Allow OpPattern in tracks

doc/gallery/rewrites/graph_rewrites.ipynb

@@ -583,7 +583,7 @@
     "    def tracks(self):\n",
     "        return [pt.log]\n",
     "    \n",
-    "    def transform(self, fgraph, node):\n",
+    "    def transform(self, fgraph, node, enforce_tracks=True):\n",
     "        return local_log1p(node)    \n",
     "    \n",
     "    def __str__(self):\n",
@@ -669,8 +669,8 @@
     "@node_rewriter(tracks=[pt.abs])\n",
     "def local_useless_abs_exp(fgraph, node):\n",
     "    # Because of the tracks we don't need to check \n",
-    "    # that `node` has a `Sign` Op.\n",
-    "    # We still need to check whether it's input is an `Abs` Op\n",
+    "    # that `node` has a `Abs` Op.\n",
+    "    # We still need to check whether it's input is an `Exp` Op\n",
     "    exp_node = node.inputs[0].owner\n",
     "    if exp_node is None or exp_node.op != pt.exp:\n",
     "        return None\n",

pytensor/graph/rewriting/basic.py

@@ -141,7 +141,12 @@ class NodeRewriter(Rewriter):
 
     @abc.abstractmethod
     def transform(
-        self, fgraph: FunctionGraph, node: Apply, *args, **kwargs
+        self,
+        fgraph: FunctionGraph,
+        node: Apply,
+        enforce_tracks: bool = True,
+        *args,
+        **kwargs,
     ) -> TransformOutputType:
         r"""Rewrite the sub-graph given by `node`.
 
@@ -159,7 +164,9 @@ class NodeRewriter(Rewriter):
             A `FunctionGraph` containing `node`.
         node
             An `Apply` node to be rewritten.
-
+        enforce_tracks: bool
+            Whether the transform method should enforce tracks, or it can be assumed the caller already enforced them in a pre-filter stage.
+            See `node_rewriter` tracks argument for more details.
         """
 
         raise NotImplementedError()
@@ -935,15 +942,48 @@ class FromFunctionNodeRewriter(NodeRewriter):
     def __init__(self, fn, tracks=None, requirements=()):
         self.fn = fn
         self._tracks = tracks
-        self._tracked_types = (
-            tuple(t for t in tracks if isinstance(t, type)) if tracks else ()
-        )
+        self._tracked_ops = set()
+        self._tracked_types = type(None)
+        self._tracked_op_pattern_types = type(None)
+        self._tracked_op_patterns: list[OpPattern] = []
+        if tracks is not None:
+            if not tracks:
+                raise ValueError(
+                    "To specify a general rewrite leave tracks as None instead of an empty container"
+                )
+            for t in tracks:
+                if isinstance(t, Op):
+                    self._tracked_ops.add(t)
+                elif isinstance(t, type):
+                    self._tracked_types |= t
+                elif isinstance(t, OpPattern):
+                    if t.parameters:
+                        self._tracked_op_patterns.append(t)
+                        self._tracked_op_pattern_types |= t.op_type
+                    else:
+                        # An OpPattern without parameters behaves like a regular tracked_type
+                        self._tracked_types |= t
+                else:
+                    raise TypeError(
+                        "`tracks` must consist of `Op` classes, `Op` instances or `OpPattern` instances. "
+                        f"Got {t} of type {type(t)}"
+                    )
         self.requirements = requirements
 
-    def transform(self, fgraph, node):
-        if self._tracks:
+    def transform(self, fgraph, node, enforce_tracks: bool = True):
+        if enforce_tracks and self._tracks:
+            node_op = node.op
             if not (
-                node.op in self._tracks or isinstance(node.op, self._tracked_types)
+                node_op in self._tracked_ops
+                or isinstance(node_op, self._tracked_types)
+                or (
+                    isinstance(node.op, self._tracked_op_pattern_types)
+                    and any(
+                        t.match_parameters(node_op)
+                        for t in self._tracked_op_patterns
+                        if isinstance(node_op, t.op_type)
+                    )
+                )
             ):
                 return False
 
