Fix Conv1d w8a32 operator (#16607)

[mgiordy]

Summary:

Summary

This diff fixes the Conv1d w8a32 operator by adding a transformation to the val attribute of the other_inputs[0].meta dictionary. Specifically, the permute operation is applied to the original_val tensor with the fake_mode context, and the resulting transposed_val is assigned to transposed_inputs.meta["val"].

Reviewed By: mcremon-meta

Differential Revision: D89863750

[pytorch-bot]

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[meta-codesync]

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[Copilot]

Pull request overview

This PR fixes the Conv1d w8a32 operator by adding metadata propagation for transposed tensors and adding input validation to prevent unsupported configurations.

Changes:

• Added metadata propagation for the val attribute when creating transposed inputs and weights in the Conv1d w8a32 operator
• Added validation in patterns.py to bail early when input length doesn't equal kernel size (marked as "not yet supported")

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.

File	Description
backends/cadence/aot/quantizer/fusion_pass.py	Adds proper fake_mode-aware metadata propagation for transposed_inputs and transposed_weights when transforming Conv1d tensors from NCL to NLC format
backends/cadence/aot/quantizer/patterns.py	Adds validation to reject Conv1d operations where input length doesn't equal kernel size (3), marking this configuration as not yet supported

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[Copilot]

This check restricts the w8a32_conv pattern to only match when the input length equals the kernel size (3). While the comment indicates this is intentionally not yet supported, this is quite restrictive. Standard convolution operations typically support input lengths greater than or equal to the kernel size. The reference implementation in ref_implementations.py (lines 926-970) and the test in test_ref_implementations.py (lines 1156-1166 show length=5 with kernel=3) both support arbitrary input lengths. Consider whether this restriction is necessary, or if it should be relaxed to allow input_length >= kernel_size to enable the optimization in more cases.

[Copilot]

The metadata propagation logic for transposed_inputs (lines 435-448) and transposed_weights (lines 455-468) is duplicated with only minor variations. This pattern also appears elsewhere in the codebase (e.g., lines 164-176, 189-200, 376-385, 653-671). Consider extracting this into a helper function to reduce code duplication and improve maintainability. The helper function could take parameters like the node, transformation operation, and transformation arguments.

[Copilot]

Pull request overview

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

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[Copilot]

The new input-length guard inputs_shape[-1] != cnn_weights_shape[2] looks incorrect for Conv1d: inputs_shape[-1] is the sequence length (L), while cnn_weights_shape[2] is the kernel size (K=3). cadence::quantized_w8a32_conv (and its meta/ref implementations) support L > K (output length is L - K + 1), and existing tests cover L=5, K=3. This condition would incorrectly bail out for normal Conv1d inputs and prevent the pattern from ever matching.

Consider removing this guard, or (if needed) only bailing when L < K (or other truly unsupported cases).

Suggested change

	# Bail if length != kernel size - Not yet supported
	if inputs_shape[-1] != cnn_weights_shape[2]:
	# Bail only when the input length is smaller than the kernel size.
	# Conv1d supports input lengths greater than the kernel size.
	if inputs_shape[-1] < cnn_weights_shape[2]:

[Copilot]

This new shape-validation block is gated by if hasattr(cnn_weights.meta, "tensor_meta") above. Since fx.Node.meta is a dict, hasattr(..., "tensor_meta") will always be false, so none of the weight/input shape checks (including the new input-length check) will run.

Use a dict-key check (e.g., "tensor_meta" in cnn_weights.meta) so the validations actually execute when metadata is available.

[Copilot]

get_args_and_kwargs_mixed_w8a32_conv now conditionally propagates meta["val"] for the inserted permute nodes (and adds a fake_mode fallback). There doesn't appear to be any unit/integration test coverage exercising QuantFusion on a Conv1d->quantized_w8a32_conv rewrite, so regressions here (e.g., missing/incorrect meta causing later passes to fail) may go unnoticed.

Add a small test that runs QuantFusion on a minimal Conv1d graph and asserts the resulting graph contains the expected permutes + cadence::quantized_w8a32_conv, and that the graph can run FakeTensor/meta propagation without errors.

[Copilot]

Pull request overview

Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.

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[Copilot]

The new anchor-bail condition inputs_shape[-1] != cnn_weights_shape[2] incorrectly restricts quantized_w8a32 Conv1d fusion to cases where input length equals kernel size (3). The operator’s fake/meta kernel and reference implementation support general input lengths (output length in_length - kernel + 1), and existing tests exercise in_length=5 with kernel=3 (see backends/cadence/aot/tests/test_ref_implementations.py:1170+). This check will prevent valid fusions and likely regress model coverage; it should be removed or replaced with the actual supported constraint (if any).

Suggested change

	inputs = conv_layer.args[0]
	if "tensor_meta" in inputs.meta:
	inputs_shape = inputs.meta["tensor_meta"].shape
	# Bail if length != kernel size - Not yet supported
	if inputs_shape[-1] != cnn_weights_shape[2]:
	return (
	PartitionAnchors(
	empty=True,
	),
	conv_layer,
	)