Faster vectorize by walking sorted nodes

pytensor/graph/replace.py

@@ -3,7 +3,13 @@ from collections.abc import Iterable, Mapping, Sequence
 from functools import partial, singledispatch
 from typing import Optional, Union, cast, overload
 
-from pytensor.graph.basic import Apply, Constant, Variable, truncated_graph_inputs
+from pytensor.graph.basic import (
+    Apply,
+    Constant,
+    Variable,
+    io_toposort,
+    truncated_graph_inputs,
+)
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.op import Op
 
@@ -295,19 +301,14 @@ def vectorize_graph(
     inputs = truncated_graph_inputs(seq_outputs, ancestors_to_include=replace.keys())
     new_inputs = [replace.get(inp, inp) for inp in inputs]
 
-    def transform(var: Variable) -> Variable:
-        if var in inputs:
-            return new_inputs[inputs.index(var)]
+    vect_vars = dict(zip(inputs, new_inputs))
+    for node in io_toposort(inputs, seq_outputs):
+        vect_inputs = [vect_vars.get(inp, inp) for inp in node.inputs]
+        vect_node = vectorize_node(node, *vect_inputs)
+        for output, vect_output in zip(node.outputs, vect_node.outputs):
+            vect_vars[output] = vect_output
 
-        node = var.owner
-        batched_inputs = [transform(inp) for inp in node.inputs]
-        batched_node = vectorize_node(node, *batched_inputs)
-        batched_var = batched_node.outputs[var.owner.outputs.index(var)]
-
-        return cast(Variable, batched_var)
-
-    # TODO: MergeOptimization or node caching?
-    seq_vect_outputs = [transform(out) for out in seq_outputs]
+    seq_vect_outputs = [vect_vars[out] for out in seq_outputs]
 
     if isinstance(outputs, Sequence):
         return seq_vect_outputs