[Qualcomm] Support native_layer_norm and affine-free LayerNorm in QNN backend

[KevinUW114514]

[Qualcomm] Support native_layer_norm and affine-free LayerNorm in QNN backend

Summary

Adds QNN backend support for aten.native_layer_norm.default (which is the decomposed form of torch.nn.LayerNorm) and handles models where weight/bias are not provided (elementwise_affine=False).

Problem

When exporting models with torch.native_layer_norm or torch.nn.LayerNorm(affine=False) to the QNN backend, the following issues occur:

1. Missing native_layer_norm visitor: The original LayerNormVisitor only targets aten.layer_norm.default, but PyTorch decomposes torch.nn.LayerNorm to aten.native_layer_norm.default during export.

2. None weight/bias: When elementwise_affine=False, the weight and bias arguments are None. QNN x86_64 runtime cannot handle None tensor inputs, causing AttributeError when calling get_parameter().

Solution

1. Update visitor target (op_layer_norm.py)

Change the visitor target from aten.layer_norm.default to aten.native_layer_norm.default:

# Before
target = ["aten.layer_norm.default"]

# After
target = ["aten.native_layer_norm.default"]

This is correct because during ExecuTorch export, aten.layer_norm.default is decomposed to aten.native_layer_norm.default before the QNN lowering stage.

2. Handle None weight/bias (op_layer_norm.py)

When weight/bias are None, create synthetic tensors:

• Missing weight → torch.ones(normalized_shapes) (identity transform)
• Missing bias → torch.zeros(normalized_shapes) (no offset)

Create synthetic fx.Node objects to register these as QNN static tensors:

weight_tensor = torch.ones(normalized_shapes, dtype=torch.float32)
weight_node = torch.fx.Node(
    node.graph,
    node.name + "_runtime_weight",
    "call_function",
    exir_ops.edge.aten.tensor.default,
    (),
    {},
)
# Preserve quant_attrs with zero_point=0 for QNN compatibility

3. Use same annotator for both ops (htp_rules.py)

The quantizer annotator registers both aten.layer_norm.default and aten.native_layer_norm.default to the same LayerNorm class, since both ops have identical argument schemas:

@register_annotator(
    [torch.ops.aten.layer_norm.default, torch.ops.aten.native_layer_norm.default],
    QnnConstants.OpLayerNorm.op_name,
)

4. Add None check to get_parameter() (utils.py)

Guard against None nodes to prevent AttributeError:

if node is None:
    return None

Files Changed

File	Changes
builders/op_layer_norm.py	Add native_layer_norm support + handle None weight/bias
builders/utils.py	Add None guard in get_parameter()
quantizer/annotators/htp_rules.py	Register annotator for both ops
tests/models.py	Add NativeLayerNorm test model
tests/test_qnn_delegate.py	Add floating-point and quantized tests

Test Plan

Run QNN delegate tests for layer_norm:

python backends/qualcomm/tests/test_qnn_delegate.py \
    -k "test_qnn_backend_layer_norm or test_qnn_backend_native_layer_norm" \
    --soc_model SM8650 \
    --build_folder build-x86/ \
    --executorch_root . \
    --enable_x86_64

Expected: 4 tests pass (2 floating-point, 2 quantized).

Release Notes

• Release notes: qualcomm

Related Issues

This resolves the issue where FLUX2 transformer export fails with:

• [QNN Delegate Op Builder]: LayerNorm weight is None, skipping
• AttributeError: 'NoneType' object has no attribute 'name'

Fixes #18989

• Labels: bug, module:qnn

@abhinaykukkadapu

[pytorch-bot]

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[KevinUW114514]

@pytorchbot label "release notes: none"

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[Copilot]

Pull request overview

Fixes a crash in the Qualcomm QNN PT2E quantizer by making _mark_nodes_as_annotated robust to None entries in node lists (e.g., when aten.layer_norm has optional affine args like weight=None).

Changes:

• Skip None entries in _mark_nodes_as_annotated to avoid AttributeError when accessing node.meta.

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[abhinaykukkadapu]

Hi @KevinUW114514 thank you for your contribution. I think the root cause is that we need to guard weight and bias creation in rules files for htp and lpai similar to #18219, let me know if you are willing to change it. Adding the guard might silently propagate bad configs like these in the pipeline and i think we should fail loudly. CC: @shewu-quic

[KevinUW114514]

Hi @abhinaykukkadapu , thanks for the follow-up! Actually I also realized this root issue as I encountered the error in my downstream tasks. I am currently working on fixing this. I can edit the issue and PR to re-state the issue and submit a complete fix for it. Let me know if any concern. Thank you!

[abhinaykukkadapu]

Hi @abhinaykukkadapu , thanks for the follow-up! Actually I also realized this root issue as I encountered the error in my downstream tasks. I am currently working on fixing this. I can edit the issue and PR to re-state the issue and submit a complete fix for it. Let me know if any concern. Thank you!

Thanks, that would be awesome, will look forward to your changes.