:orphan:

:py:mod:`neural_compressor.conf.pythonic_config`
================================================

.. py:module:: neural_compressor.conf.pythonic_config


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.conf.pythonic_config.QuantizationConfig




.. py:class:: QuantizationConfig(inputs=[], outputs=[], backend='default', device='cpu', approach='post_training_static_quant', calibration_sampling_size=[100], op_type_dict=None, op_name_dict=None, strategy='basic', strategy_kwargs=None, objective='performance', timeout=0, max_trials=100, performance_only=False, reduce_range=None, use_bf16=True, quant_level='auto', accuracy_criterion=accuracy_criterion, use_distributed_tuning=False)




   Basic class for quantization config. Inherited by PostTrainingQuantConfig and QuantizationAwareTrainingConfig.

   :param inputs: Inputs of model, only required in tensorflow.
   :param outputs: Outputs of model, only required in tensorflow.
   :param backend: Backend for model execution. Support 'default', 'itex', 'ipex', 'onnxrt_trt_ep', 'onnxrt_cuda_ep'
   :param domain: Model domain. Support 'auto', 'cv', 'object_detection', 'nlp' and 'recommendation_system'.
                  Adaptor will use specific quantization settings for different domains automatically, and
                  explicitly specified quantization settings will override the automatic setting.
                  If users set domain as auto, automatic detection for domain will be executed.
   :param recipes: Recipes for quantiztaion, support list is as below.
                   'smooth_quant': whether do smooth quant
                   'smooth_quant_args': parameters for smooth_quant
                   'fast_bias_correction': whether do fast bias correction
                   'weight_correction': whether do weight correction
                   'gemm_to_matmul': whether convert gemm to matmul and add, only valid for onnx models
                   'graph_optimization_level': support 'DISABLE_ALL', 'ENABLE_BASIC', 'ENABLE_EXTENDED', 'ENABLE_ALL'
                                             only valid for onnx models
                   'first_conv_or_matmul_quantization': whether quantize the first conv or matmul
                   'last_conv_or_matmul_quantization': whether quantize the last conv or matmul
                   'pre_post_process_quantization': whether quantize the ops in preprocess and postprocess
                   'add_qdq_pair_to_weight': whether add QDQ pair for weights, only vaild for onnxrt_trt_ep
                   'optypes_to_exclude_output_quant': don't quantize output of specified optypes
                   'dedicated_qdq_pair': whether dedicate QDQ pair, only vaild for onnxrt_trt_ep
   :param quant_format: Support 'default', 'QDQ' and 'QOperator', only required in ONNXRuntime.
   :param device: Support 'cpu' and 'gpu'.
   :param calibration_sampling_size: Number of calibration sample.
   :param op_type_dict: Tuning constraints on optype-wise  for advance user to reduce tuning space.
                        User can specify the quantization config by op type:
                        example:
                        {
                            'Conv': {
                                'weight': {
                                    'dtype': ['fp32']
                                },
                                'activation': {
                                    'dtype': ['fp32']
                                }
                            }
                        }
   :param op_name_dict: Tuning constraints on op-wise for advance user to reduce tuning space.
                        User can specify the quantization config by op name:
                        example:
                        {
                            "layer1.0.conv1": {
                                "activation": {
                                    "dtype": ["fp32"]
                                },
                                "weight": {
                                    "dtype": ["fp32"]
                                }
                            },
                        }
   :param strategy: Strategy name used in tuning, Please refer to docs/source/tuning_strategies.md.
   :param strategy_kwargs: Parameters for strategy, Please refer to docs/source/tuning_strategies.md.
   :param objective: Objective with accuracy constraint guaranteed, support 'performance', 'modelsize', 'footprint'.
                     Please refer to docs/source/objective.md.
                     Default value is 'performance'.
   :param timeout: Tuning timeout (seconds). default value is 0 which means early stop
   :param max_trials: Max tune times. default value is 100. Combine with timeout field to decide when to exit
   :param performance_only: Whether do evaluation
   :param reduce_range: Whether use 7 bit to quantization.
   :param example_inputs: Used to trace PyTorch model with torch.jit/torch.fx.
   :param excluded_precisions: Precisions to be excluded, Default value is empty list.
                               Neural compressor enable the mixed precision with fp32 + bf16 + int8 by default.
                               If you want to disable bf16 data type, you can specify excluded_precisions = ['bf16].
   :param quant_level: Support auto, 0 and 1, 0 is conservative strategy, 1 is basic or user-specified
                       strategy, auto (default) is the combination of 0 and 1.
   :param accuracy_criterion: Accuracy constraint settings.
   :param use_distributed_tuning: Whether use distributed tuning or not.