convert_fx

class torch.ao.quantization.quantize_fx.convert_fx(graph_module, convert_custom_config=None, _remove_qconfig=True, qconfig_mapping=None, backend_config=None) [source]

Convert a calibrated or trained model to a quantized model

Parameters

• graph_module (*) – A prepared and calibrated/trained model (GraphModule)
• convert_custom_config (*) – custom configurations for convert function. See ConvertCustomConfig for more details
• _remove_qconfig (*) – Option to remove the qconfig attributes in the model after convert.

• qconfig_mapping (*) –

  config for specifying how to convert a model for quantization.

  The keys must include the ones in the qconfig_mapping passed to prepare_fx or prepare_qat_fx, with the same values or None. Additional keys can be specified with values set to None.

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

  qconfig_mapping = QConfigMapping
      .set_global(qconfig_from_prepare)
      .set_object_type(torch.nn.functional.add, None)  # skip quantizing torch.nn.functional.add
      .set_object_type(torch.nn.functional.linear, qconfig_from_prepare)
      .set_module_name("foo.bar", None)  # skip quantizing module "foo.bar"
  

  • backend_config (BackendConfig): A configuration for the backend which describes how

    operators should be quantized in the backend, this includes quantization mode support (static/dynamic/weight_only), dtype support (quint8/qint8 etc.), observer placement for each operators and fused operators. See BackendConfig for more details

Returns

A quantized model (torch.nn.Module)

Return type

GraphModule

Example:

# prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
# convert_fx converts a calibrated/trained model to a quantized model for the
# target hardware, this includes converting the model first to a reference
# quantized model, and then lower the reference quantized model to a backend
# Currently, the supported backends are fbgemm (onednn), qnnpack (xnnpack) and
# they share the same set of quantized operators, so we are using the same
# lowering procedure
#
# backend_config defines the corresponding reference quantized module for
# the weighted modules in the model, e.g. nn.Linear
# TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
# e.g. backend_config = get_default_backend_config("fbgemm")
quantized_model = convert_fx(prepared_model)