:orphan:

:py:mod:`neural_compressor.torch.algorithms.static_quant.static_quant`
======================================================================

.. py:module:: neural_compressor.torch.algorithms.static_quant.static_quant


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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.torch.algorithms.static_quant.static_quant.StaticQuantQuantizer




.. py:class:: StaticQuantQuantizer(quant_config: collections.OrderedDict = {})




   The base quantizer for all algorithm quantizers.

   The `Quantizer` unifies the interfaces across various quantization algorithms, including GPTQ, RTN, etc.
   Given a float model, `Quantizer` apply the quantization algorithm to the model according to the `quant_config`.

   To implement a new quantization algorithm,, inherit from `Quantizer` and implement the following methods:
       - `prepare`: prepare a given model for convert.
       - `convert`: convert a prepared model to a quantized model.
   Note: `quantize` and `execute` are optional for new quantization algorithms.