Rename LocalOptGroup to SequentialNodeRewriter

aesara/graph/opt.py

@@ -1204,7 +1204,7 @@ class OpToRewriterTracker:
         )
 
 
-class LocalOptGroup(NodeRewriter):
+class SequentialNodeRewriter(NodeRewriter):
     r"""An optimizer that applies a list of `NodeRewriter`\s to a node.
 
     Attributes
@@ -1272,7 +1272,7 @@ class LocalOptGroup(NodeRewriter):
         return getattr(
             self,
             "__name__",
-            f"LocalOptGroup({','.join([str(o) for o in self.opts])})",
+            f"{type(self).__name__}({','.join([str(o) for o in self.opts])})",
         )
 
     def tracks(self):
@@ -1332,15 +1332,15 @@ class LocalOptGroup(NodeRewriter):
             repl = new_repl
             node = new_vars[0].owner
 
-    @staticmethod
-    def print_profile(stream, prof, level=0):
+    @classmethod
+    def print_profile(cls, stream, prof, level=0):
         (time_opts, process_count, applied_true, node_created, profile) = prof
 
         if not profile:
             return
 
         blanc = "    " * int(level)
-        print(blanc, "LocalOptGroup", file=stream)
+        print(blanc, f"{cls.__name__}", file=stream)
         print(blanc, "---------------------", file=stream)
         count_opt = []
         not_used = []
@@ -2064,7 +2064,7 @@ class TopoOptimizer(NavigatorOptimizer):
         print(blanc, "  init io_toposort", io_t, file=stream)
         print(blanc, "  loop time", loop_t, file=stream)
         print(blanc, "  callback_time", callback_time, file=stream)
-        if isinstance(node_rewriter, LocalOptGroup):
+        if isinstance(node_rewriter, SequentialNodeRewriter):
             if node_rewriter.profile:
                 node_rewriter.print_profile(
                     stream,
@@ -2089,14 +2089,14 @@ def topogroup_optimizer(
     failure_callback=TopoOptimizer.warn_inplace,
     **kwargs,
 ):
-    """Apply `node_rewriters` from the input/output nodes to the output/input nodes of a graph.
+    r"""Apply `node_rewriters` from the input/output nodes to the output/input nodes of a graph.
 
-    This constructs `TopoOptimizer`s, and uses a `LocalOptGroup` when there's
+    This constructs `TopoOptimizer`\s, and uses a `SequentialNodeRewriter` when there's
     more than one entry in `node_rewriters`.
     """
     if len(node_rewriters) > 1:
         # Don't wrap it uselessly if their is only 1 optimization.
-        node_rewriters = LocalOptGroup(*node_rewriters)
+        node_rewriters = SequentialNodeRewriter(*node_rewriters)
     else:
         (node_rewriters,) = node_rewriters
         if not name:
@@ -3168,6 +3168,11 @@ DEPRECATED_NAMES = [
         "`LocalOptTracker` is deprecated: use `OpToRewriterTracker` instead.",
         OpToRewriterTracker,
     ),
+    (
+        "LocalOptGroup",
+        "`LocalOptGroup` is deprecated: use `SequentialNodeRewriter` instead.",
+        SequentialNodeRewriter,
+    ),
 ]
 
 

aesara/graph/optdb.py

@@ -457,13 +457,13 @@ class SequenceDB(OptimizationDatabase):
 
 
 class LocalGroupDB(SequenceDB):
-    r"""A database that generates `NodeRewriter`\s of type `LocalOptGroup`."""
+    r"""A database that generates `NodeRewriter`\s of type `SequentialNodeRewriter`."""
 
