About attention mask
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Locate the DIT and U-Net attention implementations and trace how self- and cross-attention masks are handled. Compare the U-Net key/value multiplication with masking logits before softmax, then inspect any existing attention tests or examples. Done means establishing whether both implementations are correct and documenting the evidence or required follow-up.
Written by the indexing model from the issue text.
Description
Why are there no attention masks in DIT and U-Net?
DIT directly removes the attention masks, including both self and cross attention.
In U-Net, the mask is applied by multiplying the keys (k) and values (v) with the mask before the softmax, rather than the more common approach of assigning the masked parts a value close to negative infinity before softmax.
Is these implementation reasonable?
- Dominant language
- Python
- Stars
- 3.9k
- Forks
- 488
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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