add inplace_copy test case.

dicp/test/op/test_inplace_copy.py

@@ -0,0 +1,50 @@
+import pytest
+from common.utils import (
+    torch,
+    dynamo,
+    parse_args,
+    compile_model,
+    get_device,
+    Size,
+    update_dynamo_config,
+)
+
+
+class OpModule(torch.nn.Module):
+    def forward(self, a, b):
+        res_Tensor = torch.ops.aten.add.Tensor(a, b)
+        a.copy_(res_Tensor)
+        return res_Tensor
+
+
+model = OpModule()
+args = parse_args()
+compiled_model = compile_model(model, args.backend, args.dynamic)
+
+
+class TestInplaceCopy():
+    @pytest.mark.parametrize("dtype", [torch.float32])
+    @pytest.mark.parametrize("sizes", [Size((5,), (5, 3)), Size((3, 5), (5, 3)), Size((2, 3, 4), (2, 4))])
+    @pytest.mark.parametrize("compiled_model", compiled_model)
+    def test_torch_add(self, sizes, dtype, compiled_model):
+        device = get_device()
+        size = sizes.dynamic if compiled_model.dynamic else sizes.static
+        input1 = torch.ones(size, dtype=dtype)
+        input2 = torch.ones(size, dtype=dtype)
+
+        dicp_input1 = input1.to(device)
+        dicp_input2 = input2.to(device)
+
+        output = model(input1, input2)
+        dynamo.reset()
+        update_dynamo_config(compiled_model.dynamic)
+        dicp_output = compiled_model.model(dicp_input1, dicp_input2)
+
+        for i, item in enumerate(output):
+            if isinstance(item, torch.Tensor):
+                assert torch.allclose(item, dicp_output[i].cpu(), equal_nan=True)
+            else:
+                assert item == dicp_output[i]
+
+        # Confirm the correctness of the inplace copy result.
+        assert torch.allclose(dicp_input1.cpu(), dicp_output.cpu(), equal_nan=True)