update docs

pytorch_lightning/trainer/training_tricks.py

@@ -161,6 +161,13 @@ def scale_batch_size(self,
                algorithm is terminated
 
             batch_arg_name: name of the attribute that stores the batch size.
+                It is expected that the user has provided a model or datamodule that has a hyperparameter
+                with that name. We will look for this attribute name in the following places
+
+                - `model`
+                - `model.hparams`
+                - `model.datamodule`
+                - `trainer.datamodule` (the datamodule passed to the tune method)
 
             **fit_kwargs: remaining arguments to be passed to .fit(), e.g., dataloader
                 or datamodule.
@@ -263,16 +270,12 @@ def _adjust_batch_size(trainer,
                        factor: float = 1.0,
                        value: Optional[int] = None,
                        desc: str = None) -> Tuple[int, bool]:
-    """ Function for adjusting the batch size. It is expected that the user
-        has provided a model that has a hparam field called `batch_size` i.e.
-        `model.hparams.batch_size` should exist. Additionally there can be a
-        datamodule attached to either Trainer or model, in that case the attribute
-        also gets updated when present.
+    """ Helper function for adjusting the batch size.
 
     Args:
         trainer: instance of pytorch_lightning.Trainer
 
-        batch_arg_name: field where batch_size is stored in `model.hparams`
+        batch_arg_name: name of the field where batch_size is stored.
 
         factor: value which the old batch size is multiplied by to get the
             new batch size