diff --git a/pytorch_lightning/lite/lite.py b/pytorch_lightning/lite/lite.py
index bb07c763156aa..c26783c4d8bb9 100644
--- a/pytorch_lightning/lite/lite.py
+++ b/pytorch_lightning/lite/lite.py
@@ -16,7 +16,7 @@
 from contextlib import contextmanager
 from functools import partial
 from pathlib import Path
-from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, cast, Dict, Generator, List, Optional, Sequence, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -188,7 +188,7 @@ def setup(
 
     def setup_dataloaders(
         self, *dataloaders: DataLoader, replace_sampler: bool = True, move_to_device: bool = True
-    ) -> Union[Iterable, List[Iterable]]:
+    ) -> Union[DataLoader, List[DataLoader]]:
         """Setup one or multiple dataloaders for accelerated training. If you need different settings for each
         dataloader, call this method individually for each one.
 
@@ -213,7 +213,7 @@ def setup_dataloaders(
 
     def _setup_dataloader(
         self, dataloader: DataLoader, replace_sampler: bool = True, move_to_device: bool = True
-    ) -> Iterable:
+    ) -> DataLoader:
         """Setup a single dataloader for accelerated training.
 
         Args:
@@ -246,7 +246,9 @@ def _setup_dataloader(
 
         dataloader = self._strategy.process_dataloader(dataloader)
         device = self.device if move_to_device and not isinstance(self._strategy, TPUSpawnPlugin) else None
-        return _LiteDataLoader(dataloader=dataloader, device=device)
+        lite_dataloader = _LiteDataLoader(dataloader=dataloader, device=device)
+        lite_dataloader = cast(DataLoader, lite_dataloader)
+        return lite_dataloader
 
     def backward(self, tensor: Tensor, *args: Any, model: Optional[_LiteModule] = None, **kwargs: Any) -> None:
         """Replaces ``loss.backward()`` in your training loop. Handles precision and automatically for you.
diff --git a/pytorch_lightning/lite/wrappers.py b/pytorch_lightning/lite/wrappers.py
index ff95e89d1d2cf..938eb72afe622 100644
--- a/pytorch_lightning/lite/wrappers.py
+++ b/pytorch_lightning/lite/wrappers.py
@@ -15,7 +15,7 @@
 import inspect
 from contextlib import contextmanager
 from itertools import chain
-from typing import Any, Callable, Dict, Generator, Iterable, Iterator, Optional, Set, Sized, Type, Union
+from typing import Any, Callable, Dict, Generator, Iterator, Optional, Set, Type, Union
 
 import torch
 from torch import nn as nn
@@ -157,29 +157,26 @@ def _replace_dataloader_init_method() -> Generator:
 
 
 class _LiteDataLoader:
-    def __init__(self, dataloader: Union[Iterable, DataLoader], device: Optional[torch.device] = None) -> None:
-        """The LiteDataLoader is an extension of an Iterator. It would move the data to the device automatically if
-        the device is specified.
+    def __init__(self, dataloader: DataLoader, device: Optional[torch.device] = None) -> None:
+        """The LiteDataLoader is a wrapper for the :class:`~torch.utils.data.DataLoader`. It moves the data to the
+        device automatically if the device is specified.
 
         Args:
-            dataloader: The current dataloader to be used.
+            dataloader: The dataloader to wrap
             device: The device to which the data should be moved. By default the device is `None` and no data
                 transfers will be made (identical behavior as :class:`~torch.utils.data.DataLoader`).
         """
-        super().__init__()
-        self.__dict__.update(getattr(dataloader, "__dict__", {}))
+        self.__dict__.update(dataloader.__dict__)
         self._dataloader = dataloader
         self._device = device
 
-    def __len__(self) -> Union[int, float]:
-        if isinstance(self._dataloader, Sized):
-            return len(self._dataloader)
-        return float("inf")
-
     @property
     def device(self) -> Optional[torch.device]:
         return self._device
 
+    def __len__(self) -> int:
+        return len(self._dataloader)
+
     def __iter__(self) -> Union[Iterator[Any], Generator[Any, None, None]]:
         iterator = iter(self._dataloader)
         if self._device is None: