Fix DecomposeLayerNormPass to handle 6-arg layer_norm

[apullin]

Summary:

Problem

When using nn.LayerNorm in models that go through the ARM backend's quantization flow, the DecomposeLayerNormPass fails with:

ValueError: DecomposeLayerNormPass: too many values to unpack (expected 2)

This happens because torch.ops.aten.layer_norm.default has 6 arguments:

layer_norm(input, normalized_shape, weight, bias, eps, cudnn_enable)

But DecomposeLayerNormPass only handled up to 5 arguments (for native_layer_norm).

The error occurs during transform_for_annotation_pipeline in the ARM quantizer, which runs before edge transformation when the op is still aten.layer_norm.default.

Solution

Add case 6: to the match len(args) block in DecomposeLayerNormPass.call() to handle the 6th argument (cudnn_enable). This argument is simply ignored during decomposition since it's only relevant for cuDNN GPU optimization.

Testing

Added a new test file test_layernorm_modai_compat.py that:

1. Creates a simple Linear -> LayerNorm -> Linear model
2. Exports it via torch.export
3. Runs it through transform_for_annotation_pipeline (the exact path that was failing)
4. Verifies LayerNorm is decomposed correctly through the full TOSA pipelines

[pytorch-bot]

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❌ 1 New Failure, 1 Cancelled Job, 1 Unrelated Failure

As of commit 49be1d5 with merge base 10f72fc ():

NEW FAILURE - The following job has failed:

• pull / test-models-linux-basic (vit, xnnpack-quantization-delegation, cmake, linux.arm64.2xlarge, execut... / linux-job (gh)
  RuntimeError: Command docker exec -t 01ffd57123509feb234b814d8be608a830616e36524151a4d68f64693d4fa99e /exec failed with exit code 1

CANCELLED JOB - The following job was cancelled. Please retry:

• pull / unittest-buck / macos / macos-job (gh)
  ##[error]The operation was canceled.

UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:

• pull / android / run-emulator (gh) (#16137)
  Timeout waiting for emulator to boot.

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[meta-codesync]

@apullin has exported this pull request. If you are a Meta employee, you can view the originating Diff in D90395786.

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