:py:mod:`neural_compressor.compression.pruner.pruning`
======================================================

.. py:module:: neural_compressor.compression.pruner.pruning

.. autoapi-nested-parse::

   Pruning.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.compression.pruner.pruning.BasePruning
   neural_compressor.compression.pruner.pruning.BasicPruning
   neural_compressor.compression.pruner.pruning.SparseGPTPruning
   neural_compressor.compression.pruner.pruning.RetrainFreePruning



Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.compression.pruner.pruning.register_pruning



.. py:function:: register_pruning(name)

   Class decorator to register a pruning subclass to the registry.

   Decorator function used before a pruner subclass.
   Make sure that the pruning class decorated by this function can be registered in PRUNINGS.

   :param cls: The subclass of register.
   :type cls: class
   :param name: A string. Define the pruning type.

   :returns: The class of register.
   :rtype: cls


.. py:class:: BasePruning(config, model, opt=None)


   Pruning.

   The main class to do pruning; it contains at least one Pruner object.

   :param config: a string representing the path to a config file. For config file template, please refer to
                  https://github.com/intel/neural-compressor/tree/master/examples/pytorch/nlp/huggingface_models/text-classification/pruning/pytorch_pruner/eager/

   .. attribute:: model

      The model object to prune.

   .. attribute:: config_file_path

      A string representing the path to a config file.

   .. attribute:: pruners

      A list. A list of Pruner objects.

   .. attribute:: pruner_info

      A config dict object that contains pruners' information.


.. py:class:: BasicPruning(config, model, opt=None)




   Pruning.

   The main class to do pruning; it contains at least one Pruner object.

   :param config: a string representing the path to a config file. For config file template, please refer to
                  https://github.com/intel/neural-compressor/tree/master/examples/pytorch/nlp/huggingface_models/text-classification/pruning/pytorch_pruner/eager/

   .. attribute:: model

      The model object to prune.

   .. attribute:: config_file_path

      A string representing the path to a config file.

   .. attribute:: pruners

      A list. A list of Pruner objects.

   .. attribute:: pruner_info

      A config dict object that contains pruners' information.


.. py:class:: SparseGPTPruning(config, model, dataloader, framework='pytorch', device=None)




   SparseGPT Pruning
   The SparseGPT pruning_class is derived from BasePruning.

   :param config: A config dict object that contains the pruner information.
   :param model: The model that need to be pruned.
   :param dataloader: Processed datasets, which is necessary for sparseGPT pruning.
   :param device: available device of pruning.


.. py:class:: RetrainFreePruning(config, model, dataloader=None, loss_func=None, framework='pytorch')




   Pruning.

   The main class to do pruning; it contains at least one Pruner object.

   :param config: a string representing the path to a config file. For config file template, please refer to
                  https://github.com/intel/neural-compressor/tree/master/examples/pytorch/nlp/huggingface_models/text-classification/pruning/pytorch_pruner/eager/

   .. attribute:: model

      The model object to prune.

   .. attribute:: config_file_path

      A string representing the path to a config file.

   .. attribute:: pruners

      A list. A list of Pruner objects.

   .. attribute:: pruner_info

      A config dict object that contains pruners' information.