convert_fx

class torch.quantization.quantize_fx.convert_fx(graph_module, is_reference=False, convert_custom_config_dict=None, _remove_qconfig=True)

Convert a calibrated or trained model to a quantized model

Parameters

• graph_module (*) – A prepared and calibrated/trained model (GraphModule)

• is_reference (*) – flag for whether to produce a reference quantized model, which will be a common interface between pytorch quantization with other backends like accelerators

• convert_custom_config_dict (*) –

  dictionary for custom configurations for convert function:

  convert_custom_config_dict = {
  
    # additional object (module/operator) mappings that will overwrite the default
    # module mappinng
    "additional_object_mapping": {
       "static": {
          FloatModule: QuantizedModule,
          float_op: quantized_op
       },
       "dynamic": {
          FloatModule: DynamicallyQuantizedModule,
          float_op: dynamically_quantized_op
       },
    },
  
    # user will manually define the corresponding quantized
    # module class which has a from_observed class method that converts
    # observed custom module to quantized custom module
    "observed_to_quantized_custom_module_class": {
       "static": {
           ObservedCustomModule: QuantizedCustomModule
       },
       "dynamic": {
           ObservedCustomModule: QuantizedCustomModule
       },
       "weight_only": {
           ObservedCustomModule: QuantizedCustomModule
       }
    },
  
    # Attributes that are not used in forward function will
    # be removed when constructing GraphModule, this is a list of attributes
    # to preserve as an attribute of the GraphModule even when they are
    # not used in the code
    "preserved_attributes": ["preserved_attr"],
  }
  

• _remove_qconfig (*) – Option to remove the qconfig attributes in the model after convert.

Returns

A quantized model (GraphModule)

Example:

# prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
quantized_model = convert_fx(prepared_model)