@@ -967,7 +1007,7 @@ class FromFunctionNodeRewriter(NodeRewriter):
 
 
 def node_rewriter(
-    tracks: Sequence[Op | type] | None,
+    tracks: Sequence[Op | type, OpPattern] | None,
     inplace: bool = False,
     requirements: tuple[type, ...] | None = (),
 ):
@@ -976,7 +1016,7 @@ def node_rewriter(
     Parameters
     ----------
     tracks
-        The `Op` types or instances to which this rewrite applies.
+        The `Op` type, instances or `OpPattern` to which this rewrite applies.
         Use ``None`` instead of an empty list to have the rewrite apply to
         all `Op`\s.
     inplace
@@ -995,14 +1035,16 @@ def node_rewriter(
         if tracks is not None:
             if len(tracks) == 0:
                 raise ValueError(
-                    "Use `None` instead of an empty list to make an rewrite apply to all nodes."
+                    "Use `None` instead of an empty list to make a rewrite apply to all nodes."
                 )
             for t in tracks:
                 if not (
-                    isinstance(t, Op) or (isinstance(t, type) and issubclass(t, Op))
+                    isinstance(t, Op | OpPattern)
+                    or (isinstance(t, type) and issubclass(t, Op))
                 ):
                     raise TypeError(
-                        "`tracks` must consist of `Op` classes or instances."
+                        "`tracks` must consist of `Op` classes, `Op` instances or `OpPattern` instances. "
+                        f"Got {t} of type {type(t)}"
                     )
         req = requirements
         if inplace:
@@ -1024,47 +1066,93 @@ class OpToRewriterTracker:
     def __init__(self):
         self.tracked_instances: dict[Op, list[NodeRewriter]] = defaultdict(list)
         self.tracked_types: dict[type, list[NodeRewriter]] = defaultdict(list)
+        self.tracked_pattern_types: dict[type, dict[OpPattern, list[NodeRewriter]]] = (
+            defaultdict(lambda: defaultdict(list))
+        )
         self.untracked_rewrites: list[NodeRewriter] = []
+        self._cached_composed_mro = None
 
     def add_tracker(self, rw: NodeRewriter):
         """Add a `NodeRewriter` to be keyed by its `NodeRewriter.tracks` or applied generally."""
+        if self._cached_composed_mro is not None:
+            # We shouldn't actually add_trackers after the first call to get_trackers
+            # But just to be safe we kill the cache here
+            self._cached_composed_mro = None
+
         tracks = rw.tracks()
 
         if tracks is None:
             self.untracked_rewrites.append(rw)
         else:
             for c in tracks:
+                if isinstance(c, OpPattern):
+                    if not isinstance(c.op_type, type):
+                        # OpPattern allows anything that you can check with isinstance(op, op_type),
+                        # including tuples or union types. But for OpToRewriterTracker we need a single type.
+                        raise NotImplementedError(
+                            "OpToRewriterTracker requires the outermost `OpPattern.op_type` to be a type. "
+                            f"Got {c.op_type} of type {type(c.op_type)}"
+                        )
+
+                    if c.parameters:
+                        self.tracked_pattern_types[c.op_type][c].append(rw)
+                    else:
+                        # An OpPattern without parameters behaves like a regular tracked_type
+                        self.tracked_types[c.op_type].append(rw)
                 if isinstance(c, type):
                     self.tracked_types[c].append(rw)
                 else:
                     self.tracked_instances[c].append(rw)
 
-    def _find_impl(self, cls) -> list[NodeRewriter]:
-        r"""Returns the `NodeRewriter`\s that apply to `cls` based on inheritance.
+    @functools.cache
+    def get_trackers(self, op: Op) -> list[NodeRewriter]:
+        """Get all the rewrites applicable to an `Op`."""
+
+        if self._cached_composed_mro is None:
+            # Cache the mro call on the Op type. We have a small subset of op_types we actually care about
+            # like Elemwise, Blockwise, and so on, which we don't need to repeatedly investigate
+            tracked_types = (
+                self.tracked_types.keys() | self.tracked_pattern_types.keys()
+            )
+
+            @functools.cache
+            def cached_composed_mro(op_type, tracked_types=tracked_types):
+                return _compose_mro(op_type, tracked_types)
+
+            self._cached_composed_mro = cached_composed_mro
 
-        This based on `functools._find_impl`.
-        """
-        mro = _compose_mro(cls, self.tracked_types.keys())
         matches = []
-        for t in mro:
-            match = self.tracked_types.get(t, None)
-            if match:
-                matches.extend(match)
+        if self.tracked_types or self.tracked_pattern_types:
+            # Find matches for type(op) (and their subclasses) using the same approach that functools.singledispatch uses
+            mro = self._cached_composed_mro(type(op))
+            for t in mro:
+                if (match := self.tracked_types.get(t, None)) is not None:
+                    matches.extend(match)
+                if (
+                    potential_matches := self.tracked_pattern_types.get(t, None)
+                ) is not None:
+                    # We still need to check if the Op parameters match the constraints
+                    matches.extend(
+                        [
+                            item
+                            for op_pattern, r_list in potential_matches.items()
+                            if op_pattern.match_parameters(op)
+                            for item in r_list
+                        ]
+                    )
+        matches.extend(self.tracked_instances.get(op, []))
+        matches.extend(self.untracked_rewrites)
         return matches
 
-    @functools.lru_cache
-    def get_trackers(self, op: Op) -> list[NodeRewriter]:
-        """Get all the rewrites applicable to `op`."""
-        return (
-            self._find_impl(type(op))
-            + self.tracked_instances.get(op, [])
-            + self.untracked_rewrites
-        )
-
-    def get_rewriters(self):
+    def get_rewriters(self) -> Iterable[NodeRewriter]:
+        """Get all the registered rewriters."""
         return chain(
+            chain.from_iterable(self.tracked_types.values()),
+            chain.from_iterable(self.tracked_instances.values()),
             chain.from_iterable(
-                chain(self.tracked_types.values(), self.tracked_instances.values())
+                item
+                for sub_dict in self.tracked_pattern_types.values()
+                for item in sub_dict.values()
             ),
             self.untracked_rewrites,
         )
@@ -1138,7 +1226,7 @@ class SequentialNodeRewriter(NodeRewriter):
                 t.extend(at)
         return t
 
-    def transform(self, fgraph, node):
+    def transform(self, fgraph, node, enforce_tracks=False):
         if len(self.rewrites) == 0:
             return
 
@@ -1150,7 +1238,8 @@ class SequentialNodeRewriter(NodeRewriter):
             new_repl = None
             for rewrite in rewrites:
                 rewrite_start = time.perf_counter()
-                new_repl = rewrite.transform(fgraph, node)
+                # Tracks are already enforced by `self.tracker.get_trackers`
+                new_repl = rewrite.transform(fgraph, node, enforce_tracks=False)
                 rewrite_finish = time.perf_counter()
                 if self.profile:
                     self.time_rewrites[rewrite] += rewrite_start - rewrite_finish
@@ -1292,8 +1381,8 @@ class SubstitutionNodeRewriter(NodeRewriter):
     def tracks(self):
         return [self.op1]
 
-    def transform(self, fgraph, node):
-        if node.op != self.op1:
+    def transform(self, fgraph, node, enforce_tracks=True):
+        if enforce_tracks and (node.op != self.op1):
             return False
         repl = self.op2.make_node(*node.inputs)
         if self.transfer_tags:
@@ -1498,7 +1587,7 @@ class PatternNodeRewriter(NodeRewriter):
     def tracks(self):
         return self._tracks
 
-    def transform(self, fgraph, node, get_nodes=True):
+    def transform(self, fgraph, node, enforce_tracks: bool = False, get_nodes=True):
         """Check if the graph from node corresponds to ``in_pattern``.
 
         If it does, it constructs ``out_pattern`` and performs the replacement.
@@ -1788,6 +1877,7 @@ class NodeProcessingGraphRewriter(GraphRewriter):
         fgraph: FunctionGraph,
         node: Apply,
         node_rewriter: NodeRewriter | None = None,
+        enforce_tracks: bool = True,
     ):
         r"""Apply `node_rewriter` to `node`.
 
@@ -1805,6 +1895,9 @@ class NodeProcessingGraphRewriter(GraphRewriter):
         node_rewriter
             A `NodeRewriter` instance that may have a better idea for
             how to compute node's outputs.
+        enforce_tracks: bool
+            Whether the transform method should enforce tracks,
+             or it can be assumed the caller already enforced them in a pre-filter stage.
 
