:py:mod:`neural_compressor.strategy.hawq_v2`
============================================

.. py:module:: neural_compressor.strategy.hawq_v2

.. autoapi-nested-parse::

   The HAWQ_V2 tuning strategy.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.strategy.hawq_v2.HAWQ_V2TuneStrategy




.. py:class:: HAWQ_V2TuneStrategy(model, conf, q_dataloader=None, q_func=None, eval_dataloader=None, eval_func=None, resume=None, q_hooks=None)

   Bases: :py:obj:`neural_compressor.strategy.strategy.TuneStrategy`

   The HAWQ V2 tuning strategy.

   HAWQ_V2 implements the "Hawq-v2: Hessian aware trace-weighted quantization of neural networks".
   We made a small change to it by using the hessian trace to score the op impact and then
   fallback the OPs according to the scoring result.


   .. py:method:: next_tune_cfg()

      Generate and yield the next tuning config using HAWQ v2 search in tuning space.

      :Yields: *tune_config (dict)* -- A dict containing the tuning configuration for quantization.