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