:py:mod:`neural_compressor.compression.pruner.pruners.retrain_free`
===================================================================

.. py:module:: neural_compressor.compression.pruner.pruners.retrain_free

.. autoapi-nested-parse::

   Retrain free pruner.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.compression.pruner.pruners.retrain_free.PytorchRetrainFreePruner




.. py:class:: PytorchRetrainFreePruner(config, modules)




   Pruning Pruner.
   The retrain_free pruner_class is derived from BasePruner.
   This pruner references the mask search and mask rearrangement strategies in fast retraining free.
   RetrainFreePruner supports one-shot pruning (same effect as fast retraining free) and iterative pruning.
   Please refer to A Fast Post-Training Pruning Framework for Transformers
       (https://arxiv.org/abs/2204.09656)

   1. Defines pruning functions called at step begin/end, before/after optimize and epoch begin/end.
   2. Defines the pruning criterion and fixed weight parameters.
   3. Obtain block masks and its grads.
   4. Rearrange block masks.

   :param modules: A dict {"module_name": Tensor} that stores the pruning modules' weights.
   :param config: A config dict object that contains the pruner information.

   .. attribute:: pattern

      A Pattern object that defines pruning weights' arrangements within space.

   .. attribute:: criterion

      A Criterion Object that defines which weights are to be pruned

   .. attribute:: scheduler

      A Scheduler object that defines how the model's sparsity changes as training/pruning proceeds.

   .. attribute:: reg

      A Reg object that defines regulization terms.