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

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pytorch/pytorch#180491Fetched 2026-04-17 08:22:04
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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: dynamo

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 4 workflow(s) with 8 failures and 4 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_find_learning_rate_ensembling_FeatureAlphaDropout_mechanism_make_functional_cpu
  4. There should be several instances run (as flaky tests are rerun in CI) from which you can study the logs.

Test file path: functorch/test_eager_transforms.py

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

cc @chauhang @penguinwu @Chillee @samdow @kshitij12345

extent analysis

TL;DR

The test test_find_learning_rate_ensembling_FeatureAlphaDropout_mechanism_make_functional_cpu is likely failing due to flakiness and needs to be debugged by analyzing the workflow logs.

Guidance

  • To debug the issue, follow the provided debugging instructions: click on the workflow logs, expand the Test step, and grep for the test name to find relevant log snippets.
  • Study the logs from multiple instances of the test run to identify patterns or common errors that may be causing the flakiness.
  • Check the test file path functorch/test_eager_transforms.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 other related tests that may be experiencing similar issues.

Notes

The provided information suggests that the test is flaky, but the root cause is not immediately clear. Analyzing the workflow logs and test code will be necessary to determine the underlying issue.

Recommendation

Apply workaround: temporarily enable the test and run it locally to reproduce the issue and gather more information about the failure, then debug and fix the underlying cause of the flakiness.

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