pytorch - 💡(How to fix) Fix DISABLED test_vmap_free_const (__main__.FuncTorchHigherOrderOpTestsWithCompiledAutograd) [2 comments, 1 participants]

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pytorch/pytorch#180366Fetched 2026-04-16 06:35:00
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Root Cause

This test was disabled because it is failing in CI. See recent examples and the most recent trunk workflow logs.

RAW_BUFFERClick to expand / collapse

Platforms: linux, slow

This test was disabled because it is failing in CI. See recent examples and the most recent trunk workflow logs.

Over the past 6 hours, it has been determined flaky in 54 workflow(s) with 108 failures and 54 successes.

Debugging instructions (after clicking on the recent samples link): DO NOT ASSUME THINGS ARE OKAY IF THE CI IS GREEN. We now shield flaky tests from developers so CI will thus be green but it will be harder to parse the logs. To find relevant log snippets:

  1. Click on the workflow logs linked above
  2. Click on the Test step of the job so that it is expanded. Otherwise, the grepping will not work.
  3. Grep for test_vmap_free_const
  4. There should be several instances run (as flaky tests are rerun in CI) from which you can study the logs.

Test file path: inductor/test_compiled_autograd.py

For all disabled tests (by GitHub issue), see https://hud.pytorch.org/disabled.

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben @jataylo

extent analysis

TL;DR

The test test_vmap_free_const is likely failing due to flakiness and can be debugged by analyzing the workflow logs and test step output.

Guidance

  • To debug the issue, follow the provided debugging instructions: click on the workflow logs, expand the Test step, and grep for test_vmap_free_const to find relevant log snippets.
  • Study the logs from multiple instances of the test run to identify patterns or common errors.
  • Check the test file path inductor/test_compiled_autograd.py for any potential issues or recent changes that may be contributing to the flakiness.
  • Review the list of disabled tests at https://hud.pytorch.org/disabled to see if there are any related issues or patterns.

Notes

The issue lacks specific technical details about the error messages or logs, so a more detailed analysis of the logs is necessary to determine the root cause of the flakiness.

Recommendation

Apply workaround: Analyze the workflow logs and test step output to identify the cause of the flakiness and potentially modify the test or environment to make it more stable.

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