Fix unsafe copies in EXIR and permute transforms

backends/transforms/postpone_permute_below_squeeze_view.py

@@ -1,12 +1,12 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
+# Copyright 2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
 # pyre-unsafe
 
-import copy
 from typing import cast, List
 
 import torch
@@ -108,7 +108,7 @@ def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
                 # view_node_shape is almost same as permute_node_shape
                 # except it has one more dim somewhere
                 # and the extra dim has value of 1.
-                new_view_shape = copy.deepcopy(pred_shape)
+                new_view_shape = list(pred_shape)
                 new_view_shape.insert(index, 1)
                 new_permute_dims = [x + 1 if x >= index else x for x in permute_dims]
                 new_permute_dims.insert(index, index)
@@ -132,7 +132,7 @@ def maybe_remove_or_replace(self, node: torch.fx.Node) -> bool:
                 # and the extra dim has value of 1.
                 # Convert permute_dims to list of ints
                 index_to_remove = permute_dims[index]
-                new_view_shape = copy.deepcopy(pred_shape)
+                new_view_shape = list(pred_shape)
                 del new_view_shape[index_to_remove]
                 new_permute_dims = [
                     x - 1 if x > index_to_remove else x for x in permute_dims

backends/transforms/test/test_permute_optimization_passes.py

@@ -1,5 +1,6 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
+# Copyright 2026 Arm Limited and/or its affiliates.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -25,6 +26,8 @@
 from executorch.backends.transforms.replace_nop_transpose_or_permute_with_view import (
     ReplaceNopTransposeOrPermuteWithViewPass,
 )
+
+from executorch.exir import EdgeCompileConfig, to_edge
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import PassResult
 from torch.utils import _pytree as pytree
@@ -477,6 +480,38 @@ def test_permute4_view3_chains(self) -> None:
             "PostponePermuteOpBelowSqueezeOrUnsqueezeLikeView",
         )
 
+    def test_postpone_permute_with_symbolic_shapes(self) -> None:
+        class DynamicPermuteViewModule(torch.nn.Module):
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                y = x.view(x.shape[0], 12, 64)
+                y = y.permute(1, 0, 2)
+                y = y.view(1, 12, x.shape[0], 64)
+                return y.permute(0, 1, 3, 2)
+
+        exported_program = torch.export.export(
+            DynamicPermuteViewModule(),
+            (torch.randn(3, 1, 768),),
+            dynamic_shapes={"x": {0: torch.export.Dim("batch", min=1, max=8)}},
+        )
+        edge_program = to_edge(
+            exported_program,
+            compile_config=EdgeCompileConfig(_check_ir_validity=False),
+        )
+        graph_module = edge_program.exported_program().graph_module
+
+        result = cast(
+            PassResult,
+            PostponePermuteOpBelowSqueezeOrUnsqueezeLikeView().call(graph_module),
+        )
+
+        self.assertTrue(result.modified)
+        self.assertEqual(
+            count_node(result.graph_module, exir_ops.edge.aten.view_copy.default), 2
+        )
+        self.assertEqual(
+            count_node(result.graph_module, exir_ops.edge.aten.permute_copy.default), 2
+        )
+
     def test_negative_not_squeeze_like(self) -> None:
         """View that reshapes (not just squeeze/unsqueeze) should NOT be reordered."""
         builder = GraphBuilder()

exir/tensor.py

@@ -10,8 +10,6 @@
 # pyre-ignore-all-errors[16]
 from __future__ import annotations
 
-import copy
-
 import math
 import typing
 from typing import Dict, List, NamedTuple, Optional, Tuple, Union
@@ -112,7 +110,7 @@ def stride_from_dim_order(sizes: List[int], dim_order: List[int]) -> List[int]:
     """
     if len(sizes) == 0:
         return []
-    strides = copy.deepcopy(sizes)
+    strides = list(sizes)
     ndim = len(sizes)
     strides[dim_order[ndim - 1]] = 1
     for i in range(ndim - 2, -1, -1):

exir/tests/test_tensor.py

@@ -388,6 +388,26 @@ def test_strides_from_dim_order(self) -> None:
         strides = stride_from_dim_order(sizes, dim_order)
         self.assertEqual(expected_strides, strides)
 
+    def test_strides_from_dim_order_with_symbolic_sizes(self) -> None:
+        class ViewModule(torch.nn.Module):
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                return x.view(x.shape[0], -1)
+
+        exported_program = torch.export.export(
+            ViewModule(),
+            (torch.randn(2, 3, 4),),
+            dynamic_shapes={"x": {0: torch.export.Dim("batch", min=1, max=8)}},
+        )
+        placeholder = next(
+            node
+            for node in exported_program.graph_module.graph.nodes
+            if node.op == "placeholder"
+        )
+        sizes = list(placeholder.meta["val"].shape)
+
+        self.assertIsInstance(sizes[0], torch.SymInt)
+        self.assertEqual([12, 4, 1], stride_from_dim_order(sizes, [0, 1, 2]))
+
     def test_num_bytes_from_shape_and_dtype(self) -> None:
         shape = (2, 3, 4)
         self.assertEqual(24, num_bytes_from_shape_and_dtype(shape, torch.int8))