LightningModule¶

A LightningModule organizes your PyTorch code into 6 sections:

Initialization (__init__ and setup()).
Train Loop (training_step())
Validation Loop (validation_step())
Test Loop (test_step())
Prediction Loop (predict_step())
Optimizers and LR Schedulers (configure_optimizers())

When you convert to use Lightning, the code IS NOT abstracted - just organized. All the other code that’s not in the LightningModule has been automated for you by the Trainer.

net = MyLightningModuleNet()
trainer = Trainer()
trainer.fit(net)

There are no .cuda() or .to(device) calls required. Lightning does these for you.

# don't do in Lightning
x = torch.Tensor(2, 3)
x = x.cuda()
x = x.to(device)

# do this instead
x = x  # leave it alone!

# or to init a new tensor
new_x = torch.Tensor(2, 3)
new_x = new_x.to(x)

When running under a distributed strategy, Lightning handles the distributed sampler for you by default.

# Don't do in Lightning...
data = MNIST(...)
sampler = DistributedSampler(data)
DataLoader(data, sampler=sampler)

# do this instead
data = MNIST(...)
DataLoader(data)

A LightningModule is a torch.nn.Module but with added functionality. Use it as such!

net = Net.load_from_checkpoint(PATH)
net.freeze()
out = net(x)

Thus, to use Lightning, you just need to organize your code which takes about 30 minutes, (and let’s be real, you probably should do anyway).

Starter Example¶

Here are the only required methods.

import lightning.pytorch as pl
import torch

from lightning.pytorch.demos import Transformer


class LightningTransformer(pl.LightningModule):
    def __init__(self, vocab_size):
        super().__init__()
        self.model = Transformer(vocab_size=vocab_size)

    def forward(self, inputs, target):
        return self.model(inputs, target)

    def training_step(self, batch, batch_idx):
        inputs, target = batch
        output = self(inputs, target)
        loss = torch.nn.functional.nll_loss(output, target.view(-1))
        return loss

    def configure_optimizers(self):
        return torch.optim.SGD(self.model.parameters(), lr=0.1)

Which you can train by doing:

from lightning.pytorch.demos import WikiText2
from torch.utils.data import DataLoader

dataset = WikiText2()
dataloader = DataLoader(dataset)
model = LightningTransformer(vocab_size=dataset.vocab_size)

trainer = pl.Trainer(fast_dev_run=100)
trainer.fit(model=model, train_dataloaders=dataloader)

The LightningModule has many convenient methods, but the core ones you need to know about are:

Name	Description
`__init__` and `setup()`	Define initialization here
`forward()`	To run data through your model only (separate from `training_step`)
`training_step()`	the complete training step
`validation_step()`	the complete validation step
`test_step()`	the complete test step
`predict_step()`	the complete prediction step
`configure_optimizers()`	define optimizers and LR schedulers

Training¶

Training Loop¶

To activate the training loop, override the training_step() method.

class LightningTransformer(pl.LightningModule):
    def __init__(self, vocab_size):
        super().__init__()
        self.model = Transformer(vocab_size=vocab_size)

    def training_step(self, batch, batch_idx):
        inputs, target = batch
        output = self.model(inputs, target)
        loss = torch.nn.functional.nll_loss(output, target.view(-1))
        return loss

Under the hood, Lightning does the following (pseudocode):

# put model in train mode and enable gradient calculation
model.train()
torch.set_grad_enabled(True)

for batch_idx, batch in enumerate(train_dataloader):
    loss = training_step(batch, batch_idx)

    # clear gradients
    optimizer.zero_grad()

    # backward
    loss.backward()

    # update parameters
    optimizer.step()

Train Epoch-level Metrics¶

If you want to calculate epoch-level metrics and log them, use log().

def training_step(self, batch, batch_idx):
    inputs, target = batch
    output = self.model(inputs, target)
    loss = torch.nn.functional.nll_loss(output, target.view(-1))

    # logs metrics for each training_step,
    # and the average across the epoch, to the progress bar and logger
    self.log("train_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
    return loss

The log() method automatically reduces the requested metrics across a complete epoch and devices. Here’s the pseudocode of what it does under the hood:

outs = []
for batch_idx, batch in enumerate(train_dataloader):
    # forward
    loss = training_step(batch, batch_idx)
    outs.append(loss.detach())

    # clear gradients
    optimizer.zero_grad()
    # backward
    loss.backward()
    # update parameters
    optimizer.step()

# note: in reality, we do this incrementally, instead of keeping all outputs in memory
epoch_metric = torch.mean(torch.stack(outs))

Train Epoch-level Operations¶

In the case that you need to make use of all the outputs from each training_step(), override the on_train_epoch_end() method.

class LightningTransformer(pl.LightningModule):
    def __init__(self, vocab_size):
        super().__init__()
        self.model = Transformer(vocab_size=vocab_size)
        self.training_step_outputs = []

    def training_step(self, batch, batch_idx):
        inputs, target = batch
        output = self.model(inputs, target)
        loss = torch.nn.functional.nll_loss(output, target.view(-1))
        preds = ...
        self.training_step_outputs.append(preds)
        return loss

    def on_train_epoch_end(self):
        all_preds = torch.stack(self.training_step_outputs)
        # do something with all preds
        ...
        self.training_step_outputs.clear()  # free memory

Validation¶

Validation Loop¶

To activate the validation loop while training, override the validation_step() method.

