{ 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 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 { 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 }; }