forked from mirrors/nixpkgs
0d4abe5d4b
This compiles in usually about 2h15m with a 2-core build, but about 10m on a big-parallel machine.
313 lines
12 KiB
Nix
313 lines
12 KiB
Nix
{ stdenv, lib, fetchFromGitHub, fetchpatch, buildPythonPackage, python,
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cudaSupport ? false, cudatoolkit, cudnn, nccl, magma,
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mklDnnSupport ? true, useSystemNccl ? true,
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MPISupport ? false, mpi,
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buildDocs ? false,
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cudaArchList ? null,
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# Native build inputs
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cmake, util-linux, linkFarm, symlinkJoin, which,
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# Build inputs
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numactl,
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# Propagated build inputs
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dataclasses, numpy, pyyaml, cffi, click, typing-extensions,
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# Unit tests
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hypothesis, psutil,
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# virtual pkg that consistently instantiates blas across nixpkgs
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# See https://github.com/NixOS/nixpkgs/pull/83888
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blas,
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# ninja (https://ninja-build.org) must be available to run C++ extensions tests,
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ninja,
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# dependencies for torch.utils.tensorboard
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pillow, six, future, tensorflow-tensorboard, protobuf,
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isPy3k, pythonOlder }:
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# assert that everything needed for cuda is present and that the correct cuda versions are used
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assert !cudaSupport || (let majorIs = lib.versions.major cudatoolkit.version;
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in majorIs == "9" || majorIs == "10" || majorIs == "11");
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# confirm that cudatoolkits are sync'd across dependencies
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assert !(MPISupport && cudaSupport) || mpi.cudatoolkit == cudatoolkit;
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assert !cudaSupport || magma.cudatoolkit == cudatoolkit;
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let
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setBool = v: if v then "1" else "0";
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cudatoolkit_joined = symlinkJoin {
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name = "${cudatoolkit.name}-unsplit";
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# nccl is here purely for semantic grouping it could be moved to nativeBuildInputs
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paths = [ cudatoolkit.out cudatoolkit.lib nccl.dev nccl.out ];
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};
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# Give an explicit list of supported architectures for the build, See:
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# - pytorch bug report: https://github.com/pytorch/pytorch/issues/23573
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# - pytorch-1.2.0 build on nixpks: https://github.com/NixOS/nixpkgs/pull/65041
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#
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# This list was selected by omitting the TORCH_CUDA_ARCH_LIST parameter,
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# observing the fallback option (which selected all architectures known
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# from cudatoolkit_10_0, pytorch-1.2, and python-3.6), and doing a binary
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# searching to find offending architectures.
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#
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# NOTE: Because of sandboxing, this derivation can't auto-detect the hardware's
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# cuda architecture, so there is also now a problem around new architectures
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# not being supported until explicitly added to this derivation.
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#
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# FIXME: CMake is throwing the following warning on python-1.2:
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#
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# ```
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# CMake Warning at cmake/public/utils.cmake:172 (message):
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# In the future we will require one to explicitly pass TORCH_CUDA_ARCH_LIST
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# to cmake instead of implicitly setting it as an env variable. This will
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# become a FATAL_ERROR in future version of pytorch.
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# ```
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# If this is causing problems for your build, this derivation may have to strip
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# away the standard `buildPythonPackage` and use the
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# [*Adjust Build Options*](https://github.com/pytorch/pytorch/tree/v1.2.0#adjust-build-options-optional)
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# instructions. This will also add more flexibility around configurations
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# (allowing FBGEMM to be built in pytorch-1.1), and may future proof this
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# derivation.
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brokenArchs = [ "3.0" ]; # this variable is only used as documentation.
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cudaCapabilities = rec {
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cuda9 = [
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"3.5"
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"5.0"
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"5.2"
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"6.0"
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"6.1"
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"7.0"
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"7.0+PTX" # I am getting a "undefined architecture compute_75" on cuda 9
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# which leads me to believe this is the final cuda-9-compatible architecture.
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];
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cuda10 = cuda9 ++ [
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"7.5"
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"7.5+PTX" # < most recent architecture as of cudatoolkit_10_0 and pytorch-1.2.0
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];
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cuda11 = cuda10 ++ [
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"8.0"
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"8.0+PTX" # < CUDA toolkit 11.0
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"8.6"
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"8.6+PTX" # < CUDA toolkit 11.1
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];
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};
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final_cudaArchList =
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if !cudaSupport || cudaArchList != null
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then cudaArchList
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else cudaCapabilities."cuda${lib.versions.major cudatoolkit.version}";
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# Normally libcuda.so.1 is provided at runtime by nvidia-x11 via
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# LD_LIBRARY_PATH=/run/opengl-driver/lib. We only use the stub
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# libcuda.so from cudatoolkit for running tests, so that we don’t have
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# to recompile pytorch on every update to nvidia-x11 or the kernel.
