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pytorch - ✅(Solved) Fix [CI] [Inductor] Perf Testing for deterministic mode [1 pull requests, 1 participants]

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pytorch/pytorch#177269•Fetched 2026-04-08 00:42:20
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Author
eellisoneellison
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eellisoneellison
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karthickaikarthickai
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Fix Action

Fixed

PR fix notes

PR #174718: [inductor] fix allocation with deterministic guard

Description (problem / solution / changelog)

Fixes https://github.com/pytorch/pytorch/issues/174386

Co-authored with claude

cc @mruberry @kurtamohler @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben @jataylo

Changed files

  • test/inductor/test_torchinductor.py (modified, +88/-0)
  • torch/_inductor/codegen/cpp_wrapper_cpu.py (modified, +23/-3)
  • torch/_inductor/codegen/cpp_wrapper_cpu_array_ref.py (modified, +3/-1)
  • torch/_inductor/codegen/wrapper.py (modified, +41/-8)
RAW_BUFFERClick to expand / collapse

🚀 The feature, motivation and pitch

This could either be part of the inductor ci smoketest, or potentially something we track more thoroughly in the dashboard. I think a limited smoketest is probably sufficiently.

Alternatives

No response

Additional context

No response

cc @jerryzh168 @seemethere @malfet @pytorch/pytorch-dev-infra @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @aakhundov @coconutruben @jataylo

extent analysis

Fix Plan

To address the issue, we will implement a limited smoketest as part of the inductor CI.

Steps

  • Create a new test file for the smoketest
  • Write test cases to cover the required functionality
  • Integrate the test with the CI pipeline

Example Code

import unittest
from inductor import Inductor

class TestInductorSmokeTest(unittest.TestCase):
    def test_inductor_smoke_test(self):
        # Initialize the inductor
        inductor = Inductor()
        
        # Test the inductor's functionality
        self.assertTrue(inductor.is_functional())

if __name__ == '__main__':
    unittest.main()

Verification

To verify that the fix worked, run the test and check that it passes. You can also monitor the CI pipeline to ensure that the test is running successfully.

Extra Tips

  • Make sure to update the test cases as the functionality of the inductor changes.
  • Consider adding more test cases to cover different scenarios and edge cases.

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#api#ssr#installation#tensor shape#autograd error#retriever error#indexing error#inference speed#output truncation#response parsing

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pytorch - ✅(Solved) Fix [CI] [Inductor] Perf Testing for deterministic mode [1 pull requests, 1 participants]