pytorch-lightning/docs/Trainer/hooks.md

Hooks

[Github Code]

There are cases when you might want to do something different at different parts of the training/validation loop. To enable a hook, simply override the method in your LightningModule and the trainer will call it at the correct time.

Contributing If there's a hook you'd like to add, simply:

1. Fork PyTorchLightning.
2. Add the hook here.
3. Add the correct place in the Trainer where it should be called.

---

on_epoch_start

Called in the training loop at the very beginning of the epoch.

def on_epoch_start(self):
    # do something when the epoch starts

---

on_epoch_end

Called in the training loop at the very end of the epoch.

def on_epoch_end(self):
    # do something when the epoch ends 

---

on_batch_start

Called in the training loop before anything happens for that batch.

def on_batch_start(self):
    # do something when the batch starts

---

on_batch_end

Called in the training loop after the batch.

def on_batch_end(self):
    # do something when the batch ends 

---

on_pre_performance_check

Called at the very beginning of the validation loop.

def on_pre_performance_check(self):
    # do something before validation starts 

---

on_post_performance_check

Called at the very end of the validation loop.

def on_post_performance_check(self):
    # do something before validation end

---

optimizer_step

Calls .step() and .zero_grad for each optimizer.
You can override this method to adjust how you do the optimizer step for each optimizer

Called once per optimizer

# DEFAULT
def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i, second_order_closure=None):
    optimizer.step()   
    optimizer.zero_grad()   
    
# Alternating schedule for optimizer steps (ie: GANs)    
def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i, second_order_closure=None):
    # 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 

This step allows you to do a lot of non-standard training tricks such as learning-rate warm-up:

# learning rate warm-up
def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i, second_order_closure=None):
    # 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() 

---

on_before_zero_grad

Called in the training loop after taking an optimizer step and before zeroing grads. Good place to inspect weight information with weights updated.

Called once per optimizer

def on_before_zero_grad(self, optimizer):
    # do something with the optimizer or inspect it. 

---

backward

Called to perform backward step. Feel free to override as needed.

The loss passed in has already been scaled for accumulated gradients if requested.

def backward(self, use_amp, loss, optimizer):
    """
    Override backward with your own implementation if you need to
    :param use_amp: Whether amp was requested or not
    :param loss: Loss is already scaled by accumulated grads
    :param optimizer: Current optimizer being used
    :return:
    """
    if use_amp:
        with amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward()
    else:
        loss.backward()

---

on_after_backward

Called in the training loop after model.backward() This is the ideal place to inspect or log gradient information

def on_after_backward(self):
    # example to inspect gradient information in tensorboard
    if self.trainer.global_step % 25 == 0:  # don't make the tf file huge
        params = self.state_dict()
        for k, v in params.items():
            grads = v
            name = k
            self.logger.experiment.add_histogram(tag=name, values=grads, global_step=self.trainer.global_step)

---

tbptt_split_batch

Called in the training loop after on_batch_start if truncated_bptt_steps > 0. Each returned batch split is passed separately to training_step(...).

def tbptt_split_batch(self, batch, split_size):
  splits = []
  for t in range(0, time_dims[0], split_size):
      batch_split = []
      for i, x in enumerate(batch):
          if isinstance(x, torch.Tensor):
              split_x = x[:, t:t + split_size]
          elif isinstance(x, collections.Sequence):
              split_x = [None] * len(x)
              for batch_idx in range(len(x)):
                  split_x[batch_idx] = x[batch_idx][t:t + split_size]

          batch_split.append(split_x)

      splits.append(batch_split)

  return splits