Investigating The Disabled `test_copy_non_blocking_is_pinned_cuda` Test In PyTorch

by StackCamp Team 83 views

Hey everyone! Let's dive into why the test_copy_non_blocking_is_pinned_cuda test in PyTorch has been disabled. It looks like this test, part of the AOTInductorTestABICompatibleGpu suite, has been consistently failing in Continuous Integration (CI). This article will break down the issue, how to debug it, and what the error messages indicate. So, let's get started!

Understanding the Issue: Why Was the Test Disabled?

The main reason for disabling a test in any software project, especially in a large one like PyTorch, is instability. In our case, the test_copy_non_blocking_is_pinned_cuda test was disabled because it exhibited flaky behavior in the CI environment. A flaky test is one that sometimes passes and sometimes fails without any apparent changes in the code. This inconsistency makes it unreliable and can mask genuine issues.

According to the information, this test has been flaky in 3 workflows over the past 6 hours, with 10 failures and only 3 successes. This high failure rate clearly indicates a problem that needs addressing. The frequent failures are a red flag, suggesting that there might be an underlying issue with the test itself or the code it's testing.

To put it simply, we can't trust a test that doesn't consistently give us the same result under the same conditions. Disabling the test is a temporary measure to prevent it from causing false alarms and disrupting the development process. Now, the real work begins: figuring out what's causing these failures.

Debugging the Flaky Test: A Step-by-Step Guide

Debugging flaky tests can be a bit tricky, but PyTorch provides some helpful tools and guidelines to make the process easier. Here’s a structured approach you can follow:

1. Accessing the Logs

The first step in debugging is to examine the logs from the CI runs where the test failed. The provided information includes a link to recent examples and the most recent trunk workflow logs. These logs contain valuable information about the failures, including error messages, stack traces, and other diagnostic data.

2. Expanding the Test Step

When you open the workflow logs, make sure to expand the "Test" step of the job. This is crucial because the logs are quite extensive, and you need to ensure that the relevant test execution details are visible for searching.

3. Grepping for the Test Name

Once the test step is expanded, use the grep command (or your browser's search function) to find instances of test_copy_non_blocking_is_pinned_cuda. The CI system reruns flaky tests, so you should find multiple instances of the test run in the logs. This allows you to compare different runs and look for patterns.

4. Analyzing the Error Messages and Stack Traces

Now comes the most important part: analyzing the error messages and stack traces. These will give you clues about what went wrong during the test execution. Look for any exceptions, assertion failures, or other anomalies that might indicate the root cause of the problem.

5. Identifying Patterns and Common Issues

By examining multiple test runs, you can start to identify patterns and common issues. Are the failures happening consistently in the same part of the code? Are there any specific hardware or software configurations that seem to trigger the failures? Answering these questions can help you narrow down the potential causes.

6. Reproducing the Issue Locally

Once you have a good understanding of the problem, the next step is to try to reproduce it locally. This will allow you to debug the test in a controlled environment, where you can use debugging tools and modify the code more easily. You can use the command provided in the error message to run the test:

python test/inductor/test_aot_inductor.py AOTInductorTestABICompatibleGpu.test_copy_non_blocking_is_pinned_cuda

7. Investigating the Code

If you can reproduce the issue locally, it's time to dive into the code. Use a debugger to step through the test and the code it's testing. Pay close attention to the values of variables, the flow of execution, and any potential race conditions or other concurrency issues.

Deciphering the Sample Error Message

The provided sample error message gives us some specific clues about the failure. Let's break it down:

Traceback (most recent call last):
  File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 14259, in new_test
    return value(self)
  File "/var/lib/jenkins/workspace/test/inductor/test_aot_inductor.py", line 7231, in test_copy_non_blocking_is_pinned
    self.assertEqual(outputs, outputs_aoti)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/_dynamo/test_case.py", line 111, in assertEqual
    return super().assertEqual(x, y, *args, **kwargs)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 4226, in assertEqual
    raise error_metas.pop()[0].to_error(  # type: ignore[index]
AssertionError: Tensor-likes are not close!

