:py:mod:`neural_compressor.onnxrt.algorithms.smoother.core`
===========================================================

.. py:module:: neural_compressor.onnxrt.algorithms.smoother.core

.. autoapi-nested-parse::

   Smoother for onnxrt.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.onnxrt.algorithms.smoother.core.Smoother




.. py:class:: Smoother(model: Union[onnx.ModelProto, neural_compressor.onnxrt.utils.onnx_model.ONNXModel, pathlib.Path, str], dataloader: neural_compressor.onnxrt.quantization.calibrate.CalibrationDataReader, providers: List[str] = ['CPUExecutionProvider'])


   Fake input channel quantization.

   For more details please refer to:
   [1] SmoothQuant: Accurate and Efficient
   Post-Training Quantization for Large Language Models
   [2] SPIQ: Data-Free Per-Channel Static Input Quantization
   We only support inplace mode which means the model weights will be changed,
   you can call recover function to recover the weights if needed.