Improve memory efficiency of FP16 optimizer
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- Dominant language
- Python
- Stars
- 9k
- Forks
- 1.5k
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 3
Description
The pytorch/fairseq team improved the memory effiency of their FP16 optimizer by converting the FP16 parameters to FP32 on the fly instead of keeping a static copy, see https://github.com/pytorch/fairseq/pull/404.
Are there any plans to implement this optimization here?
Thanks!
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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
The issue names no Apex files, tests, or entry points. Start by comparing the linked fairseq pull request with Apex's FP16 optimizer implementation; done would mean an agreed implementation of on-the-fly FP16-to-FP32 conversion that improves memory efficiency, with validation for optimizer behavior and memory use.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100