forked from mirrors/nixpkgs
bc7b404b1a
The library name fixup was attempted on the dev output, whereas it should be applied to the lib output.
290 lines
11 KiB
Nix
290 lines
11 KiB
Nix
{ stdenv, lib, fetchFromGitHub, fetchpatch, buildPythonPackage, python,
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cudaSupport ? false, cudatoolkit ? null, cudnn ? null, nccl ? null, magma ? null,
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mklDnnSupport ? true, useSystemNccl ? true,
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openMPISupport ? false, openmpi ? null,
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buildDocs ? false,
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cudaArchList ? null,
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numpy, pyyaml, cffi, click, typing, cmake, oneDNN, hypothesis, numactl, psutil,
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linkFarm, symlinkJoin,
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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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utillinux, which, isPy3k }:
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assert !openMPISupport || openmpi != null;
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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 || cudatoolkit != null;
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assert cudnn == null || cudatoolkit != null;
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assert !cudaSupport || (let majorIs = lib.versions.major cudatoolkit.version;
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in majorIs == "9" || majorIs == "10");
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let
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hasDependency = dep: pkg: lib.lists.any (inp: inp == dep) pkg.buildInputs;
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matchesCudatoolkit = hasDependency cudatoolkit;
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in
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# confirm that cudatoolkits are sync'd across dependencies
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assert !(openMPISupport && cudaSupport) || matchesCudatoolkit openmpi;
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assert !cudaSupport || matchesCudatoolkit magma;
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let
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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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cuda9ArchList = [
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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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cuda10ArchList = cuda9ArchList ++ [
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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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final_cudaArchList =
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if !cudaSupport || cudaArchList != null
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then cudaArchList
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else
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if lib.versions.major cudatoolkit.version == "9"
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then cuda9ArchList
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else cuda10ArchList; # the assert above removes any ambiguity here.
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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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version = "1.6.0";
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pname = "pytorch";
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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 = "14hhjsi6fnpaw9m1a3bhvdinsks6fhss6bbcrfk6jgns64abqdaz";
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};
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patches = lib.optionals stdenv.isAarch64 [
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# GNU aarch64 assembler does not support 4s on neon mov:
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# https://github.com/pytorch/pytorch/issues/33124
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#
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# Fix from:
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# https://github.com/pytorch/pytorch/pull/40584
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#
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# This patch can be removed with the next major version (1.7.0).
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(fetchpatch {
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name = "qnnpack-neon-fix.patch";
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url = "https://github.com/pytorch/pytorch/commit/7676682584d0caf9243bce74ea0a88711ec4a807.diff";
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sha256 = "13spncaqlpsp8qk2850yly7xqwmhhfwznhmzkk8jgpslkbx75vgq";
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})
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] ++ 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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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=${cudnn}/include
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'';
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# Use pytorch's custom configurations
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dontUseCmakeConfigure = true;
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BUILD_NAMEDTENSOR = true;
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BUILD_DOCS = buildDocs;
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USE_MKL = blas.implementation == "mkl";
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# Unlike MKL, MKLDNN is FOSS, so we enable support for it by default. Note
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# that this was renamed to dnnl and then renamed again to oneDNN upstream, but
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# pytorch still calls it by the old name mkldnn.
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USE_MKLDNN = mklDnnSupport;
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USE_MKLDNN_CBLAS = mklDnnSupport;
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preBuild = ''
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export MAX_JOBS=$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=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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utillinux
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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 oneDNN ]
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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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# the following are required for tensorboard support
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pillow six future tensorflow-tensorboard protobuf
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] ++ lib.optionals openMPISupport [ openmpi ];
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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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mkdir $lib
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cp -r $out/${python.sitePackages}/torch/lib $lib/lib
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'';
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postFixup = stdenv.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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meta = {
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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 = lib.licenses.bsd3;
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platforms = with lib.platforms; linux ++ lib.optionals (!cudaSupport) darwin;
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maintainers = with lib.maintainers; [ teh thoughtpolice tscholak ]; # tscholak esp. for darwin-related builds
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};
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}
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