Speedup truncated_graph_inputs

.github/workflows/test.yml

@@ -139,7 +139,7 @@ jobs:
       - name: Install dependencies
         shell: bash -l {0}
         run: |
-          mamba install --yes -q "python~=${PYTHON_VERSION}=*_cpython" mkl numpy scipy pip mkl-service graphviz cython pytest coverage pytest-cov pytest-benchmark sympy
+          mamba install --yes -q "python~=${PYTHON_VERSION}=*_cpython" mkl numpy scipy pip mkl-service graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock sympy
           # numba-scipy downgrades the installed scipy to 1.7.3 in Python 3.9, but
           # not numpy, even though scipy 1.7 requires numpy<1.23. When installing
           # PyTensor next, pip installs a lower version of numpy via the PyPI.

environment.yml

@@ -31,6 +31,7 @@ dependencies:
   - pytest-cov
   - pytest-xdist
   - pytest-benchmark
+  - pytest-mock
   # For building docs
   - sphinx>=5.1.0,<6
   - sphinx_rtd_theme

pyproject.toml

@@ -86,6 +86,7 @@ tests = [
     "pytest-cov>=2.6.1",
     "coverage>=5.1",
     "pytest-benchmark",
+    "pytest-mock",
 ]
 rtd = [
     "sphinx>=5.1.0,<6",

pytensor/graph/basic.py

@@ -1003,14 +1003,14 @@ def applys_between(
 def truncated_graph_inputs(
     outputs: Sequence[Variable],
     ancestors_to_include: Optional[Collection[Variable]] = None,
-) -> List[Variable]:
+) -> list[Variable]:
     """Get the truncate graph inputs.
 
     Unlike :func:`graph_inputs` this function will return
-    the closest nodes to outputs that do not depend on
+    the closest variables to outputs that do not depend on
     ``ancestors_to_include``. So given all the returned
-    variables provided there is no missing node to
-    compute the output and all nodes are independent
+    variables provided there is no missing variable to
+    compute the output and all variables are independent
     from each other.
 
     Parameters
@@ -1027,7 +1027,7 @@ def truncated_graph_inputs(
 
     Examples
     --------
-    The returned nodes marked in (parenthesis), ancestors nodes are ``c``, output nodes are ``o``
+    The returned variables marked in (parenthesis), ancestors variables are ``c``, output variables are ``o``
 
     * No ancestors to include
 
@@ -1047,7 +1047,7 @@ def truncated_graph_inputs(
 
         (c) - (c) - o
 
-    * Additional nodes are present
+    * Additional variables are present
 
     .. code-block::
 
@@ -1076,58 +1076,60 @@ def truncated_graph_inputs(
 
     """
     # simple case, no additional ancestors to include
-    truncated_inputs = list()
-    # blockers have known independent nodes and ancestors to include
+    truncated_inputs: list[Variable] = list()
+    # blockers have known independent variables and ancestors to include
     candidates = list(outputs)
     if not ancestors_to_include:  # None or empty
         # just filter out unique variables
-        for node in candidates:
-            if node not in truncated_inputs:
-                truncated_inputs.append(node)
+        for variable in candidates:
+            if variable not in truncated_inputs:
+                truncated_inputs.append(variable)
         # no more actions are needed
         return truncated_inputs
 
-    blockers: Set[Variable] = set(ancestors_to_include)
-    # enforce O(1) check for node in ancestors to include
+    blockers: set[Variable] = set(ancestors_to_include)
+    # variables that go here are under check already, do not repeat the loop for them
+    seen: set[Variable] = set()
+    # enforce O(1) check for variable in ancestors to include
     ancestors_to_include = blockers.copy()
 
     while candidates:
         # on any new candidate
-        node = candidates.pop()
-
-        # There was a repeated reference to this node, we have already investigated it
-        if node in truncated_inputs:
+        variable = candidates.pop()
+        # we've looked into this variable already
+        if variable in seen:
             continue
-
-        # check if the node is independent, never go above blockers;
-        # blockers are independent nodes and ancestors to include
-        if node in ancestors_to_include:
-            # The case where node is in ancestors to include so we check if it depends on others
+        # check if the variable is independent, never go above blockers;
+        # blockers are independent variables and ancestors to include
+        elif variable in ancestors_to_include:
+            # The case where variable is in ancestors to include so we check if it depends on others
             # it should be removed from the blockers to check against the rest
-            dependent = variable_depends_on(node, ancestors_to_include - {node})
+            dependent = variable_depends_on(variable, ancestors_to_include - {variable})
             # ancestors to include that are present in the graph (not disconnected)
             # should be added to truncated_inputs
-            truncated_inputs.append(node)
+            truncated_inputs.append(variable)
             if dependent:
                 # if the ancestors to include is still dependent we need to go above, the search is not yet finished
-                # owner can never be None for a dependent node
-                candidates.extend(node.owner.inputs)
+                # owner can never be None for a dependent variable
+                candidates.extend(n for n in variable.owner.inputs if n not in seen)
         else:
-            # A regular node to check
-            dependent = variable_depends_on(node, blockers)
-            # all regular nodes fall to blockers
+            # A regular variable to check
+            dependent = variable_depends_on(variable, blockers)
+            # all regular variables fall to blockers
             # 1. it is dependent - further search irrelevant
-            # 2. it is independent - the search node is inside the closure
-            blockers.add(node)
-            # if we've found an independent node and it is not in blockers so far
-            # it is a new independent node not present in ancestors to include
+            # 2. it is independent - the search variable is inside the closure
+            blockers.add(variable)
+            # if we've found an independent variable and it is not in blockers so far
+            # it is a new independent variable not present in ancestors to include
             if dependent:
-                # populate search if it's not an independent node
-                # owner can never be None for a dependent node
-                candidates.extend(node.owner.inputs)
+                # populate search if it's not an independent variable
+                # owner can never be None for a dependent variable
+                candidates.extend(n for n in variable.owner.inputs if n not in seen)
             else:
                 # otherwise, do not search beyond
-                truncated_inputs.append(node)
+                truncated_inputs.append(variable)
+        # add variable to seen, no point in checking it once more
+        seen.add(variable)
     return truncated_inputs
 
 

tests/graph/test_basic.py

@@ -795,3 +795,18 @@ def test_repeated_nested_input(self):
         o2.name = "o2"
 
         assert truncated_graph_inputs([o2], [trunc_inp]) == [trunc_inp, x]
+
+    def test_single_pass_per_node(self, mocker):
+        import pytensor.graph.basic
+
+        inspect = mocker.spy(pytensor.graph.basic, "variable_depends_on")
+        x = at.dmatrix("x")
+        m = x.shape[0][None, None]
+
+        f = x / m
+        w = x / m - f
+        truncated_graph_inputs([w], [x])
+        # make sure there were exactly the same calls as unique variables seen by the function
+        assert len(inspect.call_args_list) == len(
+            {a for ((a, b), kw) in inspect.call_args_list}
+        )