_fix_outputs for BatchNormalization

[liaopeiyuan]

In newer versions of ONNX (e.g. torch.onnx outputs), BatchNormalization has multiple outputs when training mode is enabled. This is a quick fix similar to the one done for Dropout.

[liaopeiyuan]

@Huyuwei @siju-samuel @FrozenGene

[FrozenGene]

Thanks for contributing @liaopeiyuan However, I think it is better to add one unit testing case. Do you think so?

[liaopeiyuan]

yes, I will add it shortly.

[FrozenGene]

Any update? @liaopeiyuan

[liaopeiyuan]

Sorry, this is part of a larger patch, which is an on-going research project. We will add the test cases when we have time or are done with the internal patch.

[tqchen]

gentle ping @liaopeiyuan

[liaopeiyuan]

where should I place the newly added unit test?

[tqchen]

https://github.com/apache/tvm/tree/main/tests/python/frontend/onnx

[liaopeiyuan]

Does the onnxruntime build on CI come with training mode enabled? Seems like the problem is onnxruntime being unhappy with batchnorm containing multiple outputs whiletraining_mode=False by default. And, taking a step back at this PR, what would be our ideal behavior for batchnorm if the user brings an ONNX model in training mode? Do we also include the extra mean and var in outputs, or do we throw them away for them? If the latter is what we want, then it doesn't make sense to write a unit test case since ONNX and TVM behavior will be different.

[jroesch]

This PR appears to be out of date, please feel free to reopen it if this is not the case.

As part of the new year we are attempting to triage the project's open pull requests to ensure that code which
is ready for review and/or merging receives adequate attention.

Thanks again for your contribution, and feel free to reach out to discuss these changes.