Produce permutes instead of transposes

backends/cadence/aot/compiler_utils.py

@@ -87,6 +87,21 @@ def broadcastable(shape_1: Sequence[int], shape_2: Sequence[int]) -> bool:
     )
 
 
+def transpose_dims_to_permute_order(ndim: int, dim0: int, dim1: int) -> List[int]:
+    """
+    Convert transpose(dim0, dim1) to an equivalent permute order list.
+    E.g., transpose(0, 1) on a 3D tensor gives [1, 0, 2].
+    """
+    # Normalize negative dims
+    if dim0 < 0:
+        dim0 += ndim
+    if dim1 < 0:
+        dim1 += ndim
+    order = list(range(ndim))
+    order[dim0], order[dim1] = order[dim1], order[dim0]
+    return order
+
+
 def get_transposed_dims(
     node: torch.fx.Node, dims: Optional[List[int]] = None
 ) -> List[int]:

backends/cadence/aot/quantizer/fusion_pass.py

@@ -678,10 +678,13 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
                             quant_node,
                         )
                     elif isinstance(pattern, AddmmPattern):
-                        # Transpose the weight tensor
+                        # Transpose the weight tensor using permute
+                        weight_ndim = len(weights_inputs[0].meta["val"].shape)
+                        perm = list(range(weight_ndim))
+                        perm[0], perm[1] = perm[1], perm[0]
                         transposed_weights = graph_module.graph.call_function(
-                            torch.ops.aten.transpose.int,
-                            (weights_inputs[0], 0, 1),
+                            torch.ops.aten.permute.default,
+                            (weights_inputs[0], perm),
                         )
                         assert (
                             "val" in weights_inputs[0].meta
@@ -692,7 +695,7 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
                         ), "fake_mode is None on weight node"
                         with original_val.fake_mode:
                             transposed_weights.meta["val"] = (
-                                torch.ops.aten.transpose.int(original_val, 0, 1)
+                                torch.ops.aten.permute.default(original_val, perm)
                             )
                         copy_node_metadata(transposed_weights, weights_inputs[0])
 

backends/cadence/aot/replace_ops.py

@@ -1,3 +1,3 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
 # Copyright 2025 Arm Limited and/or its affiliates.
@@ -19,7 +19,10 @@
 
 import torch
 import torch.fx
-from executorch.backends.cadence.aot.compiler_utils import quantize_tensor_multiplier
+from executorch.backends.cadence.aot.compiler_utils import (
+    quantize_tensor_multiplier,
+    transpose_dims_to_permute_order,
+)
 from executorch.backends.cadence.aot.fuse_ops import FuseCascadedTransposeOrPermuteOps
 from executorch.backends.cadence.aot.pass_utils import (
     CadencePassAttribute,
@@ -355,9 +358,9 @@
 
         # Handle transpose: if mat2 is a transpose op, extract the original tensor
         transposed_mat2 = False
-        if (
-            mat2.op == "call_function"
-            and mat2.target == exir_ops.edge.aten.transpose_copy.int
+        if mat2.op == "call_function" and (
+            mat2.target == exir_ops.edge.aten.transpose_copy.int
+            or mat2.target == exir_ops.edge.aten.permute_copy.default
         ):
             # mat2 is already transposed, so we use the input to the transpose
             mat2 = cast(torch.fx.Node, mat2.args[0])
@@ -405,9 +408,11 @@
         # Transpose mat2 if it wasn't already transposed
         if not transposed_mat2:
             with graph.inserting_before(node):
+                ndim = len(mat2.meta["val"].shape)
+                perm = transpose_dims_to_permute_order(ndim, -1, -2)
                 mat2 = graph.call_function(
-                    exir_ops.edge.aten.transpose_copy.int,
-                    args=(mat2, -1, -2),
+                    exir_ops.edge.aten.permute_copy.default,
+                    args=(mat2, perm),
                 )
 