class LightningTransformer(pl.LightningModule):
    def validation_step(self, batch, batch_idx):
        inputs, target = batch
        output = self.model(inputs, target)
        loss = F.cross_entropy(y_hat, y)
        self.log("val_loss", loss)

Under the hood, Lightning does the following (pseudocode):

# ...
for batch_idx, batch in enumerate(train_dataloader):
    loss = model.training_step(batch, batch_idx)
    loss.backward()
    # ...

    if validate_at_some_point:
        # disable grads + batchnorm + dropout
        torch.set_grad_enabled(False)
        model.eval()

        # ----------------- VAL LOOP ---------------
        for val_batch_idx, val_batch in enumerate(val_dataloader):
            val_out = model.validation_step(val_batch, val_batch_idx)
        # ----------------- VAL LOOP ---------------

        # enable grads + batchnorm + dropout
        torch.set_grad_enabled(True)
        model.train()

You can also run just the validation loop on your validation dataloaders by overriding validation_step() and calling validate().

model = LightningTransformer(vocab_size=dataset.vocab_size)
trainer = pl.Trainer()
trainer.validate(model)

Note

It is recommended to validate on single device to ensure each sample/batch gets evaluated exactly once. This is helpful to make sure benchmarking for research papers is done the right way. Otherwise, in a multi-device setting, samples could occur duplicated when DistributedSampler is used, for eg. with strategy="ddp". It replicates some samples on some devices to make sure all devices have same batch size in case of uneven inputs.

Validation Epoch-level Metrics¶

In the case that you need to make use of all the outputs from each validation_step(), override the on_validation_epoch_end() method. Note that this method is called before on_train_epoch_end().

class LightningTransformer(pl.LightningModule):
    def __init__(self, vocab_size):
        super().__init__()
        self.model = Transformer(vocab_size=vocab_size)
        self.validation_step_outputs = []

    def validation_step(self, batch, batch_idx):
        x, y = batch
        inputs, target = batch
        output = self.model(inputs, target)
        loss = torch.nn.functional.nll_loss(output, target.view(-1))
        pred = ...
        self.validation_step_outputs.append(pred)
        return pred

    def on_validation_epoch_end(self):
        all_preds = torch.stack(self.validation_step_outputs)
        # do something with all preds
        ...
        self.validation_step_outputs.clear()  # free memory

Testing¶

Test Loop¶

The process for enabling a test loop is the same as the process for enabling a validation loop. Please refer to the section above for details. For this you need to override the test_step() method.

The only difference is that the test loop is only called when test() is used.

model = LightningTransformer(vocab_size=dataset.vocab_size)
dataloader = DataLoader(dataset)
trainer = pl.Trainer()
trainer.fit(model=model, train_dataloaders=dataloader)

# automatically loads the best weights for you
trainer.test(model)

There are two ways to call test():

# call after training
trainer = pl.Trainer()
trainer.fit(model=model, train_dataloaders=dataloader)

# automatically auto-loads the best weights from the previous run
trainer.test(dataloaders=test_dataloaders)

# or call with pretrained model
model = LightningTransformer.load_from_checkpoint(PATH)
dataset = WikiText2()
test_dataloader = DataLoader(dataset)
trainer = pl.Trainer()
trainer.test(model, dataloaders=test_dataloader)

Note

WikiText2 is used in a manner that does not create a train, test, val split. This is done for illustrative purposes only. A proper split can be created in lightning.pytorch.core.LightningModule.setup() or lightning.pytorch.core.LightningDataModule.setup().

Note

It is recommended to validate on single device to ensure each sample/batch gets evaluated exactly once. This is helpful to make sure benchmarking for research papers is done the right way. Otherwise, in a multi-device setting, samples could occur duplicated when DistributedSampler is used, for eg. with strategy="ddp". It replicates some samples on some devices to make sure all devices have same batch size in case of uneven inputs.

Inference¶

Prediction Loop¶

By default, the predict_step() method runs the forward() method. In order to customize this behaviour, simply override the predict_step() method.

For the example let’s override predict_step:

class LightningTransformer(pl.LightningModule):
    def __init__(self, vocab_size):
        super().__init__()
        self.model = Transformer(vocab_size=vocab_size)

    def predict_step(self, batch):
        inputs, target = batch
        return self.model(inputs, target)

Under the hood, Lightning does the following (pseudocode):

# disable grads + batchnorm + dropout
torch.set_grad_enabled(False)
model.eval()
all_preds = []

for batch_idx, batch in enumerate(predict_dataloader):
    pred = model.predict_step(batch, batch_idx)
    all_preds.append(pred)

There are two ways to call predict():

# call after training
trainer = pl.Trainer()
trainer.fit(model=model, train_dataloaders=dataloader)

# automatically auto-loads the best weights from the previous run
predictions = trainer.predict(dataloaders=predict_dataloader)

# or call with pretrained model
model = LightningTransformer.load_from_checkpoint(PATH)
dataset = WikiText2()
test_dataloader = DataLoader(dataset)
trainer = pl.Trainer()
predictions = trainer.predict(model, dataloaders=test_dataloader)

Inference in Research¶

If you want to perform inference with the system, you can add a forward method to the LightningModule.

Note

When using forward, you are responsible to call eval() and use the no_grad() context manager.

class LightningTransformer(pl.LightningModule):
    def __init__(self, vocab_size):
        super().__init__()
        self.model = Transformer(vocab_size=vocab_size)

    def forward(self, batch):
        inputs, target = batch
        return self.model(inputs, target)

    def training_step(self, batch, batch_idx):
        inputs, target = batch
        output = self.model(inputs, target)
        loss = torch.nn.functional.nll_loss(output, target.view(-1))
        return loss

    def configure_optimizers(self):
        return torch.optim.SGD(self.model.parameters(), lr=0.1)


model = LightningTransformer(vocab_size=dataset.vocab_size)

model.eval()
with torch.no_grad():
    batch = dataloader.dataset[0]
    pred = model(batch)

The advantage of adding a forward is that in complex systems, you can do a much more involved inference procedure, such as text generation:

class Seq2Seq(pl.LightningModule):
    def forward(self, x):
        embeddings = self(x)
        hidden_states = self.encoder(embeddings)
        for h in hidden_states:
            # decode
            ...
        return decoded

In the case where you want to scale your inference, you should be using predict_step().

class Autoencoder(pl.LightningModule):
    def forward(self, x):
        return self.decoder(x)

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        # this calls forward
        return self(batch)


data_module = ...
model = Autoencoder()
trainer = Trainer(accelerator="gpu", devices=2)
trainer.predict(model, data_module)

Inference in Production¶

For cases like production, you might want to iterate different models inside a LightningModule.

from torchmetrics.functional import accuracy


class ClassificationTask(pl.LightningModule):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.model(x)
        loss = F.cross_entropy(y_hat, y)
        return loss

    def validation_step(self, batch, batch_idx):
        loss, acc = self._shared_eval_step(batch, batch_idx)
        metrics = {"val_acc": acc, "val_loss": loss}
        self.log_dict(metrics)
        return metrics

    def test_step(self, batch, batch_idx):
        loss, acc = self._shared_eval_step(batch, batch_idx)
        metrics = {"test_acc": acc, "test_loss": loss}
        self.log_dict(metrics)
        return metrics

    def _shared_eval_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.model(x)
        loss = F.cross_entropy(y_hat, y)
        acc = accuracy(y_hat, y)
        return loss, acc

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        x, y = batch
        y_hat = self.model(x)
        return y_hat

    def configure_optimizers(self):
        return torch.optim.Adam(self.model.parameters(), lr=0.02)

Then pass in any arbitrary model to be fit with this task

for model in [resnet50(), vgg16(), BidirectionalRNN()]:
    task = ClassificationTask(model)

    trainer = Trainer(accelerator="gpu", devices=2)
    trainer.fit(task, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)

Tasks can be arbitrarily complex such as implementing GAN training, self-supervised or even RL.

class GANTask(pl.LightningModule):
    def __init__(self, generator, discriminator):
        super().__init__()
        self.generator = generator
        self.discriminator = discriminator

    ...

When used like this, the model can be separated from the Task and thus used in production without needing to keep it in a LightningModule.

The following example shows how you can run inference in the Python runtime:

task = ClassificationTask(model)
trainer = Trainer(accelerator="gpu", devices=2)
trainer.fit(task, train_dataloader, val_dataloader)
trainer.save_checkpoint("best_model.ckpt")

# use model after training or load weights and drop into the production system
model = ClassificationTask.load_from_checkpoint("best_model.ckpt")
x = ...
model.eval()
with torch.no_grad():
    y_hat = model(x)

Check out Inference in Production guide to learn about the possible ways to perform inference in production.

Save Hyperparameters¶

Often times we train many versions of a model. You might share that model or come back to it a few months later at which point it is very useful to know how that model was trained (i.e.: what learning rate, neural network, etc…).

Lightning has a standardized way of saving the information for you in checkpoints and YAML files. The goal here is to improve readability and reproducibility.

save_hyperparameters¶

Use save_hyperparameters() within your LightningModule’s __init__ method. It will enable Lightning to store all the provided arguments under the self.hparams attribute. These hyperparameters will also be stored within the model checkpoint, which simplifies model re-instantiation after training.

class LitMNIST(pl.LightningModule):
    def __init__(self, layer_1_dim=128, learning_rate=1e-2):
        super().__init__()
        # call this to save (layer_1_dim=128, learning_rate=1e-4) to the checkpoint
        self.save_hyperparameters()

        # equivalent
        self.save_hyperparameters("layer_1_dim", "learning_rate")

        # Now possible to access layer_1_dim from hparams
        self.hparams.layer_1_dim

In addition, loggers that support it will automatically log the contents of self.hparams.

Excluding hyperparameters¶

By default, every parameter of the __init__ method will be considered a hyperparameter to the LightningModule. However, sometimes some parameters need to be excluded from saving, for example when they are not serializable. Those parameters should be provided back when reloading the LightningModule. In this case, exclude them explicitly:

class LitMNIST(pl.LightningModule):
    def __init__(self, loss_fx, generator_network, layer_1_dim=128):
        super().__init__()
        self.layer_1_dim = layer_1_dim
        self.loss_fx = loss_fx

        # call this to save only (layer_1_dim=128) to the checkpoint
        self.save_hyperparameters("layer_1_dim")

        # equivalent
        self.save_hyperparameters(ignore=["loss_fx", "generator_network"])

load_from_checkpoint¶

LightningModules that have hyperparameters automatically saved with save_hyperparameters() can conveniently be loaded and instantiated directly from a checkpoint with load_from_checkpoint():

# to load specify the other args
model = LitMNIST.load_from_checkpoint(PATH, loss_fx=torch.nn.SomeOtherLoss, generator_network=MyGenerator())

If parameters were excluded, they need to be provided at the time of loading:

# the excluded parameters were `loss_fx` and `generator_network`
model = LitMNIST.load_from_checkpoint(PATH, loss_fx=torch.nn.SomeOtherLoss, generator_network=MyGenerator())

Child Modules¶

Research projects tend to test different approaches to the same dataset. This is very easy to do in Lightning with inheritance.

