ExecuTorch backends implement PT2E quantizer for PTQ/QAT
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Description
🚀 The feature, motivation and pitch
ExecuTorch backends implement and document PT2E quantizer for PTQ/QAT flows. This is recommendation 1 in post: https://fb.workplace.com/groups/pytorch.edge2.team/permalink/1168405054415345/.
Alternatives
No response
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
The issue names no files, tests, or entry points. Start by reviewing the PT2E quantizer requirements and the linked recommendation, then identify the ExecuTorch backends and existing PTQ/QAT documentation. Done means the backends implement and document the requested PT2E quantizer flows.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100