Mismatched elements: 8 / 8 (100.0%)
Greatest absolute difference: 2.249572992324829 at index (0, 0) (up to 1e-05 allowed)
Greatest relative difference: 3.5533642768859863 at index (0, 0) (up to 1.3e-06 allowed)

Understanding the Traceback

The traceback shows the sequence of function calls that led to the error. Here's a breakdown:

  • The error originated in test_aot_inductor.py at line 7231, in the test_copy_non_blocking_is_pinned function. This is the specific test that failed.
  • The failure occurred in an assertEqual call, which is used to compare two values and raise an error if they are not equal.
  • The error message indicates that the tensor-like objects being compared were not close. This means that the values in the tensors were different beyond a certain tolerance.

Analyzing the Mismatched Elements and Differences

The error message provides more details about the differences between the tensors:

  • Mismatched elements: 8 / 8 (100.0%): This means that every element in the tensors being compared was different.
  • Greatest absolute difference: 2.249572992324829 at index (0, 0) (up to 1e-05 allowed): The largest difference between any two corresponding elements was approximately 2.25, but the allowed difference was only 1e-05. This is a significant difference.
  • Greatest relative difference: 3.5533642768859863 at index (0, 0) (up to 1.3e-06 allowed): The largest relative difference was approximately 3.55, while the allowed relative difference was only 1.3e-06. Again, this is a substantial discrepancy.

Potential Causes and Next Steps

Based on this error message, here are some potential causes for the failure:

  1. Numerical instability: The computations performed in the test might be producing slightly different results due to floating-point arithmetic issues. This is a common problem in numerical computing, especially when dealing with CUDA tensors.
  2. Race conditions or concurrency issues: If the test involves multiple threads or processes, there might be race conditions that lead to inconsistent results. This is particularly relevant given the "non-blocking" aspect of the test name.
  3. Incorrect memory handling: The test might be incorrectly copying or moving data between CPU and GPU memory, leading to data corruption.
  4. Bugs in the AOT Inductor: The AOT Inductor itself might have a bug that causes it to generate incorrect code for this specific test case.

To further investigate, you should:

  • Examine the code in test_copy_non_blocking_is_pinned and the functions it calls.
  • Look for potential sources of numerical instability, such as divisions or exponentiations.
  • Check for any synchronization issues or race conditions.
  • Verify that the memory copies are being performed correctly.
  • Consider whether the AOT Inductor is generating the correct code for this operation.

Test File Path and Further Investigation

The information provided also includes the test file path: inductor/test_aot_inductor.py. This is helpful because you can directly navigate to this file in the PyTorch codebase and examine the test implementation. The test name, test_copy_non_blocking_is_pinned_cuda, gives a clue about what the test is supposed to do: it likely tests the functionality of copying data between pinned (CPU) memory and CUDA (GPU) memory in a non-blocking manner.

The "non-blocking" aspect is crucial here. Non-blocking operations are asynchronous, meaning they don't wait for the operation to complete before returning. This can improve performance but also introduces complexity, as you need to ensure that the data is ready when you try to use it. This asynchronous nature can sometimes lead to race conditions or timing issues, which might explain the flakiness of the test.

To dig deeper, you should:

  1. Read the code for test_copy_non_blocking_is_pinned_cuda in inductor/test_aot_inductor.py.
  2. Understand how it sets up the test tensors, performs the copy operation, and verifies the results.
  3. Look for any potential issues in the test logic itself.
  4. Examine the code that implements the non-blocking copy operation.

Disabled Tests and Communication

It's also worth noting the link provided for all disabled tests: https://hud.pytorch.org/disabled. This page gives an overview of all the tests that are currently disabled in PyTorch and the reasons for their disablement. This can be a valuable resource for understanding the overall health of the PyTorch test suite.

Finally, the information includes a list of people to cc: @clee2000 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben. These are likely the developers and maintainers who are responsible for this part of the PyTorch codebase. By including them in the discussion, you can ensure that the right people are aware of the issue and can contribute to the solution.

Wrapping Up: A Collaborative Effort

Debugging flaky tests in a large project like PyTorch is often a collaborative effort. By following the steps outlined above, analyzing the error messages, and working with the relevant developers, you can help identify the root cause of the issue and get the test re-enabled. Remember, a stable and reliable test suite is crucial for the long-term health of any software project. So, let's get those tests passing consistently! Understanding the intricacies of memory management, asynchronous operations, and numerical stability is key to resolving this issue. Keep digging, and you'll get there!

By thoroughly examining the logs, the test code, and the underlying implementation of the non-blocking copy operation, you can pinpoint the source of the flakiness and propose a fix. Good luck, and happy debugging!