For example, imagine we now want to train an AutoEncoder to use as a feature extractor for images. The only things that change in the LitAutoEncoder model are the init, forward, training, validation and test step.

class Encoder(torch.nn.Module):
    ...


class Decoder(torch.nn.Module):
    ...


class AutoEncoder(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = Encoder()
        self.decoder = Decoder()

    def forward(self, x):
        return self.decoder(self.encoder(x))


class LitAutoEncoder(LightningModule):
    def __init__(self, auto_encoder):
        super().__init__()
        self.auto_encoder = auto_encoder
        self.metric = torch.nn.MSELoss()

    def forward(self, x):
        return self.auto_encoder.encoder(x)

    def training_step(self, batch, batch_idx):
        x, _ = batch
        x_hat = self.auto_encoder(x)
        loss = self.metric(x, x_hat)
        return loss

    def validation_step(self, batch, batch_idx):
        self._shared_eval(batch, batch_idx, "val")

    def test_step(self, batch, batch_idx):
        self._shared_eval(batch, batch_idx, "test")

    def _shared_eval(self, batch, batch_idx, prefix):
        x, _ = batch
        x_hat = self.auto_encoder(x)
        loss = self.metric(x, x_hat)
        self.log(f"{prefix}_loss", loss)

and we can train this using the Trainer:

auto_encoder = AutoEncoder()
lightning_module = LitAutoEncoder(auto_encoder)
trainer = Trainer()
trainer.fit(lightning_module, train_dataloader, val_dataloader)

And remember that the forward method should define the practical use of a LightningModule. In this case, we want to use the LitAutoEncoder to extract image representations:

some_images = torch.Tensor(32, 1, 28, 28)
representations = lightning_module(some_images)

LightningModule API¶

Methods¶

all_gather¶

LightningModule.all_gather(data, group=None, sync_grads=False)[source]

Gather tensors or collections of tensors from multiple processes.

This method needs to be called on all processes and the tensors need to have the same shape across all processes, otherwise your program will stall forever.

Parameters:

data¶ (Union[Tensor, Dict, List, Tuple]) – int, float, tensor of shape (batch, …), or a (possibly nested) collection thereof.
group¶ (Optional[Any]) – the process group to gather results from. Defaults to all processes (world)
sync_grads¶ (bool) – flag that allows users to synchronize gradients for the all_gather operation

Return type:

Union[Tensor, Dict, List, Tuple]

Returns:

A tensor of shape (world_size, batch, …), or if the input was a collection the output will also be a collection with tensors of this shape.

configure_callbacks¶

LightningModule.configure_callbacks()[source]

Configure model-specific callbacks. When the model gets attached, e.g., when .fit() or .test() gets called, the list or a callback returned here will be merged with the list of callbacks passed to the Trainer’s callbacks argument. If a callback returned here has the same type as one or several callbacks already present in the Trainer’s callbacks list, it will take priority and replace them. In addition, Lightning will make sure ModelCheckpoint callbacks run last.

Return type:: Union[Sequence[Callback], Callback]
Returns:: A callback or a list of callbacks which will extend the list of callbacks in the Trainer.

Example:

def configure_callbacks(self):
    early_stop = EarlyStopping(monitor="val_acc", mode="max")
    checkpoint = ModelCheckpoint(monitor="val_loss")
    return [early_stop, checkpoint]

configure_optimizers¶

LightningModule.configure_optimizers()[source]

Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple. Optimization with multiple optimizers only works in the manual optimization mode.

Return type:

Union[Optimizer, Sequence[Optimizer], Tuple[Sequence[Optimizer], Sequence[Union[LRScheduler, ReduceLROnPlateau, LRSchedulerConfig]]], OptimizerLRSchedulerConfig, None]

Returns:

Any of these 6 options.

Single optimizer.
List or Tuple of optimizers.
Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple lr_scheduler_config).
Dictionary, with an "optimizer" key, and (optionally) a "lr_scheduler" key whose value is a single LR scheduler or lr_scheduler_config.
None - Fit will run without any optimizer.

The lr_scheduler_config is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.

lr_scheduler_config = {
    # REQUIRED: The scheduler instance
    "scheduler": lr_scheduler,
    # The unit of the scheduler's step size, could also be 'step'.
    # 'epoch' updates the scheduler on epoch end whereas 'step'
    # updates it after a optimizer update.
    "interval": "epoch",
    # How many epochs/steps should pass between calls to
    # `scheduler.step()`. 1 corresponds to updating the learning
    # rate after every epoch/step.
    "frequency": 1,
    # Metric to to monitor for schedulers like `ReduceLROnPlateau`
    "monitor": "val_loss",
    # If set to `True`, will enforce that the value specified 'monitor'
    # is available when the scheduler is updated, thus stopping
    # training if not found. If set to `False`, it will only produce a warning
    "strict": True,
    # If using the `LearningRateMonitor` callback to monitor the
    # learning rate progress, this keyword can be used to specify
    # a custom logged name
    "name": None,
}

When there are schedulers in which the .step() method is conditioned on a value, such as the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on.

