318 lines
12 KiB
Nix
318 lines
12 KiB
Nix
{ 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 don’t 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;
|
||
};
|
||
}
|