Introduce the get_fake_quant_model API

backends/cadence/aot/compiler.py

@@ -172,29 +172,18 @@ def fuse_pt2(
     return converted_graph_module
 
 
-def quantize_pt2(
+# Note: quantizer is not optional here to force the user to supply a quantizer
+# and ensure consistency is more likely to be maintained.
+def get_fake_quant_model(
     model: torch.nn.Module,
     inputs: tuple[object, ...],
-    quantizer: Optional[CadenceQuantizer] = None,
+    quantizer: CadenceQuantizer,
     calibration_data: Optional[list[tuple[object, ...]]] = None,
     dump_graphs: bool = False,
-) -> ExportedProgram:
-    """
-    Trace, prepare, convert and fuse the model using the given quantizer.
-    If calibration data is provided, it will be used to calibrate the model. If
-    not, the inputs will be used for calibration instead, which is useful for
-    unit tests but should not be used for end-to-end use cases.
-    Returns a GraphModule with the quantized model.
-    Note: this function should not be called directly in general. Please use
-    quantize_and_export_to_executorch for most needs.
-    """
+) -> torch.fx.GraphModule:
     # Make the model inference mode by calling model.eval()
     model.eval()
 
-    # Instantiate the quantizer to CadenceQuantizer if not supplied
-    if not quantizer:
-        quantizer = CadenceDefaultQuantizer()
-
     program = trace(model, inputs, dump_graphs=dump_graphs)
 
     if dump_graphs:
@@ -214,6 +203,37 @@ def quantize_pt2(
 
     # Get converted graph module
     converted_gm = convert_pt2(prepared_gm, dump_graphs=dump_graphs)
+    return converted_gm
+
+
+def quantize_pt2(
+    model: torch.nn.Module,
+    inputs: tuple[object, ...],
+    quantizer: Optional[CadenceQuantizer] = None,
+    calibration_data: Optional[list[tuple[object, ...]]] = None,
+    dump_graphs: bool = False,
+) -> ExportedProgram:
+    """
+    Trace, prepare, convert and fuse the model using the given quantizer.
+    If calibration data is provided, it will be used to calibrate the model. If
+    not, the inputs will be used for calibration instead, which is useful for
+    unit tests but should not be used for end-to-end use cases.
+    Returns a GraphModule with the quantized model.
+    Note: this function should not be called directly in general. Please use
+    quantize_and_export_to_executorch for most needs.
+    """
+    # Instantiate the quantizer to CadenceQuantizer if not supplied
+    if not quantizer:
+        quantizer = CadenceDefaultQuantizer()
+
+    # Get the converted (aka fake quant) graph module
+    converted_gm = get_fake_quant_model(
+        model,
+        inputs,
+        quantizer=quantizer,
+        calibration_data=calibration_data,
+        dump_graphs=dump_graphs,
+    )
 
     # Get fused model
     fused_gm = fuse_pt2(converted_gm, quantizer)
@@ -237,7 +257,7 @@ def quantize_pt2(
     torch.ops.aten.angle.default,
     torch.ops.aten.rms_norm.default,
 ]
-TO_EDGE_PRESERVE_OPS: list[torch._ops.OpOverload, ...] = [
+TO_EDGE_PRESERVE_OPS: list[torch._ops.OpOverload] = [
     torch.ops.aten.rms_norm.default,
 ]