149 lines
3.8 KiB
Nix
149 lines
3.8 KiB
Nix
{
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lib,
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stdenv,
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buildPythonPackage,
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autoAddDriverRunpath,
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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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callPackage,
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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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torch-bin,
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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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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 = "2.3.1";
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in
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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 "3.13");
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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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autoAddDriverRunpath
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];
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buildInputs = lib.optionals stdenv.isLinux (
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with cudaPackages;
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[
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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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);
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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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dependencies = [
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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) [ triton ];
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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/${python.sitePackages}/torch/lib"
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'';
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# See https://github.com/NixOS/nixpkgs/issues/296179
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#
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# This is a quick hack to add `libnvrtc` to the runpath so that torch can find
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# it when it is needed at runtime.
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extraRunpaths = lib.optionals stdenv.hostPlatform.isLinux [ "${lib.getLib cudaPackages.cuda_nvrtc}/lib" ];
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postPhases = lib.optionals stdenv.isLinux [ "postPatchelfPhase" ];
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postPatchelfPhase = ''
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while IFS= read -r -d $'\0' elf ; do
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for extra in $extraRunpaths ; do
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echo patchelf "$elf" --add-rpath "$extra" >&2
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patchelf "$elf" --add-rpath "$extra"
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done
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done < <(
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find "''${!outputLib}" "$out" -type f -iname '*.so' -print0
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)
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'';
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# The wheel-binary is not stripped to avoid the error of `ImportError: libtorch_cuda_cpp.so: ELF load command address/offset not properly aligned.`.
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dontStrip = true;
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pythonImportsCheck = [ "torch" ];
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passthru.tests = callPackage ./tests.nix {};
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meta = {
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description = "PyTorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration";
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homepage = "https://pytorch.org/";
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changelog = "https://github.com/pytorch/pytorch/releases/tag/v${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 lib.licenses; [
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bsd3
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issl
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unfreeRedistributable
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];
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sourceProvenance = with lib.sourceTypes; [ binaryNativeCode ];
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platforms = [
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"aarch64-darwin"
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"aarch64-linux"
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"x86_64-linux"
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];
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hydraPlatforms = [ ]; # output size 3.2G on 1.11.0
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maintainers = with lib.maintainers; [ junjihashimoto ];
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};
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}
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