Allow defining customized PythonOp shape inferer#17093
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### Allow defining customized PythonOp shape inferer For `torch.autograd.Function`, we converted it to PythonOp in MSDomain, there are two places to do shape inferencing for it: 1. in SymbolicShapeInfer, there is one. 2. in PythonOp op definition. For common PythonOp, since we don't know the relation ship between inputs and outputs, so we only infer the rank from output ranks, and generate symbolic dimensions for each dim. While this will introduce many meaningless symbolic dimensions, sometimes blocking our graph transformers to do op fusion. This PR provide a way to define custom shape inferencing for `torch.autograd.Function` we defined, to propagate the original dimensions across the PythonOp at the best efforts. But the 2rd one is not covered yet, we could refine that later. Fixing 1st one is enough for ORTModule training/evaluation. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. -->
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Allow defining customized PythonOp shape inferer
For
torch.autograd.Function, we converted it to PythonOp in MSDomain, there are two places to do shape inferencing for it:For common PythonOp, since we don't know the relation ship between inputs and outputs, so we only infer the rank from output ranks, and generate symbolic dimensions for each dim. While this will introduce many meaningless symbolic dimensions, sometimes blocking our graph transformers to do op fusion.
This PR provide a way to define custom shape inferencing for
torch.autograd.Functionwe defined, to propagate the original dimensions across the PythonOp at the best efforts.But the 2rd one is not covered yet, we could refine that later. Fixing 1st one is enough for ORTModule training/evaluation.
Motivation and Context