lightning/docs/source/optimizers.rst

.. _optimizers:

Optimization
===============

Learning rate scheduling
------------------------
Every optimizer you use can be paired with any `LearningRateScheduler <https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate>`_.

.. testcode::

   # no LR scheduler
   def configure_optimizers(self):
      return Adam(...)

   # Adam + LR scheduler
   def configure_optimizers(self):
      optimizer = Adam(...)
      scheduler = ReduceLROnPlateau(optimizer, ...)
      return [optimizer], [scheduler]

   # Two optimizers each with a scheduler
   def configure_optimizers(self):
      optimizer1 = Adam(...)
      optimizer2 = SGD(...)
      scheduler1 = ReduceLROnPlateau(optimizer1, ...)
      scheduler2 = LambdaLR(optimizer2, ...)
      return [optimizer1, optimizer2], [scheduler1, scheduler2]

   # Same as above with additional params passed to the first scheduler
   def configure_optimizers(self):
      optimizers = [Adam(...), SGD(...)]
      schedulers = [
         {
            'scheduler': ReduceLROnPlateau(optimizers[0], ...),
            'monitor': 'val_recall', # Default: val_loss
            'interval': 'epoch',
            'frequency': 1
         },
         LambdaLR(optimizers[1], ...)
      ]
      return optimizers, schedulers

----------

Use multiple optimizers (like GANs)
-----------------------------------
To use multiple optimizers return > 1 optimizers from :meth:`pytorch_lightning.core.LightningModule.configure_optimizers`

.. testcode::

   # one optimizer
   def configure_optimizers(self):
      return Adam(...)

   # two optimizers, no schedulers
   def configure_optimizers(self):
      return Adam(...), SGD(...)

   # Two optimizers, one scheduler for adam only
   def configure_optimizers(self):
      return [Adam(...), SGD(...)], [ReduceLROnPlateau()]

Lightning will call each optimizer sequentially:

.. code-block:: python

   for epoch in epochs:
      for batch in data:
         for opt in optimizers:
            train_step(opt)
            opt.step()

      for scheduler in scheduler:
         scheduler.step()

----------

Step optimizers at arbitrary intervals
--------------------------------------
To do more interesting things with your optimizers such as learning rate warm-up or odd scheduling,
override the :meth:`optimizer_step` function.

For example, here step optimizer A every 2 batches and optimizer B every 4 batches

.. testcode::

    def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, second_order_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False):
        optimizer.step()

    def optimizer_zero_grad(self, current_epoch, batch_idx, optimizer, opt_idx):
      optimizer.zero_grad()

    # Alternating schedule for optimizer steps (ie: GANs)
    def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, second_order_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False):
        # update generator opt every 2 steps
        if optimizer_i == 0:
            if batch_nb % 2 == 0 :
                optimizer.step()
                optimizer.zero_grad()

        # update discriminator opt every 4 steps
        if optimizer_i == 1:
            if batch_nb % 4 == 0 :
                optimizer.step()
                optimizer.zero_grad()

        # ...
        # add as many optimizers as you want

Here we add a learning-rate warm up

.. testcode::

    # learning rate warm-up
    def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, second_order_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False):
        # warm up lr
        if self.trainer.global_step < 500:
            lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
            for pg in optimizer.param_groups:
                pg['lr'] = lr_scale * self.hparams.learning_rate

        # update params
        optimizer.step()
        optimizer.zero_grad()

----------

Using the closure functions for optimization
--------------------------------------------

When using optimization schemes such as LBFGS, the `second_order_closure` needs to be enabled. By default, this function is defined by wrapping the `training_step` and the backward steps as follows

.. testcode::

    def second_order_closure(pl_module, split_batch, batch_idx, opt_idx, optimizer, hidden):
        # Model training step on a given batch
        result = pl_module.training_step(split_batch, batch_idx, opt_idx, hidden)

        # Model backward pass
        pl_module.backward(result, optimizer, opt_idx)

        # on_after_backward callback
        pl_module.on_after_backward(result.training_step_output, batch_idx, result.loss)

        return result

    # This default `second_order_closure` function can be enabled by passing it directly into the `optimizer.step`
    def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, second_order_closure, on_tpu=False, using_native_amp=False, using_lbfgs=False):
        # update params
        optimizer.step(second_order_closure)