# The ReduceLROnPlateau scheduler requires a monitor
def configure_optimizers(self):
    optimizer = Adam(...)
    return {
        "optimizer": optimizer,
        "lr_scheduler": {
            "scheduler": ReduceLROnPlateau(optimizer, ...),
            "monitor": "metric_to_track",
            "frequency": "indicates how often the metric is updated"
            # If "monitor" references validation metrics, then "frequency" should be set to a
            # multiple of "trainer.check_val_every_n_epoch".
        },
    }


# In the case of two optimizers, only one using the ReduceLROnPlateau scheduler
def configure_optimizers(self):
    optimizer1 = Adam(...)
    optimizer2 = SGD(...)
    scheduler1 = ReduceLROnPlateau(optimizer1, ...)
    scheduler2 = LambdaLR(optimizer2, ...)
    return (
        {
            "optimizer": optimizer1,
            "lr_scheduler": {
                "scheduler": scheduler1,
                "monitor": "metric_to_track",
            },
        },
        {"optimizer": optimizer2, "lr_scheduler": scheduler2},
    )

Metrics can be made available to monitor by simply logging it using self.log('metric_to_track', metric_val) in your LightningModule.

Note

Some things to know:

Lightning calls .backward() and .step() automatically in case of automatic optimization.
If a learning rate scheduler is specified in configure_optimizers() with key "interval" (default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s .step() method automatically in case of automatic optimization.
If you use 16-bit precision (precision=16), Lightning will automatically handle the optimizer.
If you use torch.optim.LBFGS, Lightning handles the closure function automatically for you.
If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them yourself.
If you need to control how often the optimizer steps, override the optimizer_step() hook.

forward¶

LightningModule.forward(*args, **kwargs)[source]

Same as torch.nn.Module.forward().

Parameters:

*args¶ (Any) – Whatever you decide to pass into the forward method.
**kwargs¶ (Any) – Keyword arguments are also possible.

Return type:

Any

Returns:

Your model’s output

freeze¶

LightningModule.freeze()[source]

Freeze all params for inference.

Example:

model = MyLightningModule(...)
model.freeze()

Return type:: None

log¶

LightningModule.log(name, value, prog_bar=False, logger=None, on_step=None, on_epoch=None, reduce_fx='mean', enable_graph=False, sync_dist=False, sync_dist_group=None, add_dataloader_idx=True, batch_size=None, metric_attribute=None, rank_zero_only=False)[source]

Log a key, value pair.

Example:

self.log('train_loss', loss)

The default behavior per hook is documented here: Automatic Logging.

Parameters:

name¶ (str) – key to log.
value¶ (Union[Metric, Tensor, int, float]) – value to log. Can be a float, Tensor, or a Metric.
prog_bar¶ (bool) – if True logs to the progress bar.
logger¶ (Optional[bool]) – if True logs to the logger.
on_step¶ (Optional[bool]) – if True logs at this step. The default value is determined by the hook. See Automatic Logging for details.
on_epoch¶ (Optional[bool]) – if True logs epoch accumulated metrics. The default value is determined by the hook. See Automatic Logging for details.
reduce_fx¶ (Union[str, Callable]) – reduction function over step values for end of epoch. torch.mean() by default.
enable_graph¶ (bool) – if True, will not auto detach the graph.
sync_dist¶ (bool) – if True, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.
sync_dist_group¶ (Optional[Any]) – the DDP group to sync across.
add_dataloader_idx¶ (bool) – if True, appends the index of the current dataloader to the name (when using multiple dataloaders). If False, user needs to give unique names for each dataloader to not mix the values.
batch_size¶ (Optional[int]) – Current batch_size. This will be directly inferred from the loaded batch, but for some data structures you might need to explicitly provide it.
metric_attribute¶ (Optional[str]) – To restore the metric state, Lightning requires the reference of the torchmetrics.Metric in your model. This is found automatically if it is a model attribute.
rank_zero_only¶ (bool) – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.

Return type:

None

log_dict¶

LightningModule.log_dict(dictionary, prog_bar=False, logger=None, on_step=None, on_epoch=None, reduce_fx='mean', enable_graph=False, sync_dist=False, sync_dist_group=None, add_dataloader_idx=True, batch_size=None, rank_zero_only=False)[source]

Log a dictionary of values at once.

Example:

values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n}
self.log_dict(values)

Parameters:

dictionary¶ (Union[Mapping[str, Union[Metric, Tensor, int, float]], MetricCollection]) – key value pairs. The values can be a float, Tensor, Metric, or MetricCollection.
prog_bar¶ (bool) – if True logs to the progress base.
logger¶ (Optional[bool]) – if True logs to the logger.
on_step¶ (Optional[bool]) – if True logs at this step. None auto-logs for training_step but not validation/test_step. The default value is determined by the hook. See Automatic Logging for details.
on_epoch¶ (Optional[bool]) – if True logs epoch accumulated metrics. None auto-logs for val/test step but not training_step. The default value is determined by the hook. See Automatic Logging for details.
reduce_fx¶ (Union[str, Callable]) – reduction function over step values for end of epoch. torch.mean() by default.
enable_graph¶ (bool) – if True, will not auto-detach the graph
sync_dist¶ (bool) – if True, reduces the metric across GPUs/TPUs. Use with care as this may lead to a significant communication overhead.
sync_dist_group¶ (Optional[Any]) – the ddp group to sync across.
add_dataloader_idx¶ (bool) – if True, appends the index of the current dataloader to the name (when using multiple). If False, user needs to give unique names for each dataloader to not mix values.
batch_size¶ (Optional[int]) – Current batch size. This will be directly inferred from the loaded batch, but some data structures might need to explicitly provide it.
rank_zero_only¶ (bool) – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.

