Optimize max_pooling for inference for MKL-DNN/IDEEP device#10156
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jgong5 wants to merge 3 commits intopytorch:masterfrom
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Optimize max_pooling for inference for MKL-DNN/IDEEP device#10156jgong5 wants to merge 3 commits intopytorch:masterfrom
jgong5 wants to merge 3 commits intopytorch:masterfrom
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yinghai
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Aug 2, 2018
| mkldnn::prop_kind::forward_training : mkldnn::prop_kind::forward_inference; | ||
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| ideep::pooling_forward::compute(X, Y_dims, *Y, | ||
| stride_, kernel_, pad_tl(), pad_br(), algo_); |
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Aug 15, 2018
…10156) Summary: Optimize the max_pooling operation for inference path by setting the "inference" flag to the underlying MKL-DNN, saving the computation and store of max indices which is only needed for training. To make the API compatible, training mode is still the default and inference mode is set in the optimizeForIdeep path. Test shows the speed-up of a single max_pooling operation is up to 7X on BDW. Pull Request resolved: pytorch#10156 Differential Revision: D9276755 Pulled By: yinghai fbshipit-source-id: ad533d53aabb8ccb3b592da984d6269d9b794a8a
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Optimize the max_pooling operation for inference path by setting the "inference" flag to the underlying MKL-DNN, saving the computation and store of max indices which is only needed for training. To make the API compatible, training mode is still the default and inference mode is set in the optimizeForIdeep path.
Test shows the speed-up of a single max_pooling operation is up to 7X on BDW.