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Re-landing #68111/#74596 ## Description v0.5 PR of this [RFC](https://github.com/pytorch/pytorch/issues/49444). On the basis of #50256, the below improvements are included: * The [v0.5 release branch](https://github.com/oneapi-src/oneDNN/releases/tag/graph-v0.5) of the oneDNN Graph API is used * The fuser now works with the profiling graph executor. We have inserted type check nodes to guard the profiled tensor properties. ### User API: The optimization pass is disabled by default. Users could enable it by: ``` torch.jit.enable_onednn_fusion(True) ``` `torch.jit.freeze` should be used after tracing (recommended) or scripting a model. ### Performance: [pytorch/benchmark](https://github.com/pytorch/benchmark) tool is used to compare the performance: * SkyLake 8180 (1 socket of 28 cores):  * SkyLake 8180 (single thread):  * By mapping hardswish to oneDNN Graph, it’s 8% faster than PyTorch JIT (NNC + OFI) ** We expect performance gain after mapping transpose, contiguous & view to oneDNN graph ops ### Directory structure of the integration code Fuser-related code is placed under: ``` torch/csrc/jit/codegen/onednn/ ``` Optimization pass registration is done in: ``` torch/csrc/jit/passes/onednn_graph_fuser.h ``` CMake for the integration code is in: ``` caffe2/CMakeLists.txt cmake/public/mkldnn.cmake cmake/Modules/FindMKLDNN.cmake ``` ## Limitations * In this PR, we only support Pytorch-oneDNN-Graph integration on Linux platform. Support on Windows and MacOS will be enabled as a next step. * We have only optimized the inference use-case. Pull Request resolved: https://github.com/pytorch/pytorch/pull/76622 Approved by: https://github.com/eellison |
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