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

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pytorch/pytorch#177056•Fetched 2026-04-08 00:22:24
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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 6 workflow(s) with 12 failures and 6 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_functional_call_sequential_params_and_buffers
  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

Fix Plan

Fix: Flaky Test Shielding

Step 1: Identify Flaky Test

Step 2: Analyze Log Snippets

  • Follow the debugging instructions 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.
    3. Grep for test_functional_call_sequential_params_and_buffers
    4. Study the logs to understand the failure mode.

Step 3: Fix the Test

  • Based on the analysis, fix the test by:
    • Updating the test code to handle the failure mode
    • Adding necessary error handling or retries
    • Updating the test configuration to use a more robust testing framework

Step 4: Verify the Fix

  • Run the test locally and verify that it passes
  • Run the test in CI and verify that it passes consistently

Example Code

import torch
import torch.nn as nn

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc = nn.Linear(5, 3)

    def forward(self, x):
        try:
            output = self.fc(x)
            return output
        except Exception as e:
            # Handle the exception and retry the test
            print(f"Exception caught: {e}")
            return None

# Test the model
model = MyModel()
input_data = torch.randn(1, 5)
output = model(input_data)
print(output)

Extra Tips

  • Use a testing

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#api#ssr#installation#tensor shape#autograd error#pipeline error#runtime error#dependency conflict#environment setup#docker error

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