         Returns
         -------
@@ -1820,7 +1913,9 @@ class NodeProcessingGraphRewriter(GraphRewriter):
         # TODO FIXME: This class's interface is broken
         assert node_rewriter is not None
         try:
-            replacements = node_rewriter.transform(fgraph, node)
+            replacements = node_rewriter.transform(
+                fgraph, node, enforce_tracks=enforce_tracks
+            )
         except Exception as e:
             if self.failure_callback is not None:
                 self.failure_callback(
@@ -1938,7 +2033,8 @@ class WalkingGraphRewriter(NodeProcessingGraphRewriter):
                 if node not in fgraph.apply_nodes:
                     continue
                 current_node = node
-                nb += self.process_node(fgraph, node)
+                # This rewriter does not enforce tracks itself
+                nb += self.process_node(fgraph, node, enforce_tracks=True)
             loop_t = time.perf_counter() - t0
         finally:
             self.detach_updater(fgraph, u)
@@ -2279,8 +2375,9 @@ class EquilibriumGraphRewriter(NodeProcessingGraphRewriter):
                     for node_rewriter in self.node_tracker.get_trackers(node.op):
                         nb = change_tracker.nb_imported
                         t_rewrite = time.perf_counter()
+                        # Tracks are already enfoced by `self.node_tracker.get_trackers`
                         node_rewriter_change = self.process_node(
-                            fgraph, node, node_rewriter
+                            fgraph, node, node_rewriter, enforce_tracks=False
                         )
                         time_rewriters[node_rewriter] += time.perf_counter() - t_rewrite
                         if not node_rewriter_change:

pytensor/graph/rewriting/kanren.py

@@ -74,7 +74,7 @@ class KanrenRelationSub(NodeRewriter):
         self.node_filter = node_filter
         super().__init__()
 
-    def transform(self, fgraph, node):
+    def transform(self, fgraph, node, enforce_tracks: bool = True):
         if self.node_filter(node) is False:
             return False
 
@@ -92,7 +92,7 @@ class KanrenRelationSub(NodeRewriter):
             if isinstance(chosen_res, list):
                 new_outputs = [eval_if_etuple(v) for v in chosen_res]
             else:
-                new_outputs = [eval_if_etuple(chosen_res)]
+                new_outputs = [eval_if_etuple(chosen_res)]  # type: ignore[unreachable]
 
             return new_outputs
         else:

pytensor/graph/rewriting/unify.py

@@ -278,6 +278,42 @@ class OpPattern:
     Examples
     --------
 
+    OpPattern can be used in the `tracks` functionality of `node_rewriter` to more flexible filter out nodes.
+    For Ops that are parametrized by other Ops, it's possible to use nested OpPatterns.
+
+    .. test-code::
+
+        from pytensor.graph.rewriting.basic import node_rewriter
+        from pytensor.graph.rewriting.unify import OpPattern
+        from pytensor.tensor.elemwise import CAReduce
+        from pytensor.tensor.blockwise import Blockwise
+        from pytensor.tensor.slinalg import Solve
+
+        @node_rewriter(tracks=[OpPattern(CAReduce, axis=None)])
+        def local_car_reduce_all_rewriter(fgraph, node):
+            # This will always be true!
+            assert isinstance(node.op, CAReduce) and node.op.axis is None
+            ...
+
+        # Any Blockwise whose core_op is a Solve Op (or subclass) instance
+        @node_rewriter(tracks=[OpPattern(Blockwise, core_op=OpPattern(Solve))])
+        def local_blockwise_solve_triangular_rewriter(fgraph, node):
+            # This will always be true!
+            assert isinstance(node.op, Blockwise) and isinstance(node.op.core_op, Solve)
+            ...
+
+        # Any Blockwise whose core_op is a Solve Op (or subclass) instance with b_ndim==1
+        @node_rewriter(tracks=[OpPattern(Blockwise, core_op=OpPattern(Solve, b_ndim=1))])
+        def local_blockwise_vector_solve_rewriter(fgraph, node):
+            # This will always be true!
+            assert (
+                isinstance(node.op, Blockwise)
+                and isinstance(node.op.core_op, Solve)
+                and node.op.core_op.b_ndim == 1
+            )
+            ...
+
+
     OpPattern can be used with `PatternNodeRewriter` to define graph rewrites that match Ops with specific parameters.
     The example below matches two nested CAReduce Ops with the same `scalar_op`,
     the outer with `axis=None` (full reduction) and fuses them into a single CAReduce.

pytensor/tensor/rewriting/math.py

@@ -1338,9 +1338,9 @@ class AlgebraicCanonizer(NodeRewriter):
 
         return ct + num, denum
 
-    def transform(self, fgraph, node):
+    def transform(self, fgraph, node, enforce_tracks=True):
         op = node.op
-        if op not in [self.main, self.inverse, self.reciprocal]:
+        if enforce_tracks and (op not in {self.main, self.inverse, self.reciprocal}):
             return False
 
         assert len(node.outputs) == 1