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[DCP] Add DefaultStager example to distributed async checkpoint recipe #3711
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3711
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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Hi @LucasLLC, gentle ping on this. Just to clarify context: This tutorial covers an existing feature (DefaultStager) and is independent of my other open PRs. It does not require any upstream changes. It has been passing CI for two weeks. Could you please take a look when you have a moment? Thanks! |
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Just a few grammar and minor changes. Otherwise this looks good to me.
Co-authored-by: Alanna Burke <[email protected]>
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Hi @AlannaBurke , thanks for the review! I've applied your grammar and other suggestions. Extra Fix: While previewing the file locally ( The file should be perfect now. Ready for another look! |
Fixes #3710
Description
This PR updates the
distributed_async_checkpoint_recipeto include theDefaultStagerfunctionality introduced in PyTorch 2.9.Motivation:
In large-scale training, even with standard
async_save, the initial memory copy (Staging phase, GPU -> CPU) occurs on the main thread. This blocks the training loop. This PR introducesDefaultStager, which offloads this copy to a background thread, enabling full computation-communication overlap.Key Changes:
.. versionadded:: 2.9to indicate version requirements.staging_completionafter backward but beforeoptimizer.step()to ensure data consistency while maximizing parallel execution.upload_completionbefore the next save to manage memory backpressure.Checklist
cc @LucasLLC @MeetVadakkanchery @mhorowitz @pradeepfn @ekr0 @haochengsong @Saiteja64