Accepting request 839525 from home:Guillaume_G:branches:science:machinelearning
- Rename mkl-dnn to onednn to follow upstream
- Obsoletes mkl-dnn* <= %{version}
OBS-URL: https://build.opensuse.org/request/show/839525
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/onednn?expand=0&rev=1
This commit is contained in:
Tomáš Chvátal
Git OBS Bridge
175
onednn.spec
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175
onednn.spec
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#
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# spec file for package onednn
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#
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# Copyright (c) 2020 SUSE LLC
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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# Please submit bugfixes or comments via https://bugs.opensuse.org/
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#
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%ifarch x86_64
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%bcond_without opencl
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%else
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# Build broken on non-x86, with openCL
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%bcond_with opencl
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%endif
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%define libname libdnnl1
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Name: onednn
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Version: 1.6.3
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Release: 0
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Summary: Intel(R) Math Kernel Library for Deep Neural Networks
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License: Apache-2.0
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URL: https://01.org/onednn
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Source0: https://github.com/oneapi-src/oneDNN/archive/v%{version}/%{name}-%{version}.tar.gz
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BuildRequires: cmake
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BuildRequires: doxygen
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BuildRequires: fdupes
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BuildRequires: gcc-c++
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BuildRequires: graphviz
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BuildRequires: texlive-dvips-bin
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%if %{with opencl}
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BuildRequires: opencl-headers
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BuildRequires: pkgconfig
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BuildRequires: pkgconfig(OpenCL)
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%endif
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ExclusiveArch: x86_64 aarch64 ppc64le
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Provides: mkl-dnn = %{version}
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Obsoletes: mkl-dnn <= %{version}
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Provides: oneDNN = %{version}
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%description
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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%package -n benchdnn
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Summary: Header files of Intel(R) Math Kernel Library
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Requires: %{libname} = %{version}
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%description -n benchdnn
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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This package only includes the benchmark utility including its input files.
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%package devel
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Summary: Header files of Intel(R) Math Kernel Library
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Requires: %{libname} = %{version}
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Provides: mkl-dnn-devel = %{version}
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Obsoletes: mkl-dnn-devel <= %{version}
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Provides: oneDNN-devel = %{version}
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%description devel
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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This package includes the required headers and library files to develop software
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with the Intel(R) MKL-DNN.
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%package doc
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Summary: Reference documentation for the Intel(R) Math Kernel Library
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BuildArch: noarch
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%description doc
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The reference documentation for the Intel(R) Math Kernel Library can be installed
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with this package.
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%package -n %{libname}
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Summary: Header files of Intel(R) Math Kernel Library
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%description -n %{libname}
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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%prep
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%setup -q -n oneDNN-%{version}
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%autopatch -p1
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%build
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%cmake \
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-DCMAKE_INSTALL_LIBDIR=%{_lib} \
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-DMKLDNN_ARCH_OPT_FLAGS="" \
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-DDNNL_CPU_RUNTIME=OMP \
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%if %{with opencl}
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-DDNNL_GPU_RUNTIME=OCL \
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%endif
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-DDNNL_INSTALL_MODE=DEFAULT \
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-DDNNL_BUILD_TESTS=ON \
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-DDNNL_WERROR=OFF
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%cmake_build
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%cmake_build doc
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%install
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%cmake_install
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# move the built doxygen data to normal location
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mkdir -p %{buildroot}%{_docdir}/%{name}
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mv %{buildroot}%{_datadir}/doc/dnnl/reference/* %{buildroot}%{_docdir}/%{name}
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%fdupes %{buildroot}%{_docdir}/%{name}
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# do use macros to install license/docu
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rm -r %{buildroot}%{_datadir}/doc/dnnl
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# Keep compatibility with mkl-dnn
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pushd %{buildroot}%{_includedir}
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ln -s . mkl-dnn
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popd
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# install the benchmark
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install -D build/tests/benchdnn/benchdnn %{buildroot}/%{_bindir}/benchdnn
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#move install shared lib
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mkdir -vp %{buildroot}%{_datadir}/benchdnn
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cp -vr build/tests/benchdnn/inputs %{buildroot}%{_datadir}/benchdnn
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%check
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# do not use macro so we can exclude all gpu and cross (gpu and cpu) tests (they need gpu set up)
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pushd build
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export LD_LIBRARY_PATH=%{buildroot}%{_libdir}
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ctest --output-on-failure --force-new-ctest-process %{_smp_mflags} -E '(gpu|cross)'
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popd
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%post -n %{libname} -p /sbin/ldconfig
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%postun -n %{libname} -p /sbin/ldconfig
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%files -n benchdnn
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%{_bindir}/benchdnn
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%{_datadir}/benchdnn
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%files devel
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%{_includedir}/mkl-dnn
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%{_includedir}/mkldnn*.h*
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%{_includedir}/dnnl*.h*
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%{_libdir}/libdnnl.so
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%{_libdir}/libmkldnn.so
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%dir %{_libdir}/cmake/dnnl
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%{_libdir}/cmake/dnnl/*.cmake
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%dir %{_libdir}/cmake/mkldnn
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%{_libdir}/cmake/mkldnn/*.cmake
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%files doc
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%{_docdir}/%{name}
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%files -n %{libname}
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%license LICENSE
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%doc README.md
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%{_libdir}/libdnnl.so.*
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%{_libdir}/libmkldnn.so.*
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%changelog
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