Debugging PyTorch: Investigating Test_upper_bound_i64_cuda

Aug 5, 2025 by Felix Dubois 59 views

Investigating the DISABLED test_upper_bound_i64_cuda in PyTorch AOTInductor

Hey guys! Today, we're diving deep into a tricky issue in PyTorch: the DISABLED test_upper_bound_i64_cuda test within the AOTInductor. This test has been causing some headaches in the PyTorch continuous integration (CI) system, and we need to figure out why it's failing and how to fix it. It's crucial to address these flaky tests to ensure the stability and reliability of PyTorch, especially when dealing with advanced features like AOTInductor that are vital for performance optimization.

The Mystery of the Flaky Test

So, what's the deal with this test? The test_upper_bound_i64_cuda test is designed to verify the correct behavior of the AOTInductor when handling upper bounds with 64-bit integers on CUDA-enabled GPUs. The AOTInductor, short for Ahead-of-Time Inductor, is a powerful tool in PyTorch for optimizing model execution by compiling parts of the model ahead of time. This can lead to significant performance improvements, but it also means that any bugs in the compilation or execution process can have serious consequences. When a test is marked as flaky, it means that it passes sometimes and fails other times, even without any code changes. This can be incredibly frustrating because it makes it difficult to pinpoint the root cause of the problem.

Recent Failures and Flakiness

As highlighted in the issue, this particular test has been flagged as flaky in numerous recent workflows. We're talking about a significant number of failures – 23 workflows showing flakiness with 46 failures and 23 successes in just the past 6 hours! That's a pretty high rate of failure, and it's a clear sign that something isn't quite right. These types of intermittent issues are particularly challenging because they don't consistently appear, making them tough to debug and resolve. The inconsistency often points to underlying problems such as race conditions, memory corruption, or hardware-specific issues that only manifest under certain conditions.

Why We Can't Ignore Flaky Tests

Now, you might be thinking, "Why bother with a test that sometimes passes?" Well, the truth is, we can't afford to ignore flaky tests. In a large project like PyTorch, stability and reliability are paramount. Flaky tests can mask real bugs, leading to unexpected behavior in production and eroding user trust. Moreover, they can significantly slow down the development process. Imagine trying to merge a pull request when you know there's a chance a flaky test might fail, even if your changes are correct. It's a recipe for frustration and wasted time. Therefore, addressing flaky tests is not just about making the CI green; it's about ensuring the long-term health and robustness of the PyTorch ecosystem.

Diving into the Debugging Instructions

The issue provides some handy debugging instructions, which are super important to follow when tackling flaky tests. The key takeaway here is: DO NOT ASSUME THINGS ARE OKAY IF THE CI IS GREEN. This is crucial because PyTorch now shields flaky tests from developers, meaning the CI might show a green status even if there are underlying problems. This shielding is designed to prevent flaky tests from blocking merges, but it also means we need to be extra vigilant in checking the logs ourselves.

Step-by-Step Debugging

The recommended debugging process involves these steps:

Click on the workflow logs: The provided links to recent examples and trunk workflow logs are your starting point. These logs contain the detailed output of the test runs, including any error messages or stack traces.
Expand the Test step: This is a critical step. The logs are often quite verbose, and the relevant information might be hidden if the Test step isn't expanded. Expanding it ensures that all the output is available for searching.
Grep for test_upper_bound_i64_cuda: This is where the magic happens. Using grep (or your favorite text search tool), you can quickly find all instances of the test being run within the logs. Since flaky tests are rerun in CI, you'll likely find multiple instances, giving you more data points to analyze.
Study the Logs: This is the most time-consuming but also the most rewarding part. By carefully examining the logs for each test run, you can start to identify patterns and potential causes of the failure. Look for error messages, stack traces, and any other clues that might shed light on what's going wrong.

Analyzing Error Messages and Stack Traces

The provided sample error message is a great starting point for our investigation. Let's break it down:

Traceback (most recent call last):
  File "/var/lib/jenkins/workspace/test/inductor/test_torchinductor.py", line 13672, in new_test
    return value(self)
  File "/var/lib/jenkins/workspace/test/inductor/test_aot_inductor.py", line 6753, in test_upper_bound_i64
    m(*inp)
    ~^^^^^^
  File "/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/__init__.py", line 182, in optimized
    flat_outputs = runner.run(flat_inputs)
RuntimeError: run_func_( container_handle_, input_handles.data(), input_handles.size(), output_handles.data(), output_handles.size(), reinterpret_cast<AOTInductorStreamHandle>(stream_handle), proxy_executor_handle_) API call failed at /var/lib/jenkins/workspace/torch/csrc/inductor/aoti_runner/model_container_runner.cpp, line 145

To execute this test, run the following from the base repo dir:
    python test/inductor/test_aot_inductor.py AOTInductorTestABICompatibleGpu.test_upper_bound_i64_cuda

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

This traceback gives us some valuable information:

The error occurs during the execution of the model (m(*inp)) within the test_upper_bound_i64 function in test_aot_inductor.py.
The error happens when calling runner.run(flat_inputs) within the optimized function in torch/_export/__init__.py. This suggests an issue during the execution of the AOTInductor-optimized model.
The RuntimeError indicates that the run_func_ API call failed in model_container_runner.cpp. This is a low-level error, pointing to a problem in the AOTInductor runtime.

This error message is a great starting point, but it doesn't tell us the exact cause of the failure. To get a clearer picture, we need to examine the logs from both successful and failed runs to see what differences might be triggering the issue. Look for variations in input data, hardware configurations, or other environmental factors that might be contributing to the flakiness.

Understanding the Test File Path

The issue also mentions the test file path: inductor/test_aot_inductor.py. This tells us exactly where to find the test code, which is crucial for further investigation. By looking at the test code, we can understand what the test is trying to achieve and how it's doing it. This can help us identify potential bugs in the test itself or in the code it's testing.

Locating the Test Function

Within test_aot_inductor.py, the specific test function we're interested in is AOTInductorTestABICompatibleGpu.test_upper_bound_i64_cuda. This naming convention gives us a clue about the test's purpose: it's part of a test class (AOTInductorTestABICompatibleGpu) and it specifically tests the handling of upper bounds with 64-bit integers on CUDA GPUs. By examining the code within this function, we can gain a deeper understanding of the test's logic and how it interacts with the AOTInductor.

Next Steps: Reproducing the Error and Root Cause Analysis

So, what's next? The key to fixing this flaky test is to reproduce the error consistently. The error message provides a command to run the test locally:

python test/inductor/test_aot_inductor.py AOTInductorTestABICompatibleGpu.test_upper_bound_i64_cuda

Running this command locally, ideally in an environment similar to the CI environment, is the first step in narrowing down the issue. If we can reproduce the error locally, we can then use debugging tools and techniques to pinpoint the root cause.

Potential Areas of Investigation

Based on the error message and the nature of the test, here are some potential areas to investigate:

CUDA-Specific Issues: The test name explicitly mentions CUDA, so it's possible that the issue is related to CUDA drivers, libraries, or hardware. Make sure the CUDA environment is correctly set up and that the drivers are compatible with the PyTorch version.
64-bit Integer Handling: The test focuses on 64-bit integers, so there might be a bug in how the AOTInductor handles these large integers. Check for potential overflows, incorrect type conversions, or other issues related to integer arithmetic.
AOTInductor Runtime: The error message points to model_container_runner.cpp, which is part of the AOTInductor runtime. This suggests that the issue might be in the code that executes the compiled model. Look for potential race conditions, memory corruption, or other runtime errors.
Input Data Sensitivity: Flaky tests often indicate that the issue is sensitive to the input data. Try varying the input data to the test and see if that affects the failure rate. This can help you identify specific input patterns that trigger the bug.

Collaboration and Communication

Finally, it's important to remember that debugging complex issues like this is often a collaborative effort. The issue includes a list of people (cc @clee2000 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben) who might have insights into this area of the code. Don't hesitate to reach out to them for help, share your findings, and work together to find a solution. Open communication and collaboration are key to resolving these types of issues efficiently.

Conclusion: The Road to a Stable PyTorch

Investigating flaky tests like test_upper_bound_i64_cuda is a crucial part of maintaining a robust and reliable PyTorch ecosystem. By following the debugging instructions, analyzing the error messages, and collaborating with the community, we can track down the root cause of these issues and ensure the stability of PyTorch for everyone. Remember, every fixed flaky test brings us one step closer to a more stable and dependable machine learning framework. Let's get to work and squash this bug, guys! This journey of debugging and fixing flaky tests not only ensures the immediate stability of PyTorch but also enriches our understanding of the intricate workings of the framework, making us better developers and contributors in the long run. So, let's embrace the challenge and contribute to making PyTorch even more robust and reliable.

For those interested in further exploring disabled tests, the provided link to https://hud.pytorch.org/disabled offers a comprehensive view of all disabled tests in PyTorch, providing a broader perspective on the ongoing efforts to enhance the framework's reliability. By actively engaging with these challenges, we collectively contribute to the advancement and refinement of PyTorch, solidifying its position as a leading platform for machine learning innovation.