:py:mod:`neural_compressor.adaptor.tf_utils.graph_rewriter.generic.graph_cse_optimizer`
=======================================================================================

.. py:module:: neural_compressor.adaptor.tf_utils.graph_rewriter.generic.graph_cse_optimizer

.. autoapi-nested-parse::

   CSE Graph Rewriter.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.adaptor.tf_utils.graph_rewriter.generic.graph_cse_optimizer.GraphCseOptimizer




.. py:class:: GraphCseOptimizer(model)




   We introduce the CSE optimizer to optimize the nodes that contains identical op type.

   Case 1. Node A has three output nodes(B,C,D) and those child nodes has their own outputs
           (B1/C1C2/D1).
           Node A
           x    x    x
           x    x     x
   NODE B   NODE C  NODE D
   x        x   x     x
   B1       C1  C2    D1
   If Node B/C/D have the identical memory-bound op, like relu/relu6. The graph will be
   converted as below.
   We removed the Node C & Node D, updated the B as the input of C1/C2/D1.
           Node A
           x
           Node B
           x x  x  x
           x  x   x  x
       x   x    x  x
       B1  C1   C2  D1
   Case 2.  Node A has three output nodes(B,C,D) and those child nodes has their own outputs
           (B1/C1C2/D1).
           Node A
           x   x    x
           x    x     x
   NODE B   NODE C  NODE D
       x      x   x     x
       B1     C1  C2    D1
   If Node B and C have the identical memory-bound op, like relu/relu6. The graph will be
   converted as below.
   We removed the Node C, updated the B as the input of C1/C2.
               Node A
               x     x
           Node B  Node D
           x   x  x    x
           x   |   x    x
           x   |    x    x
           B1   C1    C2   D1

   :returns: A optimized graphdef object.
   :rtype: [graphdef]