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# 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-util/nvidia-cuda-toolkit-12.4.0:=[profiler]
)
fbgemm? ( >=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?]
dev-cpp/cpp-httplib
)
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
"${FILESDIR}"/${PN}-2.4.0-libfmt-11.patch
"${FILESDIR}"/${P}-cpp-httplib.patch
"${FILESDIR}"/${P}-cstdint.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
}
|
