Allow passing in calibration data to convert_pt2 (#9791)

Summary:

As titled. The data should be passed in as a list of inputs, and will be used to calibrate the PTQ model.

Differential Revision: D71289674

Author: mcremon-meta

backends/cadence/aot/compiler.py

@@ -56,6 +56,7 @@ def convert_pt2(
     model: torch.nn.Module,
     inputs: tuple[object, ...],
     quantizer: CadenceQuantizer,
+    calibration_data: Optional[list[tuple[object, ...]]] = None,
     dump_graphs: bool = False,
 ) -> torch.fx.GraphModule:
     """
@@ -64,6 +65,9 @@ def convert_pt2(
     fuse the model later, if applicable. If you do not expect that behavior,
     please use quantize_and_fuse_pt2 instead, which will instantiate a
     default quantizer for you if needed.
+    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 converted model.
     """
 
@@ -95,7 +99,12 @@ def convert_pt2(
     prepared_model = prepare_pt2e(model_gm, quantizer)
 
     # Calibrate
-    prepared_model(*inputs)
+    # If no calibration data is provided, use the inputs
+    if calibration_data is None:
+        calibration_data = [inputs]
+
+    for samples in calibration_data:
+        prepared_model(*samples)
 
     # Convert
     converted_model = convert_pt2e(prepared_model)
@@ -136,10 +145,14 @@ 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,
 ) -> torch.fx.GraphModule:
     """
     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.
     """
     # Make the model inference mode by calling model.eval()
@@ -150,7 +163,9 @@ def quantize_pt2(
         quantizer = CadenceDefaultQuantizer()
 
     # Get converted graph module
-    converted_gm = convert_pt2(model, inputs, quantizer, dump_graphs)
+    converted_gm = convert_pt2(
+        model, inputs, quantizer, calibration_data, dump_graphs=dump_graphs
+    )
 
     # Get fused model
     fused_gm = fuse_pt2(converted_gm, quantizer)