8000 DISABLED test_max_pool2d_with_indices_backward5_mps (__main__.GPUTests) · Issue #153721 · pytorch/pytorch · GitHub
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pytorch-bot bot opened this issue May 16, 2025 · 1 comment
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DISABLED test_max_pool2d_with_indices_backward5_mps (__main__.GPUTests) #153721

pytorch-bot bot opened this issue May 16, 2025 · 1 comment
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module: flaky-tests Problem is a flaky test in CI module: inductor module: macos Mac OS related issues oncall: pt2 skipped Denotes a (flaky) test currently skipped in CI. triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module

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pytorch-bot bot commented May 16, 2025 •
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Platforms: mac, macos

This test was disabled because it is failing in CI. See recent examples and the most recent trunk workflow logs.

Over the past 3 hours, it has been determined flaky in 4 workflow(s) with 6 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_max_pool2d_with_indices_backward5_mps
  4. There should be several instances run (as flaky tests are rerun in CI) from which you can study the logs.
Sample error message 
Traceback (most recent call last):
  File "/Users/ec2-user/runner/_work/pytorch/pytorch/test/inductor/test_torchinductor.py", line 9425, in test_max_pool2d_with_indices_backward5
    self.common(
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/Users/ec2-user/runner/_work/pytorch/pytorch/test/inductor/test_torchinductor.py", line 658, in check_model_gpu
    check_model(
  File "/Users/ec2-user/runner/_work/pytorch/pytorch/test/inductor/test_torchinductor.py", line 499, in check_model
    actual = run(*example_inputs, **kwargs)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_dynamo/eval_frame.py", line 702, in _fn
    raise e.remove_dynamo_frames() from None  # see TORCHDYNAMO_VERBOSE=1
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 1636, in _call_user_compiler
    raise BackendCompilerFailed(
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_dynamo/output_graph.py", line 1611, in _call_user_compiler
    compiled_fn = compiler_fn(gm, self.example_inputs())
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_dynamo/repro/after_dynamo.py", line 150, in __call__
    compiled_gm = compiler_fn(gm, example_inputs)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/__init__.py", line 2409, in __call__
    return self.compiler_fn(model_, inputs_, **self.kwargs)
  File "/Users/ec2-user/runner/_work/pytorch/pytorch/test/inductor/test_torchinductor.py", line 491, in compile_fx_wrapper
    return compile_fx(model_, example_inputs_)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_inductor/compile_fx.py", line 2324, in compile_fx
    return aot_autograd(
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_dynamo/backends/common.py", line 106, in __call__
    cg = aot_module_simplified(gm, example_inputs, **self.kwargs)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 1189, in aot_module_simplified
    compiled_fn = AOTAutogradCache.load(
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_functorch/_aot_autograd/autograd_cache.py", line 923, in load
    compiled_fn = dispatch_and_compile()
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 1174, in dispatch_and_compile
    compiled_fn, _ = create_aot_dispatcher_function(
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 576, in create_aot_dispatcher_function
    return _create_aot_dispatcher_function(
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_functorch/aot_autograd.py", line 687, in _create_aot_dispatcher_function
    fw_metadata = run_functionalized_fw_and_collect_metadata(
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/contextlib.py", line 126, in __exit__
    next(self.gen)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 723, in dynamo_timed
    yield
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/contextlib.py", line 532, in __exit__
    raise exc_details[1]
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/contextlib.py", line 517, in __exit__
    if cb(*exc_details):
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/autograd/profiler.py", line 788, in __exit__
    torch.ops.profiler._record_function_exit._RecordFunction(record)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_ops.py", line 1045, in __call__
    return self._op(*args, **kwargs)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_ops.py", line 890, in handler
    return torch._library.utils.handle_dispatch_mode(
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_library/utils.py", line 296, in handle_dispatch_mode
    return curr_mode.__torch_dispatch__(op_overload, overload_types, args, kwargs)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/utils/_stats.py", line 27, in wrapper
    return fn(*args, **kwargs)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1346, in __torch_dispatch__
    return self.dispatch(func, types, args, kwargs)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 2021, in dispatch
    return self._cached_dispatch_impl(func, types, args, kwargs)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1444, in _cached_dispatch_impl
    entry = cache.get(key, None)
  File "/Users/ec2-user/runner/_work/_temp/conda_environment_15063694737/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1074, in __eq__
    return isinstance(other, _DispatchCacheKey) and self.key == other.key
torch._dynamo.exc.BackendCompilerFailed: backend='compile_fx_wrapper' raised:
NotImplementedError: '__eq__' is not implemented for __torch__.torch.classes.profiler._RecordFunction

Set TORCHDYNAMO_VERBOSE=1 for the internal stack trace (please do this especially if you're reporting a bug to PyTorch). For even more developer context, set TORCH_LOGS="+dynamo"


To execute this test, run the following from the base repo dir:
    python test/inductor/test_torchinductor.py GPUTests.test_max_pool2d_with_indices_backward5_mps

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

Test file path: inductor/test_torchinductor.py

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

cc @clee2000 @malfet @albanD @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov
@pytorch-bot pytorch-bot bot added triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module module: flaky-tests Problem is a flaky test in CI module: macos Mac OS related issues skipped Denotes a (flaky) test currently skipped in CI. module: inductor oncall: pt2 labels May 16, 2025
@pytorch-bot PyTorch Bot
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pytorch-bot bot commented May 16, 2025
Hello there! From the DISABLED prefix in this issue title, it looks like you are attempting to disable a test in PyTorch CI. The information I have parsed is below:
  • Test name: test_max_pool2d_with_indices_backward5_mps (__main__.GPUTests)
  • Platforms for which to skip the test: mac, macos
  • Disabled by pytorch-bot[bot]

Within ~15 minutes, test_max_pool2d_with_indices_backward5_mps (__main__.GPUTests) will be disabled in PyTorch CI for these platforms: mac, macos. Please verify that your test name looks correct, e.g., test_cuda_assert_async (__main__.TestCuda).

To modify the platforms list, please include a line in the issue body, like below. The default action will disable the test for all platforms if no platforms list is specified.

Platforms: case-insensitive, list, of, platforms

We currently support the following platforms: asan, dynamo, inductor, linux, mac, macos, rocm, slow, win, windows.

How to re-enable a test

To re-enable the test globally, close the issue. To re-enable a test for only a subset of platforms, remove the platforms from the list in the issue body. This may take some time to propagate. To re-enable a test only for a PR, put Fixes #153721 in the PR body and rerun the test jobs. Note that if a test is flaky, it maybe be difficult to tell if the test is still flaky on the PR.

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module: flaky-tests Problem is a flaky test in CI module: inductor module: macos Mac OS related issues oncall: pt2 skipped Denotes a (flaky) test currently skipped in CI. triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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