Arm backend: Add support for detach_copy

backends/arm/operator_support/tosa_profile_supported_op_lists.py

@@ -123,6 +123,7 @@
     exir_ops.edge.aten.copy.default,
     exir_ops.edge.aten.tan.default,
     exir_ops.edge.aten.index_put.default,
+    exir_ops.edge.aten.detach_copy.default,
 }
 
 
@@ -244,6 +245,7 @@
     exir_ops.edge.aten.floor_divide.default,
     exir_ops.edge.aten.tan.default,
     exir_ops.edge.aten.index_put.default,
+    exir_ops.edge.aten.detach_copy.default,
 }
 
 

backends/arm/operators/ops_identity.py

@@ -77,3 +77,4 @@ def define_node(
 
 
 identity_operator_factory("aten.alias_copy.default")
+identity_operator_factory("aten.detach_copy.default")

backends/arm/quantizer/quantization_annotator.py

@@ -459,6 +459,7 @@ def _match_pattern(
     # That is why we force it to use the same qparams and avoid
     # dequant -> neg -> requant chain.
     torch.ops.aten.neg.default,
+    torch.ops.aten.detach_copy.default,
 ]
 
 _one_to_one_shared_input_or_input_act_qspec = [

backends/arm/test/ops/test_detach_copy.py

@@ -0,0 +1,56 @@
+# 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.
+
+from typing import Tuple
+
+import torch
+
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.test_pipeline import (
+    TosaPipelineFP,
+    TosaPipelineINT,
+)
+
+input_t1 = Tuple[torch.Tensor]
+
+aten_op = "torch.ops.aten.detach_copy.default"
+exir_op = "executorch_exir_dialects_edge__ops_aten__detach_copy_default"
+
+test_data_suite = {
+    "zeros_2d": torch.zeros(3, 5),
+    "ones_3d": torch.ones(2, 3, 4),
+    "rand_2d": torch.rand(10, 10) - 0.5,
+    "ramp_1d": torch.arange(-8.0, 8.0, 0.5),
+}
+
+
+class DetachCopy(torch.nn.Module):
+    aten_op = aten_op
+    exir_op = exir_op
+
+    def forward(self, x: torch.Tensor):
+        return torch.detach_copy(x)
+
+
+@common.parametrize("test_data", test_data_suite)
+def test_detach_tosa_FP(test_data: torch.Tensor):
+    pipeline = TosaPipelineFP[input_t1](
+        DetachCopy(),
+        (test_data,),
+        aten_op=DetachCopy.aten_op,
+        exir_op=DetachCopy.exir_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", test_data_suite)
+def test_detach_tosa_INT(test_data: torch.Tensor):
+    pipeline = TosaPipelineINT[input_t1](
+        DetachCopy(),
+        (test_data,),
+        aten_op=DetachCopy.aten_op,
+        exir_op=DetachCopy.exir_op,
+    )
+    pipeline.run()