diff --git a/test/test_maskedtensor.py b/test/test_maskedtensor.py
index db1ffbc38c1..03c05c7ea6d 100644
--- a/test/test_maskedtensor.py
+++ b/test/test_maskedtensor.py
@@ -236,6 +236,32 @@ class TestBasics(TestCase):
             _compare_mt_t(sparse_mt, data)
             _compare_mt_t(mt.grad, data.grad)
 
+    def test_to_device(self, device):
+        for sample in _generate_sample_data(device=device):
+            data = sample.input
+            mask = sample.kwargs["mask"]
+            mt = masked_tensor(data, mask, requires_grad=True)
+
+            new_device = torch.device("cuda") if device != "cuda" and torch.cuda.is_available() else torch.device("cpu")
+            mt_device = mt.to(new_device)
+
+            self.assertEqual(mt_device.device.type, new_device.type)
+            self.assertEqual(mt_device.get_mask().device.type, new_device.type)
+            self.assertEqual(mt_device.get_data().device.type, new_device.type)
+
+    def test_to_dtype(self, device):
+        for sample in _generate_sample_data(device=device):
+            data = sample.input
+            mask = sample.kwargs["mask"]
+            mt = masked_tensor(data, mask, requires_grad=True)
+
+            new_dtype = torch.float64 if data.dtype == torch.float32 else torch.float32
+            mt_dtype = mt.to(new_dtype)
+
+            self.assertEqual(mt_dtype.dtype, new_dtype)
+            self.assertEqual(mt_dtype.get_mask().dtype, torch.bool)
+            self.assertEqual(mt_dtype.get_data().dtype, new_dtype)
+
     def test_to_dense(self, device):
         samples = _generate_sample_data(
             device=device,
diff --git a/torch/masked/maskedtensor/_ops_refs.py b/torch/masked/maskedtensor/_ops_refs.py
index 719df7eac46..8135f149a1b 100644
--- a/torch/masked/maskedtensor/_ops_refs.py
+++ b/torch/masked/maskedtensor/_ops_refs.py
@@ -351,7 +351,10 @@ def _apply_fn_on_data(func, *args, **kwargs):
 @register_dispatch_func([torch.ops.aten._to_copy])
 def _to_copy(func, *args, **kwargs):
     new_data = func(_get_data(args[0]), *args[1:], **kwargs)
-    return MaskedTensor(new_data, _maybe_get_mask(args[0]))
+    cloned_kwargs = kwargs.copy()
+    cloned_kwargs["dtype"] = torch.bool
+    new_mask = func(_maybe_get_mask(args[0]), *args[1:], **cloned_kwargs)
+    return MaskedTensor(new_data, new_mask)
 
 
 @register_dispatch_func([torch.ops.aten._softmax])