ConvertCustomConfig

class torch.ao.quantization.fx.custom_config.ConvertCustomConfig

Custom configuration for convert_fx().

Example usage:

convert_custom_config = ConvertCustomConfig()             .set_observed_to_quantized_mapping(ObservedCustomModule, QuantizedCustomModule)             .set_preserved_attributes(["attr1", "attr2"])

classmethod from_dict(convert_custom_config_dict)

Create a ConvertCustomConfig from a dictionary with the following items:

“observed_to_quantized_custom_module_class”: a nested dictionary mapping from quantization mode to an inner mapping from observed module classes to quantized module classes, e.g.:: { “static”: {FloatCustomModule: ObservedCustomModule}, “dynamic”: {FloatCustomModule: ObservedCustomModule}, “weight_only”: {FloatCustomModule: ObservedCustomModule} } “preserved_attributes”: a list of attributes that persist even if they are not used in forward

This function is primarily for backward compatibility and may be removed in the future.

Return type

ConvertCustomConfig

set_observed_to_quantized_mapping(observed_class, quantized_class, quant_type=QuantType.STATIC)

Set the mapping from a custom observed module class to a custom quantized module class.

The quantized module class must have a from_observed class method that converts the observed module class to the quantized module class.

Return type

ConvertCustomConfig

set_preserved_attributes(attributes)

Set the names of the attributes that will persist in the graph module even if they are not used in the model’s forward method.

Return type

ConvertCustomConfig

to_dict()

Convert this ConvertCustomConfig to a dictionary with the items described in from_dict().

Return type

Dict[str, Any]