     def __init__(
         self,
         apply_all_opts: bool = False,
         profile: bool = False,
-        node_rewriter=aesara_opt.LocalOptGroup,
+        node_rewriter=aesara_opt.SequentialNodeRewriter,
     ):
         super().__init__(failure_callback=None)
         self.apply_all_opts = apply_all_opts

aesara/tensor/math_opt.py

@@ -10,9 +10,9 @@ import aesara.scalar.basic as aes
 import aesara.scalar.math as aes_math
 from aesara.graph.basic import Constant, Variable
 from aesara.graph.opt import (
-    LocalOptGroup,
     NodeRewriter,
     PatternSub,
+    SequentialNodeRewriter,
     copy_stack_trace,
     in2out,
     node_rewriter,
@@ -2117,7 +2117,7 @@ def local_add_specialize(fgraph, node):
 
 
 mul_canonizer = in2out(
-    LocalOptGroup(local_mul_canonizer, local_fill_sink, apply_all_opts=True),
+    SequentialNodeRewriter(local_mul_canonizer, local_fill_sink, apply_all_opts=True),
     name="mul_canonizer_groups",
 )
 
@@ -2344,7 +2344,7 @@ def add_calculate(num, denum, aslist=False, out_type=None):
 
 local_add_canonizer = AlgebraicCanonizer(add, sub, neg, add_calculate)
 add_canonizer = in2out(
-    LocalOptGroup(local_add_canonizer, local_fill_sink, apply_all_opts=True),
+    SequentialNodeRewriter(local_add_canonizer, local_fill_sink, apply_all_opts=True),
     name="add_canonizer_group",
 )
 

tests/graph/test_opt.py

@@ -7,12 +7,12 @@ from aesara.graph.fg import FunctionGraph
 from aesara.graph.op import Op
 from aesara.graph.opt import (
     EquilibriumOptimizer,
-    LocalOptGroup,
     MergeOptimizer,
     OpKeyOptimizer,
     OpSub,
     OpToRewriterTracker,
     PatternSub,
+    SequentialNodeRewriter,
     TopoOptimizer,
     in2out,
     logging,
@@ -664,7 +664,7 @@ def test_patternsub_different_output_lengths():
     assert fgraph.outputs[0].owner.op == op1
 
 
-class TestLocalOptGroup:
+class TestSequentialNodeRewriter:
     def test_optimizer_verbose(self, capsys):
 
         x = MyVariable("x")
@@ -685,7 +685,7 @@ class TestLocalOptGroup:
                 res = op2(x, *node.inputs[1:])
                 return [res]
 
-        opt_group = LocalOptGroup(local_opt_1, local_opt_2)
+        opt_group = SequentialNodeRewriter(local_opt_1, local_opt_2)
 
         with config.change_flags(optimizer_verbose=True):
             (new_res,) = opt_group.transform(fgraph, o1.owner)

tests/tensor/test_math_opt.py

@@ -19,7 +19,7 @@ from aesara.configdefaults import config
 from aesara.graph.basic import Apply, Constant, equal_computations
 from aesara.graph.fg import FunctionGraph
 from aesara.graph.opt import (
-    LocalOptGroup,
+    SequentialNodeRewriter,
     TopoOptimizer,
     check_stack_trace,
     in2out,
@@ -191,7 +191,7 @@ class TestGreedyDistribute:
         g = FunctionGraph([a, b, c, d, x, y, z], [e])
         mul_canonizer.optimize(g)
         TopoOptimizer(
-            LocalOptGroup(local_greedy_distributor), order="out_to_in"
+            SequentialNodeRewriter(local_greedy_distributor), order="out_to_in"
         ).optimize(g)
         assert str(pprint(g.outputs[0])) == "((a * x) + (b * z))"
 
@@ -200,7 +200,7 @@ class TestGreedyDistribute:
         g = FunctionGraph([a, b, x], [e])
         mul_canonizer.optimize(g)
         TopoOptimizer(
-            LocalOptGroup(local_greedy_distributor), order="out_to_in"
+            SequentialNodeRewriter(local_greedy_distributor), order="out_to_in"
         ).optimize(g)
         assert str(pprint(g.outputs[0])) == "(a + (b * x))"
 
@@ -3053,7 +3053,7 @@ class TestLocalErfc:
             fg = FunctionGraph(inputs, [no_match], clone=False)
 
             TopoOptimizer(
-                LocalOptGroup(local_grad_log_erfc_neg), order="out_to_in"
+                SequentialNodeRewriter(local_grad_log_erfc_neg), order="out_to_in"
             ).optimize(fg)
 
             # Make sure that the graph hasn't been changed