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Revert "feature : add blis and other BLAS implementation support (#1502)"
This reverts commit 07e9ace0f9.
This commit is contained in:
Georgi Gerganov
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07e9ace0f9
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67
BLIS.md
67
BLIS.md
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BLIS Installation Manual
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------------------------
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BLIS is a portable software framework for high-performance BLAS-like dense linear algebra libraries. It has received awards and recognition, including the 2023 James H. Wilkinson Prize for Numerical Software and the 2020 SIAM Activity Group on Supercomputing Best Paper Prize. BLIS provides a new BLAS-like API and a compatibility layer for traditional BLAS routine calls. It offers features such as object-based API, typed API, BLAS and CBLAS compatibility layers.
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Project URL: https://github.com/flame/blis
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### Prepare:
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Compile BLIS:
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```bash
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git clone https://github.com/flame/blis
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cd blis
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./configure --enable-cblas -t openmp,pthreads auto
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# will install to /usr/local/ by default.
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make -j
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```
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Install BLIS:
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```bash
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sudo make install
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```
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We recommend using openmp since it's easier to modify the cores been used.
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### llama.cpp compilation
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Makefile:
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```bash
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make LLAMA_BLIS=1 -j
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# make LLAMA_BLIS=1 benchmark-matmult
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```
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CMake:
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```bash
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mkdir build
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cd build
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cmake -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=FLAME ..
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make -j
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```
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### llama.cpp execution
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According to the BLIS documentation, we could set the following
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environment variables to modify the behavior of openmp:
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```
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export GOMP_GPU_AFFINITY="0-19"
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export BLIS_NUM_THREADS=14
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```
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And then run the binaries as normal.
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### Intel specific issue
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Some might get the error message saying that `libimf.so` cannot be found.
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Please follow this [stackoverflow page](https://stackoverflow.com/questions/70687930/intel-oneapi-2022-libimf-so-no-such-file-or-directory-during-openmpi-compila).
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### Reference:
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1. https://github.com/flame/blis#getting-started
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2. https://github.com/flame/blis/blob/master/docs/Multithreading.md
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@ -65,8 +65,7 @@ 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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option(LLAMA_OPENBLAS "llama: use OpenBLAS" OFF)
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option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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@ -146,28 +145,36 @@ if (APPLE AND LLAMA_ACCELERATE)
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endif()
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endif()
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if (LLAMA_BLAS)
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if (LLAMA_OPENBLAS)
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if (LLAMA_STATIC)
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set(BLA_STATIC ON)
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endif()
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if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22)
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set(BLA_SIZEOF_INTEGER 8)
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endif()
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set(BLA_VENDOR ${LLAMA_BLAS_VENDOR})
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set(BLA_VENDOR OpenBLAS)
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find_package(BLAS)
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if (BLAS_FOUND)
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message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}")
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message(STATUS "OpenBLAS found")
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add_compile_options(${BLAS_LINKER_FLAGS})
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add_compile_definitions(GGML_USE_OPENBLAS)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES})
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add_link_options(${BLAS_LIBRARIES})
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} openblas)
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message("${BLAS_LIBRARIES}")
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include_directories(${BLAS_INCLUDE_DIRS})
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# find header file
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set(OPENBLAS_INCLUDE_SEARCH_PATHS
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/usr/include
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/usr/include/openblas
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/usr/include/openblas-base
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/usr/local/include
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/usr/local/include/openblas
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/usr/local/include/openblas-base
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/opt/OpenBLAS/include
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$ENV{OpenBLAS_HOME}
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$ENV{OpenBLAS_HOME}/include
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)
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find_path(OPENBLAS_INC NAMES cblas.h PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS})
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add_compile_options(-I${OPENBLAS_INC})
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else()
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message(WARNING "BLAS not found, please refer to "
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"https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
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" to set correct LLAMA_BLAS_VENDOR")
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message(WARNING "OpenBLAS not found")
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endif()
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endif()
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4
Makefile
4
Makefile
@ -122,10 +122,6 @@ ifdef LLAMA_OPENBLAS
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LDFLAGS += -lopenblas
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endif
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endif
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ifdef LLAMA_BLIS
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CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis
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LDFLAGS += -lblis -L/usr/local/lib
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endif
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ifdef LLAMA_CUBLAS
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CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
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CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
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19
README.md
19
README.md
@ -56,7 +56,7 @@ The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quant
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- Mixed F16 / F32 precision
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- 4-bit, 5-bit and 8-bit integer quantization support
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- Runs on the CPU
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- Supports OpenBLAS/Apple BLAS/ARM Performance Lib/ATLAS/BLIS/Intel MKL/NVHPC/ACML/SCSL/SGIMATH and [more](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) in BLAS
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- OpenBLAS support
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- cuBLAS and CLBlast support
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The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022).
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@ -274,25 +274,10 @@ Building the program with BLAS support may lead to some performance improvements
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```bash
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mkdir build
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cd build
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cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
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cmake .. -DLLAMA_OPENBLAS=ON
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cmake --build . --config Release
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```
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- BLIS
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Check [BLIS.md](BLIS.md) for more information.
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- Intel MKL
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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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```bash
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mkdir build
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cd build
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cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
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cmake --build . -config Release
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```
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- cuBLAS
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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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