                 # Metadata copy important
@@ -430,47 +435,6 @@
         return True
 
 
-@register_cadence_pass(CadencePassAttribute(opt_level=1))
-class ReplacePermuteWithTransposePass(RemoveOrReplacePassInterface):
-    """
-    Replace permute op with transpose if the permutation is only along
-    two dimensions.
-    """
-
-    @property
-    def targets(self) -> list[EdgeOpOverload]:
-        return [exir_ops.edge.aten.permute_copy.default]
-
-    def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
-        # Get the old dim and new dim order
-        in_tensor = node.args[0]
-        assert isinstance(in_tensor, torch.fx.Node)
-        in_shape = in_tensor.meta["val"].shape
-        old_dims = tuple(range(len(in_shape)))
-        new_dims = cast(Sequence[int], node.args[1])
-
-        # Compute the number of positions in which the old and new order differ
-        diff = [od for od, nd in zip(old_dims, new_dims) if od != nd]
-
-        # If the difference is zero, replace with identity (just the input)
-        if len(diff) == 0:
-            node.replace_all_uses_with(in_tensor)
-            return True
-
-        # If the difference is in two dimensions, we can replace this permute op
-        # with transpose op.
-        if len(diff) == 2:
-            with node.graph.inserting_before(node):
-                new_node = node.graph.call_function(
-                    exir_ops.edge.aten.transpose_copy.int,
-                    args=(node.args[0], diff[0], diff[1]),
-                )
-                new_node.meta = node.meta
-            node.replace_all_uses_with(new_node)
-            return True
-
-        return False
-
 
 @register_cadence_pass(CadencePassAttribute(opt_level=0))
 class ReplaceConvolutionOptionalArgsWithConcreteArgsPass(RemoveOrReplacePassInterface):
@@ -798,9 +762,11 @@
                 # gather stencil. Also, the first two dimensions of weight must be
                 # transposed/interchanged.
                 assert isinstance(weight, torch.fx.Node)
+                weight_ndim = len(weight.meta["val"].shape)
+                perm = transpose_dims_to_permute_order(weight_ndim, 0, 1)
                 transposed_weight = node.graph.call_function(
-                    exir_ops.edge.aten.transpose_copy.int,
-                    args=(weight, 0, 1),
+                    exir_ops.edge.aten.permute_copy.default,
+                    args=(weight, perm),
                 )
                 transposed_weight.meta = weight.meta
 
@@ -1036,18 +1002,19 @@
     def _transpose_dims(
         self, graph: torch.fx.Graph, node: torch.fx.Node, dim0: int, dim1: int
     ) -> torch.fx.Node:
-        """Helper function to transpose dims of a node."""
+        """Helper function to transpose dims of a node using permute."""
         shape = node.meta["val"].shape
         dim0, dim1 = (
             canonicalize_transposed_dim(dim0, shape),
             canonicalize_transposed_dim(dim1, shape),
         )
         dim0, dim1 = min(dim0, dim1), max(dim0, dim1)
-        transpose_node = graph.call_function(
-            exir_ops.edge.aten.transpose_copy.int, (node, dim0, dim1), {}
+        perm = transpose_dims_to_permute_order(len(shape), dim0, dim1)
+        permute_node = graph.call_function(
+            exir_ops.edge.aten.permute_copy.default, (node, perm), {}
         )
-        transpose_node.meta = node.meta
-        return transpose_node
+        permute_node.meta = node.meta
+        return permute_node
 
     def _change_nchw_to_nhwc(
         self, graph: torch.fx.Graph, node: torch.fx.Node
@@ -1263,18 +1230,19 @@
     def _transpose_dims(
         self, graph: torch.fx.Graph, node: torch.fx.Node, dim0: int, dim1: int
     ) -> torch.fx.Node:
-        """Helper function to transpose dims of a node."""
+        """Helper function to transpose dims of a node using permute."""
         shape = node.meta["val"].shape
         dim0, dim1 = (
             canonicalize_transposed_dim(dim0, shape),
             canonicalize_transposed_dim(dim1, shape),
         )
         dim0, dim1 = min(dim0, dim1), max(dim0, dim1)
-        transpose_node = graph.call_function(
-            exir_ops.edge.aten.transpose_copy.int, (node, dim0, dim1), {}
+        perm = transpose_dims_to_permute_order(len(shape), dim0, dim1)
+        permute_node = graph.call_function(
+            exir_ops.edge.aten.permute_copy.default, (node, perm), {}
         )
-        transpose_node.meta = node.meta
-        return transpose_node
+        permute_node.meta = node.meta
+        return permute_node
 
     def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
         # Get the dimension argument
@@ -1526,8 +1494,8 @@
         if not channel_last:
             with graph.inserting_before(node):
                 linear_res = graph.call_function(
-                    exir_ops.edge.aten.transpose_copy.int,
-                    args=(linear_res, 1, 2),
+                    exir_ops.edge.aten.permute_copy.default,
+                    args=(linear_res, [0, 2, 1]),
                 )
                 linear_res.meta = node.meta
 
@@ -1717,8 +1685,8 @@
         if not channel_last:
             with graph.inserting_before(node):
                 linear_res = graph.call_function(
-                    exir_ops.edge.aten.transpose_copy.int,
-                    args=(linear_res, 1, 2),
+                    exir_ops.edge.aten.permute_copy.default,
+                    args=(linear_res, [0, 2, 1]),
                 )
                 linear_res.meta = node.meta
 
@@ -2375,9 +2343,11 @@
 
         # Transpose Y_arg
         with graph.inserting_before(node):
+            Y_ndim = len(Y_tensor_val.shape)
+            perm = transpose_dims_to_permute_order(Y_ndim, -1, -2)
             Y_arg_t = graph.call_function(
-                exir_ops.edge.aten.transpose_copy.int,
-                args=(Y_arg, -1, -2),
+                exir_ops.edge.aten.permute_copy.default,
+                args=(Y_arg, perm),
             )
             Y_arg_t.meta = node.meta
 
@@ -2410,13 +2380,10 @@
         result = super().call(graph_module)
         modified = modified or result.modified
         if modified:
-            # Fuse any inserted transpose node with transpose/permute nodes
+            # Fuse any inserted permute node with transpose/permute nodes
             # surrounding it.
             result = FuseCascadedTransposeOrPermuteOps().call(result.graph_module)
             modified = modified or result.modified
-            # Replace permute with transpose.
-            result = ReplacePermuteWithTransposePass().call(result.graph_module)
-            modified = modified or result.modified
 
         return PassResult(result.graph_module, modified)
 
@@ -2643,7 +2610,6 @@
         ReplaceAtenLinalgSvdWithCadenceLinalgSvdPass,
         ReplaceEmptyTensorsWithFullPass,
         ReplaceFunctionallyEquivalentOpTargets,
-        ReplacePermuteWithTransposePass,
         ReplaceConvolutionOptionalArgsWithConcreteArgsPass,
         ReplaceAddMMWithLinearPass,
         ReplacePadWithCatPass,

backends/cadence/aot/tests/test_replace_ops_passes.py

@@ -37,7 +37,6 @@
     ReplaceMulTensorWithMulAndFullOpsPass,
     ReplaceNopTransposeOrPermuteWithViewPass,
     ReplacePadWithCatPass,
-    ReplacePermuteWithTransposePass,
     ReplacePowWithMulPass,
     ReplaceRepeatWithCatPass,
     ReplaceScalarTensorWithFullPass,
@@ -965,10 +964,7 @@ def test_replace_linear_with_fully_connected(self) -> None:
         builder.output([mm])
         original_gm = builder.get_graph_module()
 
-        gm = cast(
-            PassResult, ReplacePermuteWithTransposePass()(original_gm)
-        ).graph_module
-        gm = cast(PassResult, ReplaceMMWithAddMMPass()(gm)).graph_module
+        gm = cast(PassResult, ReplaceMMWithAddMMPass()(original_gm)).graph_module
 
         gm_before_linear = copy.deepcopy(gm)
         pass_result = cast(PassResult, ReplaceAddMMWithLinearPass()(gm))
@@ -1029,12 +1025,8 @@ def test_replace_addmm_with_linear(self) -> None:
         builder.output([addmm])
         original_gm = builder.get_graph_module()
 
-        gm = cast(
-            PassResult, ReplacePermuteWithTransposePass()(original_gm)
-        ).graph_module
-
-        gm_before_linear = copy.deepcopy(gm)
-        pass_result = cast(PassResult, ReplaceAddMMWithLinearPass()(gm))
+        gm_before_linear = copy.deepcopy(original_gm)
+        pass_result = cast(PassResult, ReplaceAddMMWithLinearPass()(original_gm))
         self.assertTrue(pass_result.modified)
         graph_after_passes = pass_result.graph_module
 
@@ -1077,11 +1069,7 @@ def test_no_replace_addmm_with_linear(self) -> None:
         builder.output([addmm])
         original_gm = builder.get_graph_module()
 
-        gm = cast(
-            PassResult, ReplacePermuteWithTransposePass()(original_gm)
-        ).graph_module
-
-        pass_result = cast(PassResult, ReplaceAddMMWithLinearPass()(gm))
+        pass_result = cast(PassResult, ReplaceAddMMWithLinearPass()(original_gm))
         self.assertFalse(pass_result.modified)
 
     @torch.no_grad()
@@ -1715,63 +1703,6 @@ def test_replace_nop_permute_with_view(
             count_node(graph_after_passes, exir_ops.edge.aten.view_copy.default), 1
         )
 
-    @expand(
-        [
-            # permutations replaced by transpose
-            [(3, 4), (1, 0)],
-            [(3, 4, 6), (0, 2, 1)],
-        ]
-    )
-    @torch.no_grad()
-    def test_replace_permute_with_transpose(
-        self, shape: Tuple[int], dims: Tuple[int]
-    ) -> None:
-        x = torch.randn(shape)
-        original_gm = single_op_builder(
-            placeholders=(x,),
-            op=exir_ops.edge.aten.permute_copy.default,
-            args=(x, dims),
-        )
-
-        gm_before = copy.deepcopy(original_gm)
-        p = ReplacePermuteWithTransposePass()
-        result = cast(PassResult, p(original_gm))
-        self.assertTrue(result.modified)
-        graph_after_passes = result.graph_module
-        inputs = [x]
-        validate(
-            gm_before, graph_after_passes, inputs, "ReplacePermuteWithTransposePass"
-        )
-
-        # Assert that permute op was replaced by a transpose op
-        self.assertEqual(
-            count_node(graph_after_passes, exir_ops.edge.aten.permute_copy.default), 0
-        )
-        self.assertEqual(
-            count_node(graph_after_passes, exir_ops.edge.aten.transpose_copy.int), 1
-        )
-
-    @torch.no_grad()
-    def test_replace_permute_with_transpose_nop(
-        self,
-    ) -> None:
-        x = torch.randn(3, 4)
-        original_gm = single_op_builder(
-            placeholders=(x,),
-            op=exir_ops.edge.aten.permute_copy.default,
-            args=(x, [0, 1]),
-        )
-        p = ReplacePermuteWithTransposePass()
-        graph_after_passes = cast(PassResult, p(original_gm)).graph_module
-
-        # Assert that permute op was replaced by a transpose op
-        self.assertEqual(
-            count_node(graph_after_passes, exir_ops.edge.aten.permute_copy.default), 0
-        )
-        self.assertEqual(
-            count_node(graph_after_passes, exir_ops.edge.aten.transpose_copy.int), 0
-        )
-
 
 class TestReplaceWhereWithFullArgsWithWhereScalar(unittest.TestCase):
     def test_replace_aten_where_with_cadence(self) -> None: