115 lines
3 KiB
Nix
115 lines
3 KiB
Nix
{ lib, stdenv
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, buildPythonPackage
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, fetchurl
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, python
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, pythonAtLeast
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, pythonOlder
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, addOpenGLRunpath
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, cudaPackages
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, future
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, numpy
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, autoPatchelfHook
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, pyyaml
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, requests
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, setuptools
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, typing-extensions
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, sympy
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, jinja2
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, networkx
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, filelock
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, openai-triton
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}:
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let
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pyVerNoDot = builtins.replaceStrings [ "." ] [ "" ] python.pythonVersion;
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srcs = import ./binary-hashes.nix version;
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unsupported = throw "Unsupported system";
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version = "2.2.1";
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in buildPythonPackage {
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inherit version;
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pname = "torch";
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# Don't forget to update torch to the same version.
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format = "wheel";
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disabled = (pythonOlder "3.8") || (pythonAtLeast "3.12");
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src = fetchurl srcs."${stdenv.system}-${pyVerNoDot}" or unsupported;
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nativeBuildInputs = lib.optionals stdenv.isLinux [
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addOpenGLRunpath
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autoPatchelfHook
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cudaPackages.autoAddOpenGLRunpathHook
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];
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buildInputs = lib.optionals stdenv.isLinux (with cudaPackages; [
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# $out/${sitePackages}/nvfuser/_C*.so wants libnvToolsExt.so.1 but torch/lib only ships
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# libnvToolsExt-$hash.so.1
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cuda_nvtx
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cuda_cudart
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cuda_cupti
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cuda_nvrtc
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cudnn
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libcublas
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libcufft
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libcurand
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libcusolver
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libcusparse
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nccl
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]);
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autoPatchelfIgnoreMissingDeps = lib.optionals stdenv.isLinux [
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# This is the hardware-dependent userspace driver that comes from
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# nvidia_x11 package. It must be deployed at runtime in
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# /run/opengl-driver/lib or pointed at by LD_LIBRARY_PATH variable, rather
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# than pinned in runpath
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"libcuda.so.1"
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];
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propagatedBuildInputs = [
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future
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numpy
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pyyaml
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requests
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setuptools
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typing-extensions
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sympy
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jinja2
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networkx
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filelock
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] ++ lib.optionals (stdenv.isLinux && stdenv.isx86_64) [
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openai-triton
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];
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postInstall = ''
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# ONNX conversion
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rm -rf $out/bin
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'';
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postFixup = lib.optionalString stdenv.isLinux ''
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addAutoPatchelfSearchPath "$out/${python.sitePackages}/torch/lib"
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'';
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# The wheel-binary is not stripped to avoid the error of `ImportError: libtorch_cuda_cpp.so: ELF load command address/offset not properly aligned.`.
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dontStrip = true;
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pythonImportsCheck = [ "torch" ];
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meta = with lib; {
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description = "PyTorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration";
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homepage = "https://pytorch.org/";
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changelog = "https://github.com/pytorch/pytorch/releases/tag/v${version}";
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# Includes CUDA and Intel MKL, but redistributions of the binary are not limited.
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# https://docs.nvidia.com/cuda/eula/index.html
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# https://www.intel.com/content/www/us/en/developer/articles/license/onemkl-license-faq.html
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# torch's license is BSD3.
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# torch-bin used to vendor CUDA. It still links against CUDA and MKL.
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license = with licenses; [ bsd3 issl unfreeRedistributable ];
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sourceProvenance = with sourceTypes; [ binaryNativeCode ];
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platforms = [ "aarch64-darwin" "aarch64-linux" "x86_64-darwin" "x86_64-linux" ];
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hydraPlatforms = []; # output size 3.2G on 1.11.0
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maintainers = with maintainers; [ junjihashimoto ];
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};
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}
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