Make ExecuTorch Q/DQ representation default and resilient
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module: quantization
triaged
- Dominant language
- Python
- Stars
- 5k
- Forks
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- Avg merge
- 2d 10h
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Description
🚀 The feature, motivation and pitch
This is recommendations 6/7: https://fb.workplace.com/groups/pytorch.edge2.team/permalink/1168405054415345/
Alternatives
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Additional context
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RFC (Optional)
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cc @kimishpatel @jerryzh168
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 linked recommendation and clarifying the expected Q/DQ representation behavior. Before implementation, identify the relevant ExecuTorch subsystem and tests, with completion defined as the representation being the default and resilient as specified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning, python
- Domain
- embedded-iot, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100