nixpkgs/pkgs/development/python-modules/pytorch/default.nix
Alexander Kiselyov 717538e908
python3Packages.torchvision: added cudaSupport option (#132917)
Co-authored-by: Sandro <sandro.jaeckel@gmail.com>
2021-08-08 19:42:58 +02:00

319 lines
12 KiB
Nix
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

{ stdenv, lib, fetchFromGitHub, fetchpatch, buildPythonPackage, python,
cudaSupport ? false, cudatoolkit, cudnn, nccl, magma,
mklDnnSupport ? true, useSystemNccl ? true,
MPISupport ? false, mpi,
buildDocs ? false,
cudaArchList ? null,
# Native build inputs
cmake, util-linux, linkFarm, symlinkJoin, which, pybind11,
# Build inputs
numactl,
# Propagated build inputs
dataclasses, 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, tensorflow-tensorboard, protobuf,
isPy3k, pythonOlder }:
# 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:+:}$LD_LIBRARY_PATH ";
in buildPythonPackage rec {
pname = "pytorch";
# Don't forget to update pytorch-bin to the same version.
version = "1.9.0";
disabled = !isPy3k;
outputs = [
"out" # output standard python package
"dev" # output libtorch headers
"lib" # output libtorch libraries
];
src = fetchFromGitHub {
owner = "pytorch";
repo = "pytorch";
rev = "v${version}";
fetchSubmodules = true;
sha256 = "sha256-gZmEhV1zzfr/5T2uNfS+8knzyJIxnv2COWVyiAzU9jM=";
};
patches = lib.optionals stdenv.isDarwin [
# 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
];
# The dataclasses module is included with Python >= 3.7. This should
# be fixed with the next PyTorch release.
postPatch = ''
substituteInPlace setup.py \
--replace "'dataclasses'" "'dataclasses; python_version < \"3.7\"'"
'';
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;
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 $f
done
'';
# Override the (weirdly) wrong version set by default. See
# https://github.com/NixOS/nixpkgs/pull/52437#issuecomment-449718038
# https://github.com/pytorch/pytorch/blob/v1.0.0/setup.py#L267
PYTORCH_BUILD_VERSION = version;
PYTORCH_BUILD_NUMBER = 0;
USE_SYSTEM_NCCL=setBool useSystemNccl; # don't build pytorch's third_party NCCL
# Suppress a weird warning in mkl-dnn, part of ideep in pytorch
# (upstream seems to have fixed this in the wrong place?)
# https://github.com/intel/mkl-dnn/commit/8134d346cdb7fe1695a2aa55771071d455fae0bc
# https://github.com/pytorch/pytorch/issues/22346
#
# Also of interest: pytorch ignores CXXFLAGS uses CFLAGS for both C and C++:
# https://github.com/pytorch/pytorch/blob/v1.2.0/setup.py#L17
NIX_CFLAGS_COMPILE = lib.optionals (blas.implementation == "mkl") [ "-Wno-error=array-bounds" ];
nativeBuildInputs = [
cmake
util-linux
which
ninja
pybind11
] ++ lib.optionals cudaSupport [ cudatoolkit_joined ];
buildInputs = [ blas blas.provider ]
++ lib.optionals cudaSupport [ cudnn magma nccl ]
++ lib.optionals stdenv.isLinux [ numactl ];
propagatedBuildInputs = [
cffi
click
numpy
pyyaml
typing-extensions
# the following are required for tensorboard support
pillow six future tensorflow-tensorboard protobuf
] ++ lib.optionals MPISupport [ mpi ]
++ lib.optionals (pythonOlder "3.7") [ dataclasses ];
checkInputs = [ hypothesis ninja psutil ];
# Tests take a long time and may be flaky, so just sanity-check imports
doCheck = false;
pythonImportsCheck = [
"torch"
];
checkPhase = with lib.versions; with lib.strings; concatStringsSep " " [
cudaStubEnv
"${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 = ''
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
cp -r $out/${python.sitePackages}/torch/lib $lib/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/libcaffe2_observers.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libcaffe2_observers.dylib
install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libcaffe2_module_test_dynamic.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libcaffe2_module_test_dynamic.dylib
install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libcaffe2_detectron_ops.dylib
install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libcaffe2_detectron_ops.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;
cudaArchList = final_cudaArchList;
};
meta = with lib; {
description = "Open source, prototype-to-production deep learning platform";
homepage = "https://pytorch.org/";
license = licenses.bsd3;
platforms = with platforms; linux ++ lib.optionals (!cudaSupport) darwin;
maintainers = with maintainers; [ danieldk teh thoughtpolice tscholak ]; # tscholak esp. for darwin-related builds
# error: use of undeclared identifier 'noU'; did you mean 'no'?
broken = stdenv.isDarwin;
};
}