fix pytorch deepcopy trace error

mmdeploy/pytorch/functions/__init__.py

@@ -5,6 +5,7 @@
 from . import cat  # noqa: F401,F403
 from . import chunk  # noqa: F401,F403
 from . import clip  # noqa: F401,F403
+from . import copy  # noqa: F401,F403
 from . import expand  # noqa: F401,F403
 from . import flatten  # noqa: F401,F403
 from . import getattribute  # noqa: F401,F403

mmdeploy/pytorch/functions/copy.py

@@ -0,0 +1,17 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from torch import Tensor
+
+from mmdeploy.core import FUNCTION_REWRITER
+
+
+@FUNCTION_REWRITER.register_rewriter(func_name='copy.deepcopy')
+def copy__default(tensor: Tensor, *args, **kwargs) -> Tensor:
+    """Rewrite `copy.deepcopy` for default backend.
+
+    Replace it with tensor.clone(), or may raise `NYI: Named tensors are not
+    supported with the tracer`
+    """
+    ctx = FUNCTION_REWRITER.get_context()
+    if isinstance(tensor, Tensor) and args == () and kwargs == {}:
+        return tensor.clone()
+    return ctx.origin_func(tensor, *args, **kwargs)

tests/test_pytorch/test_pytorch_functions.py

@@ -667,3 +667,23 @@ def test_cat__tensorrt(dtype, dynamic_axes):
         rewrite_output[0].cpu().float(),
         rtol=1e-3,
         atol=1e-5)
+
+
+@backend_checker(Backend.TENSORRT)
+def test_copy__default():
+    import copy
+    input = torch.rand(2, 4)
+    model = WrapFunction(
+        lambda input: [copy.deepcopy(input) for i in range(3)])
+    pytorch_output = model(input)
+    rewrite_output, _ = get_rewrite_outputs(
+        model,
+        model_inputs={'input': input},
+        deploy_cfg=get_trt_config(['output'], shape=[2, 4], dynamic_axes=None),
+        run_with_backend=True)
+    for pytorch_out, rewrite_out in zip(pytorch_output, rewrite_output):
+        assert torch.allclose(
+            pytorch_out.cpu().float(),
+            rewrite_out.cpu().float(),
+            rtol=1e-3,
+            atol=1e-5)