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4 Commits (b3b8e2e96a6af8b9e838c07b3095b8633c701526)

Author SHA1 Message Date
Xida Ren (Cedar) 5342aa70cf
Support onnx.GRU and onnx.RNN (#3447) 2024-07-10 14:04:17 -04:00
Xida Ren (Cedar) dd967eb199
[ONNX] Support onnx.LSTM (#2969) 
This PR only performs a lit test. In lieu of an e2e test, https://github.com/nod-ai/SHARK-TestSuite/pull/142 makede sure that the lowering works & the numbers check out.

Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
 2024-04-08 12:23:33 -07:00
Chi_Liu c7452af4fa
[MLIR][ONNX] Add OnnxToTorch support for Maxpool Op (#2695) 
Add Maxpool ONNX op support.
Add Utils.h/cpp files to create a constant int list for ONNX.
 2024-01-12 14:54:38 -08:00
Stella Laurenzo e06efc5136
Initial TorchOnnxToTorch conversion pipeline. (#2585) 
Adds a pipeline to convert custom ops and metadata represented as
`torch.operator` custom ops to corresponding `torch` ops where possible.

This is part of a multi-part approach for building ONNX import in as a
regular feature of torch-mlir. It is focused on the conversions vs the
infra. We will end up maintaining a [pure-python
importer](https://github.com/nod-ai/SHARK-Turbine/blob/main/python/shark_turbine/importers/onnx_importer.py)
to go with this in torch-mlir, and we will also maintain test case
generation utilities derived from it.

I have left substantial documentation in the README of the conversion
directory, including the recommended approach that we will take to keep
building this out.

(note that this organizes the code to coincide with the refactoring in
#2442 versus the current flat arrangement)
 2023-11-21 21:02:55 -08:00