mirror of https://github.com/llvm/torch-mlir
e2343cf4ce
* Also adds the basic scaffolding for handling more of these, which will be needed for cond, while, etc. * Refactors some of the support in the generic OpOverload emitter so it can be shared with these other special forms. This has been on my list for a while, but it just so happens that as part of upgrading to PyTorch 2.3 and a pure upstream flow in Turbine, we were using a feature that required integration with auto_functionalized. This is perhaps the "weirdest" of the higher-order ops and a poor place to start, but needs must. We have testing for this in Turbine. Full support in Turbine has an entire custom ops facility. I've reduced this down to a unit test in torch-mlir. |
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| .. | ||
| torch_mlir | ||
| CMakeLists.txt | ||
| TorchMLIRModule.cpp | ||