Add support for torch.ops.aten._to_copy.default

backends/arm/operator_support/__init__.py

@@ -8,6 +8,7 @@
 from . import (  # noqa
     mean_dim_support,
     right_shift_support,
+    to_copy_support,
     tosa_supported_operators,
     var_correction_support,
 )

backends/arm/operator_support/to_copy_support.py

@@ -0,0 +1,120 @@
+# Copyright 2024 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 logging
+
+import torch
+
+import torch.fx as fx
+
+from executorch.backends.arm.operator_support.tosa_supported_operators import (
+    register_tosa_support_check,
+    SupportedTOSAOperatorCheck,
+)
+from executorch.backends.arm.tosa_specification import TosaSpecification
+from executorch.exir.dialects._ops import ops as exir_ops
+
+logger = logging.getLogger(__name__)
+
+
+@register_tosa_support_check
+class ToCopySupported(SupportedTOSAOperatorCheck):
+    targets = [exir_ops.edge.aten._to_copy.default]
+
+    tosa_specs = [
+        TosaSpecification.create_from_string("TOSA-0.80.0+BI"),
+        TosaSpecification.create_from_string("TOSA-0.80.0+MI"),
+    ]
+
+    SupportedTypeDict = dict[torch.dtype, list[torch.dtype]]
+
+    @staticmethod
+    def _merge_supported_types(
+        dtypes1: SupportedTypeDict, dtypes2: SupportedTypeDict
+    ) -> SupportedTypeDict:
+        merged_dtypes = dtypes1
+        for k, v in dtypes2.items():
+            merged_dtypes[k] = merged_dtypes.get(k, []) + v
+        return merged_dtypes
+
+    SUPPORTED_INT_TYPES: SupportedTypeDict = {
+        torch.bool: [torch.int8, torch.int16, torch.int32],
+        torch.int8: [torch.bool, torch.int16, torch.int32],
+        torch.int16: [torch.bool, torch.int8, torch.int32],
+        torch.int32: [torch.bool, torch.int8, torch.int16],
+    }
+    SUPPORTED_FLOAT_TYPES: SupportedTypeDict = {
+        torch.int8: [torch.float16, torch.bfloat16, torch.float32],
+        torch.int16: [torch.float16, torch.bfloat16, torch.float32],
+        torch.int32: [torch.float16, torch.bfloat16, torch.float32],
+        torch.bfloat16: [torch.int8, torch.int16, torch.int32, torch.float32],
+        torch.float16: [torch.int8, torch.int16, torch.int32, torch.float32],
+        torch.float32: [
+            torch.int8,
+            torch.int16,
+            torch.int32,
+            torch.bfloat16,
+            torch.float16,
+        ],
+    }
+    ALL_SUPPORTED_TYPES = _merge_supported_types(
+        SUPPORTED_INT_TYPES, SUPPORTED_FLOAT_TYPES
+    )
+    POSSIBLE_TYPE_CONVERSIONS = {torch.int64: torch.int32}
+
+    def is_node_supported(self, node: fx.Node, tosa_spec: TosaSpecification) -> bool:
+        assert node.target in self.targets
+
+        if tosa_spec not in self.tosa_specs:
+            return False
+
+        assert tosa_spec.support_integer()
+        supported_dtypes = (
+            self.ALL_SUPPORTED_TYPES
+            if tosa_spec.support_float()
+            else self.SUPPORTED_INT_TYPES
+        )
+        # Take into account possible type conversions
+        supported_dtypes.update(
+            (k, supported_dtypes[v])
+            for k, v in self.POSSIBLE_TYPE_CONVERSIONS.items()
+            if v in supported_dtypes
+        )
+
+        # Check input type
+        assert len(node.all_input_nodes) == 1
+        input_val = node.all_input_nodes[0].meta["val"]
+        assert isinstance(input_val, torch._subclasses.FakeTensor)
+        input_dtype = input_val.dtype
+        if input_dtype not in supported_dtypes:
+            logger.info(
+                f"Input dtype {input_val.dtype} is not supported in "
+                f"{node.target.name()}."
+            )
+            return False
+
+        # Check output type
+        output_val = node.meta["val"]
+        assert isinstance(output_val, torch._subclasses.FakeTensor)
+        if output_val.dtype not in supported_dtypes[input_dtype]:
+            logger.info(
+                f"Output dtype {output_val.dtype} is not supported in "
+                f"{node.target.name()} for input dtype {input_dtype}. "
+                f"Supported output types: "
+                f"{''.join(str(t) for t in supported_dtypes[input_dtype])}"
+            )
+            return False
+
+        # Check memory format
+        if "memory_format" in node.kwargs:
+            if node.kwargs["memory_format"] in (torch.preserve_format,):
+                logger.info(
+                    f"Argument 'memory_format' is not supported for "
+                    f"{node.target.name()} right now."
+                )
+                return False
+
+        return True

backends/arm/operators/__init__.py

@@ -36,6 +36,7 @@
     op_sub,
     op_sum,
     op_tanh,
+    op_to_copy,
     op_transpose,
     op_unsqueeze,
     op_upsample_nearest2d,

backends/arm/operators/op_to_copy.py

@@ -0,0 +1,43 @@
+# Copyright 2024 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
+from typing import List
+
+import serializer.tosa_serializer as ts
+import torch
+import tosa.Op as TosaOp
+
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.tosa_mapping import TosaArg
+
+
+@register_node_visitor
+class ToCopyVisitor(NodeVisitor):
+    """
+    Implement the type cast functionality of _to_copy.
+
+    Other features like setting of the memory_format or moving a tensor to a
+    different device are not supported.
+
+    Also note that the node should not be quantized.
+    """
+
+    target = "aten._to_copy.default"
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+        is_quant_node: bool,
+    ) -> None:
+        assert not is_quant_node, "Casting of quantized values is not supported."
+        assert inputs
+        tosa_graph.addOperator(TosaOp.Op().CAST, [inputs[0].name], [output.name])

backends/arm/test/ops/test_scalars.py

@@ -153,9 +153,21 @@ def _test_add_tosa_BI_pipeline(self, module: torch.nn.Module, test_data: tuple):
             .run_method_and_compare_outputs(inputs=test_data)
         )
 
-    # Most MI tests fail, just show one working for now.
-    @parameterized.expand((tensor_scalar_tests[6],))
+    @parameterized.expand(tensor_scalar_tests)
     def test_MI(self, test_name: str, op: torch.nn.Module, x, y):
+        expected_exception = None
+        if any(token in test_name for token in ("Sub_int", "Sub__int")):
+            expected_exception = RuntimeError
+        elif test_name.endswith("_st"):
+            expected_exception = AttributeError
+
+        if expected_exception:
+            with self.assertRaises(
+                expected_exception, msg=f"Test {test_name} is expected to fail."
+            ):
+                self._test_add_tosa_MI_pipeline(op, (x, y))
+            return
+
         self._test_add_tosa_MI_pipeline(op, (x, y))
 
     # op(Scalar float, tensor) works if the scalar is constant.

backends/arm/test/ops/test_to_copy.py

@@ -0,0 +1,70 @@
+# Copyright 2024 Arm Limited and/or its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+#
+# Tests the _to_copy op which is interpreted as a cast for our purposes.
+#
+
+import unittest
+
+import torch
+
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.arm_tester import ArmTester
+
+from parameterized import parameterized
+
+
+class Cast(torch.nn.Module):
+    def __init__(self, target_dtype):
+        super().__init__()
+        self.target_dtype = target_dtype
+
+    def forward(self, x: torch.Tensor):
+        return x.to(dtype=self.target_dtype)
+
+
+class TestToCopy(unittest.TestCase):
+    """
+    Tests the _to_copy operation.
+
+    Only test unquantized graphs as explicit casting of dtypes messes with the
+    quantization.
+
+    Note: This is also covered by test_scalars.py.
+    """
+
+    _TO_COPY_TEST_DATA = (
+        (torch.rand((1, 2, 3, 4), dtype=torch.float16), torch.float32),
+        (torch.rand((1, 2, 3, 4), dtype=torch.float32), torch.float16),
+        (torch.randint(-127, 128, (1, 2, 3, 4), dtype=torch.int8), torch.float32),
+        (torch.randint(-127, 128, (1, 2, 3, 4), dtype=torch.int8), torch.int32),
+        (torch.randint(-127, 128, (1, 2, 3, 4), dtype=torch.int32), torch.int8),
+    )
+
+    def _test_to_copy_tosa_MI_pipeline(
+        self, module: torch.nn.Module, test_data: torch.Tensor
+    ):
+        (
+            ArmTester(
+                module,
+                example_inputs=test_data,
+                compile_spec=common.get_tosa_compile_spec("TOSA-0.80.0+MI"),
+            )
+            .export()
+            .dump_artifact()
+            .check_count({"torch.ops.aten._to_copy.default": 1})
+            .to_edge()
+            .dump_artifact()
+            .partition()
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+            .run_method_and_compare_outputs(inputs=test_data)
+        )
+
+    @parameterized.expand(_TO_COPY_TEST_DATA)
+    def test_view_tosa_MI(self, test_tensor: torch.Tensor, new_dtype):
+        self._test_to_copy_tosa_MI_pipeline(Cast(new_dtype), (test_tensor,))