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Mentioned helpful tooling to convert Onnx models to Torch MLIR (#2683) 

- Going through the `#torch-mlir` channel on the `llvm` discord, I
realize that there are some useful commands that would be extremely
helpful in creating Onnx lowers to Torch MLIR. Seems a lot of people are
contributing to this. So, I thought it would be good to add this
information to the docs.

These tools helped streamlined the development of this PR:
https://github.com/llvm/torch-mlir/pull/2682
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John Wu 2023-12-21 07:26:20 -08:00 committed by GitHub
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docs/importers/onnx_importer.md
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@ -28,13 +28,25 @@ are relatively straight-forward to map, following this general procedure:
`tools/torch-mlir/test/python/onnx_importer/Output`. The `.mlir` files
under there should provide good variants to drive lit test coverage of
conversion.
* (Optionally) If there is an Onnx file that uses the op of interest,
convert that file to Onnx MLIR form using the following Python command,
`python -m torch_mlir.tools.import_onnx my_model.onnx`.
* There are often many variants of tests for checking conformance of
different historic ONNX encodings, but these are often not load bearing
at the MLIR level.
* Pick a handful of test cases and add them to
`test/Conversion/TorchOnnxToTorch/simple_ops_x_to_y.mlir` corresponding to an
alphabetic breakdown. At this time, ignore tests that are not exercising
`test/Conversion/TorchOnnxToTorch/simple_ops_x_to_y.mlir` corresponding to
an alphabetic breakdown. At this time, ignore tests that are not exercising
useful differences in the pattern implementations.
* (Optionally) Use `torch-mlir-opt` to validate the outputs of the new op.
First, build the project using
`cmake --build build --target tools/torch-mlir/all`. This will generate
the conversion binary, `torch-mlir-opt`. Then call `torch-mlir-opt` with
the MLIR pass `convert-torch-onnx-to-torch`:
```
build/bin/torch-mlir-opt -convert-torch-onnx-to-torch \
-split-input-file [DESIRED_ONNX_FILE].mlir
```
* Generate failure test cases:
* Some ops have forms that do not (easily) map to torch-mlir. If you leave
an op under-implemented, add a failing test case to