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

Convert a calibrated or trained model to a quantized model

  • 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.

  • qconfig_dict (*) –

    qconfig_dict with either same keys as what is passed to the qconfig_dict in prepare_fx API, with same values or None, or additional keys with values set to None

    For each entry whose value is set to None, we skip quantizing that entry in the model:

    qconfig_dict = {
      # used for object_type, skip quantizing torch.nn.functional.add
      "object_type": [
        (torch.nn.functional.add, None),
        (torch.nn.functional.linear, qconfig_from_prepare)
      # sed for module names, skip quantizing ""
      "module_name": [
        ("", None)


A quantized model (GraphModule)


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


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