XNNPack linear kernel for group size 16 / padding weights
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module: xnnpack
- Dominant language
- Python
- Stars
- 5k
- Forks
- 1.2k
- Avg merge
- 2d 10h
- Merged PRs (30d)
- 581
Description
🚀 The feature, motivation and pitch
For models with hidden dims that are indivisible by 32. Padding up to 32 could also work.
Alternatives
No response
Additional context
No response
RFC (Optional)
No response
cc @digantdesai @mcr229 @cbilgin
Contributor guide
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
Start by locating the XNNPack linear kernel and its current handling of hidden dimensions; no file or test is named in the issue. Clarify the expected group-size-16 behavior and whether padding weights to 32 is required, then define validation for models whose hidden dimensions are not divisible by 32.
Written by the indexing model from the issue text.
Assessment
- Domain
- embedded-iot, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100