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Paul Guyot
239 lines
8.5 KiB
Tcl
239 lines
8.5 KiB
Tcl
# -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4
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PortSystem 1.0
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PortGroup github 1.0
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PortGroup mpi 1.0
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PortGroup python 1.0
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github.setup pytorch pytorch 2.12.0 v
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revision 0
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name py-${github.project}
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license BSD
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maintainers nomaintainer
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supported_archs arm64 x86_64
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github.tarball_from releases
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description Tensors and dynamic neural networks in Python \
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with strong GPU acceleration
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long_description PyTorch is a Python package that provides two \
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high-level features: Tensor computation (like \
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NumPy) with strong GPU acceleration\; Deep neural \
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networks built on a tape-based autograd \
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system. You can reuse your favorite Python \
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packages such as NumPy, SciPy and Cython to extend \
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PyTorch when needed.
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homepage https://pytorch.org/
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distname ${github.project}-${github.tag_prefix}${version}
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checksums rmd160 9e979813d9fe7a417b9c127a8773b83d6c6a607d \
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sha256 7cc1deb309f402ad67e9f45bbe311a40def4db19d66fddb9b01950f9bfc5ccb1 \
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size 430864016
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python.versions 310 311 312 313 314
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# third_party/pthreadpool needs DISPATCH_APPLY_AUTO (as of torch 2.0.0), requiring 10.12+
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# But builds fail for 10.12 - 10.14, so exclude those too
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platforms {darwin >= 19}
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mpi.setup
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# Compiler selection
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compiler.cxx_standard 2017
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compiler.blacklist-append *gcc*
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compiler.blacklist-append {clang < 1700}
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variant mkl description {Enable Intel Math Kernel Library support} { }
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# py-mkl supports x86_64 and 10.12 and later only
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if {${configure.build_arch} eq "x86_64" && !($universal_possible && [variant_isset universal])
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&& !(${os.platform} eq "darwin" && ${os.major} <= 15)} {
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default_variants-append +mkl
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}
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platform darwin {
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if {${os.major} >= 18} {
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variant mps description {Enable Apple Metal Performance Shaders (MPS) support} {
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use_xcode yes
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# Align the sysroot with the Xcode SDK so AvailabilityMacros.h defines
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# __MAC_15_0 (and __MAC_26_0 etc.), preventing the forward-compat shims
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# in MPSGraphSequoiaOps.h from being compiled when the Xcode SDK already
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# provides those Metal/MPS symbols via its framework headers.
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configure.sdkroot \
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[exec xcrun --sdk macosx --show-sdk-path]
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build.env-append \
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APPLE=ON \
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USE_MPS=ON \
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USE_PYTORCH_METAL=ON \
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USE_PYTORCH_METAL_EXPORT=ON
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notes-append \
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"
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The port ${subport} is built with Apple Metal Performance Shaders (MPS)\
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support for GPU hardware acceleration. To enable Apple GPU devices,\
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use device \"mps\". Matrix multiplication example:
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import torch
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mpsDevice = torch.device(\"mps\" if\
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torch.backends.mps.is_available() else \"cpu\")
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x = torch.randn((10_000, 1_000), device=mpsDevice)
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cov = (x.T @ x)/x.shape\[0]
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"
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}
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default_variants-append +mps
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}
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}
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if {${name} ne ${subport}} {
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depends_build-append \
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port:git \
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path:bin/doxygen:doxygen \
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port:cctools \
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path:bin/cmake:cmake \
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path:bin/ninja:ninja \
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port:py${python.version}-requests
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depends_lib-append \
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path:share/pkgconfig/eigen3.pc:eigen3 \
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port:gflags \
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port:google-glog \
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port:libomp \
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port:protobuf3-cpp \
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port:py${python.version}-click \
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port:py${python.version}-future \
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port:py${python.version}-numpy \
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port:py${python.version}-pybind11 \
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port:py${python.version}-six \
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port:py${python.version}-sympy \
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port:py${python.version}-typing_extensions \
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port:py${python.version}-yaml \
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port:zstd
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depends_run-append \
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port:py${python.version}-onnx \
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port:py${python.version}-packaging \
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port:py${python.version}-zstd
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# remove unnecessary dependencies and version pinning
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patchfiles-append patch-pyproject_toml.diff
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# Patch to fix init issue with google-glog 0.5.0, caused by breaking API change.
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# Refer to patch comments for detailed background.
