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

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

.. autoapi-nested-parse::

   pruning patterns.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.compression.pruner.patterns.BasePattern
   neural_compressor.compression.pruner.patterns.PatternNxM
   neural_compressor.compression.pruner.patterns.PatternNInM



Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.compression.pruner.patterns.register_pattern
   neural_compressor.compression.pruner.patterns.get_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:function:: get_pattern(config, modules)

   Get registered pattern class.

   Get a Pattern object from PATTERNS.

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

   :returns: A Pattern object.

   :raises AssertionError: Currently only support patterns which have been registered in PATTERNS.


.. 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: Torch 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

      Torch 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:: PatternNxM(config, modules)




   Pruning Pattern.

   A Pattern class derived from BasePattern. In this pattern, the weights in a NxM block will be pruned or kept
   during one pruning step.

   :param config: A config dict object that contains the pattern information.

   .. attribute:: block_size

      A list of two integers representing the height and width of the block.

   .. attribute:: Please note that the vertical direction of a Linear layer's weight refers to the output channel.

      because PyTorch's tensor matmul has a hidden transpose operation.


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




   Pruning Pattern.

   A Pattern class derived from Pattern. In this pattern, N out of every M continuous weights will be pruned.
   For more info of this pattern, please refer to :
   https://github.com/intel/neural-compressor/blob/master/docs/sparsity.md

   :param config: A config dict object that contains the pattern information.

   .. attribute:: N

      The number of elements to be pruned in a weight sequence.

   .. attribute:: M

      The size of the weight sequence.