diff --git a/pytorch_lightning/core/decorators.py b/pytorch_lightning/core/decorators.py
index 3979a4fc6f7ee4..0553615e4d2046 100644
--- a/pytorch_lightning/core/decorators.py
+++ b/pytorch_lightning/core/decorators.py
@@ -1,4 +1,5 @@
-from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning import LightningModule
+from pytorch_lightning.utilities import rank_zero_warn, transfer_batch_to_device
 
 
 def data_loader(fn):
@@ -12,3 +13,36 @@ def data_loader(fn):
     def inner_fx(self):
         return fn(self)
     return inner_fx
+
+
+def auto_move_data(fn):
+    """
+    Decorator for :class:`~pytorch_lightning.core.lightning.LightningModule` methods for which
+    input arguments should be moved automatically to the correct device.
+    It as no effect if applied to a method of an object that is not an instance of
+    :class:`~pytorch_lightning.core.lightning.LightningModule` and is typically applied to ``__call__``
+    or ``forward``.
+
+    Args:
+        fn: A LightningModule method for which the arguments should be moved to the device
+            the parameters are on.
+
+    Example:
+
+        .. code-block:: python
+
+            model = model.cuda(0)
+            model.prepare_data()
+            loader = model.train_dataloader()
+            for x, y in loader:
+                output = model(x)
+    """
+    def auto_transfer_args(self, *args, **kwargs):
+        if not isinstance(self, LightningModule):
+            return fn(self, *args, **kwargs)
+
+        args = transfer_batch_to_device(args, self.device)
+        kwargs = transfer_batch_to_device(kwargs, self.device)
+        return fn(self, *args, **kwargs)
+
+    return auto_transfer_args
diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index 1eafb4fb03f63c..568af7e0ddad36 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -100,35 +100,6 @@ def forward(self, x):
         if self.trainer.proc_rank == 0:
             print(*args, **kwargs)
 
-    def __call__(self, *data, **kwargs):
-        r"""
-        Automatically moves data to correct device if possible, then call torch.nn.Module.__call__
-        Lightning will warn you if it automatically moves any data
-
-        Args:
-            *data: Any positional arguments for torch.nn.Module.__call__. These are typically input data
-            **kwargs: Any keyword arguments for torch.nn.Module.__call__
-
-        Example:
-
-            .. code-block:: python
-
-                model = model.cuda(0)
-                model.prepare_data()
-                loader = model.train_dataloader()
-                for x, y in loader:
-                    output = model(x)  # Lightning will automove data here and warn you of it
-
-        """
-        devices = [p.device for p in self.parameters()]
-        # All parameters must be on same device to automove data
-        # Otherwise we just do what nn.Module does normally
-        if len(set(devices)) == 1:
-            device = devices[0]
-            data = transfer_data_to_device(data, device.type, device.index, warn_on_transfer=True)
-            kwargs = transfer_data_to_device(kwargs, device.type, device.index, warn_on_transfer=True)
-        return super(LightningModule, self).__call__(*data, **kwargs)
-
     @abstractmethod
     def forward(self, *args, **kwargs):
         r"""