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Support affine int8 weight specifications in Arm quantization (#24337) - #24337

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@AdithyaReddy9

@AdithyaReddy9 AdithyaReddy9 commented Oct 30, 2026 •

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Summary:

Allow static per-tensor and per-channel affine weight specifications. Retain the channel axis when deriving bias quantization parameters. Serialize identical per-channel convolution weight zero points as the scalar required by TOSA and reject conflicting zero points explicitly.

Reviewed By: rascani

Differential Revision: D128346146

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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24337

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

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@AdithyaReddy9 has exported this pull request. If you are a Meta employee, you can view the originating Diff in D128346146.

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CLA Signed
The committers listed above are authorized under a signed CLA.

  • ✅ login: AdithyaReddy9 / name: Adithya Reddy Nallabolu (ab974ba)

@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 Oct 30, 2026
@github-actions github-actions Bot added ciflow/trunk module: arm Issues related to arm backend labels Oct 30, 2026
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This PR needs a release notes: label

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@rascani
rascani requested a review from AdrianLundell October 30, 2026 15:18

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Thanks for the contribution, the change looks good but please add full E2E integration tests to ensure affine weights are supported across the full pipeline, preferably using pytest and the test pipelines used in other tests, and make sure the linter passes.

@meta-codesync meta-codesync Bot changed the title Support affine int8 weight specifications in Arm quantization Support affine int8 weight specifications in Arm quantization (#24337) Nov 2, 2026
AdithyaReddy9 added a commit to AdithyaReddy9/executorch that referenced this pull request Nov 2, 2026
…h#24337)

Summary:
Pull Request resolved: pytorch#24337

Allow static per-tensor and per-channel affine weight specifications. Retain the channel axis when deriving bias quantization parameters. Serialize identical per-channel convolution weight zero points as the scalar required by TOSA and reject conflicting zero points explicitly.

Differential Revision: D128346146
Signed-off-by: Adithya Reddy Nallabolu <adithyareddy@meta.com>
@AdithyaReddy9

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Thanks for the contribution, the change looks good but please add full E2E integration tests to ensure affine weights are supported across the full pipeline, preferably using pytest and the test pipelines used in other tests, and make sure the linter passes.

Added pytest E2E coverage through quantization, export, TOSA lowering and numerical reference comparison, including nonzero affine weight zero points and bias. All 10 local tests pass

@rascani

rascani commented Nov 2, 2026

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Looks like the lintrunner is failing (this happens sometimes with internal diffs exported). Can you fix that?

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Review automatically exported from Phabricator review in Meta.

AdithyaReddy9 added a commit to AdithyaReddy9/executorch that referenced this pull request Nov 2, 2026
…h#24337)

Summary:

Allow static per-tensor and per-channel affine weight specifications. Retain the channel axis when deriving bias quantization parameters. Serialize identical per-channel convolution weight zero points as the scalar required by TOSA and reject conflicting zero points explicitly.

Reviewed By: rascani

Differential Revision: D128346146
…h#24337)

Summary:

Allow static per-tensor and per-channel affine weight specifications. Retain the channel axis when deriving bias quantization parameters. Serialize identical per-channel convolution weight zero points as the scalar required by TOSA and reject conflicting zero points explicitly.

Reviewed By: rascani

Differential Revision: D128346146

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