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pytorch - 💡(How to fix) Fix [torch 2.12] umbrella issue - vLLM CI failures

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WHEN ARE THE CRITICAL DATES FOR THE PYTORCH 2.12 RELEASE?

  • M1: Release Announcement (27/3/26)
  • M2: All PRs landed in PyTorch repo / Feature Submission Closed (10/4/26)
  • M3.1: Release branch cut (13/4/26 - 15/4/26)
  • M4: Release branch finalized, Announce final launch date, Feature classifications published (week of 27/4/26) - Final RC is produced.
  • M4.1: Tutorial drafts submission deadline (6/5/26)
  • M5: External-Facing Content Finalized (8/5/26)
  • M6: Release Day (13/5/26)

TEST PR: https://github.com/vllm-project/vllm/pull/40077

April 20, 2026: buildkite.com/vllm/ci/builds/62138

  1. https://github.com/pytorch/pytorch/issues/180905
  2. https://github.com/pytorch/pytorch/issues/180906
  3. https://github.com/pytorch/pytorch/issues/180908
  4. https://github.com/pytorch/pytorch/issues/180909
  5. https://github.com/pytorch/pytorch/issues/180910
  6. https://github.com/pytorch/pytorch/issues/180911
  7. https://github.com/pytorch/pytorch/issues/180912
  8. https://github.com/pytorch/pytorch/issues/180913

cc @seemethere @malfet @pytorch/pytorch-dev-infra

extent analysis

TL;DR

Review the provided critical dates for the PyTorch 2.12 release to ensure timely completion of tasks and submissions.

Guidance

  • Check the release schedule to plan submissions and feature implementations accordingly.
  • Verify that all PRs are landed in the PyTorch repo by the M2 deadline (10/4/26).
  • Review the linked GitHub issues to address any open problems or discussions related to the release.
  • Ensure tutorial drafts are submitted by the M4.1 deadline (6/5/26) to meet the release timeline.

Notes

The provided information focuses on the release schedule and deadlines for PyTorch 2.12, without detailing specific technical issues or fixes.

Recommendation

Apply the provided schedule and deadlines to guide the release process, ensuring that all tasks and submissions are completed on time to meet the release date of May 13, 2026.

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#model save/load#optimization#mixed precision#training loop#device allocation

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