Back out "Support regex-style matching for Any and Oneof (#82853)" (#…

…83922)

Test Plan:
contbuild & OSS CI, see https://hud.pytorch.org/commit/pytorch/pytorch/fc470cf9806643efdbc1df650f9e8eafb671ba17

Original Phabricator Test Plan:

Reviewed By: yinghai, hl475, khabinov, terrycsy

Differential Revision: D38945806

fbshipit-source-id: 225a79458a38181904f780ffcfb2ea65786cc9f9

test/test_fx_passes.py

@@ -572,6 +572,7 @@ def pattern(a):
         TestCase(False, True, 0),
     ]
 
+
 class MultipleOutputsHorizontalPattern:
     @staticmethod
     def forward(x):
@@ -598,61 +599,6 @@ def pattern(a):
         TestCase(True, True, 0)
     ]
 
-class PatternWithPseudoAny:
-    @staticmethod
-    def forward(x):
-        x = x.relu()
-        x = x.sigmoid()
-
-        y = x.relu()
-        y = y + 1
-
-        z = y.relu()
-        z = z.relu()
-
-        return z
-
-    @staticmethod
-    def pattern(a):
-        y = a.relu()
-        z = torch.ops.pseudo.any(y)
-        return z
-
-    test_cases = [
-        # match_output, match_placeholder, num_matches
-        TestCase(False, False, 3),
-        TestCase(True, False, 1),
-        TestCase(False, True, 1),
-        TestCase(True, True, 0)
-    ]
-
-class PatternWithPseudoOneof:
-    @staticmethod
-    def forward(x):
-        x = x.relu()
-        x = torch.sigmoid(x)
-
-        z = x.relu()
-        z = torch.relu(z)
-
-        y = x.relu()
-        y = y + 1
-
-        return y
-
-    @staticmethod
-    def pattern(a):
-        y = a.relu()
-        z = torch.ops.pseudo.oneof(y, targets=["torch.sigmoid", "operator.add"])
-        return z
-
-    test_cases = [
-        # match_output, match_placeholder, num_matches
-        TestCase(False, False, 2),
-        TestCase(True, False, 1),
-        TestCase(False, True, 1),
-        TestCase(True, True, 0)
-    ]
 
 @instantiate_parametrized_tests
 class TestFXMatcherUtils(JitTestCase):
@@ -670,9 +616,7 @@ class TestFXMatcherUtils(JitTestCase):
         MultipleOutputsMultipleOverlappingMatches,
         MultipleOutputsMultipleNonOverlappingMatches,
         MultipleOutputsIdenticalAnchor,
-        MultipleOutputsHorizontalPattern,
-        PatternWithPseudoAny,
-        PatternWithPseudoOneof,
+        MultipleOutputsHorizontalPattern
     ])
     def test_subgraph_matcher(self, test_model):
         traced = symbolic_trace(test_model.forward)

torch/fx/passes/utils/matcher_utils.py

@@ -1,7 +1,6 @@
 from dataclasses import dataclass, field
 from collections import defaultdict
 import copy
-import torch.library
 from torch.fx.graph import Graph
 from torch.fx.node import Node
 from torch.fx._compatibility import compatibility
@@ -10,42 +9,6 @@
 __all__ = ['SubgraphMatcher', 'InternalMatch']
 
 
-pseudo = torch.library.Library("pseudo", "DEF")
-
-pseudo.define("any() -> ()")
-"""
-pseudo.any is a wildcard node that can be matched with any fx node with arbitrary number of inputs and outputs.
-For example, to match relu followed by one fx node:
-    def pattern(a):
-        y = a.relu()
-        z = torch.ops.pseudo.any(y)
-        return z
-"""
-
-pseudo.define("oneof(*, str[] targets) -> ()")
-"""
-pseudo.oneof is a special node that can be matched with a fx node whose target is in the permissible list.
-`targets` must be be a list of qualified name for operators, e.g. ["operator.add", "torch.sigmoid",
-"torch.ops.aten.foo", "torch.ops.prims.bar"]
-
-For example, using following pattern with pseudo.oneof
-    def pattern(a):
-        y = a.relu()
-        z = torch.ops.pseudo.oneof(y, targets=["relu", "torch.sigmoid", "operator.add"])
-        return z
-
-It will have 3 matches in the following function
-    def forward(y):
-        z = y.relu()
-        x = z.relu()    # first match
-
-        x = x.relu()
-        x = torch.sigmoid(x)    # second match
-
-        x = x.relu()
-        return x + 1    # third match
-"""
-
 @compatibility(is_backward_compatible=False)
 @dataclass
 class InternalMatch():
@@ -117,18 +80,6 @@ def _nodes_are_equal(self, pn: Node, gn: Node) -> bool:
         if not self.match_placeholder and pn.op == "placeholder":
             return True
 
-        if pn.target == torch.ops.pseudo.any:
-            return True
-
-        if pn.target == torch.ops.pseudo.oneof:
-            permissible_targets: List[str] = pn.kwargs.get("targets", list())  # type: ignore[assignment]
-            assert isinstance(permissible_targets, list), \
-                "pseudo.oneof(permissible_targets=[\"foo\", \"bar\"]) only accept targets as a list"
-            assert len(permissible_targets) > 0, "please specific as least one target for pseudo.oneof"
-
-            if gn._pretty_print_target(gn.target) in permissible_targets:
-                return True
-
         if pn.op == gn.op:
             if pn.op == "placeholder" or pn.op == "output":
                 return True