Fix and enable few ORTModule Unit Tests #19847
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
pengwa
added
the
training
baijumeswani
approved these changes
Mar 12, 2024
|
Thanks @baijumeswani ! |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Fix and enable few ORTModule Unit Tests
Fix 'test_bert_inputs_with_dynamic_shape' and 'test_bert_result_with_layerwise_recompute' generate Nan loss in ORT run.
The root cause is, the logic to generatic attention mask test data is not correct, only 0 or 1 is allowed in the dataset, but we see lots of other numbers. ( The reason we don't have this using old version of transformers for example v4.4.2 or 4.16.2 is because they don't contains such huggingface/transformers@d3cb288, which increase the scaling to a bigger number, causing a overflow to inf)
Another improvement during the investigation using convergence tools:
Don't dump the activations during model export phase, otherwise, the dumped data might contains some PyTorch run's result making us confused during comparing with stock PyTorch run results.
Motivation and Context