Arm Backend: Add tests for rsub.Scalar, transpose_copy.int and t_copy.default

backends/arm/quantizer/quantization_annotator.py

@@ -334,6 +334,7 @@ def _match_pattern(
     torch.ops.aten.transpose.Dimname,
     torch.ops.aten.transpose.int,
     torch.ops.aten.transpose_copy.int,
+    torch.ops.aten.t_copy.default,
     torch.ops.aten.tile.default,
     torch.ops.aten.flip.default,
     torch.ops.aten.chunk.default,
@@ -513,7 +514,8 @@ def any_or_hardtanh_min_zero(n: Node):
         quant_properties.quant_inputs = [
             _QuantProperty(0, input_act_qspec),
             _QuantProperty(
-                1, input_act_qspec if node.args[0] == node.args[1] else shared_qspec  # type: ignore[arg-type]
+                1,
+                input_act_qspec if node.args[0] == node.args[1] else shared_qspec,  # type: ignore[arg-type]
             ),
         ]
         quant_properties.quant_output = _QuantProperty(0, shared_qspec)  # type: ignore[arg-type]
@@ -532,7 +534,8 @@ def any_or_hardtanh_min_zero(n: Node):
         )
         quant_properties.quant_inputs = [_QuantProperty(0, input_qspec)]  # type: ignore[arg-type]
         quant_properties.quant_output = _QuantProperty(
-            0, SharedQuantizationSpec((node.args[0], node))  # type: ignore[arg-type]
+            0,
+            SharedQuantizationSpec((node.args[0], node)),  # type: ignore[arg-type]
         )
     elif node.target in (
         torch.ops.aten.cat.default,
@@ -565,7 +568,8 @@ def any_or_hardtanh_min_zero(n: Node):
     elif node.target in _one_to_one_shared_input_qspec:
         quant_properties.quant_inputs = [_QuantProperty(0, input_act_qspec)]
         quant_properties.quant_output = _QuantProperty(
-            0, SharedQuantizationSpec((node.args[0], node))  # type: ignore[arg-type]
+            0,
+            SharedQuantizationSpec((node.args[0], node)),  # type: ignore[arg-type]
         )
     elif node.target in [
         torch.ops.aten.eq.Tensor,
@@ -578,7 +582,8 @@ def any_or_hardtanh_min_zero(n: Node):
         quant_properties.quant_inputs = [
             _QuantProperty(0, input_act_qspec),
             _QuantProperty(
-                1, input_act_qspec if node.args[0] == node.args[1] else shared_qspec  # type: ignore[arg-type]
+                1,
+                input_act_qspec if node.args[0] == node.args[1] else shared_qspec,  # type: ignore[arg-type]
             ),
         ]
         quant_properties.quant_output = None

backends/arm/test/ops/test_rsub.py

@@ -0,0 +1,126 @@
+# 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,
+)
+
+rsub_test_data = {
+    "rand_2D_4x4": lambda: (torch.rand(4, 4), 2),
+    "rand_3D_4x4x4": lambda: (torch.rand(4, 2, 2), 1.5),
+    "rand_4D_2x2x4x4": lambda: (torch.rand(2, 2, 4, 4), -1.1),
+    "rand_4D_big_small": lambda: (
+        (10e30) * torch.randn(1, 20, 30, 40),
+        -0.25,
+    ),
+    "zero": lambda: (torch.rand(4, 4), 0),
+    # "swapped": lambda: (2, torch.rand(4, 4)), # torch.rsub(Scalar, Tensor) is not supported as it is not supported in eager mode.
+}
+
+
+class Rsub(torch.nn.Module):
+    aten_op = "torch.ops.aten.rsub.Scalar"
+    exir_op = "executorch_exir_dialects_edge__ops_aten_sub_Tensor"
+
+    def forward(self, x: torch.Tensor, y: int):
+        return torch.rsub(x, y)
+
+
+input_t1 = Tuple[torch.Tensor, torch.Tensor]
+
+
+@common.parametrize("test_data", rsub_test_data)
+def test_rsub_scalar_tosa_FP(test_data):
+    pipeline = TosaPipelineFP[input_t1](
+        Rsub(),
+        test_data(),
+        aten_op=Rsub.aten_op,
+        exir_op=Rsub.exir_op,
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", rsub_test_data)
+def test_rsub_scalar_tosa_INT(test_data):
+    """Test Subtraction (TOSA INT)"""
+    pipeline = TosaPipelineINT[input_t1](
+        Rsub(),
+        test_data(),
+        aten_op="torch.ops.aten.sub.Tensor",
+        exir_op=Rsub.exir_op,
+        use_to_edge_transform_and_lower=False,
+        qtol=0,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", rsub_test_data)
+@common.XfailIfNoCorstone300
+def test_rsub_scalar_u55_INT(test_data):
+    """Test Subtraction on Ethos-U55 (FVP Mode)"""
+    pipeline = EthosU55PipelineINT[input_t1](
+        Rsub(),
+        test_data(),
+        aten_ops="torch.ops.aten.sub.Tensor",
+        exir_ops=Rsub.exir_op,
+        run_on_fvp=True,
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", rsub_test_data)
+@common.XfailIfNoCorstone320
+def test_rsub_scalar_u85_INT(test_data):
+    """Test Subtraction on Ethos-U85 (FVP Mode)"""
+    pipeline = EthosU85PipelineINT[input_t1](
+        Rsub(),
+        test_data(),
+        aten_ops="torch.ops.aten.sub.Tensor",
+        exir_ops=Rsub.exir_op,
+        run_on_fvp=True,
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", rsub_test_data)
+@common.SkipIfNoModelConverter
+def test_rsub_scalar_vgf_FP(test_data: Tuple[torch.Tensor]):
+    """Test Subtraction (VGF FP)"""
+    pipeline = VgfPipeline[input_t1](
+        Rsub(),
+        test_data(),
+        Rsub.aten_op,
+        Rsub.exir_op,
+        tosa_version="TOSA-1.0+FP",
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", rsub_test_data)
+@common.SkipIfNoModelConverter
+def test_rsub_scalar_vgf_INT(test_data: Tuple[torch.Tensor]):
+    """Test Subtraction (VGF INT)"""
+    pipeline = VgfPipeline[input_t1](
+        Rsub(),
+        test_data(),
+        aten_op="torch.ops.aten.sub.Tensor",
+        exir_op=Rsub.exir_op,
+        tosa_version="TOSA-1.0+INT",
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()

backends/arm/test/ops/test_sub.py

@@ -149,6 +149,7 @@ def test_sub_tensor_tosa_INT_2(test_data: Tuple[torch.Tensor, torch.Tensor]):
 @common.parametrize("test_data", sub_tan_test_data)
 def test_sub_tensor_tosa_INT_3(test_data: Tuple[torch.Tensor, torch.Tensor]):
     """Test Two-Operand Subtraction (TOSA INT)"""
+    # This test has only been added to the tosa INT profile in order to catch quantization-induced errors.
     pipeline = TosaPipelineINT[input_t2](
         SubTan(), test_data(), aten_op, exir_op, qtol=0
     )

backends/arm/test/ops/test_t_copy.py

@@ -0,0 +1,115 @@
+# 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,
+)
+
+test_data_suite = {
+    # test_name: (test_data, min, max)
+    "rand": lambda: (torch.rand(2, 3),),
+    "rand_multiplied": lambda: (torch.rand(3, 4) * 10,),
+    "ones": lambda: (torch.ones(5, 10),),
+    "randn": lambda: (torch.randn(1, 10) * 2,),
+}
+
+
+class TCopy(torch.nn.Module):
+    aten_op = "torch.ops.aten.t_copy.default"
+    exir_op = "executorch_exir_dialects_edge__ops_aten_permute_copy_default"
+
+    def forward(self, x: torch.Tensor):
+        return torch.t_copy(x)
+
+
+input_t1 = Tuple[torch.Tensor]
+
+
+@common.parametrize("test_data", test_data_suite)
+def test_t_tosa_FP(test_data: Tuple):
+    pipeline = TosaPipelineFP[input_t1](
+        TCopy(),
+        test_data(),
+        aten_op=TCopy.aten_op,
+        exir_op=TCopy.exir_op,
+        use_to_edge_transform_and_lower=False,
+    )
+
+    pipeline.run()
+
+
+@common.parametrize("test_data", test_data_suite)
+def test_t_tosa_INT(test_data: Tuple):
+    pipeline = TosaPipelineINT[input_t1](
+        TCopy(),
+        test_data(),
+        aten_op=TCopy.aten_op,
+        exir_op=TCopy.exir_op,
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()
+
+
+@common.XfailIfNoCorstone300
+@common.parametrize("test_data", test_data_suite)
+def test_t_u55_INT(test_data: Tuple):
+    pipeline = EthosU55PipelineINT[input_t1](
+        TCopy(),
+        test_data(),
+        aten_ops=TCopy.aten_op,
+        exir_ops=[],
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.run()
+
+
+@common.XfailIfNoCorstone320
+@common.parametrize("test_data", test_data_suite)
+def test_t_u85_INT(test_data: Tuple):
+    pipeline = EthosU85PipelineINT[input_t1](
+        TCopy(),
+        test_data(),
+        aten_ops=TCopy.aten_op,
+        exir_ops=TCopy.exir_op,
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", test_data_suite)
+@common.SkipIfNoModelConverter
+def test_t_vgf_FP(test_data: Tuple):
+    pipeline = VgfPipeline[input_t1](
+        TCopy(),
+        test_data(),
+        aten_op=TCopy.aten_op,
+        exir_op=TCopy.exir_op,
+        tosa_version="TOSA-1.0+FP",
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", test_data_suite)
+@common.SkipIfNoModelConverter
+def test_t_vgf_INT(test_data: Tuple):
+    pipeline = VgfPipeline[input_t1](
+        TCopy(),
+        test_data(),
+        aten_op=TCopy.aten_op,
+        exir_op=TCopy.exir_op,
+        tosa_version="TOSA-1.0+INT",
+        use_to_edge_transform_and_lower=False,
+    )
+    pipeline.run()