diff --git a/pyproject.toml b/pyproject.toml
index 9d3e4fd80f..168e60e1e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,7 @@ disable_error_code = "attr-defined"
 # style choices
 warn_no_return = "False"
 
-# Changes mypy default to ignore all errors
+# Ignore mypy errors for these files
 # TODO: the goal is for this to be empty
 [[tool.mypy.overrides]]
 # the list can be generated with:
@@ -63,8 +63,6 @@ module = [
     "pytorch_lightning.core.mixins.hparams_mixin",
     "pytorch_lightning.core.saving",
     "pytorch_lightning.distributed.dist",
-    "pytorch_lightning.lite.lite",
-    "pytorch_lightning.lite.wrappers",
     "pytorch_lightning.loggers.base",
     "pytorch_lightning.loggers.comet",
     "pytorch_lightning.loggers.csv_logs",
diff --git a/pytorch_lightning/lite/lite.py b/pytorch_lightning/lite/lite.py
index 9073f5dd54..0d292dba54 100644
--- a/pytorch_lightning/lite/lite.py
+++ b/pytorch_lightning/lite/lite.py
@@ -16,7 +16,7 @@ from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from functools import partial
 from pathlib import Path
-from typing import Any, Callable, cast, Dict, Generator, List, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, cast, Dict, Generator, List, Optional, overload, Sequence, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -201,7 +201,7 @@ class LightningLite(ABC):
             for dataloader in dataloaders
         ]
         dataloaders = dataloaders[0] if len(dataloaders) == 1 else dataloaders
-        return dataloaders
+        return dataloaders  # type: ignore[return-value]
 
     def _setup_dataloader(
         self, dataloader: DataLoader, replace_sampler: bool = True, move_to_device: bool = True
@@ -284,6 +284,18 @@ class LightningLite(ABC):
         with self._precision_plugin.forward_context():
             yield
 
+    @overload
+    def to_device(self, obj: nn.Module) -> nn.Module:
+        ...
+
+    @overload
+    def to_device(self, obj: Tensor) -> Tensor:
+        ...
+
+    @overload
+    def to_device(self, obj: Any) -> Any:
+        ...
+
     def to_device(self, obj: Union[nn.Module, Tensor, Any]) -> Union[nn.Module, Tensor, Any]:
         """Move a :class:`torch.nn.Module` or a collection of tensors to the current device, if it is not already
         on that device.
diff --git a/pytorch_lightning/lite/wrappers.py b/pytorch_lightning/lite/wrappers.py
index 908ba06bdb..202404ef71 100644
--- a/pytorch_lightning/lite/wrappers.py
+++ b/pytorch_lightning/lite/wrappers.py
@@ -131,6 +131,7 @@ class _LiteDataLoader:
         iterator = iter(self._dataloader)
         if self._device is None:
             yield from iterator
+            return
 
         for item in iterator:
             yield move_data_to_device(item, self._device)
diff --git a/pytorch_lightning/plugins/training_type/training_type_plugin.py b/pytorch_lightning/plugins/training_type/training_type_plugin.py
index 7010c0e878..be51cc9f92 100644
--- a/pytorch_lightning/plugins/training_type/training_type_plugin.py
+++ b/pytorch_lightning/plugins/training_type/training_type_plugin.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 import contextlib
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Generator, Iterable, List, Mapping, Optional, Tuple, Union
+from typing import Any, Dict, Generator, List, Mapping, Optional, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -241,7 +241,7 @@ class TrainingTypePlugin(ABC):
     def test_step_end(self, output):
         return output
 
-    def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
+    def process_dataloader(self, dataloader: DataLoader) -> DataLoader:
         """Wraps the dataloader if necessary.
 
         Args: