Merge branch 'ggerganov:master' into master
This commit is contained in:
Randall Fitzgerald
GitHub
commit
48cb16a51a
11 changed files with 318 additions and 111 deletions
28
.github/workflows/build.yml
vendored
28
.github/workflows/build.yml
vendored
|
|
@ -10,10 +10,10 @@ on:
|
|||
push:
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||||
branches:
|
||||
- master
|
||||
paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.c', '**/*.cpp']
|
||||
paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp']
|
||||
pull_request:
|
||||
types: [opened, synchronize, reopened]
|
||||
paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.c', '**/*.cpp']
|
||||
paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp']
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||||
|
||||
env:
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||||
BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
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||||
|
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@ -151,21 +151,21 @@ jobs:
|
|||
env:
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||||
OPENBLAS_VERSION: 0.3.23
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OPENCL_VERSION: 2023.04.17
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CLBLAST_VERSION: 1.5.3
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CLBLAST_VERSION: 1.6.0
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||||
|
||||
strategy:
|
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matrix:
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include:
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- build: 'avx2'
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defines: ''
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defines: '-DLLAMA_BUILD_SERVER=ON'
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- build: 'avx'
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defines: '-DLLAMA_AVX2=OFF'
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defines: '-DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF'
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- build: 'avx512'
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defines: '-DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'
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defines: '-DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'
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- build: 'clblast'
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defines: '-DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH="$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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||||
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steps:
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- name: Clone
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||||
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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
|
||||
foreach ($f in (gci -Recurse -Path "$env:RUNNER_TEMP/clblast" -Filter '*.cmake')) {
|
||||
$txt = Get-Content -Path $f -Raw
|
||||
$txt.Replace('C:/dependencies/opencl/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8
|
||||
$txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', "$($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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||||
|
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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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|
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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:
|
|||
id: pack_artifacts
|
||||
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
|
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run: |
|
||||
Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt
|
||||
7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\*
|
||||
|
||||
- name: Upload artifacts
|
||||
|
|
@ -292,7 +292,7 @@ jobs:
|
|||
run: |
|
||||
mkdir build
|
||||
cd build
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cmake .. -DLLAMA_CUBLAS=ON
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||||
cmake .. -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON
|
||||
cmake --build . --config Release
|
||||
|
||||
- name: Get commit hash
|
||||
|
|
|
|||
|
|
@ -37,42 +37,44 @@ endif()
|
|||
#
|
||||
|
||||
# general
|
||||
option(LLAMA_STATIC "llama: static link libraries" OFF)
|
||||
option(LLAMA_NATIVE "llama: enable -march=native flag" OFF)
|
||||
option(LLAMA_LTO "llama: enable link time optimization" OFF)
|
||||
option(LLAMA_STATIC "llama: static link libraries" OFF)
|
||||
option(LLAMA_NATIVE "llama: enable -march=native flag" OFF)
|
||||
option(LLAMA_LTO "llama: enable link time optimization" OFF)
|
||||
|
||||
# debug
|
||||
option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" ON)
|
||||
option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
|
||||
option(LLAMA_GPROF "llama: enable gprof" OFF)
|
||||
option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" ON)
|
||||
option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
|
||||
option(LLAMA_GPROF "llama: enable gprof" OFF)
|
||||
|
||||
# sanitizers
|
||||
option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF)
|
||||
option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF)
|
||||
option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
|
||||
option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF)
|
||||
option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF)
|
||||
option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
|
||||
|
||||
# instruction set specific
|
||||
option(LLAMA_AVX "llama: enable AVX" ON)
|
||||
option(LLAMA_AVX2 "llama: enable AVX2" ON)
|
||||
option(LLAMA_AVX512 "llama: enable AVX512" OFF)
|
||||
option(LLAMA_AVX512_VBMI "llama: enable AVX512-VBMI" OFF)
|
||||
option(LLAMA_AVX512_VNNI "llama: enable AVX512-VNNI" OFF)
|
||||
option(LLAMA_FMA "llama: enable FMA" ON)
|
||||
option(LLAMA_AVX "llama: enable AVX" ON)
|
||||
option(LLAMA_AVX2 "llama: enable AVX2" ON)
|
||||
option(LLAMA_AVX512 "llama: enable AVX512" OFF)
|
||||
option(LLAMA_AVX512_VBMI "llama: enable AVX512-VBMI" OFF)
|
||||
option(LLAMA_AVX512_VNNI "llama: enable AVX512-VNNI" OFF)
|
||||
option(LLAMA_FMA "llama: enable FMA" ON)
|
||||
# in MSVC F16C is implied with AVX2/AVX512
|
||||
if (NOT MSVC)
|
||||
option(LLAMA_F16C "llama: enable F16C" ON)
|
||||
option(LLAMA_F16C "llama: enable F16C" ON)
|
||||
endif()
|
||||
|
||||
# 3rd party libs
|
||||
option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
|
||||
option(LLAMA_BLAS "llama: use BLAS" OFF)
|
||||
option(LLAMA_BLAS_VENDOR "llama: BLA_VENDOR from https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" Generic)
|
||||
option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
|
||||
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
|
||||
option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
|
||||
option(LLAMA_BLAS "llama: use BLAS" OFF)
|
||||
set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
|
||||
option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
|
||||
set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
|
||||
set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
|
||||
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
|
||||
|
||||
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_SERVER "llama: build server example" OFF)
|
||||
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_SERVER "llama: build server example" OFF)
|
||||
|
||||
#
|
||||
# Build info header
|
||||
|
|
@ -184,6 +186,8 @@ if (LLAMA_CUBLAS)
|
|||
set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
|
||||
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||||
add_compile_definitions(GGML_USE_CUBLAS)
|
||||
add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
|
||||
add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
|
||||
|
||||
if (LLAMA_STATIC)
|
||||
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
|
||||
|
|
|
|||
25
Makefile
25
Makefile
|
|
@ -1,5 +1,11 @@
|
|||
# Define the default target now so that it is always the first target
|
||||
default: main quantize quantize-stats perplexity embedding vdot
|
||||
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot
|
||||
|
||||
ifdef LLAMA_BUILD_SERVER
|
||||
BUILD_TARGETS += server
|
||||
endif
|
||||
|
||||
default: $(BUILD_TARGETS)
|
||||
|
||||
ifndef UNAME_S
|
||||
UNAME_S := $(shell uname -s)
|
||||
|
|
@ -133,9 +139,19 @@ ifdef LLAMA_CUBLAS
|
|||
OBJS += ggml-cuda.o
|
||||
NVCC = nvcc
|
||||
NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
|
||||
ifdef LLAMA_CUDA_DMMV_X
|
||||
NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
|
||||
else
|
||||
NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
|
||||
endif # LLAMA_CUDA_DMMV_X
|
||||
ifdef LLAMA_CUDA_DMMV_Y
|
||||
NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y)
|
||||
else
|
||||
NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
|
||||
endif # LLAMA_CUDA_DMMV_Y
|
||||
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
|
||||
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
|
||||
endif
|
||||
endif # LLAMA_CUBLAS
|
||||
ifdef LLAMA_CLBLAST
|
||||
CFLAGS += -DGGML_USE_CLBLAST
|
||||
CXXFLAGS += -DGGML_USE_CLBLAST
|
||||
|
|
@ -200,7 +216,7 @@ libllama.so: llama.o ggml.o $(OBJS)
|
|||
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
|
||||
|
||||
clean:
|
||||
rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state build-info.h
|
||||
rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot build-info.h
|
||||
|
||||
#
|
||||
# Examples
|
||||
|
|
@ -227,6 +243,9 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml.o llama.o common.o
|
|||
save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
|
||||
|
||||
build-info.h: $(wildcard .git/index) scripts/build-info.sh
|
||||
@sh scripts/build-info.sh > $@.tmp
|
||||
@if ! cmp -s $@.tmp $@; then \
|
||||
|
|
|
|||
104
README.md
104
README.md
|
|
@ -240,11 +240,11 @@ In order to build llama.cpp you have three different options.
|
|||
|
||||
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:
|
||||
|
||||
- Accelerate Framework:
|
||||
- **Accelerate Framework**:
|
||||
|
||||
This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
|
||||
|
||||
- OpenBLAS:
|
||||
- **OpenBLAS**:
|
||||
|
||||
This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
|
||||
|
||||
|
|
@ -278,11 +278,11 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
- BLIS
|
||||
- **BLIS**
|
||||
|
||||
Check [BLIS.md](BLIS.md) for more information.
|
||||
|
||||
- Intel MKL
|
||||
- **Intel MKL**
|
||||
|
||||
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:
|
||||
|
||||
|
|
@ -293,7 +293,7 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
cmake --build . -config Release
|
||||
```
|
||||
|
||||
- cuBLAS
|
||||
- **cuBLAS**
|
||||
|
||||
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).
|
||||
- Using `make`:
|
||||
|
|
@ -308,8 +308,81 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
cmake .. -DLLAMA_CUBLAS=ON
|
||||
cmake --build . --config Release
|
||||
```
|
||||
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.
|
||||
|
||||
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.
|
||||
- **CLBlast**
|
||||
|
||||
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.
