:py:mod:`neural_compressor.compression.pruner.patterns.base`
============================================================

.. py:module:: neural_compressor.compression.pruner.patterns.base

.. autoapi-nested-parse::

   Pruning patterns.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.compression.pruner.patterns.base.BasePattern
   neural_compressor.compression.pruner.patterns.base.PytorchBasePattern
   neural_compressor.compression.pruner.patterns.base.KerasBasePattern



Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.compression.pruner.patterns.base.register_pattern



.. py:function:: register_pattern(name)

   Class decorator used to register a Pattern subclass to the registry.

   Decorator function used before a Pattern subclasses.
   Make sure that this Pattern class can be registered in PATTERNS.

   :param name: A string defining the pattern type name to be used in a pruning process.

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


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


   Pruning Pattern.

   It defines the basic pruning unit and how this unit will be pruned during pruning, e.g. 4x1, 2:4.

   :param config: A config dict object that contains the pattern information.
   :param modules: neural network modules to be pruned with the pattern.

   .. attribute:: pattern

      A config dict object that includes information of the pattern.

   .. attribute:: is_global

      A bool determining whether the pruning takes global pruning option.
      Global pruning means that pruning scores by a pruning criterion are evaluated in all layers.
      Local pruning, by contrast, means that pruning scores by the pruning criterion are evaluated
          in every layer individually.

   .. attribute:: keep_mask_layers

      A dict that includes the layers whose mask will not be updated.

   .. attribute:: invalid_layers

      The layers whose shapes don't fit the pattern.

   .. attribute:: modules

      neural network modules to be pruned with the pattern.

   .. attribute:: config

      A config dict object that contains all the information including the pattern's.

   .. attribute:: max_sparsity_ratio_per_op

      A float representing the maximum sparsity that one layer could reach.

   .. attribute:: min_sparsity_ratio_per_op

      A float representing the minimum sparsity that one layer could reach.

   .. attribute:: target_sparsity

      A float representing the sparsity ratio of the modules after pruning.


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




   Pruning Pattern.

   It defines the basic pruning unit and how this unit will be pruned during pruning, e.g. 4x1, 2:4.

   :param config: A config dict object that contains the pattern information.
   :param modules: neural network modules to be pruned with the pattern.

   .. attribute:: pattern

      A config dict object that includes information of the pattern.

   .. attribute:: is_global

      A bool determining whether the pruning takes global pruning option.
      Global pruning means that pruning scores by a pruning criterion are evaluated in all layers.
      Local pruning, by contrast, means that pruning scores by the pruning criterion are evaluated
          in every layer individually.

   .. attribute:: keep_mask_layers

      A dict that includes the layers whose mask will not be updated.

   .. attribute:: invalid_layers

      The layers whose shapes don't fit the pattern.

   .. attribute:: modules

      neural network modules to be pruned with the pattern.

   .. attribute:: config

      A config dict object that contains all the information including the pattern's.

   .. attribute:: max_sparsity_ratio_per_op

      A float representing the maximum sparsity that one layer could reach.

   .. attribute:: min_sparsity_ratio_per_op

      A float representing the minimum sparsity that one layer could reach.

   .. attribute:: target_sparsity

      A float representing the sparsity ratio of the modules after pruning.


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




   Pruning Pattern.

   It defines the basic pruning unit and how this unit will be pruned during pruning, e.g. 4x1, 2:4.

   :param config: A config dict object that contains the pattern information.
   :param modules: neural network modules to be pruned with the pattern.

   .. attribute:: pattern

      A config dict object that includes information of the pattern.

   .. attribute:: is_global

      A bool determining whether the pruning takes global pruning option.
      Global pruning means that pruning scores by a pruning criterion are evaluated in all layers.
      Local pruning, by contrast, means that pruning scores by the pruning criterion are evaluated
          in every layer individually.

   .. attribute:: keep_mask_layers

      A dict that includes the layers whose mask will not be updated.

   .. attribute:: invalid_layers

      The layers whose shapes don't fit the pattern.

   .. attribute:: modules

      neural network modules to be pruned with the pattern.

   .. attribute:: config

      A config dict object that contains all the information including the pattern's.

   .. attribute:: max_sparsity_ratio_per_op

      A float representing the maximum sparsity that one layer could reach.

   .. attribute:: min_sparsity_ratio_per_op

      A float representing the minimum sparsity that one layer could reach.

   .. attribute:: target_sparsity

      A float representing the sparsity ratio of the modules after pruning.