Rename TopoOptimizer to WalkingGraphRewriter

aesara/graph/opt.py

@@ -1997,8 +1997,8 @@ class NodeProcessingGraphRewriter(GraphRewriter):
             )
 
 
-class TopoOptimizer(NodeProcessingGraphRewriter):
-    """An optimizer that applies a single `NodeRewriter` to each node in topological order (or reverse)."""
+class WalkingGraphRewriter(NodeProcessingGraphRewriter):
+    """A rewriter that applies a single `NodeRewriter` to each node in topological order (or reverse)."""
 
     def __init__(
         self,
@@ -2057,11 +2057,11 @@ class TopoOptimizer(NodeProcessingGraphRewriter):
             self.node_rewriter,
         )
 
-    @staticmethod
-    def print_profile(stream, prof, level=0):
+    @classmethod
+    def print_profile(cls, stream, prof, level=0):
         blanc = "    " * level
         if prof is None:  # Happen as merge_profile() isn't implemented
-            print(blanc, "TopoOptimizer merge_profile not implemented", file=stream)
+            print(blanc, f"{cls.__name__} merge_profile not implemented", file=stream)
             return
 
         (
@@ -2077,7 +2077,7 @@ class TopoOptimizer(NodeProcessingGraphRewriter):
 
         print(
             blanc,
-            "TopoOptimizer ",
+            f"{cls.__name__} ",
             getattr(opt, "name", getattr(opt, "__name__", "")),
             file=stream,
         )
@@ -2106,19 +2106,19 @@ class TopoOptimizer(NodeProcessingGraphRewriter):
                 )
 
     def __str__(self):
-        return getattr(self, "__name__", "<TopoOptimizer instance>")
+        return getattr(self, "__name__", super().__str__())
 
 
 def topogroup_optimizer(
     order,
     *node_rewriters,
     name=None,
-    failure_callback=TopoOptimizer.warn_inplace,
+    failure_callback=WalkingGraphRewriter.warn_inplace,
     **kwargs,
 ):
     r"""Apply `node_rewriters` from the input/output nodes to the output/input nodes of a graph.
 
-    This constructs `TopoOptimizer`\s, and uses a `SequentialNodeRewriter` when there's
+    This constructs `WalkingGraphRewriter`\s, and uses a `SequentialNodeRewriter` when there's
     more than one entry in `node_rewriters`.
     """
     if len(node_rewriters) > 1:
@@ -2128,7 +2128,7 @@ def topogroup_optimizer(
         (node_rewriters,) = node_rewriters
         if not name:
             name = node_rewriters.__name__
-    ret = TopoOptimizer(
+    ret = WalkingGraphRewriter(
         node_rewriters,
         order=order,
         failure_callback=failure_callback,
@@ -3220,6 +3220,11 @@ DEPRECATED_NAMES = [
         "`NavigatorOptimizer` is deprecated: use `NodeProcessingGraphRewriter` instead.",
         NodeProcessingGraphRewriter,
     ),
+    (
+        "TopoOptimizer",
+        "`TopoOptimizer` is deprecated: use `WalkingGraphRewriter` instead.",
+        WalkingGraphRewriter,
+    ),
 ]
 
 

aesara/graph/optdb.py

@@ -483,7 +483,7 @@ class LocalGroupDB(SequenceDB):
 
 
 class TopoDB(OptimizationDatabase):
-    """Generate a `GraphRewriter` of type TopoOptimizer."""
+    """Generate a `GraphRewriter` of type `WalkingGraphRewriter`."""
 
     def __init__(
         self, db, order="in_to_out", ignore_newtrees=False, failure_callback=None
@@ -495,7 +495,7 @@ class TopoDB(OptimizationDatabase):
         self.failure_callback = failure_callback
 
     def query(self, *tags, **kwtags):
-        return aesara_opt.TopoOptimizer(
+        return aesara_opt.WalkingGraphRewriter(
             self.db.query(*tags, **kwtags),
             self.order,
             self.ignore_newtrees,

aesara/sparse/opt.py

@@ -4,7 +4,7 @@ import aesara
 import aesara.scalar as aes
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply
-from aesara.graph.opt import PatternNodeRewriter, TopoOptimizer, node_rewriter
+from aesara.graph.opt import PatternNodeRewriter, WalkingGraphRewriter, node_rewriter
 from aesara.link.c.op import COp, _NoPythonCOp
 from aesara.misc.safe_asarray import _asarray
 from aesara.sparse import basic as sparse
@@ -68,7 +68,9 @@ def local_inplace_remove0(fgraph, node):
 
 aesara.compile.optdb.register(
     "local_inplace_remove0",
-    TopoOptimizer(local_inplace_remove0, failure_callback=TopoOptimizer.warn_inplace),
+    WalkingGraphRewriter(
+        local_inplace_remove0, failure_callback=WalkingGraphRewriter.warn_inplace
+    ),
     "fast_run",
     "inplace",
     position=60,
@@ -207,8 +209,8 @@ def local_inplace_addsd_ccode(fgraph, node):
 
 aesara.compile.optdb.register(
     "local_inplace_addsd_ccode",
-    TopoOptimizer(
-        local_inplace_addsd_ccode, failure_callback=TopoOptimizer.warn_inplace
+    WalkingGraphRewriter(
+        local_inplace_addsd_ccode, failure_callback=WalkingGraphRewriter.warn_inplace
     ),
     "fast_run",
     "inplace",
@@ -240,7 +242,7 @@ def local_addsd_ccode(fgraph, node):
 
 aesara.compile.optdb.register(
     "local_addsd_ccode",
-    TopoOptimizer(local_addsd_ccode),
+    WalkingGraphRewriter(local_addsd_ccode),
     # Must be after local_inplace_addsd_ccode at 60
     "fast_run",
     position=61,

aesara/tensor/math_opt.py

@@ -620,7 +620,7 @@ class AlgebraicCanonizer(NodeRewriter):
     r"""A `Rewriter` that rewrites algebraic expressions.
 
     The variable is a `node_rewriter`. It is best used
-    with a `TopoOptimizer` in in-to-out order.
+    with a `WalkingGraphRewriter` in in-to-out order.
 
     Usage: ``AlgebraicCanonizer(main, inverse, reciprocal, calculate)``
 

aesara/tensor/nnet/conv3d2d.py

@@ -3,7 +3,7 @@ from aesara import tensor as at
 from aesara.gradient import DisconnectedType
 from aesara.graph.basic import Apply
 from aesara.graph.op import Op
-from aesara.graph.opt import TopoOptimizer, copy_stack_trace, node_rewriter
+from aesara.graph.opt import WalkingGraphRewriter, copy_stack_trace, node_rewriter
 
 
 def get_diagonal_subtensor_view(x, i0, i1):
@@ -312,8 +312,9 @@ def local_inplace_DiagonalSubtensor(fgraph, node):
 
 aesara.compile.optdb.register(
     "local_inplace_DiagonalSubtensor",
-    TopoOptimizer(
-        local_inplace_DiagonalSubtensor, failure_callback=TopoOptimizer.warn_inplace
+    WalkingGraphRewriter(
+        local_inplace_DiagonalSubtensor,
+        failure_callback=WalkingGraphRewriter.warn_inplace,
     ),
     "fast_run",
     "inplace",

aesara/tensor/nnet/opt.py

@@ -8,7 +8,7 @@ from aesara.compile import optdb
 from aesara.configdefaults import config
 from aesara.graph.opt import (
     MetaNodeRewriterSkip,
-    TopoOptimizer,
+    WalkingGraphRewriter,
     copy_stack_trace,
     in2out,
     node_rewriter,
@@ -51,8 +51,9 @@ def local_inplace_sparse_block_gemv(fgraph, node):
 
 compile.optdb.register(
     "local_inplace_sparse_block_gemv",
-    TopoOptimizer(
-        local_inplace_sparse_block_gemv, failure_callback=TopoOptimizer.warn_inplace
+    WalkingGraphRewriter(
+        local_inplace_sparse_block_gemv,
+        failure_callback=WalkingGraphRewriter.warn_inplace,
     ),
     "fast_run",
     "inplace",
@@ -74,9 +75,9 @@ def local_inplace_sparse_block_outer(fgraph, node):
 
 compile.optdb.register(
     "local_inplace_sparse_block_outer",
-    TopoOptimizer(
+    WalkingGraphRewriter(
         local_inplace_sparse_block_outer,
-        failure_callback=TopoOptimizer.warn_inplace,
+        failure_callback=WalkingGraphRewriter.warn_inplace,
     ),
     "fast_run",
     "inplace",

aesara/tensor/subtensor_opt.py

@@ -7,7 +7,12 @@ import aesara
 import aesara.scalar.basic as aes
 from aesara import compile
 from aesara.graph.basic import Constant, Variable
-from aesara.graph.opt import TopoOptimizer, copy_stack_trace, in2out, node_rewriter
+from aesara.graph.opt import (
+    WalkingGraphRewriter,
+    copy_stack_trace,
+    in2out,
+    node_rewriter,
+)
 from aesara.raise_op import Assert
 from aesara.tensor.basic import (
     Alloc,
@@ -1200,9 +1205,9 @@ def local_IncSubtensor_serialize(fgraph, node):
         # print incsub_inputs, [id(i.owner.inputs[0]) for i in incsub_inputs]
 
