nixpkgs/pkgs/development/python-modules/pytorch/default.nix
Malo Bourgon 206a9575b8
python3Packages.pytorch: unbreak on Darwin (#177812)
* Add missing `buildInputs`.
* Enable Grand Central Dispatch support for `aarch64-darwin`.
* Remove `postFixup` steps for .dylib files that aren't present.
2022-06-17 10:34:54 -04:00

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{ stdenv, lib, fetchFromGitHub, fetchpatch, buildPythonPackage, python,
cudaSupport ? false, cudaPackages, magma,
mklDnnSupport ? true, useSystemNccl ? true,
MPISupport ? false, mpi,
buildDocs ? false,
cudaArchList ? null,
# Native build inputs
cmake, util-linux, linkFarm, symlinkJoin, which, pybind11, removeReferencesTo,
# Build inputs
numactl,
CoreServices, libobjc,
# Propagated build inputs
numpy, pyyaml, cffi, click, typing-extensions,
# Unit tests
hypothesis, psutil,
# virtual pkg that consistently instantiates blas across nixpkgs
# See https://github.com/NixOS/nixpkgs/pull/83888
blas,
# ninja (https://ninja-build.org) must be available to run C++ extensions tests,
ninja,
# dependencies for torch.utils.tensorboard
pillow, six, future, tensorboard, protobuf,
isPy3k, pythonOlder }:
let
inherit (cudaPackages) cudatoolkit cudnn nccl;
in
# assert that everything needed for cuda is present and that the correct cuda versions are used
assert !cudaSupport || (let majorIs = lib.versions.major cudatoolkit.version;
in majorIs == "9" || majorIs == "10" || majorIs == "11");
# confirm that cudatoolkits are sync'd across dependencies
assert !(MPISupport && cudaSupport) || mpi.cudatoolkit == cudatoolkit;
assert !cudaSupport || magma.cudatoolkit == cudatoolkit;
let
setBool = v: if v then "1" else "0";
cudatoolkit_joined = symlinkJoin {
name = "${cudatoolkit.name}-unsplit";
# nccl is here purely for semantic grouping it could be moved to nativeBuildInputs
paths = [ cudatoolkit.out cudatoolkit.lib nccl.dev nccl.out ];
};
# Give an explicit list of supported architectures for the build, See:
# - pytorch bug report: https://github.com/pytorch/pytorch/issues/23573
# - pytorch-1.2.0 build on nixpks: https://github.com/NixOS/nixpkgs/pull/65041
#
# This list was selected by omitting the TORCH_CUDA_ARCH_LIST parameter,
# observing the fallback option (which selected all architectures known
# from cudatoolkit_10_0, pytorch-1.2, and python-3.6), and doing a binary
# searching to find offending architectures.
#
# NOTE: Because of sandboxing, this derivation can't auto-detect the hardware's
# cuda architecture, so there is also now a problem around new architectures
# not being supported until explicitly added to this derivation.
#
# FIXME: CMake is throwing the following warning on python-1.2:
#
# ```
# CMake Warning at cmake/public/utils.cmake:172 (message):
# In the future we will require one to explicitly pass TORCH_CUDA_ARCH_LIST
# to cmake instead of implicitly setting it as an env variable. This will
# become a FATAL_ERROR in future version of pytorch.
# ```
# If this is causing problems for your build, this derivation may have to strip
# away the standard `buildPythonPackage` and use the
# [*Adjust Build Options*](https://github.com/pytorch/pytorch/tree/v1.2.0#adjust-build-options-optional)
# instructions. This will also add more flexibility around configurations
# (allowing FBGEMM to be built in pytorch-1.1), and may future proof this
# derivation.
brokenArchs = [ "3.0" ]; # this variable is only used as documentation.