Return type:

None

lr_schedulers¶

LightningModule.lr_schedulers()[source]

Returns the learning rate scheduler(s) that are being used during training. Useful for manual optimization.

Return type:: Union[None, List[Union[LRScheduler, ReduceLROnPlateau]], LRScheduler, ReduceLROnPlateau]
Returns:: A single scheduler, or a list of schedulers in case multiple ones are present, or None if no schedulers were returned in configure_optimizers().

manual_backward¶

LightningModule.manual_backward(loss, *args, **kwargs)[source]

Call this directly from your training_step() when doing optimizations manually. By using this, Lightning can ensure that all the proper scaling gets applied when using mixed precision.

See manual optimization for more examples.

Example:

def training_step(...):
    opt = self.optimizers()
    loss = ...
    opt.zero_grad()
    # automatically applies scaling, etc...
    self.manual_backward(loss)
    opt.step()

Parameters:

loss¶ (Tensor) – The tensor on which to compute gradients. Must have a graph attached.
*args¶ (Any) – Additional positional arguments to be forwarded to backward()
**kwargs¶ (Any) – Additional keyword arguments to be forwarded to backward()

Return type:

None

optimizers¶

LightningModule.optimizers(use_pl_optimizer=True)[source]

Returns the optimizer(s) that are being used during training. Useful for manual optimization.

Parameters:: use_pl_optimizer¶ (bool) – If True, will wrap the optimizer(s) in a LightningOptimizer for automatic handling of precision, profiling, and counting of step calls for proper logging and checkpointing. It specifically wraps the step method and custom optimizers that don’t have this method are not supported.
Return type:: Union[Optimizer, LightningOptimizer, _FabricOptimizer, List[Optimizer], List[LightningOptimizer], List[_FabricOptimizer]]
Returns:: A single optimizer, or a list of optimizers in case multiple ones are present.

print¶

LightningModule.print(*args, **kwargs)[source]

Prints only from process 0. Use this in any distributed mode to log only once.

Parameters:

*args¶ (Any) – The thing to print. The same as for Python’s built-in print function.
**kwargs¶ (Any) – The same as for Python’s built-in print function.

Return type:

None

Example:

def forward(self, x):
    self.print(x, 'in forward')

predict_step¶

LightningModule.predict_step(*args, **kwargs)[source]

Step function called during predict(). By default, it calls forward(). Override to add any processing logic.

The predict_step() is used to scale inference on multi-devices.

To prevent an OOM error, it is possible to use BasePredictionWriter callback to write the predictions to disk or database after each batch or on epoch end.

The BasePredictionWriter should be used while using a spawn based accelerator. This happens for Trainer(strategy="ddp_spawn") or training on 8 TPU cores with Trainer(accelerator="tpu", devices=8) as predictions won’t be returned.

Parameters:

batch¶ – The output of your data iterable, normally a DataLoader.
batch_idx¶ – The index of this batch.
dataloader_idx¶ – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Return type:

Any

Returns:

Predicted output (optional).

Example

class MyModel(LightningModule):

    def predict_step(self, batch, batch_idx, dataloader_idx=0):
        return self(batch)

dm = ...
model = MyModel()
trainer = Trainer(accelerator="gpu", devices=2)
predictions = trainer.predict(model, dm)

save_hyperparameters¶

LightningModule.save_hyperparameters(*args, ignore=None, frame=None, logger=True)

Save arguments to hparams attribute.

Parameters:

args¶ (Any) – single object of dict, NameSpace or OmegaConf or string names or arguments from class __init__
ignore¶ (Union[Sequence[str], str, None]) – an argument name or a list of argument names from class __init__ to be ignored
frame¶ (Optional[FrameType]) – a frame object. Default is None
logger¶ (bool) – Whether to send the hyperparameters to the logger. Default: True

Return type:

None

Example::

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class ManuallyArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # manually assign arguments
...         self.save_hyperparameters('arg1', 'arg3')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class AutomaticArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # equivalent automatic
...         self.save_hyperparameters()
...     def forward(self, *args, **kwargs):
...         ...
>>> model = AutomaticArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg2": abc
"arg3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class SingleArgModel(HyperparametersMixin):
...     def __init__(self, params):
...         super().__init__()
...         # manually assign single argument
...         self.save_hyperparameters(params)
...     def forward(self, *args, **kwargs):
...         ...
>>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))
>>> model.hparams
"p1": 1
"p2": abc
"p3": 3.14

>>> from lightning.pytorch.core.mixins import HyperparametersMixin
>>> class ManuallyArgsModel(HyperparametersMixin):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # pass argument(s) to ignore as a string or in a list
...         self.save_hyperparameters(ignore='arg2')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14

toggle_optimizer¶

LightningModule.toggle_optimizer(optimizer)[source]

Makes sure only the gradients of the current optimizer’s parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.

It works with untoggle_optimizer() to make sure param_requires_grad_state is properly reset.

