUBLAS
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ifdef LLAMA_CLBLAST
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CFLAGS += -DGGML_USE_CLBLAST
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CXXFLAGS += -DGGML_USE_CLBLAST
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# Mac provides OpenCL as a framework
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ifeq ($(UNAME_S),Darwin)
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LDFLAGS += -lclblast -framework OpenCL
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@ -145,8 +166,8 @@ ifdef LLAMA_CLBLAST
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LDFLAGS += -lclblast -lOpenCL
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endif
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OBJS += ggml-opencl.o
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ggml-opencl.o: ggml-opencl.c ggml-opencl.h
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$(CC) $(CFLAGS) -c $< -o $@
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ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
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$(CXX) $(CXXFLAGS) -c $< -o $@
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endif
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ifneq ($(filter aarch64%,$(UNAME_M)),)
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# Apple M1, M2, etc.
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@ -199,7 +220,7 @@ libllama.so: llama.o ggml.o $(OBJS)
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$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
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clean:
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rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state build-info.h
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rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot build-info.h
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#
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# Examples
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@ -226,6 +247,9 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml.o llama.o common.o
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save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
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$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
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build-info.h: $(wildcard .git/index) scripts/build-info.sh
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@sh scripts/build-info.sh > $@.tmp
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@if ! cmp -s $@.tmp $@; then \
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|
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104
README.md
104
README.md
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@ -240,11 +240,11 @@ In order to build llama.cpp you have three different options.
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Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
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- Accelerate Framework:
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- **Accelerate Framework**:
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This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
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- OpenBLAS:
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- **OpenBLAS**:
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This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
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@ -278,11 +278,11 @@ Building the program with BLAS support may lead to some performance improvements
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cmake --build . --config Release
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```
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- BLIS
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- **BLIS**
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|
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Check [BLIS.md](BLIS.md) for more information.
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|
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- Intel MKL
|
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- **Intel MKL**
|
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|
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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. You may also specify it by:
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@ -293,7 +293,7 @@ Building the program with BLAS support may lead to some performance improvements
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cmake --build . -config Release
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```
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|
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- cuBLAS
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- **cuBLAS**
|
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|
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This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
|
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- Using `make`:
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|
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@ -308,8 +308,81 @@ Building the program with BLAS support may lead to some performance improvements
|
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cmake .. -DLLAMA_CUBLAS=ON
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cmake --build . --config Release
|
||||
```
|
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Note: Because llama.cpp uses multiple CUDA streams for matrix multiplication results [are not guaranteed to be reproducible](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility). If you need reproducibility, set `GGML_CUDA_MAX_STREAMS` in the file `ggml-cuda.cu` to 1.
|
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|
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Note: Because llama.cpp uses multiple CUDA streams for matrix multiplication results [are not guaranteed to be reproducible](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility). If you need reproducibility, set `GGML_CUDA_MAX_STREAMS` in the file `ggml-cuda.cu` to 1.
|
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- **CLBlast**
|
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|
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OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
|
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|
||||
You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
|
||||
- For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed.
|
||||
|
||||
- <details>
|
||||
<summary>Installing the OpenCL SDK from source</summary>
|
||||
|
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```sh
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git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
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mkdir OpenCL-SDK/build
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cd OpenCL-SDK/build
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cmake .. -DBUILD_DOCS=OFF \
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-DBUILD_EXAMPLES=OFF \
|
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-DBUILD_TESTING=OFF \
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-DOPENCL_SDK_BUILD_SAMPLES=OFF \
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-DOPENCL_SDK_TEST_SAMPLES=OFF
|
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cmake --build . --config Release
|
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cmake --install . --prefix /some/path
|
||||
```
|
||||
</details>
|
||||
|
||||
Installing CLBlast: it may be found in your operating system's packages.
|
||||
|
||||
- <details>
|
||||
<summary>If not, then installing from source:</summary>
|
||||
|
||||
```sh
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git clone https://github.com/CNugteren/CLBlast.git
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mkdir CLBlast/build
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cd CLBLast/build
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cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
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cmake --build . --config Release
|
||||
cmake --install . --prefix /some/path
|
||||
```
|
||||
|
||||
Where `/some/path` is where the built library will be installed (default is `/usr/loca`l`).
|
||||
</details>
|
||||
|
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Building:
|
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|
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- Build with make:
|
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```sh
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make LLAMA_CLBLAST=1
|
||||
```
|
||||
- CMake:
|
||||
```sh
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_dir=/some/path
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||||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
Running:
|
||||
|
||||
The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does.
|
||||
|
||||
To select the correct platform (driver) and device (GPU), you can use the environment variables `GGML_OPENCL_PLATFORM` and `GGML_OPENCL_DEVICE`.
|
||||
The selection can be a number (starting from 0) or a text string to search:
|
||||
|
||||
```sh
|
||||
GGML_OPENCL_PLATFORM=1 ./main ...
|
||||
GGML_OPENCL_DEVICE=2 ./main ...
|
||||
GGML_OPENCL_PLATFORM=Intel ./main ...
|
||||
GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ...
|
||||
```
|
||||
|
||||
The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful.
|
||||
Using the variables it is possible to select a CPU-based driver as well, if so desired.
|
||||
|
||||
You can get a list of platforms and devices from the `clinfo -l` command, etc.
|
||||
|
||||
### Prepare Data & Run
|
||||
|
||||
|
|
@ -391,6 +464,25 @@ Note the use of `--color` to distinguish between user input and generated text.
|
|||
|
||||
|
||||
|
||||
### Persistent Interaction
|
||||
|
||||
The prompt, user inputs, and model generations can be saved and resumed across calls to `./main` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file.
|
||||
|
||||
```bash
|
||||
# Start a new chat
|
||||
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh
|
||||
|
||||
# Resume that chat
|
||||
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh
|
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|
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# Start a different chat with the same prompt/model
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PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh
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|
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# Different prompt cache for different prompt/model
|
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PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
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CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh
|
||||
```
|
||||
|
||||
### Instruction mode with Alpaca
|
||||
|
||||
1. First, download the `ggml` Alpaca model into the `./models` folder
|
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|
|
|
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|
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@ -23,8 +23,8 @@ CUR_PROMPT_CACHE="${CHAT_SAVE_DIR}/current-cache.bin"
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|||
NEXT_PROMPT_FILE="${CHAT_SAVE_DIR}/next-prompt.txt"
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NEXT_PROMPT_CACHE="${CHAT_SAVE_DIR}/next-cache.bin"
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|
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SESSION_SIZE_MSG_PATTERN='main: session file matches \d+ / \d+'
|
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SAMPLE_TIME_MSG_PATTERN='sample time =\s+\d+.\d+ ms /\s+\d+'
|
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SESSION_SIZE_MSG_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+'
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SAMPLE_TIME_MSG_PATTERN='sample time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+'
|
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SED_DELETE_MESSAGES="/^(${USER_NAME}:|${AI_NAME}:|\\.\\.\\.)/,\$d"
|
||||
|
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CTX_SIZE=2048
|
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|
|
|
|||
|
|
@ -251,6 +251,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
break;
|
||||
}
|
||||
params.model = argv[i];
|
||||
} else if (arg == "-a" || arg == "--alias") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.model_alias = argv[i];
|
||||
} else if (arg == "--lora") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -283,7 +289,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
params.n_gpu_layers = std::stoi(argv[i]);
|
||||
#else
|
||||
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
|
||||
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
||||
#endif
|
||||
} else if (arg == "--no-mmap") {
|
||||
params.use_mmap = false;
|
||||
} else if (arg == "--mtest") {
|
||||
|
|
@ -410,7 +421,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
||||
fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
|
||||
fprintf(stderr, " --no-penalize-nl do not penalize newline token\n");
|
||||
fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value\n");
|
||||
fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
||||
fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n");
|
||||
fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp);
|
||||
fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
|
||||
fprintf(stderr, " --perplexity compute perplexity over the prompt\n");
|
||||
|
|
@ -421,8 +433,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
if (llama_mmap_supported()) {
|
||||
fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
||||
}
|
||||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
|
||||
fprintf(stderr, " number of layers to store in VRAM\n");
|
||||
#endif
|
||||
fprintf(stderr, " --mtest compute maximum memory usage\n");
|
||||
fprintf(stderr, " --verbose-prompt print prompt before generation\n");
|
||||
fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
||||
|
|
|
|||
|
|
@ -45,6 +45,7 @@ struct gpt_params {
|
|||
float mirostat_eta = 0.10f; // learning rate
|
||||
|
||||
std::string model = "models/7B/ggml-model.bin"; // model path
|
||||
std::string model_alias = "unknown"; // model alias
|
||||
std::string prompt = "";
|
||||
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
|
||||
std::string input_prefix = ""; // string to prefix user inputs with
|
||||
|
|
|
|||
|
|
@ -69,8 +69,8 @@ In this section, we cover the most commonly used options for running the `main`
|
|||
- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
|
||||
- `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
|
||||
- `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
|
||||
- `-n N, --n_predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
|
||||
- `-c N, --ctx_size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
|
||||
- `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
|
||||
- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
|
||||
|
||||
## Input Prompts
|
||||
|
||||
|
|
@ -136,9 +136,9 @@ During text generation, LLaMA models have a limited context size, which means th
|
|||
|
||||
### Context Size
|
||||
|
||||
The `--ctx_size` option allows you to set the size of the prompt context used by the LLaMA models during text generation. A larger context size helps the model to better comprehend and generate responses for longer input or conversations.
|
||||
The `--ctx-size` option allows you to set the size of the prompt context used by the LLaMA models during text generation. A larger context size helps the model to better comprehend and generate responses for longer input or conversations.
|
||||
|
||||
- `-c N, --ctx_size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results.
|
||||
- `-c N, --ctx-size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results.
|
||||
|
||||
### Keep Prompt
|
||||
|
||||
|
|
@ -146,7 +146,7 @@ The `--keep` option allows users to retain the original prompt when the model ru
|
|||
|
||||
- `--keep N`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
|
||||
|
||||
By utilizing context management options like `--ctx_size` and `--keep`, you can maintain a more coherent and consistent interaction with the LLaMA models, ensuring that the generated text remains relevant to the original prompt or conversation.
|
||||
By utilizing context management options like `--ctx-size` and `--keep`, you can maintain a more coherent and consistent interaction with the LLaMA models, ensuring that the generated text remains relevant to the original prompt or conversation.
|
||||
|
||||
## Generation Flags
|
||||
|
||||
|
|
@ -154,11 +154,11 @@ The following options allow you to control the text generation process and fine-
|
|||
|
||||
### Number of Tokens to Predict
|
||||
|
||||
- `-n N, --n_predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).
|
||||
- `-n N, --n-predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).
|
||||
|
||||
The `--n_predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text. A value of -1 will cause text to be generated without limit.
|
||||
The `--n-predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text. A value of -1 will cause text to be generated without limit.
|
||||
|
||||
It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `n_predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter.
|
||||
It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `n-predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter.
|
||||
|
||||
### Temperature
|
||||
|
||||
|
|
@ -170,33 +170,33 @@ Example usage: `--temp 0.5`
|
|||
|
||||
### Repeat Penalty
|
||||
|
||||
- `--repeat_penalty N`: Control the repetition of token sequences in the generated text (default: 1.1).
|
||||
- `--repeat_last_n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx_size).
|
||||
- `--repeat-penalty N`: Control the repetition of token sequences in the generated text (default: 1.1).
|
||||
- `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
|
||||
- `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty.
|
||||
|
||||
The `repeat_penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.1.
|
||||
The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.1.
|
||||
|
||||
The `repeat_last_n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx_size`).
|
||||
The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).
|
||||
|
||||
Use the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases.
|
||||
|
||||
Example usage: `--repeat_penalty 1.15 --repeat_last_n 128 --no-penalize-nl`
|
||||
Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`
|
||||
|
||||
### Top-K Sampling
|
||||
|
||||
- `--top_k N`: Limit the next token selection to the K most probable tokens (default: 40).
|
||||
- `--top-k N`: Limit the next token selection to the K most probable tokens (default: 40).
|
||||
|
||||
Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text. The default value is 40.
|
||||
Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top-k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text. The default value is 40.
|
||||
|
||||
Example usage: `--top_k 30`
|
||||
Example usage: `--top-k 30`
|
||||
|
||||
### Top-P Sampling
|
||||
|
||||
- `--top_p N`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
|
||||
- `--top-p N`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
|
||||
|
||||
Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. The default value is 0.9.
|
||||
Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top-p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. The default value is 0.9.
|
||||
|
||||
Example usage: `--top_p 0.95`
|
||||
Example usage: `--top-p 0.95`
|
||||
|
||||
### Tail Free Sampling (TFS)
|
||||
|
||||
|
|
@ -217,16 +217,16 @@ Example usage: `--typical 0.9`
|
|||
### Mirostat Sampling
|
||||
|
||||
- `--mirostat N`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
|
||||
- `--mirostat_lr N`: Set the Mirostat learning rate, parameter eta (default: 0.1).
|
||||
- `--mirostat_ent N`: Set the Mirostat target entropy, parameter tau (default: 5.0).
|
||||
- `--mirostat-lr N`: Set the Mirostat learning rate, parameter eta (default: 0.1).
|
||||
- `--mirostat-ent N`: Set the Mirostat target entropy, parameter tau (default: 5.0).
|
||||
|
||||
Mirostat is an algorithm that actively maintains the quality of generated text within a desired range during text generation. It aims to strike a balance between coherence and diversity, avoiding low-quality output caused by excessive repetition (boredom traps) or incoherence (confusion traps).
|
||||
|
||||
The `--mirostat_lr` option sets the Mirostat learning rate (eta). The learning rate influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. The default value is `0.1`.
|
||||
The `--mirostat-lr` option sets the Mirostat learning rate (eta). The learning rate influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. The default value is `0.1`.
|
||||
|
||||
The `--mirostat_ent` option sets the Mirostat target entropy (tau), which represents the desired perplexity value for the generated text. Adjusting the target entropy allows you to control the balance between coherence and diversity in the generated text. A lower value will result in more focused and coherent text, while a higher value will lead to more diverse and potentially less coherent text. The default value is `5.0`.
|
||||
The `--mirostat-ent` option sets the Mirostat target entropy (tau), which represents the desired perplexity value for the generated text. Adjusting the target entropy allows you to control the balance between coherence and diversity in the generated text. A lower value will result in more focused and coherent text, while a higher value will lead to more diverse and potentially less coherent text. The default value is `5.0`.
|
||||
|
||||
Example usage: `--mirostat 2 --mirostat_lr 0.05 --mirostat_ent 3.0`
|
||||
Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0`
|
||||
|
||||
### Logit Bias
|
||||
|
||||
|
|
@ -264,15 +264,15 @@ These options help improve the performance and memory usage of the LLaMA models.
|
|||
|
||||
### Memory Float 32
|
||||
|
||||
- `--memory_f32`: Use 32-bit floats instead of 16-bit floats for memory key+value, allowing higher quality inference at the cost of higher memory usage.
|
||||
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended.
|
||||
|
||||
### Batch Size
|
||||
|
||||
- `-b N, --batch_size N`: Set the batch size for prompt processing (default: 512). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
|
||||
- `-b N, --batch-size N`: Set the batch size for prompt processing (default: 512). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
|
||||
|
||||
### Prompt Caching
|
||||
|
||||
- `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs.
|
||||
- `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation.
|
||||
|
||||
### Quantization
|
||||
|
||||
|
|
@ -285,5 +285,6 @@ These options provide extra functionality and customization when running the LLa
|
|||
- `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.
|
||||
- `--verbose-prompt`: Print the prompt before generating text.
|
||||
- `--mtest`: Test the model's functionality by running a series of tests to ensure it's working properly.
|
||||
- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
|
||||
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
|
||||
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
|
||||
|
|
|
|||
|
|
@ -134,8 +134,6 @@ int main(int argc, char ** argv) {
|
|||
return 0;
|
||||
}
|
||||
|
||||
// Add a space in front of the first character to match OG llama tokenizer behavior
|
||||
params.prompt.insert(0, 1, ' ');
|
||||
|
||||
std::string path_session = params.path_prompt_cache;
|
||||
std::vector<llama_token> session_tokens;
|
||||
|
|
@ -155,6 +153,7 @@ int main(int argc, char ** argv) {
|
|||
return 1;
|
||||
}
|
||||
session_tokens.resize(n_token_count_out);
|
||||
llama_set_rng_seed(ctx, params.seed);
|
||||
|
||||
fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
|
||||
} else {
|
||||
|
|
@ -163,7 +162,16 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
|
||||
// tokenize the prompt
|
||||
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
|
||||
std::vector<llama_token> embd_inp;
|
||||
|
||||
if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
|
||||
// Add a space in front of the first character to match OG llama tokenizer behavior
|
||||
params.prompt.insert(0, 1, ' ');
|
||||
|
||||
embd_inp = ::llama_tokenize(ctx, params.prompt, true);
|
||||
} else {
|
||||
embd_inp = session_tokens;
|
||||
}
|
||||
|
||||
const int n_ctx = llama_n_ctx(ctx);
|
||||
|
||||
|
|
@ -181,7 +189,9 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
n_matching_session_tokens++;
|
||||
}
|
||||
if (n_matching_session_tokens >= embd_inp.size()) {
|
||||
if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
|
||||
fprintf(stderr, "%s: using full prompt from session file\n", __func__);
|
||||
} else if (n_matching_session_tokens >= embd_inp.size()) {
|
||||
fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
|
||||
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
|
||||
fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
|
||||
|
|
|
|||
|
|
@ -285,7 +285,8 @@ Test();
|
|||
## Common Options
|
||||
|
||||
- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
|
||||
- `-c N, --ctx_size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
|
||||
- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
|
||||
- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
|
||||
- `--embedding`: Enable the embedding mode. **Completion function doesn't work in this mode**.
|
||||
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`;
|
||||
- `--port`: Set the port to listen. Default: `8080`.
|
||||
|
|
@ -304,7 +305,7 @@ The RNG seed is used to initialize the random number generator that influences t
|
|||
|
||||
### Memory Float 32
|
||||
|
||||
- `--memory_f32`: Use 32-bit floats instead of 16-bit floats for memory key+value, allowing higher quality inference at the cost of higher memory usage.
|
||||
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement but does not appear to increase generation quality in a measurable way. Not recommended.
|
||||
|
||||
## Limitations:
|
||||
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@ struct llama_server_context
|
|||
std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
|
||||
// compare the evaluated prompt with the new prompt
|
||||
int new_prompt_len = 0;
|
||||
for (int i = 0;i < prompt_tokens.size(); i++) {
|
||||
for (size_t i = 0; i < prompt_tokens.size(); i++) {
|
||||
if (i < processed_tokens.size() &&
|
||||
processed_tokens[i] == prompt_tokens[i])
|
||||
{
|
||||
|
|
@ -71,7 +71,7 @@ struct llama_server_context
|
|||
{
|
||||
embd_inp.push_back(prompt_tokens[i]);
|
||||
if(new_prompt_len == 0) {
|
||||
if(i - 1 < n_past) {
|
||||
if(int32_t(i) - 1 < n_past) {
|
||||
processed_tokens.erase(processed_tokens.begin() + i, processed_tokens.end());
|
||||
}
|
||||
// Evaluate the new fragment prompt from the last token processed.
|
||||
|
|
@ -136,7 +136,7 @@ struct llama_server_context
|
|||
{
|
||||
// out of user input, sample next token
|
||||
const float temp = params.temp;
|
||||
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
|
||||
// const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
|
||||
const float top_p = params.top_p;
|
||||
const float tfs_z = params.tfs_z;
|
||||
const float typical_p = params.typical_p;
|
||||
|
|
@ -306,12 +306,12 @@ struct llama_server_context
|
|||
// Avoid add the no show words to the response
|
||||
for (std::vector<llama_token> word_tokens : no_show_words)
|
||||
{
|
||||
int match_token = 1;
|
||||
size_t match_token = 1;
|
||||
if (tokens_predicted.front() == word_tokens.front())
|
||||
{
|
||||
bool execute_matching = true;
|
||||
if (tokens_predicted.size() > 1) { // if previus tokens had been tested
|
||||
for (int i = 1; i < word_tokens.size(); i++)
|
||||
for (size_t i = 1; i < word_tokens.size(); i++)
|
||||
{
|
||||
if (i >= tokens_predicted.size()) {
|
||||
match_token = i;
|
||||
|
|
@ -385,7 +385,9 @@ void server_print_usage(int /*argc*/, char **argv, const gpt_params ¶ms)
|
|||
fprintf(stderr, "options:\n");
|
||||
fprintf(stderr, " -h, --help show this help message and exit\n");
|
||||
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n");
|
||||
fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n");
|
||||
fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
|
||||
fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
||||
fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n");
|
||||
fprintf(stderr, " --embedding enable embedding mode\n");
|
||||
fprintf(stderr, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
|
||||
if (llama_mlock_supported())
|
||||
|
|
@ -396,12 +398,16 @@ void server_print_usage(int /*argc*/, char **argv, const gpt_params ¶ms)
|
|||
{
|
||||
fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
|
||||
}
|
||||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
|
||||
fprintf(stderr, " number of layers to store in VRAM\n");
|
||||
#endif
|
||||
fprintf(stderr, " -m FNAME, --model FNAME\n");
|
||||
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
|
||||
fprintf(stderr, " -host ip address to listen (default 127.0.0.1)\n");
|
||||
fprintf(stderr, " -port PORT port to listen (default 8080)\n");
|
||||
fprintf(stderr, " -a ALIAS, --alias ALIAS\n");
|
||||
fprintf(stderr, " set an alias for the model, will be added as `model` field in completion response\n");
|
||||
fprintf(stderr, " --host ip address to listen (default 127.0.0.1)\n");
|
||||
fprintf(stderr, " --port PORT port to listen (default 8080)\n");
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
|
||||
|
|
@ -453,6 +459,15 @@ bool server_params_parse(int argc, char **argv, server_params &sparams, gpt_para
|
|||
}
|
||||
params.model = argv[i];
|
||||
}
|
||||
else if (arg == "-a" || arg == "--alias")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
params.model_alias = argv[i];
|
||||
}
|
||||
else if (arg == "--embedding")
|
||||
{
|
||||
params.embedding = true;
|
||||
|
|
@ -462,7 +477,7 @@ bool server_params_parse(int argc, char **argv, server_params &sparams, gpt_para
|
|||
server_print_usage(argc, argv, default_params);
|
||||
exit(0);
|
||||
}
|
||||
else if (arg == "-c" || arg == "--ctx_size")
|
||||
else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
|
|
@ -471,7 +486,7 @@ bool server_params_parse(int argc, char **argv, server_params &sparams, gpt_para
|
|||
}
|
||||
params.n_ctx = std::stoi(argv[i]);
|
||||
}
|
||||
else if (arg == "--memory_f32")
|
||||
else if (arg == "--memory-f32" || arg == "--memory_f32")
|
||||
{
|
||||
params.memory_f16 = false;
|
||||
}
|
||||
|
|
@ -482,7 +497,12 @@ bool server_params_parse(int argc, char **argv, server_params &sparams, gpt_para
|
|||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
params.n_gpu_layers = std::stoi(argv[i]);
|
||||
#else
|
||||
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
|
||||
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
||||
#endif
|
||||
}
|
||||
else
|
||||
{
|
||||
|
|
@ -601,7 +621,7 @@ int main(int argc, char **argv)
|
|||
|
||||
Server svr;
|
||||
|
||||
svr.Get("/", [](const Request &req, Response &res)
|
||||
svr.Get("/", [](const Request &, Response &res)
|
||||
{ res.set_content("<h1>llama.cpp server works</h1>", "text/html"); });
|
||||
|
||||
svr.Post("/completion", [&llama](const Request &req, Response &res)
|
||||
|
|
@ -645,11 +665,12 @@ int main(int argc, char **argv)
|
|||
try
|
||||
{
|
||||
json data = {
|
||||
{"model", llama.params.model_alias },
|
||||
{"content", llama.generated_text },
|
||||
{"tokens_predicted", llama.num_tokens_predicted}};
|
||||
return res.set_content(data.dump(), "application/json");
|
||||
}
|
||||
catch (json::exception e)
|
||||
catch (const json::exception &e)
|
||||
{
|
||||
// Some tokens have bad UTF-8 strings, the json parser is very sensitive
|
||||
json data = {
|
||||
|
|
@ -701,7 +722,7 @@ int main(int argc, char **argv)
|
|||
{"content", result },
|
||||
{"stop", !llama.has_next_token }};
|
||||
return res.set_content(data.dump(), "application/json");
|
||||
} catch (json::exception e) {
|
||||
} catch (const json::exception &e) {
|
||||
// Some tokens have bad UTF-8 strings, the json parser is very sensitive
|
||||
json data = {
|
||||
{"content", "" },
|
||||
|
|
|
|||
104
ggml-cuda.cu
104
ggml-cuda.cu
|
|
@ -83,9 +83,19 @@ typedef struct {
|
|||
} block_q8_0;
|
||||
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
|
||||
|
||||
#define WARP_SIZE 32
|
||||
|
||||
#define CUDA_MUL_BLOCK_SIZE 256
|
||||
|
||||
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
|
||||
#define CUDA_DMMV_BLOCK_SIZE 32 // dmmv = dequantize_mul_mat_vec
|
||||
|
||||
// dmmv = dequantize_mul_mat_vec
|
||||
#ifndef GGML_CUDA_DMMV_X
|
||||
#define GGML_CUDA_DMMV_X 32
|
||||
#endif
|
||||
#ifndef GGML_CUDA_DMMV_Y
|
||||
#define GGML_CUDA_DMMV_Y 1
|
||||
#endif
|
||||
|
||||
static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
|
||||
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
|
@ -200,41 +210,51 @@ static __global__ void dequantize_block(const void * vx, float * y, const int k)
|
|||
dequantize_kernel(vx, ib, iqs, v0, v1);
|
||||
}
|
||||
|
||||
template <int block_size, int qk, int qr, dequantize_kernel_t dequantize_kernel>
|
||||
template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
|
||||
static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y, float * dst, const int ncols) {
|
||||
const int row = blockIdx.x;
|
||||
// qk = quantized weights per x block
|
||||
// qr = number of quantized weights per data value in x block
|
||||
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
||||
const int tid = threadIdx.x;
|
||||
|
||||
const int iter_stride = 2*GGML_CUDA_DMMV_X;
|
||||
const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
|
||||
const int y_offset = qr == 1 ? 1 : qk/2;
|
||||
|
||||
__shared__ float tmp[block_size]; // separate sum for each thread
|
||||
tmp[tid] = 0;
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = 0; i < ncols/block_size; i += 2) {
|
||||
const int col = i*block_size + 2*tid;
|
||||
const int ib = (row*ncols + col)/qk; // block index
|
||||
const int iqs = (col%qk)/qr; // quant index
|
||||
for (int i = 0; i < ncols; i += iter_stride) {
|
||||
const int col = i + vals_per_iter*tid;
|
||||
const int ib = (row*ncols + col)/qk; // x block index
|
||||
const int iqs = (col%qk)/qr; // x quant index
|
||||
const int iybs = col - col%qk; // y block start index
|
||||
|
||||
// processing >2 values per i iter is faster for fast GPUs
|
||||
#pragma unroll
|
||||
for (int j = 0; j < vals_per_iter; j += 2) {
|
||||
// process 2 vals per j iter
|
||||
|
||||
// dequantize
|
||||
float v0, v1;
|
||||
dequantize_kernel(vx, ib, iqs, v0, v1);
|
||||
dequantize_kernel(vx, ib, iqs + j/qr, v0, v1);
|
||||
// for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
|
||||
|
||||
// matrix multiplication
|
||||
tmp[tid] += v0 * y[iybs + iqs + 0];
|
||||
tmp[tid] += v1 * y[iybs + iqs + y_offset];
|
||||
tmp += v0 * y[iybs + iqs + j/qr + 0];
|
||||
tmp += v1 * y[iybs + iqs + j/qr + y_offset];
|
||||
// for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
|
||||
}
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
for (int s=block_size/2; s>0; s>>=1) {
|
||||
if (tid < s) {
|
||||
tmp[tid] += tmp[tid + s];
|
||||
}
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp[0];
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -269,33 +289,43 @@ static void dequantize_row_q8_0_cuda(const void * vx, float * y, const int k, cu
|
|||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
|
||||
dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK4_0, QR4_0, dequantize_q4_0>
|
||||
<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
|
||||
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
||||
GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
|
||||
const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
|
||||
dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
|
||||
<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
|
||||
dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK4_1, QR4_1, dequantize_q4_1>
|
||||
<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
|
||||
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
||||
GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
|
||||
const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
|
||||
dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
|
||||
<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
|
||||
dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK5_0, QR5_0, dequantize_q5_0>
|
||||
<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
|
||||
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
||||
GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
|
||||
const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
|
||||
dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
|
||||
<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
|
||||
dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK5_1, QR5_1, dequantize_q5_1>
|
||||
<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
|
||||
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
||||
GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
|
||||
const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
|
||||
dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
|
||||
<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
|
||||
dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, QK8_0, QR8_0, dequantize_q8_0>
|
||||
<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
|
||||
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
||||
GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
|
||||
const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
|
||||
dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
|
||||
<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
||||
|
|
@ -304,9 +334,11 @@ static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, c
|
|||
}
|
||||
|
||||
static void convert_mul_mat_vec_f16_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % CUDA_DMMV_BLOCK_SIZE == 0);
|
||||
dequantize_mul_mat_vec<CUDA_DMMV_BLOCK_SIZE, 32, 1, convert_f16>
|
||||
<<<nrows, CUDA_DMMV_BLOCK_SIZE, 0, stream>>>(vx, y, dst, ncols);
|
||||
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
||||
GGML_ASSERT(nrows % GGML_CUDA_DMMV_Y == 0);
|
||||
const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
|
||||
dequantize_mul_mat_vec<1, 1, convert_f16>
|
||||
<<<nrows/GGML_CUDA_DMMV_Y, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
|
||||
|
|
