dmlc--dgl
Xin Yao
8d5d8962ad
* expose GeneratePermutation * add sparse_all_to_all_push * add sparse_all_to_all_pull * add unit test * handle world_size=1 * remove python nccl wrapper * remove the nccl dependency * use pinned memory to speedup D2H copy * fix lint * resolve comments * fix lint * fix ut * resolve comments
67 行
1.7 KiB
Bash
67 行
1.7 KiB
Bash
#!/bin/bash
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set -e
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. /opt/conda/etc/profile.d/conda.sh
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if [ $# -ne 1 ]; then
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echo "Device argument required, can be cpu, gpu or cugraph"
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exit -1
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fi
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CMAKE_VARS="-DBUILD_CPP_TEST=ON -DUSE_OPENMP=ON"
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# This is a semicolon-separated list of Python interpreters containing PyTorch.
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# The value here is for CI. Replace it with your own or comment this whole
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# statement for default Python interpreter.
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if [ "$1" != "cugraph" ]; then
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# We do not build pytorch for cugraph because currently building
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# pytorch against all the supported cugraph versions is not supported
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# See issue: https://github.com/rapidsai/cudf/issues/8510
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CMAKE_VARS="$CMAKE_VARS -DBUILD_TORCH=ON -DTORCH_PYTHON_INTERPS=/opt/conda/envs/pytorch-ci/bin/python"
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else
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# Disable sparse build as cugraph docker image lacks cuDNN.
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CMAKE_VARS="$CMAKE_VARS -DBUILD_SPARSE=OFF"
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fi
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#This is implemented to detect underlying architecture and enable arch specific optimization.
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arch=`uname -m`
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if [[ $arch == *"x86"* ]]; then
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CMAKE_VARS="-DUSE_AVX=ON $CMAKE_VARS"
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fi
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if [[ $1 != "cpu" ]]; then
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CMAKE_VARS="-DUSE_CUDA=ON $CMAKE_VARS"
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fi
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if [ -d build ]; then
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rm -rf build
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fi
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mkdir build
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rm -rf _download
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pushd build
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cmake $CMAKE_VARS ..
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make -j
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popd
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pushd python
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if [[ $1 == "cugraph" ]]; then
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rm -rf build *.egg-info dist
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pip uninstall -y dgl
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# test install
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python3 setup.py install
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# test inplace build (for cython)
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python3 setup.py build_ext --inplace
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else
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for backend in pytorch mxnet tensorflow
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do
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conda activate "${backend}-ci"
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rm -rf build *.egg-info dist
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pip uninstall -y dgl
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# test install
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python3 setup.py install
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# test inplace build (for cython)
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python3 setup.py build_ext --inplace
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done
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fi
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popd
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