|
||||
|
||||
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>
|
||||
|
||||
```sh
|
||||
git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
|
||||
mkdir OpenCL-SDK/build
|
||||
cd OpenCL-SDK/build
|
||||
cmake .. -DBUILD_DOCS=OFF \
|
||||
-DBUILD_EXAMPLES=OFF \
|
||||
-DBUILD_TESTING=OFF \
|
||||
-DOPENCL_SDK_BUILD_SAMPLES=OFF \
|
||||
-DOPENCL_SDK_TEST_SAMPLES=OFF
|
||||
cmake --build . --config Release
|
||||
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
|
||||
git clone https://github.com/CNugteren/CLBlast.git
|
||||
mkdir CLBlast/build
|
||||
cd CLBLast/build
|
||||
cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
|
||||
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>
|
||||
|
||||
Building:
|
||||
|
||||
- Build with make:
|
||||
```sh
|
||||
make LLAMA_CLBLAST=1
|
||||
```
|
||||
- CMake:
|
||||
```sh
|
||||
mkdir build
|
||||
cd build
|
||||
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_dir=/some/path
|
||||
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
|
||||
|
||||
# Start a different chat with the same prompt/model
|
||||
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh
|
||||
|
||||
# Different prompt cache for different prompt/model
|
||||
PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
|
||||
CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh
|
||||
```
|
||||
|
||||
### Instruction mode with Alpaca
|
||||
|
||||
1. First, download the `ggml` Alpaca model into the `./models` folder
|
||||
|
|
|
|||
|
|
@ -23,8 +23,8 @@ CUR_PROMPT_CACHE="${CHAT_SAVE_DIR}/current-cache.bin"
|
|||
NEXT_PROMPT_FILE="${CHAT_SAVE_DIR}/next-prompt.txt"
|
||||
NEXT_PROMPT_CACHE="${CHAT_SAVE_DIR}/next-cache.bin"
|
||||
|
||||
SESSION_SIZE_MSG_PATTERN='main: session file matches \d+ / \d+'
|
||||
SAMPLE_TIME_MSG_PATTERN='sample time =\s+\d+.\d+ ms /\s+\d+'
|
||||
SESSION_SIZE_MSG_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+'
|
||||
SAMPLE_TIME_MSG_PATTERN='sample time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+'
|
||||
SED_DELETE_MESSAGES="/^(${USER_NAME}:|${AI_NAME}:|\\.\\.\\.)/,\$d"
|
||||
|
||||
CTX_SIZE=2048
|
||||
|
|
|
|||
|
|
@ -272,7 +272,7 @@ These options help improve the performance and memory usage of the LLaMA models.
|
|||
|
||||
### 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
|
||||
|
||||
|
|
|
|||
|
|
@ -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",
|
||||
|
|
|
|||
|
|
@ -82,7 +82,7 @@ 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])
|
||||
{
|
||||
|
|
@ -92,7 +92,7 @@ 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.
|
||||
|
|
@ -157,7 +157,7 @@ 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;
|
||||
|
|
@ -327,12 +327,12 @@ 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;
|
||||
|
|
@ -686,7 +686,7 @@ 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)
|
||||
|
|
@ -734,7 +734,7 @@ int main(int argc, char **argv)
|
|||
{"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 = {
|
||||
|
|
@ -786,7 +786,7 @@ 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", "" },
|
||||
|
|
|
|||
110
ggml-cuda.cu
110
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
|
||||
|
||||
// dequantize
|
||||
float v0, v1;
|
||||
dequantize_kernel(vx, ib, iqs, v0, v1);
|
||||
// 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
|
||||
|
||||
// matrix multiplication
|
||||
tmp[tid] += v0 * y[iybs + iqs + 0];
|
||||
tmp[tid] += v1 * y[iybs + iqs + y_offset];
|
||||
// dequantize
|
||||
float 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 += 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) {
|
||||
|
|
|
|||
58
ggml.c
58
ggml.c
|
|
@ -3494,7 +3494,7 @@ 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 +3749,9 @@ 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 +3808,10 @@ 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 +4024,10 @@ 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 +4072,7 @@ 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 +4088,15 @@ 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 +4105,7 @@ 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,
|
||||
};
|
||||
|
||||
|
|
@ -13792,11 +13804,19 @@ 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++;
|
||||
|
|
@ -14510,6 +14530,26 @@ 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};
|
||||
|
||||
|
|
@ -14527,7 +14567,7 @@ 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,
|
||||
|
|
@ -14541,7 +14581,7 @@ 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++) {
|
||||
|
|
@ -14549,7 +14589,7 @@ 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");
|
||||
|
|
|
|||
12
ggml.h
12
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) \
|
||||
|
|
@ -372,11 +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;
|
||||
|
|
@ -429,6 +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);
|
||||
|
||||
|
|
@ -437,6 +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);
|
||||
|
|
@ -445,6 +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,
|
||||
|
|
@ -970,6 +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);
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue