ml_switcheroo.core.graph_optimizer
==================================

.. py:module:: ml_switcheroo.core.graph_optimizer

.. autoapi-nested-parse::

   Graph-Level Rewriter for Pattern Fusion.

   This module implements the `GraphOptimizer`, which performs fusion on
   the `LogicalGraph` intermediate representation. It scans the graph for
   topological sequences that match predefined patterns (e.g., Conv -> BN -> ReLU)
   and replaces them with fused macro nodes.

   Algorithm:
       1.  Build an adjacency map for efficient traversal.
       2.  Iterate through all nodes in the graph in topological order.
       3.  For each node, check if it starts a sequence matching any known PatternDef.
       4.  If a match is found (A -> B -> C):
           a. Create a new Fused Node (Kind=Replacement).
           b. Merge metadata from A, B, C into Fused Node.
           c. Rewire input edges: Source(A) -> Fused.
           d. Rewire output edges: Fused -> Target(C).
           e. Mark A, B, C for removal.
           f. Continue scan after C.
       5.  Reconstruct graph with fused nodes and updated edges.



Classes
-------

.. autoapisummary::

   ml_switcheroo.core.graph_optimizer.GraphOptimizer


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

.. py:class:: GraphOptimizer(patterns: List[ml_switcheroo.core.dsl.PatternDef])

   Optimizes a LogicalGraph by fusing subgraphs based on defined patterns.


   .. py:attribute:: patterns


   .. py:method:: optimize(graph: ml_switcheroo.core.graph.LogicalGraph) -> ml_switcheroo.core.graph.LogicalGraph

      Runs the fusion pass on the graph.

      :param graph: The input `LogicalGraph`.

      :returns: A new `LogicalGraph` with fusion applied.