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[Backend Tester] Clean up a few test issues #13258

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@GregoryComer GregoryComer commented Aug 9, 2025

There are a few broken tests that need cleaning up. Some are failing due to missing portable kernels. These tests are now skipped if any unsupported portable ops remain post-delegation. I also fixed a few other small issues and bumped the element-wise tolerance to reduce false positives. SNR should hopefully catch most blatant correctness issues. The fp16 and quantized tests can generate occasional high element-wise error but still have decent SNR (~60+).

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pytorch-bot bot commented Aug 9, 2025

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13258

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GregoryComer added a commit that referenced this pull request Aug 9, 2025
ghstack-source-id: f04ccde
ghstack-comment-id: 3172044664
Pull-Request: #13258
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GregoryComer added a commit that referenced this pull request Aug 11, 2025
ghstack-source-id: 5c06eb7
ghstack-comment-id: 3172044664
Pull-Request: #13258
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GregoryComer added a commit that referenced this pull request Aug 11, 2025
ghstack-source-id: 8c0ec06
ghstack-comment-id: 3172044664
Pull-Request: #13258
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GregoryComer added a commit that referenced this pull request Aug 12, 2025
ghstack-source-id: 9ac560d
ghstack-comment-id: 3172044664
Pull-Request: #13258
GregoryComer added a commit that referenced this pull request Aug 12, 2025
ghstack-source-id: 9ac560d
ghstack-comment-id: 3172044664
Pull-Request: #13258
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[ghstack-poisoned]
@@ -9,6 +9,14 @@

import torch

# Set of unsupported ops that should cause tests to be skipped
UNSUPPORTED_PORTABLE_OPS = {
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If we are adding a Portable flow, how would these show up there? Some PTE_FAIL?

@@ -142,12 +159,15 @@ def build_result(
tester.run_method_and_compare_outputs(
inputs=None if generate_random_test_inputs else inputs,
statistics_callback=lambda stats: error_statistics.append(stats),
atol=1e-1,
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@digantdesai digantdesai Aug 12, 2025

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atol seems pretty high for a general default, no?

The fp16 and quantized tests can generate occasional high element-wise error but still have decent SNR (~60+).

Do you know how this is tested on PyTorch/PyTorch side? >60 SNR is good but outliers are not great esp for individual ops, if they are expected I would prefer if we set them per test basis which would allow us to reason about the math being done on that specific test warrenting high ATOL/RTOL.

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2 participants