pytorch / pytorch/executorch

Document end-to-end quantization journey in ExecuTorch

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Dominant language
Python
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Avg merge
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Merged PRs (30d)
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Description

🚀 The feature, motivation and pitch
  1. Document how PTQ/QAT/quality validation looks in ET using the PT2E flow (the common flow implemented by all backends).

  2. Document advanced research flows (e.g., GPTQ, PARQ) using quantize_.

Alternatives

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Additional context

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RFC (Optional)

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Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by locating the ExecuTorch documentation for the PT2E flow and the quantize_ APIs. Document the end-to-end PTQ, QAT, and quality-validation journey, then cover advanced flows such as GPTQ and PARQ. Done means both flow categories are documented clearly for ExecuTorch users.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
documentation, machine-learning
Issue type
Documentation
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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