Apply suggestions from code review

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

docs/source/training_tricks.rst

@@ -36,7 +36,7 @@ norm <https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_>`_
     trainer = Trainer(gradient_clip_val=0.5)
 
 Auto scaling of batch size
--------------------------------------
+--------------------------
 Auto scaling of batch size may be enabled to find the largest batch size that fits into
 memory. Larger batch size often yields better estimates of gradients, but may also result in
 longer training time.
@@ -52,17 +52,17 @@ longer training time.
     trainer = Trainer(auto_scale_batch_size=True|'power'|'binsearch')
 
 Setting the feature to `True` enables `'power'` scaling, that starting from a
-batch size of 1 keeps double the batch size until an out-of-memory (OMM) error is
-encountered. Setting the argument to `'binsearch'` continue to finetune the batch
-size by duing a binary search. 
+batch size of 1 keeps doubling the batch size until an out-of-memory (OOM) error is
+encountered. Setting the argument to `'binsearch'` continues to finetune the batch
+size by performing a binary search. 
 
 .. note:: 
 
-    This feature expects that a `batch_size` field exist in the `hparams` of your model i.e.
+    This feature expects that a `batch_size` field in the `hparams` of your model, i.e.,
     `model.hparams.batch_size` should exist and will be overridden by the results of this
     algorithm.
 
-The scaling algorithm has a number of parameters, that the user can control by
+The scaling algorithm has a number of parameters that the user can control by
 invoking the trainer method `.scale_batch_size` themself.
 
 .. code-block:: python

pytorch_lightning/trainer/__init__.py

@@ -142,15 +142,15 @@ def forward(self, x):
 
 .. code-block:: python
 
-    # defeault use by the Trainer (no scaling of batch size)
+    # default used by the Trainer (no scaling of batch size)
     trainer = Trainer(auto_scale_batch_size=False)
 
 Example::
 
-    # run batch size scaling, result override hparams.batch_size
+    # run batch size scaling, result overrides hparams.batch_size
     trainer = Trainer(auto_scale_batch_size=True)
 
-    # run batch size scaling, result override hparams.my_batch_size_arg
+    # run batch size scaling, result overrides hparams.my_batch_size_arg
     trainer = Trainer(auto_scale_batch_size='my_batch_size_arg')
 
 auto_lr_find

pytorch_lightning/trainer/trainer.py

@@ -297,9 +297,9 @@ def __init__(
 
             auto_scale_batch_size: If set to True, will `initially` run a batch size
                 finder trying to find the largest batch size that fits into memory.
-                The results will be stored in self.hparams.batch_size in the lightning module.
+                The result will be stored in self.hparams.batch_size in the LightningModule.
                 Additionally, can be set to either `power` (same as `True`) that
-                estimates the batch size through a power search or `binseach` that
+                estimates the batch size through a power search or `binsearch` that
                 estimates the batch size through a binary search.
         """
 

pytorch_lightning/trainer/training_tricks.py

@@ -95,7 +95,7 @@ def scale_batch_size(self,
                          n_max_try: int = 25):
         r"""
         Will iteratively try to find the largest batch size for a given model
-        that does not not give an out of memory (OOM) error
+        that does not give an out of memory (OOM) error.
 
         Args:
             model: Model to fit.
@@ -104,22 +104,21 @@ def scale_batch_size(self,
                 If mode is `power` we keep multiplying the batch size by 2, until
                 we get an OOM error. If mode is 'binsearch', we will initially
                 also keep multiplying by 2 and after encountering an OOM error
-                do a binary search between the last succeded batch size and the
+                do a binary search between the last successful batch size and the
                 batch size that failed.
 
             n_step_per_try: number of steps to run with a given batch size.
                 Idealy 1 should be enough to test if a OOM error occurs,
-                however in practise a few is needed
+                however in practise a few are needed
 
-            init_val: initial batch size to do the search from
+            init_val: initial batch size to start the search with
 
             n_max_try: max number of increase in batch size done before
                algorithm is terminated
 
         """
         if mode not in ['power', 'binsearch']:
-            raise ValueError('mode in method `scale_batch_size`'
-                             ' can only be `power` or `binsearch')
+            raise ValueError('mode in method `scale_batch_size` can only be `power` or `binsearch')
 
         # Arguments we adjust during the batch size finder, save for restoring
         max_steps = self.max_steps

tests/trainer/test_trainer_tricks.py

@@ -42,7 +42,7 @@ class CurrentTestModel(
 
 
 def test_trainer_reset_correctly(tmpdir):
-    ''' Check that all trainer parameters are reset correctly after scaling batch size'''
+    """ Check that all trainer parameters are reset correctly after scaling batch size. """
     tutils.reset_seed()
 
     class CurrentTestModel(
@@ -79,7 +79,7 @@ class CurrentTestModel(
 
 
 def test_trainer_arg_bool(tmpdir):
-    ''' Check that trainer arg works with bool input '''
+    """ Check that trainer arg works with bool input. """
     tutils.reset_seed()
 
     class CurrentTestModel(