Add FoldQATConvBNPass to fold QAT Conv-BN simulation chains into conv bias (#19315)

[rezaasjd]

Summary:

Add FoldQATConvBNPass to the Cadence AOT compiler pipeline to handle QAT Conv-BN simulated fusion patterns that survive into the exported graph.

When a model is exported after QAT training, the Conv-BN simulation chain (add(var+eps) -> sqrt -> div(bn_weight) -> div(conv_out/scale) -> add(orig_bias) -> batch_norm) may not be folded by TorchAO _fold_conv_bn_qat due to pattern mismatch. This leaves non-quantized add/div/sqrt nodes in the graph that cause QuantFusion to crash when it tries to fuse them as quantized add ops.

The fix has three parts:

1. Add conv1d.default to QuantizeFusedConvBnBiasAtenPass conv_targets so it matches conv1d ops and can create zero biases for convs without one (mirrors the existing conv2d support).

2. Add FoldQATConvBNPass which matches the QAT simulation chain, computes the BN correction constant C = (orig_bias - running_mean) * bn_weight / sqrt(running_var + eps) + bn_bias, folds C into the conv quantized bias tensor, and removes the simulation chain + batch_norm nodes. No new graph nodes are created.

3. Apply these passes in the correct order in both the get_fake_quant_model (pre-export, on GraphModule) and apply_pre_edge_transform_passes (post-export, on ExportedProgram) pipelines: first QuantizeFusedConvBnBiasAtenPass to create zero biases for convs that lack one, then FoldQATConvBNPass to fold the simulation chain into those biases.

Differential Revision: D103949573

[pytorch-bot]

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@rezaasjd has exported this pull request. If you are a Meta employee, you can view the originating Diff in D103949573.

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