mlcommons / mlcommons/algorithmic-efficiency
Split out train and update_batch_norm in Librispeech workloads
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- Dominant language
- Python
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- 425
- Forks
- 78
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Description
Currently update_batch_norm just runs the librispeech workloads in train mode, which also runs dropout in train mode. The purpose of having separate mode and update_batch_norm kwargs to model_fn() was so that submitters could separate which they want to update, if desired. We can update Conformer.__call__ and Deepspeech.__call__ to take both train and update_batch_norm and pass them to dropout/BN respectively.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with algorithmic_efficiency/workloads/librispeech_conformer/librispeech_jax/models.py at Conformer.call and librispeech_deepspeech/librispeech_jax/models.py at Deepspeech.call. Review how model_fn passes mode and update_batch_norm, then verify that train controls dropout while update_batch_norm controls batch normalization independently. Done means both Librispeech workloads support the separated behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 45/100