Avoid double cloning of Composite Ops created by FusionOptimizer

Author: ricardoV94

pytensor/scalar/basic.py

@@ -13,7 +13,6 @@
 import builtins
 import math
 from collections.abc import Callable
-from copy import copy
 from itertools import chain
 from textwrap import dedent
 from typing import Any, TypeAlias
@@ -4093,12 +4092,12 @@ def __init__(self, *args, **kwargs):
         self.prepare_node_called = set()
         super().__init__(*args, **kwargs)
 
-    def _cleanup_graph(self, inputs, outputs):
+    def _cleanup_graph(self, inputs, outputs, clone: bool = True):
         # TODO: We could convert to TensorVariable, optimize graph,
         # and then convert back to ScalarVariable.
         # This would introduce rewrites like `log(1 + x) -> log1p`.
 
-        fgraph = FunctionGraph(copy(inputs), copy(outputs))
+        fgraph = FunctionGraph(inputs, outputs, clone=clone)
 
         # Validate node types
         for node in fgraph.apply_nodes:
@@ -4281,7 +4280,7 @@ class Composite(ScalarInnerGraphOp):
 
     init_param: tuple[str, ...] = ("inputs", "outputs")
 
-    def __init__(self, inputs, outputs, name="Composite"):
+    def __init__(self, inputs, outputs, name="Composite", clone_graph: bool = True):
         self.name = name
         self._name = None
         # We need to clone the graph as sometimes its nodes already
@@ -4299,10 +4298,13 @@ def __init__(self, inputs, outputs, name="Composite"):
         if len(outputs) > 1 or not any(
             isinstance(var.owner.op, Composite) for var in outputs
         ):
-            # No inner Composite
-            inputs, outputs = clone(inputs, outputs)
+            if clone_graph:
+                inputs, outputs = clone(inputs, outputs)
+
         else:
             # Inner Composite that we need to flatten
+            # FIXME: There could be a composite in the middle of the graph, why is this here?
+            #  If anything it should be an optimization, but I suspect lower-level compilation can handle this anyway.
             assert len(outputs) == 1
             # 1. Create a new graph from inputs up to the
             # Composite
@@ -4321,7 +4323,8 @@ def __init__(self, inputs, outputs, name="Composite"):
             assert res[0] != inputs
             inputs, outputs = res[0], res2[1]
 
-        self.inputs, self.outputs = self._cleanup_graph(inputs, outputs)
+        # We already cloned the graph, or the user told us there was no need for it
+        self.inputs, self.outputs = self._cleanup_graph(inputs, outputs, clone=False)
         self.inputs_type = tuple(input.type for input in self.inputs)
         self.outputs_type = tuple(output.type for output in self.outputs)
         self.nin = len(inputs)

pytensor/tensor/rewriting/elemwise.py

@@ -913,12 +913,13 @@ def update_fuseable_mappings_after_fg_replace(
                 break
 
             scalar_inputs, scalar_outputs = self.elemwise_to_scalar(inputs, outputs)
-            composite_outputs = Elemwise(ps.Composite(scalar_inputs, scalar_outputs))(
-                *inputs
-            )
-            if not isinstance(composite_outputs, list):
-                composite_outputs = [composite_outputs]
-            for old_out, composite_out in zip(outputs, composite_outputs, strict=True):
+            composite_outputs = Elemwise(
+                # No need to clone Composite graph, because `self.elemwise_to_scalar` creates fresh variables
+                ps.Composite(scalar_inputs, scalar_outputs, clone_graph=False)
+            )(*inputs, return_list=True)
+            assert len(outputs) == len(composite_outputs)
+            for old_out, composite_out in zip(outputs, composite_outputs):
+                # Preserve any names on the original outputs
                 if old_out.name:
                     composite_out.name = old_out.name