Parameters:: optimizer¶ (Union[Optimizer, LightningOptimizer]) – The optimizer to toggle.
Return type:: None

test_step¶

LightningModule.test_step(*args, **kwargs)[source]

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¶ – The output of your data iterable, normally a DataLoader.
batch_idx¶ – The index of this batch.
dataloader_idx¶ – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Return type:

Union[Tensor, Mapping[str, Any], None]

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.

to_onnx¶

LightningModule.to_onnx(file_path, input_sample=None, **kwargs)[source]

Saves the model in ONNX format.

Parameters:

file_path¶ (Union[str, Path]) – The path of the file the onnx model should be saved to.
input_sample¶ (Optional[Any]) – An input for tracing. Default: None (Use self.example_input_array)
**kwargs¶ (Any) – Will be passed to torch.onnx.export function.

Return type:

None

Example:

class SimpleModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.l1 = torch.nn.Linear(in_features=64, out_features=4)

    def forward(self, x):
        return torch.relu(self.l1(x.view(x.size(0), -1)

model = SimpleModel()
input_sample = torch.randn(1, 64)
model.to_onnx("export.onnx", input_sample, export_params=True)

to_torchscript¶

LightningModule.to_torchscript(file_path=None, method='script', example_inputs=None, **kwargs)[source]

By default compiles the whole model to a ScriptModule. If you want to use tracing, please provided the argument method='trace' and make sure that either the example_inputs argument is provided, or the model has example_input_array set. If you would like to customize the modules that are scripted you should override this method. In case you want to return multiple modules, we recommend using a dictionary.

Parameters:

file_path¶ (Union[str, Path, None]) – Path where to save the torchscript. Default: None (no file saved).
method¶ (Optional[str]) – Whether to use TorchScript’s script or trace method. Default: ‘script’
example_inputs¶ (Optional[Any]) – An input to be used to do tracing when method is set to ‘trace’. Default: None (uses example_input_array)
**kwargs¶ (Any) – Additional arguments that will be passed to the torch.jit.script() or torch.jit.trace() function.

Note

Requires the implementation of the forward() method.
The exported script will be set to evaluation mode.
It is recommended that you install the latest supported version of PyTorch to use this feature without limitations. See also the torch.jit documentation for supported features.

Example:

class SimpleModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.l1 = torch.nn.Linear(in_features=64, out_features=4)

    def forward(self, x):
        return torch.relu(self.l1(x.view(x.size(0), -1)))

model = SimpleModel()
model.to_torchscript(file_path="model.pt")

torch.jit.save(model.to_torchscript(
    file_path="model_trace.pt", method='trace', example_inputs=torch.randn(1, 64))
)

Return type:: Union[ScriptModule, Dict[str, ScriptModule]]
Returns:: This LightningModule as a torchscript, regardless of whether file_path is defined or not.

training_step¶

LightningModule.training_step(*args, **kwargs)[source]

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

Parameters:

batch¶ – The output of your data iterable, normally a DataLoader.
batch_idx¶ – The index of this batch.
dataloader_idx¶ – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Return type:

Union[Tensor, Mapping[str, Any], None]

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.

unfreeze¶

LightningModule.unfreeze()[source]

Unfreeze all parameters for training.

model = MyLightningModule(...)
model.unfreeze()

Return type:: None

untoggle_optimizer¶

LightningModule.untoggle_optimizer(optimizer)[source]

Resets the state of required gradients that were toggled with toggle_optimizer().

Parameters:: optimizer¶ (Union[Optimizer, LightningOptimizer]) – The optimizer to untoggle.
Return type:: None

validation_step¶

LightningModule.validation_step(*args, **kwargs)[source]

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¶ – The output of your data iterable, normally a DataLoader.
batch_idx¶ – The index of this batch.
dataloader_idx¶ – The index of the dataloader that produced this batch. (only if multiple dataloaders used)

Return type:

Union[Tensor, Mapping[str, Any], None]

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.

Properties¶

These are properties available in a LightningModule.

current_epoch¶

The number of epochs run.

def training_step(self, batch, batch_idx):
    if self.current_epoch == 0:
        ...

device¶

The device the module is on. Use it to keep your code device agnostic.

def training_step(self, batch, batch_idx):
    z = torch.rand(2, 3, device=self.device)

global_rank¶

The global_rank is the index of the current process across all nodes and devices. Lightning will perform some operations such as logging, weight checkpointing only when global_rank=0. You usually do not need to use this property, but it is useful to know how to access it if needed.

def training_step(self, batch, batch_idx):
    if self.global_rank == 0:
        # do something only once across all the nodes
        ...

global_step¶

The number of optimizer steps taken (does not reset each epoch). This includes multiple optimizers (if enabled).

def training_step(self, batch, batch_idx):
    self.logger.experiment.log_image(..., step=self.global_step)

hparams¶

The arguments passed through LightningModule.__init__() and saved by calling save_hyperparameters() could be accessed by the hparams attribute.

def __init__(self, learning_rate):
    self.save_hyperparameters()


def configure_optimizers(self):
    return Adam(self.parameters(), lr=self.hparams.learning_rate)

logger¶

The current logger being used (tensorboard or other supported logger)

def training_step(self, batch, batch_idx):
    # the generic logger (same no matter if tensorboard or other supported logger)
    self.logger

    # the particular logger
    tensorboard_logger = self.logger.experiment

loggers¶

The list of loggers currently being used by the Trainer.