 
-# We register it in a TopoOptimizer inside the canonizer EQ optimizer.
+# We register it in a WalkingGraphRewriter inside the canonizer EQ optimizer.
 # Otherwise in some cases it was making the EQ optimizer use 45. In
-# the TopoOptimizer, the EQ only use 5 passes.
+# the WalkingGraphRewriter, the EQ only use 5 passes.
 compile.optdb.register(
     "pre_local_IncSubtensor_serialize",
     in2out(local_IncSubtensor_serialize),
@@ -1240,8 +1245,8 @@ def local_inplace_setsubtensor(fgraph, node):
 
 compile.optdb.register(
     "local_inplace_setsubtensor",
-    TopoOptimizer(
-        local_inplace_setsubtensor, failure_callback=TopoOptimizer.warn_inplace
+    WalkingGraphRewriter(
+        local_inplace_setsubtensor, failure_callback=WalkingGraphRewriter.warn_inplace
     ),
     "fast_run",
     "inplace",
@@ -1261,8 +1266,9 @@ def local_inplace_AdvancedIncSubtensor1(fgraph, node):
 
 compile.optdb.register(
     "local_inplace_AdvancedIncSubtensor1",
-    TopoOptimizer(
-        local_inplace_AdvancedIncSubtensor1, failure_callback=TopoOptimizer.warn_inplace
+    WalkingGraphRewriter(
+        local_inplace_AdvancedIncSubtensor1,
+        failure_callback=WalkingGraphRewriter.warn_inplace,
     ),
     "fast_run",
     "inplace",
@@ -1286,8 +1292,9 @@ def local_inplace_AdvancedIncSubtensor(fgraph, node):
 
 compile.optdb.register(
     "local_inplace_AdvancedIncSubtensor",
-    TopoOptimizer(
-        local_inplace_AdvancedIncSubtensor, failure_callback=TopoOptimizer.warn_inplace
+    WalkingGraphRewriter(
+        local_inplace_AdvancedIncSubtensor,
+        failure_callback=WalkingGraphRewriter.warn_inplace,
     ),
     "fast_run",
     "inplace",

aesara/typed_list/opt.py

 from aesara.compile import optdb
-from aesara.graph.opt import TopoOptimizer, node_rewriter
+from aesara.graph.opt import WalkingGraphRewriter, node_rewriter
 from aesara.typed_list.basic import Append, Extend, Insert, Remove, Reverse
 
 
@@ -18,7 +18,9 @@ def typed_list_inplace_opt(fgraph, node):
 
 optdb.register(
     "typed_list_inplace_opt",
-    TopoOptimizer(typed_list_inplace_opt, failure_callback=TopoOptimizer.warn_inplace),
+    WalkingGraphRewriter(
+        typed_list_inplace_opt, failure_callback=WalkingGraphRewriter.warn_inplace
+    ),
     "fast_run",
     "inplace",
     position=60,

doc/extending/graph_rewriting.rst

@@ -264,9 +264,9 @@ subset of them) and applies one or several local optimizers.
 >>> e = aesara.graph.fg.FunctionGraph([x, y, z], [a])
 >>> e
 FunctionGraph(add(z, mul(true_div(mul(y, x), y), true_div(z, x))))
->>> simplify = aesara.graph.opt.TopoOptimizer(local_simplify)
+>>> simplify = aesara.graph.opt.WalkingGraphRewriter(local_simplify)
 >>> simplify.optimize(e)
-(<aesara.graph.opt.TopoOptimizer object at 0x...>, 1, 5, 3, ..., ..., ...)
+(<aesara.graph.opt.WalkingGraphRewriter object at 0x...>, 1, 5, 3, ..., ..., ...)
 >>> e
 FunctionGraph(add(z, mul(x, true_div(z, x))))
 
@@ -962,14 +962,14 @@ This will output something like this:
               time_toposort 0.00311398506165
               validate_time 4.60147857666e-05
               callback_time 0.00174236297607
-           0.004569s - ('local_dot_to_dot22', 'TopoOptimizer', 0) - 0.000s
-             TopoOptimizer
+           0.004569s - ('local_dot_to_dot22', 'WalkingGraphRewriter', 0) - 0.000s
+             WalkingGraphRewriter
                nb_node (start, end, changed) (81, 81, 5)
                init io_toposort 0.00139284133911
                loop time 0.00312399864197
                callback_time 0.00172805786133
-           0.002283s - ('local_dot22_to_dot22scalar', 'TopoOptimizer', 2) - 0.000s
-             TopoOptimizer
+           0.002283s - ('local_dot22_to_dot22scalar', 'WalkingGraphRewriter', 2) - 0.000s
+             WalkingGraphRewriter
                nb_node (start, end, changed) (80, 80, 0)
                init io_toposort 0.00171804428101
                loop time 0.000502109527588
@@ -982,14 +982,14 @@ This will output something like this:
                time in local optimizers 0.000s
                time in global optimizers 0.000s
                 0 - 0.002s 0 (0.000s in global opts, 0.001s io_toposort) - 80 nodes -
-           0.002227s - ('use_c_blas', 'TopoOptimizer', 4) - 0.000s
-             TopoOptimizer
+           0.002227s - ('use_c_blas', 'WalkingGraphRewriter', 4) - 0.000s
+             WalkingGraphRewriter
                nb_node (start, end, changed) (80, 80, 0)
                init io_toposort 0.0014750957489
                loop time 0.00068998336792
                callback_time 0.0
-           0.001632s - ('use_scipy_ger', 'TopoOptimizer', 5) - 0.000s
-             TopoOptimizer
+           0.001632s - ('use_scipy_ger', 'WalkingGraphRewriter', 5) - 0.000s
+             WalkingGraphRewriter
                nb_node (start, end, changed) (80, 80, 0)
                init io_toposort 0.00138401985168
                loop time 0.000202178955078

tests/graph/test_destroyhandler.py

@@ -13,7 +13,7 @@ from aesara.graph.opt import (
     OpKeyOptimizer,
     PatternNodeRewriter,
     SubstitutionNodeRewriter,
-    TopoOptimizer,
+    WalkingGraphRewriter,
 )
 from aesara.graph.type import Type
 from aesara.graph.utils import InconsistencyError
@@ -27,7 +27,7 @@ def PatternOptimizer(p1, p2, ign=True):
 def TopoSubstitutionNodeRewriter(
     op1, op2, fail=NodeProcessingGraphRewriter.warn_ignore, ign=True
 ):
-    return TopoOptimizer(
+    return WalkingGraphRewriter(
         SubstitutionNodeRewriter(op1, op2), ignore_newtrees=ign, failure_callback=fail
     )
 

tests/graph/test_opt.py

@@ -13,7 +13,7 @@ from aesara.graph.opt import (
     PatternNodeRewriter,
     SequentialNodeRewriter,
     SubstitutionNodeRewriter,
-    TopoOptimizer,
+    WalkingGraphRewriter,
     in2out,
     logging,
     node_rewriter,
@@ -55,7 +55,7 @@ def PatternOptimizer(p1, p2, ign=False):
 
 
 def TopoPatternOptimizer(p1, p2, ign=True):
-    return TopoOptimizer(PatternNodeRewriter(p1, p2), ignore_newtrees=ign)
+    return WalkingGraphRewriter(PatternNodeRewriter(p1, p2), ignore_newtrees=ign)
 
 
 class TestPatternOptimizer:
@@ -148,7 +148,7 @@ class TestPatternOptimizer:
         assert str(g) == "FunctionGraph(Op2(Op1(Op2(Op1(Op2(Op1(x)))))))"
 
     def test_ambiguous(self):
-        # this test should always work with TopoOptimizer and the
+        # this test should always work with WalkingGraphRewriter and the
         # ignore_newtrees flag set to False. Behavior with ignore_newtrees
         # = True or with other NodeProcessingGraphRewriters may differ.
         x, y, z = MyVariable("x"), MyVariable("y"), MyVariable("z")

tests/tensor/test_math_opt.py

@@ -20,7 +20,7 @@ from aesara.graph.basic import Apply, Constant, equal_computations
 from aesara.graph.fg import FunctionGraph
 from aesara.graph.opt import (
     SequentialNodeRewriter,
-    TopoOptimizer,
+    WalkingGraphRewriter,
     check_stack_trace,
     in2out,
     out2in,
@@ -190,7 +190,7 @@ class TestGreedyDistribute:
         e = (a / z + b / x) * x * z
         g = FunctionGraph([a, b, c, d, x, y, z], [e])
         mul_canonizer.optimize(g)
-        TopoOptimizer(
+        WalkingGraphRewriter(
             SequentialNodeRewriter(local_greedy_distributor), order="out_to_in"
         ).optimize(g)
         assert str(pprint(g.outputs[0])) == "((a * x) + (b * z))"
@@ -199,7 +199,7 @@ class TestGreedyDistribute:
         e = (a / x + b) * x
         g = FunctionGraph([a, b, x], [e])
         mul_canonizer.optimize(g)
-        TopoOptimizer(
+        WalkingGraphRewriter(
             SequentialNodeRewriter(local_greedy_distributor), order="out_to_in"
         ).optimize(g)
         assert str(pprint(g.outputs[0])) == "(a + (b * x))"
@@ -3052,7 +3052,7 @@ class TestLocalErfc:
         for inputs, no_match in no_matches:
             fg = FunctionGraph(inputs, [no_match], clone=False)
 
-            TopoOptimizer(
+            WalkingGraphRewriter(
                 SequentialNodeRewriter(local_grad_log_erfc_neg), order="out_to_in"
             ).optimize(fg)