How to use fp16 training with masked operations
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 9k
- Forks
- 1.5k
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 3
Description
Hello !
I'm working on sequence training with CNNs and for this I've to operate some masked_fill operations over the padding, before softmax for example.
On float32 training, I'm masking with -1e20 value, and it seems to be training fine. Unfortunately, when training with float16 and masking with -1e15, amp loss scaling always returns "NaN" for gradients.
Do you have any idea how to combine masked_fill with amp ?
Thanks,
Morgan
Contributor guide
No contributing guide indexed for this repository
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 repository files or tests. Start by reproducing the reported NaN gradients with float16, masked_fill, softmax, and AMP loss scaling, then review the existing discussion for constraints. Done means establishing whether the behavior is a bug or documenting a supported way to combine these operations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100