Base Classes

RL4COLitModule

class rl4co.models.rl.common.base.RL4COLitModule(env, policy, batch_size=512, val_batch_size=None, test_batch_size=None, train_data_size=100000, val_data_size=10000, test_data_size=10000, optimizer='Adam', optimizer_kwargs={'lr': 0.0001}, lr_scheduler=None, lr_scheduler_kwargs={'gamma': 0.1, 'milestones': [80, 95]}, lr_scheduler_interval='epoch', lr_scheduler_monitor='val/reward', generate_default_data=False, shuffle_train_dataloader=False, dataloader_num_workers=0, data_dir='data/', log_on_step=True, metrics={}, **litmodule_kwargs)

Bases: LightningModule

Base class for Lightning modules for RL4CO. This defines the general training loop in terms of RL algorithms. Subclasses should implement mainly the shared_step to define the specific loss functions and optimization routines.

Parameters:

• env (RL4COEnvBase) – RL4CO environment

• policy (Module) – policy network (actor)

• batch_size (int) – batch size (general one, default used for training)

• val_batch_size (int) – specific batch size for validation

• test_batch_size (int) – specific batch size for testing

• train_data_size (int) – size of training dataset for one epoch

• val_data_size (int) – size of validation dataset for one epoch

• test_data_size (int) – size of testing dataset for one epoch

• optimizer (Union[str, Optimizer, partial]) – optimizer or optimizer name

• optimizer_kwargs (dict) – optimizer kwargs

• lr_scheduler (Union[str, LRScheduler, partial]) – learning rate scheduler or learning rate scheduler name

• lr_scheduler_kwargs (dict) – learning rate scheduler kwargs

• lr_scheduler_interval (str) – learning rate scheduler interval

• lr_scheduler_monitor (str) – learning rate scheduler monitor

• generate_default_data (bool) – whether to generate default datasets, filling up the data directory

• shuffle_train_dataloader (bool) – whether to shuffle training dataloader. Default is False since we recreate dataset every epoch

• dataloader_num_workers (int) – number of workers for dataloader

• data_dir (str) – data directory

• metrics (dict) – metrics

• litmodule_kwargs – kwargs for LightningModule

configure_optimizers(parameters=None)

Parameters:

parameters – parameters to be optimized. If None, will use `self.policy.parameters()

forward(td, **kwargs)

Forward pass for the model. Simple wrapper around policy. Uses env from the module if not provided.

instantiate_metrics(metrics)

Dictionary of metrics to be logged at each phase

log_metrics(metric_dict, phase, dataloader_idx=None)

Log metrics to logger and progress bar

on_train_epoch_end()

Called at the end of the training epoch. This can be used for instance to update the train dataset with new data (which is the case in RL).

post_setup_hook()

Hook to be called after setup. Can be used to set up subclasses without overriding setup

setup(stage='fit')

Base LightningModule setup method. This will setup the datasets and dataloaders

Note

We also send to the loggers all hyperparams that are not nn.Module (i.e. the policy). Apparently PyTorch Lightning does not do this by default.

setup_loggers()

Log all hyperparameters except those in nn.Module

shared_step(batch, batch_idx, phase, **kwargs)

Shared step between train/val/test. To be implemented in subclass

test_dataloader()

An iterable or collection of iterables specifying test samples.

For more information about multiple dataloaders, see this section.

For data processing use the following pattern:

• download in prepare_data()

• process and split in setup()

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

• test()

• prepare_data()

• setup()

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Note

If you don’t need a test dataset and a test_step(), you don’t need to implement this method.

test_step(batch, batch_idx, dataloader_idx=None)

Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.

Parameters:

• batch (Any) – The output of your data iterable, normally a DataLoader.

• batch_idx (int) – The index of this batch.

• dataloader_idx (int) – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

• Tensor - The loss tensor

• dict - A dictionary. Can include any keys, but must include the key 'loss'.

• None - Skip to the next batch.

# if you have one test dataloader:
def test_step(self, batch, batch_idx):
    ...


# if you have multiple test dataloaders:
def test_step(self, batch, batch_idx, dataloader_idx=0):
    ...

Examples:

# CASE 1: A single test dataset
def test_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'test_loss': loss, 'test_acc': test_acc})

If you pass in multiple test dataloaders, test_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note

If you don’t need to test you don’t need to implement this method.

Note

When the test_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.

train_dataloader()

An iterable or collection of iterables specifying training samples.

For more information about multiple dataloaders, see this section.

The dataloader you return will not be reloaded unless you set reload_dataloaders_every_n_epochs to a positive integer.

For data processing use the following pattern:

• download in prepare_data()

• process and split in setup()

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

• fit()

• prepare_data()

• setup()

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

training_step(batch, batch_idx)

Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.

Parameters:

• batch (Any) – The output of your data iterable, normally a DataLoader.

• batch_idx (int) – The index of this batch.

• dataloader_idx – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

• Tensor - The loss tensor

• dict - A dictionary. Can include any keys, but must include the key 'loss'.

• None - Skip to the next batch. This is only supported for automatic optimization.

  This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.

In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.

Example:

def training_step(self, batch, batch_idx):
    x, y, z = batch
    out = self.encoder(x)
    loss = self.loss(out, x)
    return loss

To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:

def __init__(self):
    super().__init__()
    self.automatic_optimization = False


# Multiple optimizers (e.g.: GANs)
def training_step(self, batch, batch_idx):
    opt1, opt2 = self.optimizers()

    # do training_step with encoder
    ...
    opt1.step()
    # do training_step with decoder
    ...
    opt2.step()

Note

When accumulate_grad_batches > 1, the loss returned here will be automatically normalized by accumulate_grad_batches internally.

val_dataloader()

An iterable or collection of iterables specifying validation samples.

For more information about multiple dataloaders, see this section.

The dataloader you return will not be reloaded unless you set reload_dataloaders_every_n_epochs to a positive integer.

It’s recommended that all data downloads and preparation happen in prepare_data().

• fit()

• validate()

• prepare_data()

• setup()

Note

Lightning tries to add the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.

Note

If you don’t need a validation dataset and a validation_step(), you don’t need to implement this method.

validation_step(batch, batch_idx, dataloader_idx=None)

Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.

Parameters:

• batch (Any) – The output of your data iterable, normally a DataLoader.

• batch_idx (int) – The index of this batch.

• dataloader_idx (int) – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Returns:

• Tensor - The loss tensor

• dict - A dictionary. Can include any keys, but must include the key 'loss'.

• None - Skip to the next batch.

# if you have one val dataloader:
def validation_step(self, batch, batch_idx):
    ...


# if you have multiple val dataloaders:
def validation_step(self, batch, batch_idx, dataloader_idx=0):
    ...

Examples:

# CASE 1: A single validation dataset
def validation_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'val_loss': loss, 'val_acc': val_acc})

If you pass in multiple val dataloaders, validation_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note

If you don’t need to validate you don’t need to implement this method.

Note

When the validation_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.

wrap_dataset(dataset)

Wrap dataset with policy-specific wrapper. This is useful i.e. in REINFORCE where we need to collect the greedy rollout baseline outputs.