Speedup FunctionGraph methods

pytensor/graph/fg.py

@@ -24,7 +24,6 @@ from pytensor.graph.traversal import (
     vars_between,
 )
 from pytensor.graph.utils import MetaObject, MissingInputError, TestValueError
-from pytensor.misc.ordered_set import OrderedSet
 
 
 ClientType = tuple[Apply, int]
@@ -133,7 +132,6 @@ class FunctionGraph(MetaObject):
             features = []
 
         self._features: list[Feature] = []
-
         # All apply nodes in the subgraph defined by inputs and
         # outputs are cached in this field
         self.apply_nodes: set[Apply] = set()
@@ -161,7 +159,8 @@ class FunctionGraph(MetaObject):
                     "input's owner or use graph.clone."
                 )
 
-            self.add_input(in_var, check=False)
+            self.inputs.append(in_var)
+            self.clients.setdefault(in_var, [])
 
         for output in outputs:
             self.add_output(output, reason="init")
@@ -189,16 +188,6 @@ class FunctionGraph(MetaObject):
             return
 
         self.inputs.append(var)
-        self.setup_var(var)
-
-    def setup_var(self, var: Variable) -> None:
-        """Set up a variable so it belongs to this `FunctionGraph`.
-
-        Parameters
-        ----------
-        var : pytensor.graph.basic.Variable
-
-        """
         self.clients.setdefault(var, [])
 
     def get_clients(self, var: Variable) -> list[ClientType]:
@@ -322,10 +311,11 @@ class FunctionGraph(MetaObject):
 
         """
         # Imports the owners of the variables
-        if var.owner and var.owner not in self.apply_nodes:
-            self.import_node(var.owner, reason=reason, import_missing=import_missing)
+        apply = var.owner
+        if apply is not None and apply not in self.apply_nodes:
+            self.import_node(apply, reason=reason, import_missing=import_missing)
         elif (
-            var.owner is None
+            apply is None
             and not isinstance(var, AtomicVariable)
             and var not in self.inputs
         ):
@@ -336,10 +326,11 @@ class FunctionGraph(MetaObject):
                     f"Computation graph contains a NaN. {var.type.why_null}"
                 )
             if import_missing:
-                self.add_input(var)
+                self.inputs.append(var)
+                self.clients.setdefault(var, [])
             else:
                 raise MissingInputError(f"Undeclared input: {var}", variable=var)
-        self.setup_var(var)
+        self.clients.setdefault(var, [])
         self.variables.add(var)
 
     def import_node(
@@ -356,29 +347,29 @@ class FunctionGraph(MetaObject):
         apply_node : Apply
             The node to be imported.
         check : bool
-            Check that the inputs for the imported nodes are also present in
-            the `FunctionGraph`.
+            Check that the inputs for the imported nodes are also present in the `FunctionGraph`.
         reason : str
             The name of the optimization or operation in progress.
         import_missing : bool
             Add missing inputs instead of raising an exception.
         """
         # We import the nodes in topological order. We only are interested in
-        # new nodes, so we use all variables we know of as if they were the
-        # input set.  (The functions in the graph module only use the input set
-        # to know where to stop going down.)
-        new_nodes = tuple(toposort(apply_node.outputs, blockers=self.variables))
-
+        # new nodes, so we use all nodes we know of as inputs to interrupt the toposort
+        self_variables = self.variables
+        self_clients = self.clients
+        self_apply_nodes = self.apply_nodes
+        self_inputs = self.inputs
+        for node in toposort(apply_node.outputs, blockers=self_variables):
             if check:
-            for node in new_nodes:
                 for var in node.inputs:
                     if (
                         var.owner is None
                         and not isinstance(var, AtomicVariable)
-                        and var not in self.inputs
+                        and var not in self_inputs
                     ):
                         if import_missing:
-                            self.add_input(var)
+                            self_inputs.append(var)
+                            self_clients.setdefault(var, [])
                         else:
                             error_msg = (
                                 f"Input {node.inputs.index(var)} ({var})"
@@ -390,20 +381,20 @@ class FunctionGraph(MetaObject):
                             )
                             raise MissingInputError(error_msg, variable=var)
 
-        for node in new_nodes:
-            assert node not in self.apply_nodes
-            self.apply_nodes.add(node)
-            if not hasattr(node.tag, "imported_by"):
-                node.tag.imported_by = []
-            node.tag.imported_by.append(str(reason))
+            self_apply_nodes.add(node)
+            tag = node.tag
+            if not hasattr(tag, "imported_by"):
+                tag.imported_by = [str(reason)]
+            else:
+                tag.imported_by.append(str(reason))
             for output in node.outputs:
-                self.setup_var(output)
-                self.variables.add(output)
-            for i, input in enumerate(node.inputs):
-                if input not in self.variables:
-                    self.setup_var(input)
-                    self.variables.add(input)
-                self.add_client(input, (node, i))
+                self_clients.setdefault(output, [])
+                self_variables.add(output)
+            for i, inp in enumerate(node.inputs):
+                if inp not in self_variables:
+                    self_clients.setdefault(inp, [])
+                    self_variables.add(inp)
+                self_clients[inp].append((node, i))
             self.execute_callbacks("on_import", node, reason)
 
     def change_node_input(
@@ -457,7 +448,7 @@ class FunctionGraph(MetaObject):
             self.outputs[node.op.idx] = new_var
 
         self.import_var(new_var, reason=reason, import_missing=import_missing)
-        self.add_client(new_var, (node, i))
+        self.clients[new_var].append((node, i))
         self.remove_client(r, (node, i), reason=reason)
         # Precondition: the substitution is semantically valid However it may
         # introduce cycles to the graph, in which case the transaction will be
@@ -756,10 +747,6 @@ class FunctionGraph(MetaObject):
           :meth:`FunctionGraph.orderings`.
 
         """
-        if len(self.apply_nodes) < 2:
-            # No sorting is necessary
-            return list(self.apply_nodes)
-
         return list(toposort_with_orderings(self.outputs, orderings=self.orderings()))
 
     def orderings(self) -> dict[Apply, list[Apply]]:
@@ -779,29 +766,17 @@ class FunctionGraph(MetaObject):
         take care of computing the dependencies by itself.
 
         """
-        assert isinstance(self._features, list)
-        all_orderings: list[dict] = []
-
-        for feature in self._features:
-            if hasattr(feature, "orderings"):
-                orderings = feature.orderings(self)
-                if not isinstance(orderings, dict):
-                    raise TypeError(
-                        "Non-deterministic return value from "
-                        + str(feature.orderings)
-                        + ". Nondeterministic object is "
-                        + str(orderings)
-                    )
-                if len(orderings) > 0:
-                    all_orderings.append(orderings)
-                    for node, prereqs in orderings.items():
-                        if not isinstance(prereqs, list | OrderedSet):
-                            raise TypeError(
-                                "prereqs must be a type with a "
-                                "deterministic iteration order, or toposort "
-                                " will be non-deterministic."
+        all_orderings: list[dict] = [
+            orderings
+            for feature in self._features
+            if (
+                hasattr(feature, "orderings") and (orderings := feature.orderings(self))
             )
-        if len(all_orderings) == 1:
+        ]
+
+        if not all_orderings:
+            return {}
+        elif len(all_orderings) == 1:
             # If there is only 1 ordering, we reuse it directly.
             return all_orderings[0].copy()
         else: