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%global _empty_manifest_terminate_build 0
Name: python-pytorch
Version: 2.2.2
Release: 1
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
License: BSD-3-Clause
URL: https://pytorch.org/
Source0: https://github.com/pytorch/pytorch/releases/download/v%{version}/pytorch-v%{version}.tar.gz
Source1: pocketfft.tar.gz
Source2: pthreadpool.tar.gz
Source3: cpuinfo.tar.gz
Source4: QNNPACK.tar.gz
Source5: FXdiv.tar.gz
Source6: tensorpipe.tar.gz
Source7: FP16.tar.gz
Source8: psimd.tar.gz
Source9: onnx.tar.gz
Source10: foxi.tar.gz
Source11: gloo.tar.gz
Source12: fbgemm.tar.gz
Source13: googletest.tar.gz
Source14: benchmark.tar.gz
Source15: NNPACK.tar.gz
Source16: XNNPACK.tar.gz
Source17: sleef.tar.gz
Source18: fmt.tar.gz
Source19: pybind11.tar.gz
Patch1: 0001-add-Wno-error-nonnull-for-test-cpp-api.patch
Patch2: 0002-disable-git-submodule.patch
Requires: python3-future
Requires: python3-filelock
Requires: python3-sympy
Requires: python3-networkx
Requires: python3-fsspec
%description
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.
%package -n python3-pytorch
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Provides: python-torch
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-setuptools_scm
BuildRequires: python3-pbr
BuildRequires: python3-pip
BuildRequires: python3-wheel
BuildRequires: python3-hatchling
BuildRequires: python3-astunparse
BuildRequires: python3-numpy
BuildRequires: python3-pyyaml
BuildRequires: cmake
BuildRequires: python3-typing-extensions
BuildRequires: python3-requests
BuildRequires: g++
%description -n python3-pytorch
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.
%package help
Summary: Development documents and examples for torch
Provides: python3-pytorch-doc
%description help
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.
%prep
%autosetup -p1 -n %{name}-%{version}
tar -xzf %{_sourcedir}/pocketfft.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/pocketfft ./third_party/
tar -xzf %{_sourcedir}/pthreadpool.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/pthreadpool ./third_party/
tar -xzf %{_sourcedir}/cpuinfo.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/cpuinfo ./third_party/
tar -xzf %{_sourcedir}/QNNPACK.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/QNNPACK ./third_party/
tar -xzf %{_sourcedir}/FXdiv.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/FXdiv ./third_party/
tar -xzf %{_sourcedir}/tensorpipe.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/tensorpipe ./third_party/
tar -xzf %{_sourcedir}/FP16.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/FP16 ./third_party/
tar -xzf %{_sourcedir}/psimd.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/psimd ./third_party/
tar -xzf %{_sourcedir}/onnx.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/onnx ./third_party/
tar -xzf %{_sourcedir}/foxi.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/foxi ./third_party/
tar -xzf %{_sourcedir}/gloo.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/gloo ./third_party/
tar -xzf %{_sourcedir}/fbgemm.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/fbgemm ./third_party/
tar -xzf %{_sourcedir}/googletest.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/googletest ./third_party/
tar -xzf %{_sourcedir}/benchmark.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/benchmark ./third_party/
tar -xzf %{_sourcedir}/NNPACK.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/NNPACK ./third_party/
tar -xzf %{_sourcedir}/XNNPACK.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/XNNPACK ./third_party/
tar -xzf %{_sourcedir}/sleef.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/sleef ./third_party/
tar -xzf %{_sourcedir}/fmt.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/fmt ./third_party/
tar -xzf %{_sourcedir}/kineto.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/kineto ./third_party/
tar -xzf %{_sourcedir}/pybind11.tar.gz -C %{_sourcedir} && mv %{_sourcedir}/pybind11 ./third_party/
%build
export USE_CUDA=OFF
export USE_KINETO=OFF
export USE_QNNPACK=OFF
export USE_NNPACK=OFF
export USE_TENSORPIPE=OFF
export BUILD_CUSTOM_PROTOBUF=OFF
export USE_SYSTEM_SLEEF=ON
export USE_XNNPACK=OFF
#export USE_SYSTEM_CPUINFO=1
export CFLAGS+=" -Wno-error=maybe-uninitialized -Wno-error=uninitialized -Wno-error=restrict -fPIC"
export CXXFLAGS+=" -Wno-error=maybe-uninitialized -Wno-error=uninitialized -Wno-error=restrict -fPIC"
%pyproject_build
%install
%pyproject_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/doclist.lst .
%files -n python3-pytorch
%doc *.md
%license LICENSE
%{_bindir}/convert-caffe2-to-onnx
%{_bindir}/convert-onnx-to-caffe2
%{_bindir}/torchrun
%{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jan 11 2024 menma <1316818279@qq.com> - 2.2.2-1
- init package
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