|
|||
474
ggml-opencl.c
474
ggml-opencl.c
|
|
@ -1,474 +0,0 @@
|
|||
#include "ggml-opencl.h"
|
||||
|
||||
#define CL_TARGET_OPENCL_VERSION 110
|
||||
#include <clblast_c.h>
|
||||
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <string.h>
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#define MULTILINE_QUOTE(...) #__VA_ARGS__
|
||||
static const char * program_source = MULTILINE_QUOTE(
|
||||
|
||||
typedef char int8_t;
|
||||
typedef uchar uint8_t;
|
||||
typedef int int32_t;
|
||||
typedef uint uint32_t;
|
||||
|
||||
struct __attribute__ ((packed)) block_q4_0
|
||||
{
|
||||
half d;
|
||||
uint8_t qs[16]; /* QK4_0 / 2 */
|
||||
};
|
||||
|
||||
struct __attribute__ ((packed)) block_q4_1
|
||||
{
|
||||
half d;
|
||||
half m;
|
||||
uint8_t qs[16]; /* QK4_1 / 2 */
|
||||
};
|
||||
|
||||
struct __attribute__ ((packed)) block_q5_0
|
||||
{
|
||||
half d;
|
||||
uint32_t qh;
|
||||
uint8_t qs[16]; /* QK5_0 / 2 */
|
||||
};
|
||||
|
||||
struct __attribute__ ((packed)) block_q5_1
|
||||
{
|
||||
half d;
|
||||
half m;
|
||||
uint32_t qh;
|
||||
uint8_t qs[16]; /* QK5_1 / 2 */
|
||||
};
|
||||
|
||||
struct __attribute__ ((packed)) block_q8_0
|
||||
{
|
||||
half d;
|
||||
int8_t qs[32]; /* QK8_0 */
|
||||
};
|
||||
|
||||
|
||||
__kernel void dequantize_row_q4_0(__global struct block_q4_0* x, __global float* y) {
|
||||
const uint i = get_global_id(0) / 32; /* QK4_0 */
|
||||
const uint j = get_local_id(0);
|
||||
|
||||
const float d = vload_half(0, (__global half*) &x[i].d);
|
||||
|
||||
const int x0 = (x[i].qs[j] & 0xf) - 8;
|
||||
const int x1 = (x[i].qs[j] >> 4) - 8;
|
||||
|
||||
y[i*32 + j + 0 ] = x0*d;
|
||||
y[i*32 + j + 16] = x1*d;
|
||||
}
|
||||
|
||||
__kernel void dequantize_row_q4_1(__global struct block_q4_1* x, __global float* y) {
|
||||
const uint i = get_global_id(0) / 32; /* QK4_1 */
|
||||
const uint j = get_local_id(0);
|
||||
|
||||
const float d = vload_half(0, (__global half*) &x[i].d);
|
||||
const float m = vload_half(0, (__global half*) &x[i].m);
|
||||
|
||||
const int x0 = (x[i].qs[j] & 0xf);
|
||||
const int x1 = (x[i].qs[j] >> 4);
|
||||
|
||||
y[i*32 + j + 0 ] = x0*d + m;
|
||||
y[i*32 + j + 16] = x1*d + m;
|
||||
}
|
||||
|
||||
__kernel void dequantize_row_q5_0(__global struct block_q5_0* x, __global float* y) {
|
||||
const uint i = get_global_id(0) / 32; /* QK5_0 */
|
||||
const uint j = get_local_id(0);
|
||||
|
||||
const float d = vload_half(0, (__global half*) &x[i].d);
|
||||
|
||||
uint32_t qh = x[i].qh;
|
||||
|
||||
const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10;
|
||||
|
||||
const int32_t x0 = ((x[i].qs[j] & 0xf) | xh_0) - 16;
|
||||
const int32_t x1 = ((x[i].qs[j] >> 4) | xh_1) - 16;
|
||||
|
||||
y[i*32 + j + 0 ] = x0*d;
|
||||
y[i*32 + j + 16] = x1*d;
|
||||
}
|
||||
|
||||
__kernel void dequantize_row_q5_1(__global struct block_q5_1* x, __global float* y) {
|
||||
const uint i = get_global_id(0) / 32; /* QK5_1 */
|
||||
const uint j = get_local_id(0);
|
||||
|
||||
const float d = vload_half(0, (__global half*) &x[i].d);
|
||||
const float m = vload_half(0, (__global half*) &x[i].m);
|
||||
|
||||
uint32_t qh = x[i].qh;
|
||||
|
||||
const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
|
||||
const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10;
|
||||
|
||||
const int x0 = (x[i].qs[j] & 0xf) | xh_0;
|
||||
const int x1 = (x[i].qs[j] >> 4) | xh_1;
|
||||
|
||||
y[i*32 + j + 0 ] = x0*d + m;
|
||||
y[i*32 + j + 16] = x1*d + m;
|
||||
}
|
||||
|
||||
__kernel void dequantize_row_q8_0(__global struct block_q8_0* x, __global float* y) {
|
||||
const uint i = get_global_id(0) / 32; /* QK8_0 */
|
||||
const uint j = get_local_id(0);
|
||||
|
||||
const float d = vload_half(0, (__global half*) &x[i].d);
|
||||
y[i*32 + j] = x[i].qs[j]*d;
|
||||
}
|
||||
|
||||
);
|
||||
|
||||
#define CL_CHECK(err) \
|
||||
do { \
|
||||
cl_int err_ = (err); \
|
||||
if (err_ != CL_SUCCESS) { \
|
||||
fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
|
||||
#err, err_, __FILE__, __LINE__); \
|
||||
exit(1); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
#define CLBLAST_CHECK(err) \
|
||||
do { \
|
||||
CLBlastStatusCode err_ = (err); \
|
||||
if (err_ != CLBlastSuccess) { \
|
||||
fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \
|
||||
#err, err_, __FILE__, __LINE__); \
|
||||
exit(1); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
static cl_platform_id platform;
|
||||
static cl_device_id device;
|
||||
static cl_context context;
|
||||
static cl_command_queue queue;
|
||||
static cl_program program;
|
||||
static cl_kernel kernel_q4_0, kernel_q4_1, kernel_q5_0, kernel_q5_1, kernel_q8_0;
|
||||
static cl_mem cl_buffer_a, cl_buffer_qb, cl_buffer_b, cl_buffer_c;
|
||||
static size_t cl_size_a = 0, cl_size_qb = 0, cl_size_b = 0, cl_size_c = 0;
|
||||
|
||||
static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
|
||||
cl_program p;
|
||||
char *program_log;
|
||||
size_t program_size, log_size;
|
||||
int err;
|
||||
|
||||
program_size = strlen(program_buffer);
|
||||
|
||||
p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
|
||||
if(err < 0) {
|
||||
fprintf(stderr, "OpenCL error creating program");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
err = clBuildProgram(p, 0, NULL, NULL, NULL, NULL);
|
||||
if(err < 0) {
|
||||
|
||||
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
|
||||
program_log = (char*) malloc(log_size + 1);
|
||||
program_log[log_size] = '\0';
|
||||
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
|
||||
printf("%s\n", program_log);
|
||||
free(program_log);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
return p;
|
||||
}
|
||||
|
||||
void ggml_cl_init(void) {
|
||||
cl_int err = 0;
|
||||
|
||||
struct cl_device;
|
||||
struct cl_platform {
|
||||
cl_platform_id id;
|
||||
unsigned number;
|
||||
char name[128];
|
||||
char vendor[128];
|
||||
struct cl_device * devices;
|
||||
unsigned n_devices;
|
||||
struct cl_device * default_device;
|
||||
};
|
||||
|
||||
struct cl_device {
|
||||
struct cl_platform * platform;
|
||||
cl_device_id id;
|
||||
unsigned number;
|
||||
cl_device_type type;
|
||||
char name[128];
|
||||
};
|
||||
|
||||
enum { NPLAT = 16, NDEV = 16 };
|
||||
|
||||
struct cl_platform platforms[NPLAT];
|
||||
unsigned n_platforms = 0;
|
||||
struct cl_device devices[NDEV];
|
||||
unsigned n_devices = 0;
|
||||
struct cl_device * default_device = NULL;
|
||||
|
||||
platform = NULL;
|
||||
device = NULL;
|
||||
|
||||
cl_platform_id platform_ids[NPLAT];
|
||||
CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms));
|
||||
|
||||
for (unsigned i = 0; i < n_platforms; i++) {
|
||||
struct cl_platform * p = &platforms[i];
|
||||
p->number = i;
|
||||
p->id = platform_ids[i];
|
||||
CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
|
||||
CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));
|
||||
|
||||
cl_device_id device_ids[NDEV];
|
||||
cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
|
||||
if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
|
||||
p->n_devices = 0;
|
||||
} else {
|
||||
CL_CHECK(clGetDeviceIDsError);
|
||||
}
|
||||
p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
|
||||
p->default_device = NULL;
|
||||
|
||||
for (unsigned j = 0; j < p->n_devices; j++) {
|
||||
struct cl_device * d = &devices[n_devices];
|
||||
d->number = n_devices++;
|
||||
d->id = device_ids[j];
|
||||
d->platform = p;
|
||||
CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
|
||||
CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));
|
||||
|
||||
if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
|
||||
p->default_device = d;
|
||||
}
|
||||
}
|
||||
|
||||
if (default_device == NULL && p->default_device != NULL) {
|
||||
default_device = p->default_device;
