.. testsetup:: *

    from pytorch_lightning.trainer.trainer import Trainer


Training Tricks
================
Lightning implements various tricks to help during training

----------

Accumulate gradients
--------------------
Accumulated gradients runs K small batches of size N before doing a backwards pass.
The effect is a large effective batch size of size KxN.

.. seealso:: :class:`~pytorch_lightning.trainer.trainer.Trainer`

.. testcode::

    # DEFAULT (ie: no accumulated grads)
    trainer = Trainer(accumulate_grad_batches=1)

----------

Gradient Clipping
-----------------
Gradient clipping may be enabled to avoid exploding gradients. Specifically, this will `clip the gradient
norm <https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_>`_ computed over all model parameters together.

.. seealso:: :class:`~pytorch_lightning.trainer.trainer.Trainer`

.. testcode::

    # DEFAULT (ie: don't clip)
    trainer = Trainer(gradient_clip_val=0)

    # clip gradients with norm above 0.5
    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. Inspired by https://github.com/BlackHC/toma.

.. seealso:: :class:`~pytorch_lightning.trainer.trainer.Trainer`

.. code-block:: python

    # DEFAULT (ie: don't scale batch size automatically)
    trainer = Trainer(auto_scale_batch_size=None)

    # Autoscale batch size 
    trainer = Trainer(auto_scale_batch_size=None|'power'|'binsearch')

Currently, this feature supports two modes `'power'` scaling and `'binsearch'`
scaling. In `'power'` scaling, starting from a 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 in the `hparams` of your model, i.e.,
    `model.hparams.batch_size` should exist and will be overridden by the results of this
    algorithm. Additionally, your `train_dataloader()` method should depend on this field
    for this feature to work i.e.

    .. code-block:: python
        
        def train_dataloader(self):
            return DataLoader(train_dataset, batch_size=self.batch_size)

.. warning::
            
    Due to these constraints, this features does *NOT* work when passing dataloaders directly
    to `.fit()`. 

The scaling algorithm has a number of parameters that the user can control by
invoking the trainer method `.scale_batch_size` themself (see description below).

.. code-block:: python

    # Use default in trainer construction
    trainer = Trainer()

    # Invoke method
    new_batch_size = trainer.scale_batch_size(model, ...)

    # Override old batch size
    model.hparams.batch_size = new_batch_size
    
    # Fit as normal
    trainer.fit(model)

The algorithm in short works by:
    1. Dumping the current state of the model and trainer
    2. Iteratively until convergence or maximum number of tries `max_trials` (default 25) has been reached:
        - Call `fit()` method of trainer. This evaluates `steps_per_trial` (default 3) number of 
          training steps. Each training step can trigger an OOM error if the tensors 
          (training batch, weights, gradients ect.) allocated during the steps have a 
          too large memory footprint.
        - If an OOM error is encountered, decrease batch size else increase it.
          How much the batch size is increased/decreased is determined by the choosen
          stratrgy.
    3. The found batch size is saved to `model.hparams.batch_size`
    4. Restore the initial state of model and trainer

.. autoclass:: pytorch_lightning.trainer.training_tricks.TrainerTrainingTricksMixin
   :members: scale_batch_size
   :noindex:

.. warning:: Batch size finder is not supported for DDP yet, it is coming soon.