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[RFC] PR Cherrypicking Process After a Release Branch Cut #7203

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lsy323 opened this issue Jun 5, 2024 · 1 comment
Open

[RFC] PR Cherrypicking Process After a Release Branch Cut #7203

lsy323 opened this issue Jun 5, 2024 · 1 comment
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@lsy323
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lsy323 commented Jun 5, 2024

馃殌 Feature

In this RFC, we propose the policy aiming to guide the decision-making process for determining whether Pull Requests (PRs) should be cherry-picked onto a release branch after the release branch has been cut. The goal is to maintain the stability and predictability of releases while addressing critical issues and incorporating essential improvements.

Motivation

Cherry-picking pull requests (PRs) onto a release branch can introduce additional overhead and goes against established best practices. While cherry-picks are sometimes unavoidable, we can mitigate their necessity through well-defined policies. This proposal outlines a framework for making informed decisions about when and how to cherry-pick changes.

Proposed Policies:

The following outlines the specific scenarios under which cherry-picking pull requests (PRs) onto a release branch will be considered acceptable after the official release branch cut.

  • The PR is for severe/P0 bug fixing purposes
  • The PR is for improving unforeseen code stability or security issues
  • The PR has significant impact on usability improvements
  • The PR is related to a planned release feature urgent fix
  • The PR only updates documentation, not changing any code
  • The PR is for improving release infrastructure
@lsy323 lsy323 added the RFC label Jun 5, 2024
@miladm miladm pinned this issue Jun 6, 2024
@miladm miladm changed the title [RFC] Policy on accepting cherrypicking PRs after release branch cut [RFC] PR Cherrypicking Process After a Release Branch Cut Jun 6, 2024
@JackCaoG
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couple thing I think we should consider

  1. The merge rule should be more strict as the release deadline approaches. For the first 1-2 week we can still consider taking features that's critical to the user/customer.
  2. For features that can be turned off by a flag or not enabled by default we should consider them as safer. Features that will affect the default behavior of the PyTorch/XLA is dangerous and shouldn't be taken after release unless it is a regression fix.
  3. The bar for the experimental feature can be lower since we set the user expection. The changes to a already stable feature should be more carefully review.

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