mirror of https://github.com/llvm/torch-mlir
28957adaac
This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends. |
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| main.py | ||
| xfail_sets.py | ||