fix: move check_inputs to target device if available during to_torchscript.

src/lightning/pytorch/core/module.py

@@ -1472,6 +1472,10 @@ def forward(self, x):
                     )
                 example_inputs = self.example_input_array
 
+            if kwargs.get("check_inputs") is not None:
+                kwargs["check_inputs"] = self._on_before_batch_transfer(kwargs["check_inputs"])
+                kwargs["check_inputs"] = self._apply_batch_transfer_handler(kwargs["check_inputs"])
+
             # automatically send example inputs to the right device and use trace
             example_inputs = self._on_before_batch_transfer(example_inputs)
             example_inputs = self._apply_batch_transfer_handler(example_inputs)

tests/tests_pytorch/models/test_torchscript.py

@@ -105,6 +105,26 @@ def test_torchscript_device(device_str):
     assert script_output.device == device
 
 
+@pytest.mark.parametrize(
+    "device_str",
+    [
+        "cpu",
+        pytest.param("cuda:0", marks=RunIf(min_cuda_gpus=1)),
+        pytest.param("mps:0", marks=RunIf(mps=True)),
+    ],
+)
+def test_torchscript_device_with_check_inputs(device_str):
+    """Test that scripted module is on the correct device."""
+    device = torch.device(device_str)
+    model = BoringModel().to(device)
+    model.example_input_array = torch.randn(5, 32)
+
+    check_inputs = torch.rand(5, 32)
+
+    script = model.to_torchscript(method="trace", check_inputs=check_inputs)
+    assert isinstance(script, torch.jit.ScriptModule)
+
+
 def test_torchscript_retain_training_state():
     """Test that torchscript export does not alter the training mode of original model."""
     model = BoringModel()