cudaCapabilities = rec {
cuda9 = [
"3.5"
"5.0"
"5.2"
"6.0"
"6.1"
"7.0"
"7.0+PTX" # I am getting a "undefined architecture compute_75" on cuda 9
# which leads me to believe this is the final cuda-9-compatible architecture.
];
cuda10 = cuda9 ++ [
"7.5"
"7.5+PTX" # < most recent architecture as of cudatoolkit_10_0 and pytorch-1.2.0
];
cuda11 = cuda10 ++ [
"8.0"
"8.0+PTX" # < CUDA toolkit 11.0
"8.6"
"8.6+PTX" # < CUDA toolkit 11.1
];
};
final_cudaArchList =
if !cudaSupport || cudaArchList != null
then cudaArchList
else cudaCapabilities."cuda${lib.versions.major cudatoolkit.version}";
# Normally libcuda.so.1 is provided at runtime by nvidia-x11 via
# LD_LIBRARY_PATH=/run/opengl-driver/lib. We only use the stub
# libcuda.so from cudatoolkit for running tests, so that we dont have
# to recompile pytorch on every update to nvidia-x11 or the kernel.
cudaStub = linkFarm "cuda-stub" [{
name = "libcuda.so.1";
path = "${cudatoolkit}/lib/stubs/libcuda.so";
}];
cudaStubEnv = lib.optionalString cudaSupport
"LD_LIBRARY_PATH=${cudaStub}\${LD_LIBRARY_PATH:+:}$LD_LIBRARY_PATH ";
in buildPythonPackage rec {
pname = "pytorch";
# Don't forget to update pytorch-bin to the same version.
version = "1.11.0";
format = "setuptools";
disabled = pythonOlder "3.7.0";
outputs = [
"out" # output standard python package
"dev" # output libtorch headers
"lib" # output libtorch libraries
];
src = fetchFromGitHub {
owner = "pytorch";
repo = "pytorch";
rev = "v${version}";
fetchSubmodules = true;
sha256 = "sha256-CEu63tdRBAF8CTchO3Qu8gUNObQylX6U08yDTI4/c/0=";
};
patches = [
# Fix for a breakpad incompatibility with glibc>2.33
# https://github.com/pytorch/pytorch/issues/70297
# https://github.com/google/breakpad/commit/605c51ed96ad44b34c457bbca320e74e194c317e
./breakpad-sigstksz.patch
] ++ lib.optionals (stdenv.isDarwin && stdenv.isx86_64) [
# pthreadpool added support for Grand Central Dispatch in April
# 2020. However, this relies on functionality (DISPATCH_APPLY_AUTO)
# that is available starting with macOS 10.13. However, our current
# base is 10.12. Until we upgrade, we can fall back on the older
# pthread support.
./pthreadpool-disable-gcd.diff
];
preConfigure = lib.optionalString cudaSupport ''
export TORCH_CUDA_ARCH_LIST="${lib.strings.concatStringsSep ";" final_cudaArchList}"
export CC=${cudatoolkit.cc}/bin/gcc CXX=${cudatoolkit.cc}/bin/g++
'' + lib.optionalString (cudaSupport && cudnn != null) ''
export CUDNN_INCLUDE_DIR=${cudnn}/include
'';
# Use pytorch's custom configurations
dontUseCmakeConfigure = true;
BUILD_NAMEDTENSOR = setBool true;
BUILD_DOCS = setBool buildDocs;
# We only do an imports check, so do not build tests either.
BUILD_TEST = setBool false;
# Unlike MKL, oneDNN (née MKLDNN) is FOSS, so we enable support for
# it by default. PyTorch currently uses its own vendored version
# of oneDNN through Intel iDeep.
USE_MKLDNN = setBool mklDnnSupport;
USE_MKLDNN_CBLAS = setBool mklDnnSupport;
# Avoid using pybind11 from git submodule
# Also avoids pytorch exporting the headers of pybind11
USE_SYSTEM_BIND11 = true;
preBuild = ''
export MAX_JOBS=$NIX_BUILD_CORES
${python.interpreter} setup.py build --cmake-only
${cmake}/bin/cmake build
'';
preFixup = ''
function join_by { local IFS="$1"; shift; echo "$*"; }
function strip2 {
IFS=':'
read -ra RP <<< $(patchelf --print-rpath $1)
IFS=' '
RP_NEW=$(join_by : ''${RP[@]:2})
patchelf --set-rpath \$ORIGIN:''${RP_NEW} "$1"
}
for f in $(find ''${out} -name 'libcaffe2*.so')
do
strip2 $f
done
'';
# Override the (weirdly) wrong version set by default. See
# https://github.com/NixOS/nixpkgs/pull/52437#issuecomment-449718038
# https://github.com/pytorch/pytorch/blob/v1.0.0/setup.py#L267
PYTORCH_BUILD_VERSION = version;
PYTORCH_BUILD_NUMBER = 0;
USE_SYSTEM_NCCL=setBool useSystemNccl; # don't build pytorch's third_party NCCL
# Suppress a weird warning in mkl-dnn, part of ideep in pytorch
# (upstream seems to have fixed this in the wrong place?)
# https://github.com/intel/mkl-dnn/commit/8134d346cdb7fe1695a2aa55771071d455fae0bc
# https://github.com/pytorch/pytorch/issues/22346
#
# Also of interest: pytorch ignores CXXFLAGS uses CFLAGS for both C and C++:
# https://github.com/pytorch/pytorch/blob/v1.11.0/setup.py#L17
NIX_CFLAGS_COMPILE = lib.optionals (blas.implementation == "mkl") [ "-Wno-error=array-bounds" ];
nativeBuildInputs = [
cmake
util-linux
which
ninja
pybind11
removeReferencesTo
] ++ lib.optionals cudaSupport [ cudatoolkit_joined ];
buildInputs = [ blas blas.provider pybind11 ]
++ lib.optionals cudaSupport [ cudnn magma nccl ]
++ lib.optionals stdenv.isLinux [ numactl ]
++ lib.optionals stdenv.isDarwin [ CoreServices libobjc ];
propagatedBuildInputs = [
cffi
click
numpy
pyyaml
typing-extensions
# the following are required for tensorboard support
pillow six future tensorboard protobuf
] ++ lib.optionals MPISupport [ mpi ];
checkInputs = [ hypothesis ninja psutil ];
# Tests take a long time and may be flaky, so just sanity-check imports
doCheck = false;
pythonImportsCheck = [
"torch"
];
checkPhase = with lib.versions; with lib.strings; concatStringsSep " " [
cudaStubEnv
"${python.interpreter} test/run_test.py"
"--exclude"
(concatStringsSep " " [
"utils" # utils requires git, which is not allowed in the check phase
# "dataloader" # psutils correctly finds and triggers multiprocessing, but is too sandboxed to run -- resulting in numerous errors
# ^^^^^^^^^^^^ NOTE: while test_dataloader does return errors, these are acceptable errors and do not interfere with the build
# tensorboard has acceptable failures for pytorch 1.3.x due to dependencies on tensorboard-plugins
(optionalString (majorMinor version == "1.3" ) "tensorboard")
])
];
postInstall = ''
find "$out/${python.sitePackages}/torch/include" "$out/${python.sitePackages}/torch/lib" -type f -exec remove-references-to -t ${stdenv.cc} '{}' +
mkdir $dev
cp -r $out/${python.sitePackages}/torch/include $dev/include
cp -r $out/${python.sitePackages}/torch/share $dev/share
# Fix up library paths for split outputs
substituteInPlace \
$dev/share/cmake/Torch/TorchConfig.cmake \
--replace \''${TORCH_INSTALL_PREFIX}/lib "$lib/lib"
substituteInPlace \
$dev/share/cmake/Caffe2/Caffe2Targets-release.cmake \
--replace \''${_IMPORT_PREFIX}/lib "$lib/lib"
mkdir $lib
mv $out/${python.sitePackages}/torch/lib $lib/lib
ln -s $lib/lib $out/${python.sitePackages}/torch/lib
'';
postFixup = lib.optionalString stdenv.isDarwin ''
for f in $(ls $lib/lib/*.dylib); do
install_name_tool -id $lib/lib/$(basename $f) $f || true
done
install_name_tool -change @rpath/libshm.dylib $lib/lib/libshm.dylib $lib/lib/libtorch_python.dylib
install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libtorch_python.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libtorch_python.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libtorch.dylib
install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libshm.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libshm.dylib
'';
# Builds in 2+h with 2 cores, and ~15m with a big-parallel builder.
requiredSystemFeatures = [ "big-parallel" ];
passthru = {
inherit cudaSupport cudaPackages;
cudaArchList = final_cudaArchList;
# At least for 1.10.2 `torch.fft` is unavailable unless BLAS provider is MKL. This attribute allows for easy detection of its availability.
blasProvider = blas.provider;
};
meta = with lib; {
description = "Open source, prototype-to-production deep learning platform";
homepage = "https://pytorch.org/";
license = licenses.bsd3;
maintainers = with maintainers; [ teh thoughtpolice tscholak ]; # tscholak esp. for darwin-related builds
platforms = with platforms; linux ++ lib.optionals (!cudaSupport) darwin;
broken = stdenv.isLinux && stdenv.isAarch64;
};
}