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cudaStub = linkFarm "cuda-stub" [{
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name = "libcuda.so.1";
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path = "${cudatoolkit}/lib/stubs/libcuda.so";
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}];
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cudaStubEnv = lib.optionalString cudaSupport
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"LD_LIBRARY_PATH=${cudaStub}\${LD_LIBRARY_PATH:+:}$LD_LIBRARY_PATH ";
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in buildPythonPackage rec {
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pname = "pytorch";
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# Don't forget to update pytorch-bin to the same version.
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version = "1.8.1";
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disabled = !isPy3k;
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outputs = [
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"out" # output standard python package
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"dev" # output libtorch headers
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"lib" # output libtorch libraries
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];
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src = fetchFromGitHub {
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owner = "pytorch";
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repo = "pytorch";
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rev = "v${version}";
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fetchSubmodules = true;
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sha256 = "sha256-HERbvmrfhWwH164GFHU/M0KbhVAuhI5sBZSxCZy8mRk=";
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};
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patches = lib.optionals stdenv.isDarwin [
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# pthreadpool added support for Grand Central Dispatch in April
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# 2020. However, this relies on functionality (DISPATCH_APPLY_AUTO)
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# that is available starting with macOS 10.13. However, our current
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# base is 10.12. Until we upgrade, we can fall back on the older
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# pthread support.
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./pthreadpool-disable-gcd.diff
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];
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# The dataclasses module is included with Python >= 3.7. This should
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# be fixed with the next PyTorch release.
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postPatch = ''
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substituteInPlace setup.py \
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--replace "'dataclasses'" "'dataclasses; python_version < \"3.7\"'"
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'';
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preConfigure = lib.optionalString cudaSupport ''
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export TORCH_CUDA_ARCH_LIST="${lib.strings.concatStringsSep ";" final_cudaArchList}"
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export CC=${cudatoolkit.cc}/bin/gcc CXX=${cudatoolkit.cc}/bin/g++
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'' + lib.optionalString (cudaSupport && cudnn != null) ''
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export CUDNN_INCLUDE_DIR=${cudnn}/include
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'';
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# Use pytorch's custom configurations
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dontUseCmakeConfigure = true;
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BUILD_NAMEDTENSOR = setBool true;
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BUILD_DOCS = setBool buildDocs;
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# We only do an imports check, so do not build tests either.
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BUILD_TEST = setBool false;
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# Unlike MKL, oneDNN (née MKLDNN) is FOSS, so we enable support for
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# it by default. PyTorch currently uses its own vendored version
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# of oneDNN through Intel iDeep.
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USE_MKLDNN = setBool mklDnnSupport;
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USE_MKLDNN_CBLAS = setBool mklDnnSupport;
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preBuild = ''
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export MAX_JOBS=$NIX_BUILD_CORES
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${python.interpreter} setup.py build --cmake-only
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${cmake}/bin/cmake build
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'';
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preFixup = ''
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function join_by { local IFS="$1"; shift; echo "$*"; }
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function strip2 {
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IFS=':'
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read -ra RP <<< $(patchelf --print-rpath $1)
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IFS=' '
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RP_NEW=$(join_by : ''${RP[@]:2})
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patchelf --set-rpath \$ORIGIN:''${RP_NEW} "$1"
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}
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for f in $(find ''${out} -name 'libcaffe2*.so')
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do
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strip2 $f
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done
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'';
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# Override the (weirdly) wrong version set by default. See
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# https://github.com/NixOS/nixpkgs/pull/52437#issuecomment-449718038
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# https://github.com/pytorch/pytorch/blob/v1.0.0/setup.py#L267
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PYTORCH_BUILD_VERSION = version;
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PYTORCH_BUILD_NUMBER = 0;
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USE_SYSTEM_NCCL=setBool useSystemNccl; # don't build pytorch's third_party NCCL
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# Suppress a weird warning in mkl-dnn, part of ideep in pytorch
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# (upstream seems to have fixed this in the wrong place?)