|
||||
}
|
||||
}
|
||||
|
||||
if (n_devices == 0) {
|
||||
fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
|
||||
char * user_device_string = getenv("GGML_OPENCL_DEVICE");
|
||||
int user_platform_number = -1;
|
||||
int user_device_number = -1;
|
||||
|
||||
unsigned n;
|
||||
if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
|
||||
user_platform_number = (int)n;
|
||||
}
|
||||
if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
|
||||
user_device_number = (int)n;
|
||||
}
|
||||
|
||||
struct cl_device * selected_devices = devices;
|
||||
unsigned n_selected_devices = n_devices;
|
||||
|
||||
if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
|
||||
for (unsigned i = 0; i < n_platforms; i++) {
|
||||
struct cl_platform * p = &platforms[i];
|
||||
if (strstr(p->name, user_platform_string) != NULL ||
|
||||
strstr(p->vendor, user_platform_string) != NULL) {
|
||||
user_platform_number = (int)i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (user_platform_number == -1) {
|
||||
fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
if (user_platform_number != -1) {
|
||||
struct cl_platform * p = &platforms[user_platform_number];
|
||||
selected_devices = p->devices;
|
||||
n_selected_devices = p->n_devices;
|
||||
default_device = p->default_device;
|
||||
if (n_selected_devices == 0) {
|
||||
fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
|
||||
for (unsigned i = 0; i < n_selected_devices; i++) {
|
||||
struct cl_device * d = &selected_devices[i];
|
||||
if (strstr(d->name, user_device_string) != NULL) {
|
||||
user_device_number = d->number;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (user_device_number == -1) {
|
||||
fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string);
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
if (user_device_number != -1) {
|
||||
selected_devices = &devices[user_device_number];
|
||||
n_selected_devices = 1;
|
||||
default_device = &selected_devices[0];
|
||||
}
|
||||
|
||||
GGML_ASSERT(n_selected_devices > 0);
|
||||
|
||||
if (default_device == NULL) {
|
||||
default_device = &selected_devices[0];
|
||||
}
|
||||
|
||||
fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
|
||||
fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name);
|
||||
if (default_device->type != CL_DEVICE_TYPE_GPU) {
|
||||
fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
|
||||
}
|
||||
|
||||
platform = default_device->platform->id;
|
||||
device = default_device->id;
|
||||
|
||||
cl_context_properties properties[] = {
|
||||
(intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
|
||||
};
|
||||
|
||||
CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));
|
||||
|
||||
CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
|
||||
(err != CL_INVALID_PROPERTY && err != CL_INVALID_VALUE ? err :
|
||||
(queue = clCreateCommandQueue(context, device, 0, &err), err)
|
||||
)));
|
||||
|
||||
program = build_program_from_source(context, device, program_source);
|
||||
|
||||
// Prepare dequantize kernels
|
||||
CL_CHECK((kernel_q4_0 = clCreateKernel(program, "dequantize_row_q4_0", &err), err));
|
||||
CL_CHECK((kernel_q4_1 = clCreateKernel(program, "dequantize_row_q4_1", &err), err));
|
||||
CL_CHECK((kernel_q5_0 = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
|
||||
CL_CHECK((kernel_q5_1 = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
|
||||
CL_CHECK((kernel_q8_0 = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
|
||||
}
|
||||
|
||||
static void ggml_cl_malloc(size_t req_size, size_t* cur_size, cl_mem_flags flags, cl_mem* buf) {
|
||||
if (req_size <= *cur_size) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Reallocate buffer with enough space
|
||||
if (*cur_size > 0) {
|
||||
clReleaseMemObject(*buf);
|
||||
}
|
||||
cl_int err;
|
||||
CL_CHECK((*buf = clCreateBuffer(context, flags, req_size, NULL, &err), err));
|
||||
*cur_size = req_size;
|
||||
}
|
||||
|
||||
void ggml_cl_sgemm_wrapper(
|
||||
const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b,
|
||||
const int m, const int n, const int k,
|
||||
const float alpha, const void *host_a, const int lda,
|
||||
const float *host_b, const int ldb, const float beta,
|
||||
float *host_c, const int ldc, const int btype) {
|
||||
|
||||
cl_kernel kernel;
|
||||
size_t global = n * k, local, size_qb;
|
||||
bool dequant;
|
||||
|
||||
switch (btype) {
|
||||
case GGML_TYPE_F32:
|
||||
dequant = false;
|
||||
break;
|
||||
case GGML_TYPE_Q4_0:
|
||||
dequant = true;
|
||||
kernel = kernel_q4_0;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q4_1:
|
||||
dequant = true;
|
||||
kernel = kernel_q4_1;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) * 2 + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q5_0:
|
||||
dequant = true;
|
||||
kernel = kernel_q5_0;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) + sizeof(uint32_t) + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q5_1:
|
||||
dequant = true;
|
||||
kernel = kernel_q5_1;
|
||||
local = 16;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) * 2 + sizeof(uint32_t) + local) / 32;
|
||||
break;
|
||||
case GGML_TYPE_Q8_0:
|
||||
dequant = true;
|
||||
kernel = kernel_q8_0;
|
||||
local = 32;
|
||||
size_qb = global * (sizeof(ggml_fp16_t) + local) / 32;
|
||||
break;
|
||||
default:
|
||||
fprintf(stderr, "Error: Unsupported OpenCL btype %d\n", btype);
|
||||
abort();
|
||||
}
|
||||
|
||||
const size_t size_a = m * k * sizeof(float);
|
||||
const size_t size_b = n * k * sizeof(float);
|
||||
const size_t size_c = m * n * sizeof(float);
|
||||
|
||||
// Prepare buffers
|
||||
ggml_cl_malloc(size_a, &cl_size_a, CL_MEM_READ_ONLY, &cl_buffer_a);
|
||||
if (dequant) {
|
||||
ggml_cl_malloc(size_qb, &cl_size_qb, CL_MEM_READ_ONLY, &cl_buffer_qb);
|
||||
}
|
||||
ggml_cl_malloc(size_b, &cl_size_b, CL_MEM_READ_WRITE, &cl_buffer_b);
|
||||
ggml_cl_malloc(size_c, &cl_size_c, CL_MEM_WRITE_ONLY, &cl_buffer_c);
|
||||
|
||||
cl_event ev_a, ev_qb, ev_b;
|
||||
|
||||
if (dequant) {
|
||||
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &cl_buffer_qb));
|
||||
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &cl_buffer_b));
|
||||
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_qb, CL_FALSE, 0, size_qb, host_b, 0, NULL, &ev_qb));
|
||||
} else {
|
||||
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_b, CL_FALSE, 0, size_b, host_b, 0, NULL, &ev_b));
|
||||
}
|
||||
|
||||
CL_CHECK(clEnqueueWriteBuffer(queue, cl_buffer_a, CL_FALSE, 0, size_a, host_a, 0, NULL, &ev_a));
|
||||
if (dequant) {
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &global, &local, 1, &ev_qb, &ev_b));
|
||||
CL_CHECK(clReleaseEvent(ev_qb));
|
||||
}
|
||||
CL_CHECK(clWaitForEvents(1, &ev_a));
|
||||
CL_CHECK(clWaitForEvents(1, &ev_b));
|
||||
CL_CHECK(clReleaseEvent(ev_a));
|
||||
CL_CHECK(clReleaseEvent(ev_b));
|
||||
|
||||
cl_event ev_sgemm;
|
||||
CLBLAST_CHECK(CLBlastSgemm(
|
||||
(CLBlastLayout)order,
|
||||
(CLBlastTranspose)trans_a, (CLBlastTranspose)trans_b,
|
||||
m, n, k,
|
||||
alpha,
|
||||
cl_buffer_a, 0, lda,
|
||||
cl_buffer_b, 0, ldb,
|
||||
beta,
|
||||
cl_buffer_c, 0, ldc,
|
||||
&queue, &ev_sgemm));
|
||||
|
||||
cl_event ev_c;
|
||||
CL_CHECK(clEnqueueReadBuffer(queue, cl_buffer_c, CL_TRUE, 0, size_c, host_c, 1, &ev_sgemm, &ev_c));
|
||||
|
||||
// Wait for completion
|
||||
CL_CHECK(clWaitForEvents(1, &ev_c));
|
||||
CL_CHECK(clReleaseEvent(ev_sgemm));
|
||||
CL_CHECK(clReleaseEvent(ev_c));
|
||||
}
|
||||
1028
ggml-opencl.cpp
Normal file
1028
ggml-opencl.cpp
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -1,23 +1,21 @@
|
|||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
void ggml_cl_init(void);
|
||||
|
||||
enum ggml_blas_order {
|
||||
GGML_BLAS_ORDER_ROW_MAJOR = 101,
|
||||
