nixpkgs/pkgs/development/python-modules/pytorch/default.nix
2019-10-23 10:57:43 -07:00

239 lines
9.5 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, fetchurl, fetchgit, buildPythonPackage, python, pythonOlder,
cudaSupport ? false, cudatoolkit ? null, cudnn ? null, nccl ? null, magma ? null,
mklSupport ? false, mkl ? null,
openMPISupport ? false, openmpi ? null,
buildNamedTensor ? false,
buildBinaries ? false,
cudaArchList ? null,
fetchFromGitHub, lib, numpy, pyyaml, cffi, click, typing, cmake, hypothesis, numactl,
linkFarm, symlinkJoin,
# ninja (https://ninja-build.org) must be available to run C++ extensions tests,
ninja,
# dependencies for torch.utils.tensorboard
tensorboardSupport ? true, pillow, six, future, tensorflow-tensorboard,
utillinux, which, isPy3k }:
assert !openMPISupport || openmpi != null;
assert !tensorboardSupport || tensorflow-tensorboard != null;
# assert that everything needed for cuda is present and that the correct cuda versions are used
assert !cudaSupport || cudatoolkit != null;
assert cudnn == null || cudatoolkit != null;
assert !cudaSupport || (let majorIs = lib.versions.major cudatoolkit.version;
in majorIs == "9" || majorIs == "10");
let
hasDependency = dep: pkg: lib.lists.any (inp: inp == dep) pkg.buildInputs;
matchesCudatoolkit = hasDependency cudatoolkit;
matchesMkl = hasDependency mkl;
in
# confirm that cudatoolkits are sync'd across dependencies
assert !(openMPISupport && cudaSupport) || matchesCudatoolkit openmpi;
assert !cudaSupport || matchesCudatoolkit magma;
# confirm that mkl is sync'd across dependencies
assert !mklSupport || mkl != null;
assert !(mklSupport && cudaSupport) || matchesMkl magma;
assert !mklSupport || (numpy.blasImplementation == "mkl" && numpy.blas == mkl);
let
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.
cuda9ArchList = [
"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.
];
cuda10ArchList = cuda9ArchList ++ [
"7.5"
"7.5+PTX" # < most recent architecture as of cudatoolkit_10_0 and pytorch-1.2.0
];
final_cudaArchList =
if !cudaSupport || cudaArchList != null
then cudaArchList
else
if lib.versions.major cudatoolkit.version == "9"
then cuda9ArchList
else cuda10ArchList; # the assert above removes any ambiguity here.
# 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 {
version = "1.2.0";
pname = "pytorch";
disabled = !isPy3k;
outputs = [
"out" # output standard python package
"dev" # output libtorch only
];
src = fetchFromGitHub {
owner = "pytorch";
repo = "pytorch";
rev = "v${version}";
fetchSubmodules = true;
sha256 = "1biyq2p48chakf2xw7hazzqmr5ps1nx475ql8vkmxjg5zaa071cz";
};
dontUseCmakeConfigure = true;
preConfigure = lib.optionalString cudaSupport ''
export TORCH_CUDA_ARCH_LIST="${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
'';
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 $f
done
'';
# Override the (weirdly) wrong version set by default. See
# https://github.com/NixOS/nixpkgs/pull/52437#issuecomment-449718038
# https://github.com/pytorch/pytorch/blob/v1.0.0/setup.py#L267
PYTORCH_BUILD_VERSION = version;
PYTORCH_BUILD_NUMBER = 0;
BUILD_NAMEDTENSOR = buildNamedTensor; # experimental feature
USE_SYSTEM_NCCL=true; # don't build pytorch's third_party NCCL
# Suppress a weird warning in mkl-dnn, part of ideep in pytorch
# (upstream seems to have fixed this in the wrong place?)
# https://github.com/intel/mkl-dnn/commit/8134d346cdb7fe1695a2aa55771071d455fae0bc
# https://github.com/pytorch/pytorch/issues/22346
#
# Also of interest: pytorch ignores CXXFLAGS uses CFLAGS for both C and C++:
# https://github.com/pytorch/pytorch/blob/v1.2.0/setup.py#L17
NIX_CFLAGS_COMPILE = lib.optionals (numpy.blas == mkl) [ "-Wno-error=array-bounds" ];
nativeBuildInputs = [
cmake
utillinux
which
ninja
] ++ lib.optionals cudaSupport [ cudatoolkit_joined ];
buildInputs = [
numpy.blas
] ++ lib.optionals cudaSupport [ cudnn magma nccl ]
++ lib.optionals stdenv.isLinux [ numactl ];
propagatedBuildInputs = [
cffi
click
numpy
pyyaml
] ++ lib.optionals openMPISupport [ openmpi ]
++ lib.optional (pythonOlder "3.5") typing
++ lib.optionals tensorboardSupport [pillow six future tensorflow-tensorboard];
checkInputs = [ hypothesis ninja ];
doCheck = false; # tests take a long time for channel release, so doCheck should be overridden only when developing
checkPhase = "${cudaStubEnv}python test/run_test.py"
+ " --exclude utils" # utils requires git, which is not allowed in the check phase
# Other tests which have been disabled in previous nix derivations of pytorch.
# --exclude dataloader sparse torch utils thd_distributed distributed cpp_extensions
;
postInstall = ''
mkdir $dev
cp -r $out/${python.sitePackages}/torch/lib $dev/lib
cp -r $out/${python.sitePackages}/torch/include $dev/include
'';
postFixup = stdenv.lib.optionalString stdenv.isDarwin ''
for f in $(ls $dev/lib/*.dylib); do
install_name_tool -id $dev/lib/$(basename $f) $f || true
done
install_name_tool -change @rpath/libshm.dylib $dev/lib/libshm.dylib $dev/lib/libtorch_python.dylib
install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libtorch_python.dylib
install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libtorch_python.dylib
install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libtorch.dylib
install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libcaffe2_observers.dylib
install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libcaffe2_observers.dylib
install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libcaffe2_module_test_dynamic.dylib
install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libcaffe2_module_test_dynamic.dylib
install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libcaffe2_detectron_ops.dylib
install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libcaffe2_detectron_ops.dylib
install_name_tool -change @rpath/libtorch.dylib $dev/lib/libtorch.dylib $dev/lib/libshm.dylib
install_name_tool -change @rpath/libc10.dylib $dev/lib/libc10.dylib $dev/lib/libshm.dylib
'';
meta = {
description = "Open source, prototype-to-production deep learning platform";
homepage = https://pytorch.org/;
license = lib.licenses.bsd3;
platforms = with lib.platforms; linux ++ lib.optionals (!cudaSupport) darwin;
maintainers = with lib.maintainers; [ teh thoughtpolice stites tscholak ]; # tscholak esp. for darwin-related builds
};
}