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# https://github.com/intel/mkl-dnn/commit/8134d346cdb7fe1695a2aa55771071d455fae0bc
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# https://github.com/pytorch/pytorch/issues/22346
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#
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# Also of interest: pytorch ignores CXXFLAGS uses CFLAGS for both C and C++:
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# https://github.com/pytorch/pytorch/blob/v1.2.0/setup.py#L17
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NIX_CFLAGS_COMPILE = lib.optionals (blas.implementation == "mkl") [ "-Wno-error=array-bounds" ];
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nativeBuildInputs = [
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cmake
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util-linux
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which
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ninja
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] ++ lib.optionals cudaSupport [ cudatoolkit_joined ];
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buildInputs = [ blas blas.provider ]
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++ lib.optionals cudaSupport [ cudnn magma nccl ]
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++ lib.optionals stdenv.isLinux [ numactl ];
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propagatedBuildInputs = [
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cffi
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click
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numpy
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pyyaml
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typing-extensions
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# the following are required for tensorboard support
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pillow six future tensorflow-tensorboard protobuf
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] ++ lib.optionals MPISupport [ mpi ]
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++ lib.optionals (pythonOlder "3.7") [ dataclasses ];
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checkInputs = [ hypothesis ninja psutil ];
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# Tests take a long time and may be flaky, so just sanity-check imports
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doCheck = false;
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pythonImportsCheck = [
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"torch"
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];
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checkPhase = with lib.versions; with lib.strings; concatStringsSep " " [
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cudaStubEnv
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"${python.interpreter} test/run_test.py"
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"--exclude"
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(concatStringsSep " " [
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"utils" # utils requires git, which is not allowed in the check phase
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# "dataloader" # psutils correctly finds and triggers multiprocessing, but is too sandboxed to run -- resulting in numerous errors
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# ^^^^^^^^^^^^ NOTE: while test_dataloader does return errors, these are acceptable errors and do not interfere with the build
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# tensorboard has acceptable failures for pytorch 1.3.x due to dependencies on tensorboard-plugins
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(optionalString (majorMinor version == "1.3" ) "tensorboard")
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])
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];
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postInstall = ''
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mkdir $dev
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cp -r $out/${python.sitePackages}/torch/include $dev/include
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cp -r $out/${python.sitePackages}/torch/share $dev/share
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# Fix up library paths for split outputs
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substituteInPlace \
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$dev/share/cmake/Torch/TorchConfig.cmake \
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--replace \''${TORCH_INSTALL_PREFIX}/lib "$lib/lib"
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substituteInPlace \
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$dev/share/cmake/Caffe2/Caffe2Targets-release.cmake \
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--replace \''${_IMPORT_PREFIX}/lib "$lib/lib"
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mkdir $lib
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cp -r $out/${python.sitePackages}/torch/lib $lib/lib
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'';
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postFixup = lib.optionalString stdenv.isDarwin ''
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for f in $(ls $lib/lib/*.dylib); do
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install_name_tool -id $lib/lib/$(basename $f) $f || true
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done
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install_name_tool -change @rpath/libshm.dylib $lib/lib/libshm.dylib $lib/lib/libtorch_python.dylib
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install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libtorch_python.dylib
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install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libtorch_python.dylib
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install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libtorch.dylib
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install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libcaffe2_observers.dylib
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install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libcaffe2_observers.dylib
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install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libcaffe2_module_test_dynamic.dylib
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install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libcaffe2_module_test_dynamic.dylib
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install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libcaffe2_detectron_ops.dylib
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install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libcaffe2_detectron_ops.dylib
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install_name_tool -change @rpath/libtorch.dylib $lib/lib/libtorch.dylib $lib/lib/libshm.dylib
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install_name_tool -change @rpath/libc10.dylib $lib/lib/libc10.dylib $lib/lib/libshm.dylib
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'';
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# Builds in 2+h with 2 cores, and ~15m with a big-parallel builder.
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requiredSystemFeatures = [ "big-parallel" ];
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meta = with lib; {
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description = "Open source, prototype-to-production deep learning platform";
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homepage = "https://pytorch.org/";
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license = licenses.bsd3;
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platforms = with platforms; linux ++ lib.optionals (!cudaSupport) darwin;
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maintainers = with maintainers; [ danieldk teh thoughtpolice tscholak ]; # tscholak esp. for darwin-related builds
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# error: use of undeclared identifier 'noU'; did you mean 'no'?
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broken = stdenv.isDarwin;
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};
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}
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