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Allow passing in calibration data to convert_pt2 #9791

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Apr 1, 2025
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Allow passing in calibration data to convert_pt2 #9791

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19 changes: 17 additions & 2 deletions backends/cadence/aot/compiler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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:
"""
Expand All @@ -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.
"""

Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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()
Expand All @@ -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)
Expand Down
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