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NXP backend: remove #22179 workaround for QAT channels-last segfault - #22746

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JakeStevens:fix/22179-portable-segfault
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JakeStevens:fix/22179-portable-segfault

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@JakeStevens JakeStevens commented Sep 11, 2026

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What: test_mlperf_tiny_classification_mse_cpu_vs_npu used the Python edge reference instead of the portable-kernel C++ reference for the QAT plus channels-last variant, because that configuration segfaulted in the portable kernels.

Why: the crash is fixed on current main (issue #22179). The reporter's stack predates two out-of-bounds fixes in the portable dequantize path that this model exercises at runtime (16 per-channel dequantize ops): #21517 fixed an out-of-bounds traversal for non-contiguous (channels-last) inputs, and #21773 fixed misreading int32 zero points as int64. The int32 zero points are QAT-only: QAT emits int32 bias zero points while PTQ emits int64, matching the issue's QAT-only signature.

Verification: exported the exact failing configuration (QAT, channels-last, 15-epoch training, dataset calibration, NXP edge passes) and ran it with the portable-kernel executor_runner. It runs cleanly and its outputs bit-match the eager quantized reference. The quantized_kernels_test suite passes 74/74, including the regression tests from both fixes. lintrunner reports no issues on the touched file. The NXP SDK-gated test itself was not run here (no SDK); NXP CI will exercise it.

Fixes #22179

Authored with AI assistance (Muse Code).

cc @robert-kalmar @digantdesai @rascani

…gfault

What: test_mlperf_tiny_classification_mse_cpu_vs_npu used the Python
edge reference instead of the portable-kernel C++ reference for the QAT
plus channels-last variant, because that configuration segfaulted in
the portable kernels.

Why: the crash is fixed on current main. The reporter's stack predates
two out-of-bounds fixes in the portable dequantize path that this model
exercises at runtime (16 per-channel dequantize ops): pytorch#21517 fixed an
out-of-bounds traversal for non-contiguous (channels-last) inputs, and
pytorch#21773 fixed misreading int32 zero points as int64. The int32 zero
points are QAT-only: QAT emits int32 bias zero points while PTQ emits
int64, matching the issue's QAT-only signature.

Verification: exported the exact failing configuration (QAT,
channels-last, 15-epoch training, dataset calibration, NXP edge
passes) and ran it with the portable-kernel executor_runner. It runs
cleanly and its outputs bit-match the eager quantized reference. The
quantized_kernels_test suite passes 74/74, including the regression
tests from both fixes. lintrunner reports no issues on the touched
file. The NXP SDK-gated test itself was not run here (no SDK); NXP CI
will exercise it.

Fixes pytorch#22179

Authored with AI assistance (Muse Code).
@pytorch-bot

pytorch-bot Bot commented Sep 11, 2026

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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/22746

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✅ You can merge normally! (2 Unrelated Failures)

As of commit 82c3a82 with merge base a3ae329 (image):

BROKEN TRUNK - The following jobs failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

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@meta-cla meta-cla Bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Sep 11, 2026
@JakeStevens
JakeStevens marked this pull request as draft September 11, 2026 16:03
@JakeStevens
JakeStevens requested review from novak-vaclav and removed request for robert-kalmar September 11, 2026 16:03
@JakeStevens
JakeStevens marked this pull request as ready for review September 11, 2026 16:10
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@novak-vaclav seems this was already fixed, please take a look and confirm!

@novak-vaclav novak-vaclav added module: nxp Issues related to NXP Neutron NPU delegation and code under backends/nxp/ release notes: nxp Changes to the NXP Neutron backend delegate labels Sep 15, 2026
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Segmentation fault while running model with Portable kernels

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