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pytorch - ✅(Solved) Fix AArch64 Unit Test Failure - test_ops_jit.py test_variant_consistency_jit_linalg_lstsq_cpu_complex64 [1 pull requests, 1 participants]

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pytorch/pytorch#177249•Fetched 2026-04-08 00:42:49
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robert-hardwickrobert-hardwick
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Error Message

Traceback (most recent call last): File "/builds/software-machine-learning-infra-frameworks-workspaces-robhar02/pytorch/test/test_ops_jit.py", line 109, in test_variant_consistency_jit self.indiv_variant_test_jit( File "/builds/software-machine-learning-infra-frameworks-workspaces-robhar02/pytorch/test/test_ops_jit.py", line 157, in indiv_variant_test_jit check_against_reference( File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_jit.py", line 113, in check_against_reference self.assertEqual(grads, grads_test) File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 4365, in assertEqual raise error_metas.pop()[0].to_error( # type: ignore[index] AssertionError: Tensor-likes are not close!

Mismatched elements: 6 / 9 (66.7%) Greatest absolute difference: 7.677078247070312e-05 at index (1, 1) (up to 1e-05 allowed) Greatest relative difference: 3.8056180073908763e-06 at index (2, 1) (up to 1.3e-06 allowed)

Fix Action

Fixed

PR fix notes

PR #177584: Add AArch64 xfails for inductor, nn, jit, and linalg tests

Description (problem / solution / changelog)

Stack from ghstack (oldest at bottom):

  • -> #177584

This PR marks all known unit test failures for AArch64 as xfail or skip with a small code comment referencing the github issues. The test files affected are also added to the linux-aarch64 unit test suite.

Once this PR has been merged we should be able to run ALL unit tests on all AArch64 cpus without any reported failures ( this will be a follow up PR ).

Related PRs #177243, #177244, #177245, #177247, #177249, #177250, #177251, #177254, #177255, #177258, #177264, #170787, #146483, #177327

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben @jataylo @fadara01 @aditew01 @nikhil-arm @milpuz01

Changed files

  • .ci/pytorch/test.sh (modified, +3/-0)
  • test/inductor/test_aot_inductor.py (modified, +7/-0)
  • test/inductor/test_cpu_repro.py (modified, +8/-0)
  • test/inductor/test_cpu_select_algorithm.py (modified, +8/-0)
  • test/inductor/test_fused_attention.py (modified, +14/-3)
  • test/inductor/test_torchinductor.py (modified, +5/-1)
  • test/inductor/test_torchinductor_opinfo.py (modified, +6/-2)
  • test/jit/test_freezing.py (modified, +4/-0)
  • test/nn/test_convolution.py (modified, +6/-0)
  • test/test_jit.py (modified, +4/-2)
  • test/test_jit_autocast.py (modified, +13/-1)
  • test/test_nn.py (modified, +4/-1)
  • torch/testing/_internal/common_methods_invocations.py (modified, +11/-0)
  • torch/testing/_internal/opinfo/definitions/linalg.py (modified, +21/-0)

Code Example

Traceback (most recent call last):
  File "/builds/software-machine-learning-infra-frameworks-workspaces-robhar02/pytorch/test/test_ops_jit.py", line 109, in test_variant_consistency_jit
    self.indiv_variant_test_jit(
  File "/builds/software-machine-learning-infra-frameworks-workspaces-robhar02/pytorch/test/test_ops_jit.py", line 157, in indiv_variant_test_jit
    check_against_reference(
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_jit.py", line 113, in check_against_reference
    self.assertEqual(grads, grads_test)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 4365, in assertEqual
    raise error_metas.pop()[0].to_error(  # type: ignore[index]
AssertionError: Tensor-likes are not close!

Mismatched elements: 6 / 9 (66.7%)
Greatest absolute difference: 7.677078247070312e-05 at index (1, 1) (up to 1e-05 allowed)
Greatest relative difference: 3.8056180073908763e-06 at index (2, 1) (up to 1.3e-06 allowed)
RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

Failure in test_ops_jit.py test_variant_consistency_jit_linalg_lstsq_cpu_complex64

Stacktrace

Traceback (most recent call last):
  File "/builds/software-machine-learning-infra-frameworks-workspaces-robhar02/pytorch/test/test_ops_jit.py", line 109, in test_variant_consistency_jit
    self.indiv_variant_test_jit(
  File "/builds/software-machine-learning-infra-frameworks-workspaces-robhar02/pytorch/test/test_ops_jit.py", line 157, in indiv_variant_test_jit
    check_against_reference(
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_jit.py", line 113, in check_against_reference
    self.assertEqual(grads, grads_test)
  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/testing/_internal/common_utils.py", line 4365, in assertEqual
    raise error_metas.pop()[0].to_error(  # type: ignore[index]
AssertionError: Tensor-likes are not close!

Mismatched elements: 6 / 9 (66.7%)
Greatest absolute difference: 7.677078247070312e-05 at index (1, 1) (up to 1e-05 allowed)
Greatest relative difference: 3.8056180073908763e-06 at index (2, 1) (up to 1.3e-06 allowed)

Affects Neoverse N1, V1 and V2 ( all AArch64 )

Versions

Commit - https://github.com/pytorch/pytorch/commit/08b6f48d871affbc7abe9277020aed882fdf110a

cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @snadampal @milpuz01 @aditew01 @nikhil-arm @fadara01 @nWEIdia

extent analysis

Fix Plan

The fix involves updating the tolerance values in the check_against_reference function to account for the numerical differences on AArch64 architectures.

  • Update the check_against_reference function to use a higher tolerance value for AArch64 architectures.
  • Modify the assertEqual statement to use a custom tolerance value.

Example code:

import torch

def check_against_reference(self, grads, grads_test):
    # Use a higher tolerance value for AArch64 architectures
    if torch.backends.cpp.is_aarch64():
        atol = 1e-4
        rtol = 1e-6
    else:
        atol = 1e-5
        rtol = 1.3e-6

    self.assertTrue(torch.allclose(grads, grads_test, atol=atol, rtol=rtol))

Verification

To verify the fix, re-run the test_variant_consistency_jit_linalg_lstsq_cpu_complex64 test on the affected AArch64 architectures.

Extra Tips

  • Be cautious when updating tolerance values, as it may mask underlying numerical issues.
  • Consider adding architecture-specific tolerance values for other affected tests.

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#api#ssr#installation#tensor shape#autograd error#memory optimization#batch processing#GPU compatibility#latency issue#model loading

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