optimizers.rst

Optimization

Learning rate scheduling

Every optimizer you use can be paired with any LearningRateScheduler.

.. 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:

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_i, second_order_closure, on_tpu, using_native_amp, using_lbfgs):
        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_i, second_order_closure, on_tpu, using_native_amp, using_lbfgs):
        # 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_i, second_order_closure, on_tpu, using_native_amp, using_lbfgs):
        # 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()