[torchao] support codebook/LUT in torchao
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Description
🚀 The feature, motivation and pitch
Support codebook/LUT in torchao flows (eager mode and PT2E). Add quant primitives for codebook/LUT. This is recommendation 2 in post: https://fb.workplace.com/groups/pytorch.edge2.team/permalink/1168405054415345/
Quant primitive work: https://github.com/pytorch/ao/pull/1988
Alternatives
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Additional context
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RFC (Optional)
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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 reviewing the linked quant primitive work in PR 1988 and the torchao flows named in the issue: eager mode and PT2E. Define what codebook/LUT support and the required quant primitives should cover in both flows; done means those flows support the requested codebook/LUT functionality.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100