%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