# Copyright 2022-2024 Gentoo Authors # Distributed under the terms of the GNU General Public License v2 EAPI=8 PYTHON_COMPAT=( python3_{10..12} ) ROCM_VERSION=6.1 inherit python-single-r1 cmake cuda flag-o-matic prefix rocm toolchain-funcs MYPN=pytorch MYP=${MYPN}-${PV} DESCRIPTION="A deep learning framework" HOMEPAGE="https://pytorch.org/" SRC_URI="https://github.com/pytorch/${MYPN}/archive/refs/tags/v${PV}.tar.gz -> ${MYP}.tar.gz https://dev.gentoo.org/~tupone/distfiles/${PN}-patches-20240809.tar.gz" S="${WORKDIR}"/${MYP} LICENSE="BSD" SLOT="0" KEYWORDS="~amd64" IUSE="cuda distributed fbgemm flash gloo mkl mpi nnpack +numpy onednn openblas opencl openmp qnnpack rocm xnnpack" RESTRICT="test" REQUIRED_USE=" ${PYTHON_REQUIRED_USE} mpi? ( distributed ) gloo? ( distributed ) ?? ( cuda rocm ) rocm? ( || ( ${ROCM_REQUIRED_USE} ) !flash ) " # CUDA 12 not supported yet: https://github.com/pytorch/pytorch/issues/91122 RDEPEND=" ${PYTHON_DEPS} dev-cpp/gflags:= >=dev-cpp/glog-0.5.0 dev-libs/cpuinfo dev-libs/libfmt dev-cpp/opentelemetry-cpp dev-libs/protobuf:= dev-libs/pthreadpool dev-libs/sleef virtual/lapack sci-libs/onnx sci-libs/foxi cuda? ( dev-libs/cudnn >=dev-libs/cudnn-frontend-1.0.3:0/8 =dev-libs/FBGEMM-2023.12.01 ) gloo? ( sci-libs/gloo[cuda?] ) mpi? ( virtual/mpi ) nnpack? ( sci-libs/NNPACK ) numpy? ( $(python_gen_cond_dep ' dev-python/numpy[${PYTHON_USEDEP}] ') ) onednn? ( dev-libs/oneDNN ) opencl? ( virtual/opencl ) qnnpack? ( !sci-libs/QNNPACK dev-cpp/gemmlowp ) rocm? ( =dev-util/hip-6.1* =dev-libs/rccl-6.1*[${ROCM_USEDEP}] =sci-libs/rocThrust-6.1*[${ROCM_USEDEP}] =sci-libs/rocPRIM-6.1*[${ROCM_USEDEP}] =sci-libs/hipBLAS-6.1*[${ROCM_USEDEP}] =sci-libs/hipFFT-6.1*[${ROCM_USEDEP}] =sci-libs/hipSPARSE-6.1*[${ROCM_USEDEP}] =sci-libs/hipRAND-6.1*[${ROCM_USEDEP}] =sci-libs/hipCUB-6.1*[${ROCM_USEDEP}] =sci-libs/hipSOLVER-6.1*[${ROCM_USEDEP}] =sci-libs/miopen-6.1*[${ROCM_USEDEP}] =dev-util/roctracer-6.1*[${ROCM_USEDEP}] =sci-libs/hipBLASLt-6.1* amdgpu_targets_gfx90a? ( =sci-libs/hipBLASLt-6.1*[amdgpu_targets_gfx90a] ) amdgpu_targets_gfx940? ( =sci-libs/hipBLASLt-6.1*[amdgpu_targets_gfx940] ) amdgpu_targets_gfx941? ( =sci-libs/hipBLASLt-6.1*[amdgpu_targets_gfx941] ) amdgpu_targets_gfx942? ( =sci-libs/hipBLASLt-6.1*[amdgpu_targets_gfx942] ) ) distributed? ( sci-libs/tensorpipe[cuda?] ) xnnpack? ( >=sci-libs/XNNPACK-2024.02.29 ) mkl? ( sci-libs/mkl ) openblas? ( sci-libs/openblas ) " DEPEND=" ${RDEPEND} cuda? ( >=dev-libs/cutlass-3.4.1 ) onednn? ( sci-libs/ideep ) dev-libs/psimd dev-libs/FP16 dev-libs/FXdiv dev-libs/pocketfft dev-libs/flatbuffers >=sci-libs/kineto-0.4.0_p20240525 $(python_gen_cond_dep ' dev-python/pyyaml[${PYTHON_USEDEP}] dev-python/pybind11[${PYTHON_USEDEP}] dev-python/typing-extensions[${PYTHON_USEDEP}] ') " PATCHES=( ../patches/${PN}-2.4.0-gentoo.patch ../patches/${PN}-2.4.0-install-dirs.patch ../patches/${PN}-1.12.0-glog-0.6.0.patch ../patches/${PN}-1.13.1-tensorpipe.patch ../patches/${PN}-2.3.0-cudnn_include_fix.patch ../patches/${PN}-2.1.2-fix-rpath.patch ../patches/${PN}-2.4.0-fix-openmp-link.patch ../patches/${PN}-2.4.0-rocm-fix-std-cpp17.patch ../patches/${PN}-2.2.2-musl.patch ../patches/${PN}-2.4.0-exclude-aotriton.patch ../patches/${PN}-2.3.0-fix-rocm-gcc14-clamp.patch ../patches/${PN}-2.3.0-fix-libcpp.patch ) src_prepare() { filter-lto #bug 862672 sed -i \ -e "/third_party\/gloo/d" \ cmake/Dependencies.cmake \ || die cmake_src_prepare pushd torch/csrc/jit/serialization || die flatc --cpp --gen-mutable --scoped-enums mobile_bytecode.fbs || die popd # prefixify the hardcoded paths, after all patches are applied hprefixify \ aten/CMakeLists.txt \ caffe2/CMakeLists.txt \ cmake/Metal.cmake \ cmake/Modules/*.cmake \ cmake/Modules_CUDA_fix/FindCUDNN.cmake \ cmake/Modules_CUDA_fix/upstream/FindCUDA/make2cmake.cmake \ cmake/Modules_CUDA_fix/upstream/FindPackageHandleStandardArgs.cmake \ cmake/public/LoadHIP.cmake \ cmake/public/cuda.cmake \ cmake/Dependencies.cmake \ torch/CMakeLists.txt \ CMakeLists.txt if use rocm; then sed -e "s:/opt/rocm:/usr:" \ -e "s:lib/cmake:$(get_libdir)/cmake:g" \ -e "s/HIP 1.0/HIP 1.0 REQUIRED/" \ -i cmake/public/LoadHIP.cmake || die ebegin "HIPifying cuda sources" ${EPYTHON} tools/amd_build/build_amd.py || die eend $? fi } src_configure() { if use cuda && [[ -z ${TORCH_CUDA_ARCH_LIST} ]]; then ewarn "WARNING: caffe2 is being built with its default CUDA compute capabilities: 3.5 and 7.0." ewarn "These may not be optimal for your GPU." ewarn "" ewarn "To configure caffe2 with the CUDA compute capability that is optimal for your GPU," ewarn "set TORCH_CUDA_ARCH_LIST in your make.conf, and re-emerge caffe2." ewarn "For example, to use CUDA capability 7.5 & 3.5, add: TORCH_CUDA_ARCH_LIST=7.5 3.5" ewarn "For a Maxwell model GPU, an example value would be: TORCH_CUDA_ARCH_LIST=Maxwell" ewarn "" ewarn "You can look up your GPU's CUDA compute capability at https://developer.nvidia.com/cuda-gpus" ewarn "or by running /opt/cuda/extras/demo_suite/deviceQuery | grep 'CUDA Capability'" fi local mycmakeargs=( -DBUILD_CUSTOM_PROTOBUF=OFF -DBUILD_SHARED_LIBS=ON -DUSE_CCACHE=OFF -DUSE_CUDA=$(usex cuda) -DUSE_DISTRIBUTED=$(usex distributed) -DUSE_MPI=$(usex mpi) -DUSE_FAKELOWP=OFF -DUSE_FBGEMM=$(usex fbgemm) -DUSE_FLASH_ATTENTION=$(usex flash) -DUSE_MEM_EFF_ATTENTION=OFF -DUSE_GFLAGS=ON -DUSE_GLOG=ON -DUSE_GLOO=$(usex gloo) -DUSE_KINETO=OFF # TODO -DUSE_MAGMA=OFF # TODO: In GURU as sci-libs/magma -DUSE_MKLDNN=$(usex onednn) -DUSE_NNPACK=$(usex nnpack) -DUSE_XNNPACK=$(usex xnnpack) -DUSE_SYSTEM_XNNPACK=$(usex xnnpack) -DUSE_TENSORPIPE=$(usex distributed) -DUSE_PYTORCH_QNNPACK=$(usex qnnpack) -DUSE_NUMPY=$(usex numpy) -DUSE_OPENCL=$(usex opencl) -DUSE_OPENMP=$(usex openmp) -DUSE_ROCM=$(usex rocm) -DUSE_SYSTEM_CPUINFO=ON -DUSE_SYSTEM_PYBIND11=ON -DUSE_UCC=OFF -DUSE_VALGRIND=OFF -DPython_EXECUTABLE="${PYTHON}" -DUSE_ITT=OFF -DUSE_SYSTEM_PTHREADPOOL=ON -DUSE_SYSTEM_PSIMD=ON -DUSE_SYSTEM_FXDIV=ON -DUSE_SYSTEM_FP16=ON -DUSE_SYSTEM_GLOO=ON -DUSE_SYSTEM_ONNX=ON -DUSE_SYSTEM_SLEEF=ON -DUSE_PYTORCH_METAL=OFF -DUSE_XPU=OFF -Wno-dev -DTORCH_INSTALL_LIB_DIR="${EPREFIX}"/usr/$(get_libdir) -DLIBSHM_INSTALL_LIB_SUBDIR="${EPREFIX}"/usr/$(get_libdir) ) if use mkl; then mycmakeargs+=(-DBLAS=MKL) elif use openblas; then mycmakeargs+=(-DBLAS=OpenBLAS) else mycmakeargs+=(-DBLAS=Generic -DBLAS_LIBRARIES=) fi if use cuda; then addpredict "/dev/nvidiactl" # bug 867706 addpredict "/dev/char" addpredict "/proc/self/task" # bug 926116 mycmakeargs+=( -DUSE_CUDNN=ON -DTORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-3.5 7.0}" -DUSE_NCCL=OFF # TODO: NVIDIA Collective Communication Library -DCMAKE_CUDA_FLAGS="$(cuda_gccdir -f | tr -d \")" ) elif use rocm; then export PYTORCH_ROCM_ARCH="$(get_amdgpu_flags)" mycmakeargs+=( -DUSE_NCCL=ON -DUSE_SYSTEM_NCCL=ON ) # ROCm libraries produce too much warnings append-cxxflags -Wno-deprecated-declarations -Wno-unused-result if tc-is-clang; then # fix mangling in LLVM: https://github.com/llvm/llvm-project/issues/85656 append-cxxflags -fclang-abi-compat=17 fi fi if use onednn; then mycmakeargs+=( -DUSE_MKLDNN=ON -DMKLDNN_FOUND=ON -DMKLDNN_LIBRARIES=dnnl -DMKLDNN_INCLUDE_DIR="${ESYSROOT}/usr/include/oneapi/dnnl" ) fi cmake_src_configure # do not rerun cmake and the build process in src_install sed '/RERUN/,+1d' -i "${BUILD_DIR}"/build.ninja || die } src_install() { cmake_src_install insinto "/var/lib/${PN}" doins "${BUILD_DIR}"/CMakeCache.txt rm -rf python mkdir -p python/torch/include || die mv "${ED}"/usr/lib/python*/site-packages/caffe2 python/ || die cp torch/version.py python/torch/ || die python_domodule python/caffe2 python_domodule python/torch ln -s ../../../../../include/torch \ "${D}$(python_get_sitedir)"/torch/include/torch || die # bug 923269 }