Gate FuseQATConvBN behind is_qat=True; opt in from QAT deployments

Summary:
The FuseQATConvBN pass added in D104497938 ran unconditionally inside `apply_pre_edge_transform_passes`. Its `_prep_conv_biases` step delegates to the shared `_quantize_fused_conv_bias` helper, which iterates every conv in the graph and asserts each conv input is `dequantize_per_tensor` — an invariant that only holds inside the conv-BN simulation chain `prepare_qat_pt2e` inserts. PTQ graphs trip the assert (T271158088).

Two failure modes seen in the wild:
- `test_quantized_w8a32_conv1d_out_2` uses `CadenceW8A32MixedQuantizer` so activations stay float32; the conv input is the placeholder, not a dequant.
- `test_conv2d_out_7` is `channel_last=True`, so the conv input is `aten.permute`, not a dequant; the helper only unwraps `unsqueeze` variants.

Add an `is_qat: bool = False` parameter to `apply_pre_edge_transform_passes` and only include `FuseQATConvBN` when True. Plumb through `quantize_pt2`/`get_fake_quant_model` and forward from the modai recipe lambda so `ar_*_qat_et_recipe` factories actually opt in.

QAT-trained models lowered via blobgen need a way to reach the QAT recipe. Add `is_qat: bool` to `Packaging` and have `Rt700Hifi4Deployment` pass `train=self.packaging.is_qat` to `get_recipe_with_custom_settings`. Models like `activity_classification_artemis` should set `"is_qat": True` in their `defs.bzl` packaging block.

Differential Revision: D105061752

Author: ethansfng

backends/cadence/aot/compiler.py

@@ -152,6 +152,7 @@ def convert_pt2(
 def apply_pre_edge_transform_passes(
     converted_program: ExportedProgram,
     quantizer: CadenceQuantizer,
+    is_qat: bool = False,
 ) -> ExportedProgram:
     """
     Apply pre-edge transform passes including QuantFusion and torch ops passes.
@@ -166,12 +167,11 @@ def apply_pre_edge_transform_passes(
     """
     # pyre-ignore[16]: no attribute
     patterns = [q.pattern for q in quantizer.quantizers]
-    PassManager(
-        [
-            FuseQATConvBN(converted_program),
-            QuantFusion(patterns),
-        ]
-    )(converted_program.graph_module)
+    passes = []
+    if is_qat:
+        passes.append(FuseQATConvBN(converted_program))
+    passes.append(QuantFusion(patterns))
+    PassManager(passes)(converted_program.graph_module)
 
     # Apply torch ops passes (e.g., ReplaceMulTensorWithMulAndFullOpsPass)
     fused_program = apply_torch_ops_passes(converted_program)
@@ -187,19 +187,24 @@ def get_fake_quant_model(
     quantizer: CadenceQuantizer,
     calibration_data: Optional[list[tuple[object, ...]]] = None,
     dump_graphs: bool = False,
+    is_qat: bool = False,
 ) -> torch.fx.GraphModule:
     # Make the model inference mode by calling model.eval()
     model.eval()
 
     ops_to_keep = quantizer.get_ops_to_preserve_from_decomposition()
-    program = trace(model, inputs, dump_graphs=dump_graphs, ops_to_keep=ops_to_keep)
+    program = trace(
+        model, inputs, dump_graphs=dump_graphs, ops_to_keep=ops_to_keep, is_qat=is_qat
+    )
 
     if dump_graphs:
         logging.info("Graph after trace:")
         logging.info(program.graph.print_tabular())
 
     # Get prepared graph module
-    prepared_gm = prepare_traced_pt2(program, quantizer, dump_graphs=dump_graphs)
+    prepared_gm = prepare_traced_pt2(
+        program, quantizer, dump_graphs=dump_graphs, is_qat=is_qat
+    )
 
     # Calibrate
     # If no calibration data is provided, use the inputs
@@ -221,6 +226,7 @@ def quantize_pt2(
     calibration_data: Optional[list[tuple[object, ...]]] = None,
     dump_graphs: bool = False,
     quant_input_args: Optional[list[str]] = None,
+    is_qat: bool = False,
 ) -> ExportedProgram:
     """
     Trace, prepare, convert and fuse the model using the given quantizer.
@@ -242,6 +248,7 @@ def quantize_pt2(
         quantizer=quantizer,
         calibration_data=calibration_data,
         dump_graphs=dump_graphs,
+        is_qat=is_qat,
     )
     # Wrap the model to handle quantized inputs if provided
     if quant_input_args is not None:
@@ -254,7 +261,7 @@ def quantize_pt2(
     if quant_input_args is not None:
         QuantizedInputWrapper.sink_dequants(program)
 
-    fused_program = apply_pre_edge_transform_passes(program, quantizer)
+    fused_program = apply_pre_edge_transform_passes(program, quantizer, is_qat=is_qat)
 
     if dump_graphs:
         logging.info("Graph after quantization and fusion:")