Investigate/fix DrCI flaky build classification

[malfet]

Starting from https://github.com/pytorch/pytorch/pull/122350 where two build failures clearly caused by PR were marked as flaky

[atalman]

cc @ZainRizvi

[huydhn]

To document what I have found. This is a clear example of the relationship between the accuracy of Dr.CI classification and the log classifier.

The build failure as captured by the log classifier was a generic error:

{
  workflowId: 8601773675,
  workflowUniqueId: 16535519,
  id: 23578796771,
  runnerName: 'i-058fb9e0227cfbbbc',
  authorEmail: '[email protected]',
  name: 'trunk / linux-focal-rocm6.0-py3.8 / build',
  jobName: 'linux-focal-rocm6.0-py3.8 / build',
  conclusion: 'failure',
  completed_at: '2024-04-08T19:09:52Z',
  html_url: 'https://github.com/pytorch/pytorch/actions/runs/8601773675/job/23578796771',
  head_branch: 'ciflow/trunk/122350',
  pr_number: 122350,
  head_sha: '247c64f9c271d4abd5f1d7e30f5deb59cc0ea979',
  failure_captures: [ 'Process completed with exit code 1.' ],
  failure_lines: [ '##[error]Process completed with exit code 1.' ],
  failure_context: [
    '+ echo ::endgroup::',
    '+ sccache --stop-server',
    '+ sccache --show-stats',
    "+ echo '::group::Sccache Compilation Log'",
    '+ sccache_epilogue',
    '+ python setup.py bdist_wheel',
    '+ [[ linux-focal-rocm6.0-py3.8 != *rocm* ]]',
    '+ [[ linux-focal-rocm6.0-py3.8 != *libtorch* ]]',
    '+ return 1',
    '+ set -e',
    '+ retcode=1',
    '+ python setup.py clean bad_argument'
  ],
  time: '2024-04-08T19:09:57.620867Z'
}

Given this information, this was exactly the same as an actual flaky build failure from https://github.com/pytorch/pytorch/actions/runs/8473868798/job/23219108783, which the bot retried successfully. The flaky failure captured by the log classifier was:

{
  workflowId: 8473868798,
  id: 23219108783,
  jobName: 'linux-focal-rocm6.0-py3.8 / build',
  name: 'trunk / linux-focal-rocm6.0-py3.8 / build',
  conclusion: 'failure',
  completed_at: '2024-03-28T21:38:47Z',
  html_url: 'https://github.com/pytorch/pytorch/actions/runs/8473868798/job/23219108783',
  head_sha: '53c6a0301c4d68d5afcdccd746ce4d1f667f71e9',
  head_branch: 'ciflow/trunk/122331',
  failure_captures: [ 'Process completed with exit code 1.' ],
  failure_lines: [ '##[error]Process completed with exit code 1.' ],
  failure_context: [
    '+ echo ::endgroup::',
    '+ sccache --stop-server',
    '+ sccache --show-stats',
    "+ echo '::group::Sccache Compilation Log'",
    '+ sccache_epilogue',
    '+ python setup.py bdist_wheel',
    '+ [[ linux-focal-rocm6.0-py3.8 != *rocm* ]]',
    '+ [[ linux-focal-rocm6.0-py3.8 != *libtorch* ]]',
    '+ return 1',
    '+ set -e',
    '+ retcode=1',
    '+ python setup.py clean bad_argument'
  ],
  authorEmail: ''
}

The two looks exactly the same, including the failure_context guardrail. The merge base of https://github.com/pytorch/pytorch/pull/122350 was also 11 day old, which increase the chance getting false positives (the older the base commit, the higher the chance).

Some thoughts:

1. As an mitigation, I could add a check to skip/tighten flaky classification when the base commit is older than a certain threshold of maybe 3 days.
2. In the long run, we plan to improve the log classifier accuracy. These 2 failures were completely different, but the log classifier treated them the same.

[clee2000]

@malfet: we should never treat build failures as flaky/flaky build failures should never be allowed to merge without -f. Ex infra failure -> do not merge, regardless of whether the failure is due to the PR or not

AI: Add builds to list of never marked as flaky jobs AI: Do not mark flaky when log classifier result is really generic AI: Category for "not your fault but you can't ignore this"

[huydhn]

The first and second AI make sense to me, and could be implemented easily. The last one, however, needs more clarification I think. Devs would likely force merge the PR when a failure is "not your fault but you can't ignore this". We have some mechanisms to help with this AIACT:

1. To retry infra failures on PR
2. To limit the number of failures devs can bypass on their PR to the current value of 10 (configurable)

[clee2000]

The intention of the third point is that devs should explicitly force merge the PR if they don't want to figure out some way to rerun the job

[ZainRizvi]

Do legitimate flaky failures tend to have generic error captures like ##[error]Process completed with exit code 1.?

Using a generic output like that to determine flakiness seems really risky, even if we implement the other mitigations mentioned here.

[huydhn]

Do legitimate flaky failures tend to have generic error captures like ##[error]Process completed with exit code 1.?

Yup, that one and Command docker exec -t ... from Nova, i.e. https://hud.pytorch.org/pytorch/executorch/commit/b3ac5332e72a909ca900d6dc6feb21bedce753e4

Atm, there are 2 heuristic in place to reduce the chance of having FP.

• The error is not compared alone, but is used together with the error context of N shell commands before https://github.com/pytorch/test-infra/blob/main/torchci/lib/jobUtils.ts#L291. So an exit code 1 due to build failure and an exit code 1 due to GH infra flaky looks different.
• And the fix mentioned in the issue https://github.com/pytorch/test-infra/pull/5073 narrow down the chance of having FP further.

We could also mitigate this by making it easier to add a new heuristic rule for the log classifier. The PR route is a bit unyielding IMO, so we don't create new rule often while I think we should.

[albanD]

+1 same thing for backward compat job!

[huydhn]

+1 same thing for backward compat job!

I have a fix ready here https://github.com/pytorch/test-infra/pull/5106, although it's weird that I don't see @ZainRizvi comment about lint on the issue, although I think it's there.