#!/bin/bash # This script is intended to run inside the go generate # working directory must be llm/generate/ # First we build our default built-in library which will be linked into the CGO # binary as a normal dependency. This default build is CPU based. # # Then we build a CUDA dynamic library (although statically linked with the CUDA # library dependencies for maximum portability) # # Then if we detect ROCm, we build a dynamically loaded ROCm lib. ROCm is particularly # important to be a dynamic lib even if it's the only GPU library detected because # we can't redistribute the objectfiles but must rely on dynamic libraries at # runtime, which could lead the server not to start if not present. set -ex set -o pipefail # See https://llvm.org/docs/AMDGPUUsage.html#processors for reference amdGPUs() { GPU_LIST=( "gfx803" "gfx900" "gfx906:xnack-" "gfx908:xnack-" "gfx90a:xnack+" "gfx90a:xnack-" "gfx1010" "gfx1012" "gfx1030" "gfx1100" "gfx1101" "gfx1102" ) ( IFS=$';' echo "'${GPU_LIST[*]}'" ) } echo "Starting linux generate script" if [ -z "${CUDACXX}" ]; then if [ -x /usr/local/cuda/bin/nvcc ]; then export CUDACXX=/usr/local/cuda/bin/nvcc else # Try the default location in case it exists export CUDACXX=$(command -v nvcc) fi fi COMMON_CMAKE_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off" source $(dirname $0)/gen_common.sh init_vars git_module_setup apply_patches if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then # Users building from source can tune the exact flags we pass to cmake for configuring # llama.cpp, and we'll build only 1 CPU variant in that case as the default. if [ -n "${OLLAMA_CUSTOM_CPU_DEFS}" ]; then echo "OLLAMA_CUSTOM_CPU_DEFS=\"${OLLAMA_CUSTOM_CPU_DEFS}\"" CMAKE_DEFS="${OLLAMA_CUSTOM_CPU_DEFS} -DCMAKE_POSITION_INDEPENDENT_CODE=on ${CMAKE_DEFS}" BUILD_DIR="${LLAMACPP_DIR}/build/linux/cpu" echo "Building custom CPU" build install link_server_lib else # Darwin Rosetta x86 emulation does NOT support AVX, AVX2, AVX512 # -DLLAMA_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer # -DLLAMA_F16C -- 2012 Intel Ivy Bridge & AMD 2011 Bulldozer (No significant improvement over just AVX) # -DLLAMA_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen # -DLLAMA_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver # Note: the following seem to yield slower results than AVX2 - ymmv # -DLLAMA_AVX512 -- 2017 Intel Skylake and High End DeskTop (HEDT) # -DLLAMA_AVX512_VBMI -- 2018 Intel Cannon Lake # -DLLAMA_AVX512_VNNI -- 2021 Intel Alder Lake COMMON_CPU_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off" # # CPU first for the default library, set up as lowest common denominator for maximum compatibility (including Rosetta) # CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}" BUILD_DIR="${LLAMACPP_DIR}/build/linux/cpu" echo "Building LCD CPU" build install link_server_lib # # ~2011 CPU Dynamic library with more capabilities turned on to optimize performance # Approximately 400% faster than LCD on same CPU # init_vars CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}" BUILD_DIR="${LLAMACPP_DIR}/build/linux/cpu_avx" echo "Building AVX CPU" build install link_server_lib # # ~2013 CPU Dynamic library # Approximately 10% faster than AVX on same CPU # init_vars CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on ${CMAKE_DEFS}" BUILD_DIR="${LLAMACPP_DIR}/build/linux/cpu_avx2" echo "Building AVX2 CPU" build install link_server_lib fi else echo "Skipping CPU generation step as requested" fi if [ -z "${CUDA_LIB_DIR}" ]; then # Try the default location in case it exists CUDA_LIB_DIR=/usr/local/cuda/lib64 fi if [ -d "${CUDA_LIB_DIR}" ]; then echo "CUDA libraries detected - building dynamic CUDA library" init_vars CUDA_MAJOR=$(ls "${CUDA_LIB_DIR}"/libcudart.so.* | head -1 | cut -f3 -d. || true) if [ -n "${CUDA_MAJOR}" ]; then CUDA_VARIANT=_v${CUDA_MAJOR} fi CMAKE_DEFS="-DLLAMA_CUBLAS=on ${COMMON_CMAKE_DEFS} ${CMAKE_DEFS}" BUILD_DIR="${LLAMACPP_DIR}/build/linux/cuda${CUDA_VARIANT}" build install gcc -fPIC -g -shared -o ${BUILD_DIR}/lib/libext_server.so \ -Wl,--whole-archive \ ${BUILD_DIR}/lib/libext_server.a \ ${BUILD_DIR}/lib/libcommon.a \ ${BUILD_DIR}/lib/libllama.a \ -Wl,--no-whole-archive \ ${CUDA_LIB_DIR}/libcudart_static.a \ ${CUDA_LIB_DIR}/libcublas_static.a \ ${CUDA_LIB_DIR}/libcublasLt_static.a \ ${CUDA_LIB_DIR}/libcudadevrt.a \ ${CUDA_LIB_DIR}/libculibos.a \ -lrt -lpthread -ldl -lstdc++ -lm fi if [ -z "${ROCM_PATH}" ]; then # Try the default location in case it exists ROCM_PATH=/opt/rocm fi if [ -z "${CLBlast_DIR}" ]; then # Try the default location in case it exists if [ -d /usr/lib/cmake/CLBlast ]; then export CLBlast_DIR=/usr/lib/cmake/CLBlast fi fi if [ -d "${ROCM_PATH}" ]; then echo "ROCm libraries detected - building dynamic ROCm library" if [ -f ${ROCM_PATH}/lib/librocm_smi64.so.? ]; then ROCM_VARIANT=_v$(ls ${ROCM_PATH}/lib/librocm_smi64.so.? | cut -f3 -d. || true) fi init_vars CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DLLAMA_HIPBLAS=on -DCMAKE_C_COMPILER=$ROCM_PATH/llvm/bin/clang -DCMAKE_CXX_COMPILER=$ROCM_PATH/llvm/bin/clang++ -DAMDGPU_TARGETS=$(amdGPUs) -DGPU_TARGETS=$(amdGPUs)" BUILD_DIR="${LLAMACPP_DIR}/build/linux/rocm${ROCM_VARIANT}" build install gcc -fPIC -g -shared -o ${BUILD_DIR}/lib/libext_server.so \ -Wl,--whole-archive \ ${BUILD_DIR}/lib/libext_server.a \ ${BUILD_DIR}/lib/libcommon.a \ ${BUILD_DIR}/lib/libllama.a \ -Wl,--no-whole-archive \ -lrt -lpthread -ldl -lstdc++ -lm \ -L/opt/rocm/lib -L/opt/amdgpu/lib/x86_64-linux-gnu/ \ -Wl,-rpath,/opt/rocm/lib,-rpath,/opt/amdgpu/lib/x86_64-linux-gnu/ \ -lhipblas -lrocblas -lamdhip64 -lrocsolver -lamd_comgr -lhsa-runtime64 -lrocsparse -ldrm -ldrm_amdgpu fi cleanup