Correctly track modified bit for variety of replace ops

Differential Revision: D86725366

Pull Request resolved: #15727

Author: DrJessop

backends/cadence/aot/replace_ops.py

@@ -34,6 +34,7 @@
     CadencePassAttribute,
     none_throws,
     register_cadence_pass,
+    RemoveOrReplacePassInterface,
 )
 from executorch.backends.cadence.aot.remove_ops import RemoveNopSelectOpPass
 from executorch.backends.cadence.aot.utils import get_edge_overload_packet
@@ -115,84 +116,84 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceSafeSoftmaxWithSoftmax(ExportPass):  # keep
+class ReplaceSafeSoftmaxWithSoftmax(RemoveOrReplacePassInterface):  # keep
     """
     Replace _safe_softmax with _softmax
     """
 
-    def call_operator(
-        self,
-        op,
-        args: tuple[Argument, ...],
-        kwargs: dict[str, Argument],
-        meta: NodeMetadata,
-    ) -> ProxyValue:
-        if op != torch.ops.aten._safe_softmax.default:
-            return super().call_operator(op, args, kwargs, meta)
+    @property
+    def targets(self) -> list[EdgeOpOverload]:
+        return [torch.ops.aten._safe_softmax.default]
 
+    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
         # Add False for the half_to_float argument of softmax
-        softmax_args = list(args) + [False]
+        softmax_args = tuple(list(node.args) + [False])
 
-        return super().call_operator(
-            torch.ops.aten._softmax.default,
-            tuple(softmax_args),
-            kwargs,
-            meta,
-        )
+        with node.graph.inserting_before(node):
+            new_node = node.graph.call_function(
+                torch.ops.aten._softmax.default,
+                args=softmax_args,
+                kwargs=node.kwargs,
+            )
+            new_node.meta = node.meta
+        node.replace_all_uses_with(new_node)
+        return True
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplacePT2QuantWithCadenceQuantPass(ExportPass):
+class ReplacePT2QuantWithCadenceQuantPass(RemoveOrReplacePassInterface):
     """
     Replace the pt2 quantization ops with cadence quantization ops.
     We do not link kernels to the PT2 quantization ops, so we need to
     replace them with cadence ops at all optimization levels.
     """
 
-    def call_operator(
-        self,
-        op,
-        args: Tuple[Argument, ...],
-        kwargs: Dict[str, Argument],
-        meta: NodeMetadata,
-    ) -> ProxyValue:
-        ns = exir_ops.edge if isinstance(op, EdgeOpOverload) else torch.ops
-        if op != ns.quantized_decomposed.quantize_per_tensor.default:
-            return super().call_operator(op, args, kwargs, meta)
+    @property
+    def targets(self) -> list[EdgeOpOverload]:
+        return [
+            torch.ops.quantized_decomposed.quantize_per_tensor.default,
+            exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
+        ]
 
-        return super().call_operator(
-            ns.cadence.quantize_per_tensor.default,
-            args,
-            kwargs,
-            meta,
-        )
+    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
+        ns = exir_ops.edge if isinstance(node.target, EdgeOpOverload) else torch.ops
+        with node.graph.inserting_before(node):
+            new_node = node.graph.call_function(
+                ns.cadence.quantize_per_tensor.default,
+                args=node.args,
+                kwargs=node.kwargs,
+            )
+            new_node.meta = node.meta
+        node.replace_all_uses_with(new_node)
+        return True
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplacePT2DequantWithCadenceDequantPass(ExportPass):
+class ReplacePT2DequantWithCadenceDequantPass(RemoveOrReplacePassInterface):
     """
     Replace the pt2 dequantization ops with cadence dequantization ops.
     We do not link kernels to the PT2 quantization ops, so we need to
     replace them with cadence ops at all optimization levels.
     """
 
-    def call_operator(
-        self,
-        op,
-        args: Tuple[Argument, ...],
-        kwargs: Dict[str, Argument],
-        meta: NodeMetadata,
-    ) -> ProxyValue:
-        ns = exir_ops.edge if isinstance(op, EdgeOpOverload) else torch.ops
-        if op != ns.quantized_decomposed.dequantize_per_tensor.default:
-            return super().call_operator(op, args, kwargs, meta)
+    @property
+    def targets(self) -> list[EdgeOpOverload]:
+        return [
+            torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+            exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
+        ]
 
-        return super().call_operator(
-            ns.cadence.dequantize_per_tensor.default,
-            args,
-            kwargs,
-            meta,
-        )
+    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
+        ns = exir_ops.edge if isinstance(node.target, EdgeOpOverload) else torch.ops
+        with node.graph.inserting_before(node):
+            new_node = node.graph.call_function(
+                ns.cadence.dequantize_per_tensor.default,
+                args=node.args,
+                kwargs=node.kwargs,
+            )
+            new_node.meta = node.meta
+        node.replace_all_uses_with(new_node)
+        return True
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
@@ -232,18 +233,34 @@ def call_operator(
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceFunctionallyEquivalentOpTargets(ExportPass):
+class ReplaceFunctionallyEquivalentOpTargets(RemoveOrReplacePassInterface):
     """
     Replace an op with a functionally equivalent op by just switching the op
     target, but without incurring any change to the op args.
     """
 
-    def call_operator(self, op, args, kwargs, meta):
-        if op not in functionally_equivalent_op_targets:
-            return super().call_operator(op, args, kwargs, meta)
-        return super().call_operator(
-            functionally_equivalent_op_targets[op], args, kwargs, meta
-        )
+    @property
+    def targets(self) -> list[EdgeOpOverload]:
+        return list(functionally_equivalent_op_targets.keys())
+
+    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
+        assert isinstance(node.target, EdgeOpOverload)
+        target_op = functionally_equivalent_op_targets[node.target]
+        with node.graph.inserting_before(node):
+            new_node = node.graph.call_function(
+                target_op,
+                args=node.args,
+                kwargs=node.kwargs,
+            )
+            new_node.meta = node.meta
+        node.replace_all_uses_with(new_node)
+
+        # RemoveOrReplacePassInterface calls eliminate_dead_code, but this doesn't
+        # remove impure nodes (nodes which have side effects). Not sure if that is
+        # generally safe, so instead of modifying the interface, just erasing
+        # these nodes for this pass.
+        node.graph.erase_node(node)
+        return True
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=1))
@@ -1438,82 +1455,95 @@ def call_operator(self, op, args, kwargs, meta):
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceScalarTensorWithFullPass(ExportPass):
+class ReplaceScalarTensorWithFullPass(RemoveOrReplacePassInterface):
     """
     aten.scalar_tensor can be replaced by aten.full with a shape of [1].
     scalar_tensor is not supported, so this is an opt_level=0 pass.
     """
 
-    def call_operator(
-        self,
-        op,
-        args: Tuple[Argument, ...],
-        kwargs: Dict[str, Argument],
-        meta: NodeMetadata,
-    ) -> ProxyValue:
-        if op not in {
-            exir_ops.edge.aten.scalar_tensor.default,
+    @property
+    def targets(self) -> list[EdgeOpOverload]:
+        return [
             torch.ops.aten.scalar_tensor.default,
-        }:
-            return super().call_operator(op, args, kwargs, meta)
+            exir_ops.edge.aten.scalar_tensor.default,
+        ]
 
-        return super().call_operator(
-            exir_ops.edge.aten.full.default,
-            (
-                [1],
-                args[0],
-            ),
-            {"dtype": torch.float32},
-            meta,
-        )
+    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
+        with node.graph.inserting_before(node):
+            new_node = node.graph.call_function(
+                exir_ops.edge.aten.full.default,
+                args=(
+                    [1],
+                    node.args[0],
+                ),
+                kwargs={"dtype": torch.float32},
+            )
+            new_node.meta = node.meta
+        node.replace_all_uses_with(new_node)
+        return True
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceFullLikeWithFullPass(ExportPass):
+class ReplaceFullLikeWithFullPass(RemoveOrReplacePassInterface):
     """
     aten.full_like can be replaced by aten.full with the shape of the arg tensor.
     full_like is not supported, so this is an opt_level=0 pass.
     """
 
-    def call_operator(self, op, args, kwargs, meta):
-        if op not in {
-            exir_ops.edge.aten.full_like.default,
-        }:
-            return super().call_operator(op, args, kwargs, meta)
+    @property
+    def targets(self) -> list[EdgeOpOverload]:
+        return [exir_ops.edge.aten.full_like.default]
 
-        # Get the shape of the "like" tensor, and pass that in to the full op.
-        return super().call_operator(
-            exir_ops.edge.aten.full.default,
-            (
-                args[0].to_tensor().shape,
-                args[1],
-            ),
-            {},
-            meta,
-        )
+    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
+        input_arg = node.args[0]
+        assert isinstance(input_arg, torch.fx.Node)
+        shape = input_arg.meta["val"].shape
+        fill_value = node.args[1]
+
+        with node.graph.inserting_before(node):
+            new_node = node.graph.call_function(
+                exir_ops.edge.aten.full.default,
+                args=(shape, fill_value),
+                kwargs={},
+            )
+            new_node.meta = node.meta
+        node.replace_all_uses_with(new_node)
+        return True
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceInfArgInFullWithValuePass(ExportPass):
+class ReplaceInfArgInFullWithValuePass(RemoveOrReplacePassInterface):
     """
     aten.full allows "-inf" and "inf" as inputs. The profiler cannot
     handle that, so replace them with the maximum value of the type.
     """
 
-    def call_operator(self, op, args, kwargs, meta):
-        if op not in {
-            exir_ops.edge.aten.full.default,
-        }:
-            return super().call_operator(op, args, kwargs, meta)
+    @property
+    def targets(self) -> list[EdgeOpOverload]:
+        return [exir_ops.edge.aten.full.default]
 
-        new_args = list(args)
+    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
 
-        if args[1] == float("-inf"):
+        new_args = list(node.args)
+        fill_value = node.args[1]
+        if fill_value == float("-inf"):
             new_args[1] = torch.finfo(torch.float32).min
-        elif args[1] == float("inf"):
+        elif fill_value == float("inf"):
             new_args[1] = torch.finfo(torch.float32).max
+        else:
+            return False
 
-        return super().call_operator(op, tuple(new_args), kwargs, meta)
+        new_args = tuple(new_args)
+
+        with node.graph.inserting_before(node):
+            new_node = node.graph.call_function(
+                exir_ops.edge.aten.full.default,
+                args=new_args,
+                kwargs=node.kwargs,
+            )
+            new_node.meta = node.meta
+        node.replace_all_uses_with(new_node)
+        return True
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
@@ -1713,26 +1743,6 @@ def call_operator(
         return super().call_operator(op, args, kwargs, meta)
 
 
-@register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceAtenApproxGeluWithApproxGeluPass(ExportPass):
-    """
-    Replace the aten gelu op with an approximate arg with an approximate gelu op.
-    """
-
-    def call_operator(
-        self,
-        op,
-        args: Tuple[Argument, ...],
-        kwargs: Dict[str, Argument],
-        meta: NodeMetadata,
-    ) -> ProxyValue:
-        if op not in {
-            exir_ops.edge.aten.gelu.default,
-        }:
-            return super().call_operator(op, args, kwargs, meta)
-        return super().call_operator(op, args, kwargs, meta)
-
-
 # Adapted from fbcode/pyspeech/opt_passes/replace_ops.py
 @register_cadence_pass(CadencePassAttribute(opt_level=2))
 class ReplaceSplitWithSlicePass(ExportPass):
@@ -2122,18 +2132,25 @@ class CommonReplacePasses:
 
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
-class ReplaceAtenLinalgSvdWithCadenceLinalgSvdPass(ExportPass):
+class ReplaceAtenLinalgSvdWithCadenceLinalgSvdPass(RemoveOrReplacePassInterface):
     """
     Replace aten linalg svd op with cadence custom op.
     """
 
-    def call_operator(self, op, args, kwargs, meta):
-        if op != exir_ops.edge.aten._linalg_svd.default:
-            return super().call_operator(op, args, kwargs, meta)
+    @property
+    def targets(self) -> list[EdgeOpOverload]:
+        return [exir_ops.edge.aten._linalg_svd.default]
 
-        return super().call_operator(
-            exir_ops.edge.cadence.linalg_svd.default, args, kwargs, meta
-        )
+    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
+        with node.graph.inserting_before(node):
+            new_node = node.graph.call_function(
+                exir_ops.edge.cadence.linalg_svd.default,
+                args=node.args,
+                kwargs=node.kwargs,
+            )
+            new_node.meta = node.meta
+        node.replace_all_uses_with(new_node)
+        return True
 
 
 # This class encapsulates all the functions that replace/switch one op in the
@@ -2165,6 +2182,5 @@ class CadenceReplaceOpsInGraph:
         ReplaceAdaptiveAvgPoolWithAtenAvgPoolPass,
         ReplaceAtenAvgPoolWithCadenceAvgPoolPass,
         ReplaceWhereWithFullArgsWithWhereScalar,
-        ReplaceAtenApproxGeluWithApproxGeluPass,
         ReplaceMulTensorWithMulAndFullOpsPass,
     ]