Arm Backend: Add support for copy.default

backends/arm/_passes/remove_noop_pass.py

@@ -25,6 +25,7 @@ def call_operator(self, op, args, kwargs, meta):
         if op not in (
             exir_ops.edge.dim_order_ops._clone_dim_order.default,
             exir_ops.edge.dim_order_ops._to_dim_order_copy.default,
+            exir_ops.edge.aten.copy.default,
         ):
             return super().call_operator(op, args, kwargs, meta)
 
@@ -34,4 +35,6 @@ def call_operator(self, op, args, kwargs, meta):
         if input_dtype != output_dtype:
             return super().call_operator(op, args, kwargs, meta)
 
+        if op == exir_ops.edge.aten.copy.default:
+            return args[1]
         return args[0]

backends/arm/operator_support/tosa_profile_supported_op_lists.py

@@ -118,6 +118,7 @@
     exir_ops.edge.aten.acos.default,
     exir_ops.edge.aten.elu.default,
     exir_ops.edge.aten.bitwise_not.default,
+    exir_ops.edge.aten.copy.default,
 }
 
 
@@ -233,6 +234,7 @@
     exir_ops.edge.aten.logit.default,
     exir_ops.edge.aten.acos.default,
     exir_ops.edge.aten.elu.default,
+    exir_ops.edge.aten.copy.default,
 }
 
 

backends/arm/quantizer/quantization_annotator.py

@@ -574,6 +574,16 @@ def any_or_hardtanh_min_zero(n: Node):
             0,
             SharedQuantizationSpec((input_node, node)),
         )
+    elif node.target in [torch.ops.aten.copy_.default]:
+        input_node = ensure_type(Node, node.args[1])
+        quant_properties.quant_inputs = [
+            _QuantProperty(0, input_act_qspec),
+            _QuantProperty(1, input_act_qspec),
+        ]
+        quant_properties.quant_output = _QuantProperty(
+            0,
+            SharedQuantizationSpec((input_node, node)),
+        )
     elif node.target in [
         torch.ops.aten.eq.Tensor,
         torch.ops.aten.ge.Tensor,

backends/arm/test/ops/test_copy.py

@@ -0,0 +1,171 @@
+# Copyright 2025 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 (
+    EthosU55PipelineINT,
+    EthosU85PipelineINT,
+    TosaPipelineFP,
+    TosaPipelineINT,
+    VgfPipeline,
+)
+
+
+class CopyOutput(torch.nn.Module):
+    def forward(self, x):
+        y = torch.zeros(x.shape)
+        return y.copy_(x / x) + x
+
+
+class CopyFirstArg(torch.nn.Module):
+    def forward(self, x):
+        y = torch.zeros(x.shape)
+        return y.copy_(x) + x
+
+
+class CopySecondArg(torch.nn.Module):
+    def forward(self, x):
+        y = torch.zeros(x.shape)
+        return x * y.copy_(x)
+
+
+class CopyBothArgs(torch.nn.Module):
+    def forward(self, x):
+        y = torch.zeros(x.shape)
+        return y.copy_(x) + y.copy_(x)
+
+
+class CopyAfterOtherOp(torch.nn.Module):
+    def forward(self, x):
+        y = torch.zeros(x.shape)
+        x = x * 2
+        return y.copy_(x) + x
+
+
+class CopyParallelToOtherOp(torch.nn.Module):
+    def forward(self, x):
+        y = torch.zeros(x.shape)
+        return x * 2 + y.copy_(x)
+
+
+test_suite = {
+    "copy_output": lambda: (
+        CopyOutput,
+        (torch.rand(1, 2, 3, 4, dtype=torch.float32),),
+    ),
+    "copy_first_arg": lambda: (
+        CopyFirstArg,
+        (torch.rand(1, 2, 3, 4, dtype=torch.float32),),
+    ),
+    "copy_second_arg": lambda: (
+        CopySecondArg,
+        (torch.rand(1, 2, 3, 4, dtype=torch.float32),),
+    ),
+    "copy_both_args": lambda: (
+        CopyBothArgs,
+        (torch.rand(1, 2, 3, 4, dtype=torch.float32),),
+    ),
+    "copy_after_other_op": lambda: (
+        CopyAfterOtherOp,
+        (torch.rand(1, 2, 3, 4, dtype=torch.float32),),
+    ),
+    "copy_parallel_to_other_op": lambda: (
+        CopyParallelToOtherOp,
+        (torch.rand(1, 2, 3, 4, dtype=torch.float32),),
+    ),
+}
+
+
+aten_op = "torch.ops.aten.copy_.default"
+exir_op = "executorch_exir_dialects_edge__ops_aten_copy_default"
+
+input_t = Tuple[torch.Tensor]
+
+
+@common.parametrize("input_data", test_suite)
+def test_copy_tosa_FP(input_data):
+    module, input_tensor = input_data()
+    pipeline = TosaPipelineFP[input_t](
+        module(),
+        input_tensor,
+        aten_op=aten_op,
+        exir_op=exir_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("input_data", test_suite)
+def test_copy_tosa_INT(input_data):
+    module, input_tensor = input_data()
+
+    pipeline = TosaPipelineINT[input_t](
+        module(),
+        input_tensor,
+        aten_op,
+        exir_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("input_data", test_suite)
+@common.XfailIfNoCorstone300
+def test_copy_u55_INT(input_data):
+    module, input_tensor = input_data()
+
+    pipeline = EthosU55PipelineINT[input_t](
+        module(),
+        input_tensor,
+        aten_op,
+        exir_op,
+    )
+    pipeline.run()
+
+
+@common.parametrize("input_data", test_suite)
+@common.XfailIfNoCorstone320
+def test_copy_u85_INT(input_data):
+    module, input_tensor = input_data()
+
+    pipeline = EthosU85PipelineINT[input_t](
+        module(),
+        input_tensor,
+        aten_op,
+        exir_op,
+    )
+
+    pipeline.run()
+
+
+@common.parametrize("test_data", test_suite)
+@common.SkipIfNoModelConverter
+def test_copy_vgf_FP(test_data):
+    module, input_tensor = test_data()
+    pipeline = VgfPipeline[input_t](
+        module(),
+        input_tensor,
+        aten_op=aten_op,
+        exir_op=exir_op,
+        tosa_version="TOSA-1.0+FP",
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", test_suite)
+@common.SkipIfNoModelConverter
+def test_copy_vgf_INT(test_data):
+    module, input_tensor = test_data()
+    pipeline = VgfPipeline[input_t](
+        module(),
+        input_tensor,
+        aten_op,
+        exir_op,
+        tosa_version="TOSA-1.0+INT",
+    )
+    pipeline.run()