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[core] Handle progress bar and logging in distributed environments #12806
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
| try: | ||
| import torch | ||
| except ImportError: | ||
| torch = None |
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To not introduce circular import problem.
| torch = None | ||
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| def is_torch_dist_rank_zero() -> bool: |
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Is this robust to different distributed setups such as accelerate, pure deepspeed, etc.? As a concrete example, I'm thinking about the case where diffusers models are used in distributed training and whether these changes would work as expected in that case.
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I don't think we can control external dependencies much i.e., the logs from transformers, for example.
dg845
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LGTM! Left some questions.
dg845
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Thanks!
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This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread. Please note that issues that do not follow the contributing guidelines are likely to be ignored. |
What does this PR do?
We should handle logging (including the progress bar stuff) gracefully when operating under distributed setups.
Before this PR:
With this PR:
Notice that there's still:
It's coming from T5. We cannot control the T5 logging as it comes from
transformers.Test Code