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https://github.com/saymrwulf/pytorch.git
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6de6041585
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/39868 ### Summary why disable NNPACK on iOS - To stay consistency with our internal version - It's currently blocking some external users due to its lack support of x86 architecture - https://github.com/pytorch/pytorch/issues/32040 - https://discuss.pytorch.org/t/undefined-symbols-for-architecture-x86-64-for-libtorch-in-swift-unit-test/84552/6 - NNPACK uses fast convolution algorithms (FFT, winograd) to reduce the computational complexity of convolutions with large kernel size. The algorithmic speedup is limited to specific conv params which are unlikely to appear in mobile networks. - Since XNNPACK has been enabled, it performs much better than NNPACK on depthwise-separable convolutions which is the algorithm being used by most of mobile computer vision networks. ### Test Plan - CI Checks Test Plan: Imported from OSS Differential Revision: D22087365 Pulled By: xta0 fbshipit-source-id: 89a959b0736c1f8703eff10723a8fbd02357fd4a
44 lines
1.5 KiB
Bash
44 lines
1.5 KiB
Bash
#!/bin/bash
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set -ex -o pipefail
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echo ""
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echo "DIR: $(pwd)"
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WORKSPACE=/Users/distiller/workspace
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PROJ_ROOT=/Users/distiller/project
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ARTIFACTS_DIR=${WORKSPACE}/ios
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ls ${ARTIFACTS_DIR}
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ZIP_DIR=${WORKSPACE}/zip
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mkdir -p ${ZIP_DIR}/install/lib
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mkdir -p ${ZIP_DIR}/src
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# copy header files
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cp -R ${ARTIFACTS_DIR}/arm64/include ${ZIP_DIR}/install/
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# build a FAT bianry
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cd ${ZIP_DIR}/install/lib
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target_libs=(libc10.a libclog.a libcpuinfo.a libeigen_blas.a libpytorch_qnnpack.a libtorch_cpu.a libtorch.a libXNNPACK.a)
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for lib in ${target_libs[*]}
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do
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if [ -f "${ARTIFACTS_DIR}/x86_64/lib/${lib}" ] && [ -f "${ARTIFACTS_DIR}/arm64/lib/${lib}" ]; then
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libs=("${ARTIFACTS_DIR}/x86_64/lib/${lib}" "${ARTIFACTS_DIR}/arm64/lib/${lib}")
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lipo -create "${libs[@]}" -o ${ZIP_DIR}/install/lib/${lib}
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fi
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done
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lipo -i ${ZIP_DIR}/install/lib/*.a
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# copy the umbrella header and license
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cp ${PROJ_ROOT}/ios/LibTorch.h ${ZIP_DIR}/src/
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cp ${PROJ_ROOT}/LICENSE ${ZIP_DIR}/
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# zip the library
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ZIPFILE=libtorch_ios_nightly_build.zip
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cd ${ZIP_DIR}
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#for testing
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touch version.txt
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echo $(date +%s) > version.txt
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zip -r ${ZIPFILE} install src version.txt LICENSE
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# upload to aws
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brew install awscli
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set +x
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export AWS_ACCESS_KEY_ID=${AWS_S3_ACCESS_KEY_FOR_PYTORCH_BINARY_UPLOAD}
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export AWS_SECRET_ACCESS_KEY=${AWS_S3_ACCESS_SECRET_FOR_PYTORCH_BINARY_UPLOAD}
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set +x
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# echo "AWS KEY: ${AWS_ACCESS_KEY_ID}"
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# echo "AWS SECRET: ${AWS_SECRET_ACCESS_KEY}"
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aws s3 cp ${ZIPFILE} s3://ossci-ios-build/ --acl public-read
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