GGML_BLAS_ORDER_COLUMN_MAJOR = 102,
|
||||
};
|
||||
bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
|
||||
|
||||
enum ggml_blas_op {
|
||||
GGML_BLAS_OP_N = 111,
|
||||
GGML_BLAS_OP_T = 112,
|
||||
GGML_BLAS_OP_C = 113,
|
||||
};
|
||||
void * ggml_cl_host_malloc(size_t size);
|
||||
void ggml_cl_host_free(void * ptr);
|
||||
|
||||
void ggml_cl_sgemm_wrapper(const enum ggml_blas_order order, const enum ggml_blas_op trans_a, const enum ggml_blas_op trans_b, const int m, const int n, const int k, const float alpha, const void *host_a, const int lda, const float *host_b, const int ldb, const float beta, float *host_c, const int ldc, const int btype);
|
||||
void ggml_cl_transform_tensor(struct ggml_tensor * tensor);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
143
ggml.c
143
ggml.c
|
|
@ -186,10 +186,12 @@ typedef double ggml_float;
|
|||
#if defined(_MSC_VER) || defined(__MINGW32__)
|
||||
#include <intrin.h>
|
||||
#else
|
||||
#if !defined(__riscv)
|
||||
#include <immintrin.h>
|
||||
#endif
|
||||
#endif
|
||||
#endif
|
||||
#endif
|
||||
|
||||
#ifdef __F16C__
|
||||
|
||||
|
|
@ -3494,7 +3496,7 @@ static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
|
|||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_IS_QUANTIZED is outdated");
|
||||
|
||||
static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
|
||||
static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
|
||||
"NONE",
|
||||
|
||||
"DUP",
|
||||
|
|
@ -3749,6 +3751,9 @@ const char * ggml_type_name(enum ggml_type type) {
|
|||
return GGML_TYPE_NAME[type];
|
||||
}
|
||||
|
||||
const char * ggml_op_name(enum ggml_op op) {
|
||||
return GGML_OP_NAME[op];
|
||||
}
|
||||
|
||||
size_t ggml_element_size(const struct ggml_tensor * tensor) {
|
||||
return GGML_TYPE_SIZE[tensor->type];
|
||||
|
|
@ -3805,6 +3810,10 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
|||
return wtype;
|
||||
}
|
||||
|
||||
size_t ggml_tensor_overhead(void) {
|
||||
return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE + 16;
|
||||
}
|
||||
|
||||
static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
return tensor->nb[0] > tensor->nb[1];
|
||||
}
|
||||
|
|
@ -4017,6 +4026,10 @@ size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch)
|
|||
return result;
|
||||
}
|
||||
|
||||
void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) {
|
||||
ctx->no_alloc = no_alloc;
|
||||
}
|
||||
|
||||
// IMPORTANT:
|
||||
// when creating "opt" tensors, always save and load the scratch buffer
|
||||
// this is an error prone process, but it is necessary to support inplace
|
||||
|
|
@ -4061,7 +4074,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
|
|||
struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);
|
||||
|
||||
if (ctx->scratch.data == NULL || data != NULL) {
|
||||
size_needed += sizeof(struct ggml_tensor);
|
||||
size_needed += GGML_TENSOR_SIZE;
|
||||
|
||||
if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
|
||||
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
|
||||
|
|
@ -4077,14 +4090,15 @@ struct ggml_tensor * ggml_new_tensor_impl(
|
|||
};
|
||||
} else {
|
||||
if (ctx->scratch.offs + size_needed > ctx->scratch.size) {
|
||||
GGML_PRINT("%s: not enough space in the scratch memory\n", __func__);
|
||||
GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
|
||||
__func__, ctx->scratch.offs + size_needed, ctx->scratch.size);
|
||||
assert(false);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
if (cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE > ctx->mem_size) {
|
||||
if (cur_end + GGML_TENSOR_SIZE + GGML_OBJECT_SIZE > ctx->mem_size) {
|
||||
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
|
||||
__func__, cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE, ctx->mem_size);
|
||||
__func__, cur_end + GGML_TENSOR_SIZE + GGML_OBJECT_SIZE, ctx->mem_size);
|
||||
assert(false);
|
||||
return NULL;
|
||||
}
|
||||
|
|
@ -4093,7 +4107,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
|
|||
|
||||
*obj_new = (struct ggml_object) {
|
||||
.offs = cur_end + GGML_OBJECT_SIZE,
|
||||
.size = sizeof(struct ggml_tensor),
|
||||
.size = GGML_TENSOR_SIZE,
|
||||
.next = NULL,
|
||||
};
|
||||
|
||||
|
|
@ -9431,7 +9445,7 @@ static void ggml_compute_forward_rms_norm_back(
|
|||
|
||||
// ggml_compute_forward_mul_mat
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
// helper function to determine if it is better to use BLAS or not
|
||||
// for large matrices, BLAS is faster
|
||||
static bool ggml_compute_forward_mul_mat_use_blas(
|
||||
|
|
@ -9472,7 +9486,7 @@ static void ggml_compute_forward_mul_mat_f32(
|
|||
const int64_t ne02 = src0->ne[2];
|
||||
const int64_t ne03 = src0->ne[3];
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
#endif
|
||||
const int64_t ne11 = src1->ne[1];
|
||||
|
|
@ -9536,9 +9550,16 @@ static void ggml_compute_forward_mul_mat_f32(
|
|||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
if (params->ith != 0) {
|
||||
return;
|
||||
|
|
@ -9558,21 +9579,11 @@ static void ggml_compute_forward_mul_mat_f32(
|
|||
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
// zT = y * xT
|
||||
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
|
||||
ne11, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
x, ne10,
|
||||
0.0f, d, ne01,
|
||||
GGML_TYPE_F32);
|
||||
#else
|
||||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
||||
ne11, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
//printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
|
||||
|
|
@ -9711,9 +9722,16 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
GGML_ASSERT(nb10 == sizeof(float));
|
||||
|
||||
|
|
@ -9743,20 +9761,6 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
assert(id*sizeof(float) <= params->wsize);
|
||||
}
|
||||
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
const float * x = wdata;
|
||||
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
||||
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
// zT = y * xT
|
||||
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
|
||||
ne11, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
x, ne10,
|
||||
0.0f, d, ne01,
|
||||
GGML_TYPE_F32);
|
||||
#else
|
||||
const float * x = wdata;
|
||||
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
||||
|
||||
|
|
@ -9768,7 +9772,6 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
1.0f, y, ne10,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -9931,9 +9934,16 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
if (params->ith != 0) {
|
||||
return;
|
||||
|
|
@ -9956,9 +9966,6 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
const void* x = (char *) src0->data + i03*nb03 + i02*nb02;
|
||||
#else
|
||||
{
|
||||
size_t id = 0;
|
||||
for (int64_t i01 = 0; i01 < ne01; ++i01) {
|
||||
|
|
@ -9970,23 +9977,12 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
}
|
||||
|
||||
const float * x = wdata;
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
// zT = y * xT
|
||||
ggml_cl_sgemm_wrapper(GGML_BLAS_ORDER_ROW_MAJOR, GGML_BLAS_OP_N, GGML_BLAS_OP_T,
|
||||
ne11, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
x, ne10,
|
||||
0.0f, d, ne01,
|
||||
type);
|
||||
#else
|
||||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
||||
ne11, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -13810,11 +13806,19 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor *
|
|||
// reached a leaf node, not part of the gradient graph (e.g. a constant)
|
||||
GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES);
|
||||
|
||||
if (strlen(node->name) == 0) {
|
||||
snprintf(node->name, sizeof(node->name), "leaf_%d", cgraph->n_leafs);
|
||||
}
|
||||
|
||||
cgraph->leafs[cgraph->n_leafs] = node;
|
||||
cgraph->n_leafs++;
|
||||
} else {
|
||||
GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES);
|
||||
|
||||
if (strlen(node->name) == 0) {
|
||||
snprintf(node->name, sizeof(node->name), "node_%d", cgraph->n_nodes);
|
||||
}
|
||||
|
||||
cgraph->nodes[cgraph->n_nodes] = node;
|
||||
cgraph->grads[cgraph->n_nodes] = node->grad;
|
||||
cgraph->n_nodes++;
|
||||
|
|
@ -14165,9 +14169,16 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
|||
cur = ggml_cuda_mul_mat_get_wsize(node->src0, node->src1, node);
|
||||
}
|
||||
else
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
cur = ggml_cl_mul_mat_get_wsize(node->src0, node->src1, node);
|
||||
}
|
||||
else
|
||||
#endif
|
||||
if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
|
|
@ -14181,13 +14192,13 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
|||
#endif
|
||||
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
|
||||
cur = 0;
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1;
|
||||
}
|
||||
#endif
|
||||
} else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) {
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1;
|
||||
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
|
||||
|
|
@ -14521,6 +14532,26 @@ void ggml_graph_reset(struct ggml_cgraph * cgraph) {
|
|||
}
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_get_tensor_by_name(struct ggml_cgraph * cgraph, const char * name) {
|
||||
for (int i = 0; i < cgraph->n_leafs; i++) {
|
||||
struct ggml_tensor * leaf = cgraph->leafs[i];
|
||||
|
||||
if (strcmp(leaf->name, name) == 0) {
|
||||
return leaf;
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < cgraph->n_nodes; i++) {
|
||||
struct ggml_tensor * node = cgraph->nodes[i];
|
||||
|
||||
if (strcmp(node->name, name) == 0) {
|
||||
return node;
|
||||
}
|
||||
}
|
||||
|
||||
return NULL;
|
||||
}
|
||||
|
||||
void ggml_graph_print(const struct ggml_cgraph * cgraph) {
|
||||
int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0};
|
||||
|
||||
|
|
@ -14538,7 +14569,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
|
|||
GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n",
|
||||
i,
|
||||
node->ne[0], node->ne[1], node->ne[2],
|
||||
GGML_OP_LABEL[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
|
||||
GGML_OP_NAME[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
|
||||
(double) node->perf_cycles / (double) ggml_cycles_per_ms(),
|
||||
(double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs,
|
||||
(double) node->perf_time_us / 1000.0,
|
||||
|
|
@ -14552,7 +14583,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
|
|||
GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s\n",
|
||||
i,
|
||||
node->ne[0], node->ne[1],
|
||||
GGML_OP_LABEL[node->op]);
|
||||
GGML_OP_NAME[node->op]);
|
||||
}
|
||||
|
||||
for (int i = 0; i < GGML_OP_COUNT; i++) {
|
||||
|
|
@ -14560,7 +14591,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
|
|||
continue;
|
||||
}
|
||||
|
||||
GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_LABEL[i], (double) perf_total_per_op_us[i] / 1000.0);
|
||||
GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_NAME[i], (double) perf_total_per_op_us[i] / 1000.0);
|
||||
}
|
||||
|
||||
GGML_PRINT("========================================\n");
|
||||
|
|
|
|||
13
ggml.h
13
ggml.h
|
|
@ -198,6 +198,7 @@
|
|||
#define GGML_MAX_PARAMS 256
|
||||
#define GGML_MAX_CONTEXTS 64
|
||||
#define GGML_MAX_OPT 4
|
||||
#define GGML_MAX_NAME 32
|
||||
#define GGML_DEFAULT_N_THREADS 4
|
||||
|
||||
#define GGML_ASSERT(x) \
|
||||
|
|
@ -249,6 +250,7 @@ extern "C" {
|
|||
enum ggml_backend {
|
||||
GGML_BACKEND_CPU = 0,
|
||||
GGML_BACKEND_CUDA = 1,
|
||||
GGML_BACKEND_CL = 2,
|
||||
};
|
||||
|
||||
// model file types
|
||||
|
|
@ -371,11 +373,13 @@ extern "C" {
|
|||
|
||||
void * data;
|
||||
|
||||
char name[32];
|
||||
char name[GGML_MAX_NAME];
|
||||
|
||||
char padding[16];
|
||||
};
|
||||
|
||||
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
|
||||
|
||||
// computation graph
|
||||
struct ggml_cgraph {
|
||||
int n_nodes;
|
||||
|
|
@ -428,6 +432,7 @@ extern "C" {
|
|||
GGML_API float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
|
||||
|
||||
GGML_API const char * ggml_type_name(enum ggml_type type);
|
||||
GGML_API const char * ggml_op_name (enum ggml_op op);
|
||||
|
||||
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||
|
||||
|
|
@ -436,6 +441,9 @@ extern "C" {
|
|||
// TODO: temporary until model loading of ggml examples is refactored
|
||||
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
|
||||
|
||||
// use this to compute the memory overhead of a tensor
|
||||
GGML_API size_t ggml_tensor_overhead(void);
|
||||
|
||||
// main
|
||||
|
||||
GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
|
||||
|
|
@ -444,6 +452,7 @@ extern "C" {
|
|||
GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
|
||||
GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_new_tensor(
|
||||
struct ggml_context * ctx,
|
||||
|
|
@ -969,6 +978,8 @@ extern "C" {
|
|||
GGML_API void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph);
|
||||
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_get_tensor_by_name(struct ggml_cgraph * cgraph, const char * name);
|
||||
|
||||
// print info and performance information for the graph
|
||||
GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);
|
||||
|
||||
|
|
|
|||
32
llama.cpp
32
llama.cpp
|
|
@ -12,6 +12,8 @@
|
|||
#include "ggml.h"
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#include "ggml-cuda.h"
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#include "ggml-opencl.h"
|
||||
#endif
|
||||
|
||||
#include <array>
|
||||
|
|
@ -1092,7 +1094,7 @@ static void llama_model_load_internal(
|
|||
fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__);
|
||||
}
|
||||
fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
|
||||
#else
|
||||
#elif !defined(GGML_USE_CLBLAST)
|
||||
(void) n_gpu_layers;
|
||||
#endif
|
||||
}
|
||||
|
|
@ -1125,7 +1127,33 @@ static void llama_model_load_internal(
|
|||
done_size += lt.size;
|
||||
}
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
{
|
||||
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
||||
|
||||
fprintf(stderr, "ggml_opencl: offloading %d layers to GPU\n", n_gpu);
|
||||
|
||||
size_t vram_total = 0;
|
||||
|
||||
for (int i = 0; i < n_gpu; ++i) {
|
||||
const auto & layer = model.layers[i];
|
||||
|
||||
ggml_cl_transform_tensor(layer.wq); vram_total += ggml_nbytes(layer.wq);
|
||||
ggml_cl_transform_tensor(layer.wk); vram_total += ggml_nbytes(layer.wk);
|
||||
ggml_cl_transform_tensor(layer.wv); vram_total += ggml_nbytes(layer.wv);
|
||||
ggml_cl_transform_tensor(layer.wo); vram_total += ggml_nbytes(layer.wo);
|
||||
ggml_cl_transform_tensor(layer.w1); vram_total += ggml_nbytes(layer.w1);
|
||||
ggml_cl_transform_tensor(layer.w2); vram_total += ggml_nbytes(layer.w2);
|
||||
ggml_cl_transform_tensor(layer.w3); vram_total += ggml_nbytes(layer.w3);
|
||||
}
|
||||
if (n_gpu_layers > (int) hparams.n_layer) {
|
||||
fprintf(stderr, "ggml_opencl: offloading output layer to GPU\n");
|
||||
ggml_cl_transform_tensor(model.output); vram_total += ggml_nbytes(model.output);
|
||||
}
|
||||
|
||||
fprintf(stderr, "ggml_opencl: total VRAM used: %zu MB\n", vram_total / 1024 / 1024);
|
||||
}
|
||||
#endif
|
||||
|
||||
if (progress_callback) {
|
||||
progress_callback(1.0f, progress_callback_user_data);
|
||||
|
|
|
|||
5
llama.h
5
llama.h
|
|
@ -31,6 +31,11 @@
|
|||
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
|
||||
#define LLAMA_SESSION_VERSION 1
|
||||
|
||||
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
||||
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
|
||||
#define LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
#endif
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue