Arm backend: Upcast index argument to int64 for aten.index_copy ops

backends/arm/_passes/insert_int32_casts_after_int64_placeholders.py

@@ -36,6 +36,8 @@ class InsertInt32CastsAfterInt64PlaceholdersPass(ArmPass):
     # Key: op overload; Value: zero-based indices of positional args that must be i64.
     I64_INPUT_ARG_POSITIONS = {
         torch.ops.aten.one_hot.default: (0,),
+        torch.ops.aten.index_copy_.default: (2,),
+        torch.ops.aten.index_copy.default: (2,),
     }
 
     def _insert_callsite_i32_to_i64_casts(self, graph_module: torch.fx.GraphModule):

backends/arm/test/passes/test_insert_int32_casts_after_int64_placeholders_pass.py

@@ -8,9 +8,13 @@
 import torch
 from executorch.backends.arm._passes import InsertInt32CastsAfterInt64PlaceholdersPass
 
-from executorch.backends.arm.test.tester.test_pipeline import PassPipeline
+from executorch.backends.arm.test.tester.test_pipeline import (
+    PassPipeline,
+    TosaPipelineINT,
+)
 
 input_t = Tuple[torch.Tensor, torch.Tensor]  # weights, indices
+input_t3 = Tuple[torch.Tensor, torch.LongTensor, torch.Tensor]
 
 
 class Int64InputModel(torch.nn.Module):
@@ -44,3 +48,67 @@ def test_int64_model_tosa_FP():
     )
     pipeline.pop_stage(-1)  # Do not compare output
     pipeline.run()
+
+
+class UpcastToInt64ForIndexCopyInplaceModel(torch.nn.Module):
+    aten_op = "torch.ops.aten.index_copy_.default"
+
+    def forward(self, x: torch.Tensor, index: torch.LongTensor, y: torch.tensor):
+        return x.index_copy_(0, index, y)
+
+    def get_inputs(self) -> input_t3:
+        return (
+            torch.zeros(5, 3),
+            torch.tensor([0, 4, 2]),
+            torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float),
+        )
+
+
+def test_upcast_to_int64_for_index_copy_inplace_tosa_INT():
+    module = UpcastToInt64ForIndexCopyInplaceModel()
+    pipeline = TosaPipelineINT[input_t3](
+        module,
+        module.get_inputs(),
+        aten_op=module.aten_op,
+    )
+    pipeline.pop_stage("check.quant_nodes")
+    pipeline.change_args(
+        "check_count.exir",
+        {
+            "torch.ops.higher_order.executorch_call_delegate": 0,
+        },
+    )
+    pipeline.pop_stage("run_method_and_compare_outputs")
+    pipeline.run()
+
+
+class UpcastToInt64ForIndexCopyModel(torch.nn.Module):
+    aten_op = "torch.ops.aten.index_copy.default"
+
+    def forward(self, x: torch.Tensor, index: torch.LongTensor, y: torch.tensor):
+        return x.index_copy(0, index, y)
+
+    def get_inputs(self) -> input_t3:
+        return (
+            torch.zeros(5, 3),
+            torch.tensor([0, 4, 2]),
+            torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float),
+        )
+
+
+def test_upcast_to_int64_for_index_copy_tosa_INT():
+    module = UpcastToInt64ForIndexCopyModel()
+    pipeline = TosaPipelineINT[input_t3](
+        module,
+        module.get_inputs(),
+        aten_op=module.aten_op,
+    )
+    pipeline.pop_stage("check.quant_nodes")
+    pipeline.change_args(
+        "check_count.exir",
+        {
+            "torch.ops.higher_order.executorch_call_delegate": 0,
+        },
+    )
+    pipeline.pop_stage("run_method_and_compare_outputs")
+    pipeline.run()