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Add backward compatible types to pt2e prepare (#2244) #11080

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Merged
merged 1 commit into from
May 23, 2025
Merged

Add backward compatible types to pt2e prepare (#2244) #11080

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2 changes: 1 addition & 1 deletion backends/cadence/aot/compiler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,7 +123,7 @@ def prepare_and_convert_pt2(
assert isinstance(model_gm, torch.fx.GraphModule)

# Prepare
prepared_model = prepare_pt2e(model_gm, quantizer) # pyre-ignore[6]
prepared_model = prepare_pt2e(model_gm, quantizer)

# Calibrate
# If no calibration data is provided, use the inputs
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2 changes: 1 addition & 1 deletion examples/xnnpack/quantization/utils.py
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Expand Up @@ -33,7 +33,7 @@ def quantize(
is_dynamic=is_dynamic,
)
quantizer.set_global(operator_config)
m = prepare_pt2e(model, quantizer) # pyre-ignore[6]
m = prepare_pt2e(model, quantizer)
# calibration
m(*example_inputs)
m = convert_pt2e(m)
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2 changes: 1 addition & 1 deletion extension/llm/export/builder.py
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Expand Up @@ -373,7 +373,7 @@ def pt2e_quantize(self, quantizers: Optional[List[Quantizer]]) -> "LLMEdgeManage
), "Please run export() first"
m = prepare_pt2e(
self.pre_autograd_graph_module, # pyre-ignore[6]
composed_quantizer, # pyre-ignore[6]
composed_quantizer,
)
logging.info(
f"Calibrating with tasks: {self.calibration_tasks}, limit: {self.calibration_limit}, calibration_data: {self.calibration_data}, tokenizer_path: {self.tokenizer_path}, seq_length: {self.calibration_seq_length}"
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