Arm backend: Generalize RemovePermutesAroundElementwiseTosaOps

backends/arm/_passes/arm_pass_manager.py

@@ -614,7 +614,7 @@ def _tosa_pipeline(
                 RewriteMatmulPass(),
                 RewritePadPass(),
                 FuseViewCopyTransformPass(),
-                RemovePermutesAroundElementwiseTosaOps(),
+                RemovePermutesAroundElementwiseTosaOps(exported_program),
                 PostponePermuteOpBelowSqueezeOrUnsqueezeLikeView(),
                 FuseCascadedTransposeOrPermuteOps(),
                 ConvertPermuteSingletonToViewPass(),

backends/arm/_passes/remove_permutes_around_elementwise_tosa_ops.py

@@ -3,15 +3,19 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import torch
+
+from executorch.backends.arm._passes.arm_pass_utils import is_param_node
 from executorch.backends.arm._passes.insert_table_ops import TableOps
 from executorch.backends.transforms.remove_permutes_around_elementwise_ops import (
     RemovePermutesAroundElementwiseOps,
 )
+from executorch.exir import ExportedProgram
 from executorch.exir.dialects._ops import ops as exir_ops
 
 
 class RemovePermutesAroundElementwiseTosaOps(RemovePermutesAroundElementwiseOps):
-    def __init__(self) -> None:
+    def __init__(self, exported_program: ExportedProgram) -> None:
         super().__init__(
             extra_permutable_ops={
                 *TableOps.unary_table_ops.keys(),
@@ -20,16 +24,19 @@ def __init__(self) -> None:
                 exir_ops.backend.tosa.TABLE.default,
             }
         )
+        self.exported_program = exported_program
+
+    def _is_constant(self, node: torch.fx.Node) -> bool:
+        # Override fragile string match check with exported program check
+        return super()._is_constant(node) or is_param_node(self.exported_program, node)
 
     def permute_subgraph(self, subgraph) -> bool:
-        # Original function will always permute constant nodes which is wrong for table ops
-        # Remove constant tosa.TABLE edges before running full function
+        # TABLE lookup inputs are already tied to the table layout.
         new_constant_edges_in = set()
         for const_node, user_node in subgraph.constant_edges_in:
             if user_node.target == exir_ops.backend.tosa.TABLE.default:
                 continue
-            else:
-                new_constant_edges_in.add((const_node, user_node))
+            new_constant_edges_in.add((const_node, user_node))
 
         subgraph.constant_edges_in = new_constant_edges_in
         return super().permute_subgraph(subgraph)

backends/arm/test/misc/test_transpose_counts.py

@@ -453,7 +453,7 @@ def forward(self, x: torch.Tensor):
         Model4ConvLstmLinearLayerNorm(), (torch.randn(2, 8, 32),), 3
     ),
     "model_5_dwconv_gelu_layernorm_avgpool": TransposeCountCase(
-        Model5DwConvGeluLayerNormAvgPool(), (torch.randn(1, 8, 16, 16),), 4
+        Model5DwConvGeluLayerNormAvgPool(), (torch.randn(1, 8, 16, 16),), 2
     ),
     "model_6_gru_linear": TransposeCountCase(
         Model6GruLinear(), (torch.randn(2, 16, 8),), 2

backends/arm/test/passes/test_remove_permutes_around_elementwise_tosa_ops.py

@@ -3,6 +3,9 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+from types import SimpleNamespace
+from typing import cast
+
 import torch
 from executorch.backends.arm._passes.remove_permutes_around_elementwise_tosa_ops import (
     RemovePermutesAroundElementwiseTosaOps,
@@ -11,12 +14,30 @@
     TosaLoweringContext,
     TosaSpecification,
 )
+from executorch.exir import ExportedProgram
 from executorch.exir.dialects._ops import ops as exir_ops
 
 TOSA_INT_SPEC = TosaSpecification.create_from_string("TOSA-1.0+INT")
+TOSA_FP_SPEC = TosaSpecification.create_from_string("TOSA-1.0+FP")
 PERMUTE_TARGET = exir_ops.edge.aten.permute_copy.default
 RESCALE_TARGET = exir_ops.backend.tosa.RESCALE.default
 TABLE_TARGET = exir_ops.backend.tosa.TABLE.default
+MUL_TARGET = exir_ops.edge.aten.mul.Tensor
+ADD_TARGET = exir_ops.edge.aten.add.Tensor
+ERF_TARGET = exir_ops.edge.aten.erf.default
+
+
+def _fake_exported_program() -> ExportedProgram:
+    return cast(
+        ExportedProgram,
+        SimpleNamespace(
+            graph_signature=SimpleNamespace(
+                inputs_to_buffers={},
+                inputs_to_lifted_tensor_constants={},
+                inputs_to_parameters={},
+            )
+        ),
+    )
 
 
 def _count_nodes(graph_module: torch.fx.GraphModule, target) -> int:
@@ -52,8 +73,125 @@ def test_remove_permutes_around_rescale_tosa_INT() -> None:
     graph_module = torch.fx.GraphModule({}, graph)
 
     with TosaLoweringContext(TOSA_INT_SPEC):
-        result = RemovePermutesAroundElementwiseTosaOps().call(graph_module)
+        result = RemovePermutesAroundElementwiseTosaOps(_fake_exported_program()).call(
+            graph_module
+        )
 
     assert result.modified
     assert _count_nodes(result.graph_module, PERMUTE_TARGET) == 0
     assert _count_nodes(result.graph_module, RESCALE_TARGET) == 1
+
+
+def test_remove_permutes_around_gelu_with_folded_scalar_constants_tosa_FP() -> None:
+    graph = torch.fx.Graph()
+    x = graph.placeholder("x")
+    x.meta["val"] = torch.randn(1, 2, 3, 4)
+
+    scalar_constants = []
+    for i in range(3):
+        const = graph.placeholder(f"c_scalar_{i}")
+        const.meta["val"] = torch.randn(1, 1, 1, 1)
+        scalar_constants.append(const)
+
+    permute_in = graph.create_node(
+        "call_function",
+        PERMUTE_TARGET,
+        args=(x, [0, 2, 3, 1]),
+    )
+    permute_in.meta["val"] = torch.randn(1, 3, 4, 2)
+    mul_0 = graph.create_node(
+        "call_function",
+        MUL_TARGET,
+        args=(permute_in, scalar_constants[0]),
+    )
+    mul_0.meta["val"] = torch.randn(1, 3, 4, 2)
+    erf = graph.create_node("call_function", ERF_TARGET, args=(mul_0,))
+    erf.meta["val"] = torch.randn(1, 3, 4, 2)
+    add = graph.create_node(
+        "call_function",
+        ADD_TARGET,
+        args=(erf, scalar_constants[1]),
+    )
+    add.meta["val"] = torch.randn(1, 3, 4, 2)
+    mul_1 = graph.create_node(
+        "call_function",
+        MUL_TARGET,
+        args=(add, scalar_constants[2]),
+    )
+    mul_1.meta["val"] = torch.randn(1, 3, 4, 2)
+    mul_2 = graph.create_node(
+        "call_function",
+        MUL_TARGET,
+        args=(permute_in, mul_1),
+    )
+    mul_2.meta["val"] = torch.randn(1, 3, 4, 2)
+    permute_out = graph.create_node(
+        "call_function",
+        PERMUTE_TARGET,
+        args=(mul_2, [0, 3, 1, 2]),
+    )
+    permute_out.meta["val"] = torch.randn(1, 2, 3, 4)
+    graph.output(permute_out)
+
+    graph_module = torch.fx.GraphModule({}, graph)
+
+    with TosaLoweringContext(TOSA_FP_SPEC):
+        result = RemovePermutesAroundElementwiseTosaOps(_fake_exported_program()).call(
+            graph_module
+        )
+
+    assert result.modified
+    assert _count_nodes(result.graph_module, PERMUTE_TARGET) == 3
+    assert _count_nodes(result.graph_module, ERF_TARGET) == 1
+
+
+def test_remove_permutes_skips_stale_shared_boundary_subgraph_tosa_FP() -> None:
+    graph = torch.fx.Graph()
+    x = graph.placeholder("x")
+    x.meta["val"] = torch.randn(1, 16, 16, 8)
+
+    channel_const = graph.placeholder("p_layer_norm_weight")
+    channel_const.meta["val"] = torch.randn(1, 1, 1, 8)
+
+    permute_in = graph.create_node(
+        "call_function",
+        PERMUTE_TARGET,
+        args=(x, [0, 3, 1, 2]),
+    )
+    permute_in.meta["val"] = torch.randn(1, 8, 16, 16)
+    first_mul = graph.create_node(
+        "call_function",
+        MUL_TARGET,
+        args=(permute_in, permute_in),
+    )
+    first_mul.meta["val"] = torch.randn(1, 8, 16, 16)
+    shared_permute = graph.create_node(
+        "call_function",
+        PERMUTE_TARGET,
+        args=(first_mul, [0, 2, 3, 1]),
+    )
+    shared_permute.meta["val"] = torch.randn(1, 16, 16, 8)
+    second_mul = graph.create_node(
+        "call_function",
+        MUL_TARGET,
+        args=(shared_permute, channel_const),
+    )
+    second_mul.meta["val"] = torch.randn(1, 16, 16, 8)
+    permute_out = graph.create_node(
+        "call_function",
+        PERMUTE_TARGET,
+        args=(second_mul, [0, 3, 1, 2]),
+    )
+    permute_out.meta["val"] = torch.randn(1, 8, 16, 16)
+    graph.output(permute_out)
+
+    graph_module = torch.fx.GraphModule({}, graph)
+
+    with TosaLoweringContext(TOSA_FP_SPEC):
+        result = RemovePermutesAroundElementwiseTosaOps(_fake_exported_program()).call(
+            graph_module
+        )
+
+    assert result.modified
+    assert _count_nodes(result.graph_module, PERMUTE_TARGET) == 1
+    assert second_mul.args[1] is channel_const

backends/transforms/remove_permutes_around_elementwise_ops.py

@@ -400,6 +400,10 @@ def is_node_permutable(self, node: torch.fx.Node) -> bool:
         return self._is_pointwise(node.target)
 
     def permute_subgraph(self, subgraph: Subgraph) -> bool:  # noqa: C901
+        # Ensure that the subgraph's edges have not been modified by an earlier rewrite before applying changes.
+        if not self._subgraph_edges_are_current(subgraph):
+            return False
+
         # Validate: every view_copy node's permutation rank must match its
         # input tensor rank.  A mismatch can occur when a squeeze/unsqueeze
         # view is reached via upstream traversal with a permutation that was
@@ -495,6 +499,28 @@ def permute_subgraph(self, subgraph: Subgraph) -> bool:  # noqa: C901
 
         return True
 
+    def _subgraph_edges_are_current(self, subgraph: Subgraph) -> bool:
+        """Return false if an earlier rewrite invalidated this candidate."""
+        for inp, out in subgraph.edges_in:
+            if (
+                inp.target != exir_ops.edge.aten.permute_copy.default
+                or inp not in out.all_input_nodes
+            ):
+                return False
+
+        for inp, out in subgraph.edges_out:
+            if (
+                out.target != exir_ops.edge.aten.permute_copy.default
+                or out not in inp.users
+            ):
+                return False
+
+        for const_node, user_node in subgraph.constant_edges_in:
+            if const_node not in user_node.all_input_nodes:
+                return False
+
+        return True
+
     def update_cat(self, node: torch.fx.Node, start_permute: list[int]) -> None:
         dim = get_arg(node, "dim", int)
         set_arg(node, "dim", start_permute[dim])