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# Motivation This PR is a part of RFC #114848, and it is a successor PR of #116249 and #116019. This PR would depend on oneDNN compilation in #116249. Some runtime support is needed in #116019. Aten operators like `addmm`, `baddmm` is defined in `Blas.cpp` in `aten/src/ATen/native/mkldnn/xpu/`. Accompanied with these files provide core functionaliy, `BlasImpl.h`, `Utils.h` and other file provide basic utilities for them. For instance, `Utils.h` provide common memory descriptor query utils for `Matmul.h` and these utility function will also be used in other primitive, like `convolution`. `BlasImpl.h` is a header file that provide helper for handling shape info processing in matmul related operators. It would not only help basic GEMM operator like `addmm, baddmm` but also help fusion operators used in `torch.compile` like `linear_pointwise` in #117824. In next stage, we would continually complete the oneDNN support through enabling `matmul fusion` and `convolution` related code. Co-authored-by: xiaolil1 <xiaoli.liu@intel.com> Co-authored-by: lei,zhenyuan <zhenyuan.lei@intel.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/117202 Approved by: https://github.com/EikanWang, https://github.com/jgong5, https://github.com/malfet ghstack dependencies: #117098, #117112 |
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.
Questions and Feedback
Please use GitHub issues (https://github.com/pytorch/pytorch/issues) to ask questions, report bugs, and request new features.