[rascani]

Allow unquantized spatial operators and tagged max-pooling configurations
to use portable kernels, while retaining explicit NHWC lowering for
accelerated spatial operators. Remove the AOT requirement for --quantize
now that float spatial fallback is supported.

Cover output comparison and serialization for float convolution and
pooling, and exercise max-pool fallback and activation preservation in
both layout modes. Focused host tests, M55 simulator cases, and lint pass.

Authored with AI using Codex.

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell

[rascani]

Stack from ghstack (oldest at bottom):

• Support channel broadcasting in Cortex-M explicit layout #24761
• -> Preserve portable fallback in Cortex-M explicit layout #24760

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24760

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

❌ 2 New Failures, 1 Unrelated Failure

As of commit fa94def with merge base 472953c ():

NEW FAILURES - The following jobs have failed:

• pull / android / run-emulator (gh)
  adb: failed to install android-test-debug-androidTest.apk: cmd: Can't find service: package
• pull / unittest / linux / linux-job (gh)
  extension/llm/tokenizers/test/test_hf_tokenizer.py::TestHfTokenizer::test_phi_4_mini

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

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

• trunk / test-arm-backend-vkml (test_pytest_models_vkml) / linux-job (gh) (trunk failure)
  RuntimeError: Command bash /__w/_temp/exec_script failed with exit code 1

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[github-actions]

This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

To add a label, you can comment to pytorchbot, for example
@pytorchbot label "release notes: none"

For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.