Benutzerwarnung: Es wurde eine Mutation in einem Puffer im Modell erkannt. ExecuTorch geht davon aus, dass Puffer, die i ⇐ Python

[Anonymous]

Ich habe das Modell in das Executorch-Programm exportiert und die Datei wird korrekt im Verzeichnis gespeichert. Aber ich habe eine solche Warnung erhalten

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# To see more debug info, please use `graph_module.print_readable()`
Model successfully exported to model.pte
/usr/local/lib/python3.10/dist-packages/executorch/exir/emit/_emitter.py:1512: UserWarning: Mutation on a buffer in the model is detected. ExecuTorch assumes buffers that are mutated in the graph have a meaningless initial state, only the shape and dtype will be serialized.
warnings.warn(

Ja, die Warnung wurde nicht abgeschlossen, daher war ich verwirrt, ob diese Warnung große Auswirkungen auf das Programm haben würde, das ich ausführen wollte oder nicht.
Ich verwende T4 in Google Colab, um diese Funktion auszuführen.

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def quantize_model(model: nn.Module, example_inputs: tuple) -> nn.Module:

print(f"Original model architecture:\n{model}")

# Initialize quantizer with symmetric quantization configuration
quantizer = XNNPACKQuantizer()
operator_config = get_symmetric_quantization_config(is_per_channel=False)
quantizer.set_global(operator_config)

# Prepare model for quantization
pre_autograd_model = export_for_training(model, example_inputs).module()
prepared_model = prepare_pt2e(pre_autograd_model, quantizer)

# Calibrate and convert the model
prepared_model(*example_inputs)
quantized_model = convert_pt2e(prepared_model)

print(f"Quantized model architecture:\n{quantized_model}")
return quantized_model

def export_to_executorch(model: nn.Module, example_inputs: tuple, output_path: str):

edge_program = to_edge_transform_and_lower(
export(model, example_inputs),
compile_config=EdgeCompileConfig(_check_ir_validity=True),
partitioner=[XnnpackPartitioner()]
)

# Convert to executorch program
executorch_program: exir.ExecutorchProgramManager = edge_program.to_executorch(
ExecutorchBackendConfig(passes=[])
)

# Save the program to file
with open(output_path, "wb") as file:
file.write(executorch_program.buffer)
print(f"Model successfully exported to {output_path}")

def main():
# Define example inputs
example_inputs = (torch.randn(1, 1, 256, 256),)

# Create and quantize model
model = ResEmoteNet()  # Assuming this class is defined elsewhere
quantized_model = quantize_model(model, example_inputs)

# Export the quantized model
export_to_executorch(
model=quantized_model,
example_inputs=example_inputs,
output_path="model.pte"
)

if __name__ == "__main__":
main()