class torch.quantization.prepare(model, inplace=False, allow_list=None, observer_non_leaf_module_list=None, prepare_custom_config_dict=None)[source]

Prepares a copy of the model for quantization calibration or quantization-aware training.

Quantization configuration should be assigned preemptively to individual submodules in .qconfig attribute.

The model will be attached with observer or fake quant modules, and qconfig will be propagated.

  • model – input model to be modified in-place

  • inplace – carry out model transformations in-place, the original module is mutated

  • allow_list – list of quantizable modules

  • observer_non_leaf_module_list – list of non-leaf modules we want to add observer

  • prepare_custom_config_dict – customization configuration dictionary for prepare function

# Example of prepare_custom_config_dict:
prepare_custom_config_dict = {
    # user will manually define the corresponding observed
    # module class which has a from_float class method that converts
    # float custom module to observed custom module
    "float_to_observed_custom_module_class": {
        CustomModule: ObservedCustomModule


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