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# Upstream PyTorch issue: https://github.com/pytorch/pytorch/issues/58054
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# diff -NaurdwB ./py-pytorch-orig/c10/util/Logging.cpp ./py-pytorch-new/c10/util/Logging.cpp | sed -E -e 's/\.\/py-pytorch-(orig|new)/\./g' | sed -E -e 's|/opt/local|@@PREFIX@@|g' > ~/Downloads/patch-glog-init-check.diff
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patchfiles-append patch-glog-init-check.diff
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# Use Intel Math kernel Library
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if {[variant_isset mkl]} {
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patchfiles-append FindMKL-OMP.patch
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pre-build {
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# Hacks to get search paths into builds
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reinplace "s|/opt/intel/mkl|${python.prefix}|g" \
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cmake/Modules/FindMKL.cmake
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reinplace "s|mklvers \"intel64\"|mklvers \"\"|g" \
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cmake/Modules/FindMKL.cmake
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reinplace "s|MACPORTS_PREFIX|${prefix}|g" \
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cmake/Modules/FindMKL.cmake
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}
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depends_lib-append port:py${python.version}-mkl
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depends_build-append port:py${python.version}-mkl-include
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build.env-append \
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BLAS_SET_BY_USER=ON
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}
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compiler.cpath-append \
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${prefix}/include/libomp
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compiler.library_path-append \
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${prefix}/lib/libomp
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configure.cppflags-append \
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-I${prefix}/include/libomp
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# ccache configuration
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configure.ccache yes
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set ccache_dir ${workpath}/.ccache
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if {[option configure.ccache]} {
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depends_build-append \
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path:bin/ccache:ccache
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post-patch {
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xinstall -d ${ccache_dir}
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}
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configure.env-append CCACHE_DIR=${ccache_dir} USE_CCACHE=ON
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build.env-append CCACHE_DIR=${ccache_dir} USE_CCACHE=ON
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destroot.env-append CCACHE_DIR=${ccache_dir} USE_CCACHE=ON
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} else {
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# commands to disable ccache
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configure.ccache no
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configure.env-append CCACHE_DISABLE=1 USE_CCACHE=OFF
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build.env-append CCACHE_DISABLE=1 USE_CCACHE=OFF
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destroot.env-append CCACHE_DISABLE=1 USE_CCACHE=OFF
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# Limit cores for parallel builds
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# Note: parallel builds observed to fail on 8, 24 cores without ccache
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use_parallel_build yes
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set max_build_jobs 4
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if {[option build.jobs] < ${max_build_jobs}} {
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set max_build_jobs [option build.jobs]
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}
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build.env-append \
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CMAKE_BUILD_PARALLEL_LEVEL=${max_build_jobs}
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}
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build.env-append \
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BUILD_CUSTOM_PROTOBUF=OFF \
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USE_CUDA=OFF \
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USE_DISTRIBUTED=ON \
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USE_EIGEN_SPARSE=ON \
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USE_GFLAGS=ON \
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USE_GLOG=ON \
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USE_GLOO=ON \
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USE_LITE_PROTO=ON \
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USE_NCCL=OFF \
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USE_OPENMP=ON \
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USE_RCCL=OFF \
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USE_ROCM=OFF \
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USE_SYSTEM_EIGEN_INSTALL=ON \
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USE_SYSTEM_PYBIND11=ON \
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USE_XPU=OFF
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post-destroot {
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set py_torch_root ${python.pkgd}/torch
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foreach slib [glob -directory ${destroot}${py_torch_root} *.so] {
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system "install_name_tool -add_rpath ${py_torch_root}/lib ${slib}"
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}
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# Upstream PyTorch bundles pybind11 headers in its include directory.
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# Since we use USE_SYSTEM_PYBIND11=ON, create a symlink so downstream
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# packages (torchaudio, torchvision, etc.) can find pybind11 headers.
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ln -s ${python.pkgd}/pybind11/include/pybind11 \
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${destroot}${py_torch_root}/include/pybind11
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set docdir ${prefix}/share/doc/${subport}
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xinstall -d ${destroot}${docdir}
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xinstall -m 0644 -W ${worksrcpath} LICENSE README.md \
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${destroot}${docdir}
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}
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# pytorch's tests all use GPU compilation
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if { [lsearch ${build.env} {USE_CUDA=OFF}] != -1 } {
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test.run yes
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}
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} else {
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# overload the github livecheck regex to look for versions that
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# are just numbers and '.', no letters (e.g., "3.7.3_rc2").
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livecheck.url https://github.com/pytorch/pytorch/releases
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livecheck.regex {/tree/v(([[:digit:]]+\.)+[[:digit:]]+)}
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}
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