def training_step(self, batch, batch_idx):
    # List of Logger objects
    loggers = self.loggers
    for logger in loggers:
        logger.log_metrics({"foo": 1.0})

local_rank¶

The local_rank is the index of the current process across all the devices for the current node. You usually do not need to use this property, but it is useful to know how to access it if needed. For example, if using 10 machines (or nodes), the GPU at index 0 on each machine has local_rank = 0.

def training_step(self, batch, batch_idx):
    if self.local_rank == 0:
        # do something only once across each node
        ...

precision¶

The type of precision used:

def training_step(self, batch, batch_idx):
    if self.precision == "16-true":
        ...

trainer¶

Pointer to the trainer

def training_step(self, batch, batch_idx):
    max_steps = self.trainer.max_steps
    any_flag = self.trainer.any_flag

prepare_data_per_node¶

If set to True will call prepare_data() on LOCAL_RANK=0 for every node. If set to False will only call from NODE_RANK=0, LOCAL_RANK=0.

class LitModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.prepare_data_per_node = True

automatic_optimization¶

When set to False, Lightning does not automate the optimization process. This means you are responsible for handling your optimizers. However, we do take care of precision and any accelerators used.

See manual optimization for details.

def __init__(self):
    self.automatic_optimization = False


def training_step(self, batch, batch_idx):
    opt = self.optimizers(use_pl_optimizer=True)

    loss = ...
    opt.zero_grad()
    self.manual_backward(loss)
    opt.step()

Manual optimization is most useful for research topics like reinforcement learning, sparse coding, and GAN research. It is required when you are using 2+ optimizers because with automatic optimization, you can only use one optimizer.

def __init__(self):
    self.automatic_optimization = False


def training_step(self, batch, batch_idx):
    # access your optimizers with use_pl_optimizer=False. Default is True
    opt_a, opt_b = self.optimizers(use_pl_optimizer=True)

    gen_loss = ...
    opt_a.zero_grad()
    self.manual_backward(gen_loss)
    opt_a.step()

    disc_loss = ...
    opt_b.zero_grad()
    self.manual_backward(disc_loss)
    opt_b.step()

example_input_array¶

Set and access example_input_array, which basically represents a single batch.

def __init__(self):
    self.example_input_array = ...
    self.generator = ...


def on_train_epoch_end(self):
    # generate some images using the example_input_array
    gen_images = self.generator(self.example_input_array)

Hooks¶

This is the pseudocode to describe the structure of fit(). The inputs and outputs of each function are not represented for simplicity. Please check each function’s API reference for more information.

def fit(self):
    if global_rank == 0:
        # prepare data is called on GLOBAL_ZERO only
        prepare_data()

    configure_callbacks()

    with parallel(devices):
        # devices can be GPUs, TPUs, ...
        train_on_device(model)


def train_on_device(model):
    # called PER DEVICE
    setup("fit")
    configure_optimizers()
    on_fit_start()

    # the sanity check runs here

    on_train_start()
    for epoch in epochs:
        fit_loop()
    on_train_end()

    on_fit_end()
    teardown("fit")


def fit_loop():
    model.train()
    torch.set_grad_enabled(True)

    on_train_epoch_start()

    for batch in train_dataloader():
        on_train_batch_start()

        on_before_batch_transfer()
        transfer_batch_to_device()
        on_after_batch_transfer()

        out = training_step()

        on_before_zero_grad()
        optimizer_zero_grad()

        on_before_backward()
        backward()
        on_after_backward()

        on_before_optimizer_step()
        configure_gradient_clipping()
        optimizer_step()

        on_train_batch_end(out, batch, batch_idx)

        if should_check_val:
            val_loop()

    on_train_epoch_end()


def val_loop():
    on_validation_model_eval()  # calls `model.eval()`
    torch.set_grad_enabled(False)

    on_validation_start()
    on_validation_epoch_start()

    for batch_idx, batch in enumerate(val_dataloader()):
        on_validation_batch_start(batch, batch_idx)

        batch = on_before_batch_transfer(batch)
        batch = transfer_batch_to_device(batch)
        batch = on_after_batch_transfer(batch)

        out = validation_step(batch, batch_idx)

        on_validation_batch_end(out, batch, batch_idx)

    on_validation_epoch_end()
    on_validation_end()

    # set up for train
    on_validation_model_train()  # calls `model.train()`
    torch.set_grad_enabled(True)

backward¶

LightningModule.backward(loss, *args, **kwargs)[source]

Called to perform backward on the loss returned in training_step(). Override this hook with your own implementation if you need to.

Parameters:: loss¶ (Tensor) – The loss tensor returned by training_step(). If gradient accumulation is used, the loss here holds the normalized value (scaled by 1 / accumulation steps).
Return type:: None

Example:

def backward(self, loss):
    loss.backward()

on_before_backward¶

LightningModule.on_before_backward(loss)

Called before loss.backward().

Parameters:: loss¶ (Tensor) – Loss divided by number of batches for gradient accumulation and scaled if using AMP.
Return type:: None

on_after_backward¶

LightningModule.on_after_backward(): Called after loss.backward() and before optimizers are stepped. :rtype: None

Note

If using native AMP, the gradients will not be unscaled at this point. Use the on_before_optimizer_step if you need the unscaled gradients.

on_before_zero_grad¶

LightningModule.on_before_zero_grad(optimizer)

Called after training_step() and before optimizer.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.

This is where it is called:

for optimizer in optimizers:
    out = training_step(...)

    model.on_before_zero_grad(optimizer) # < ---- called here
    optimizer.zero_grad()

    backward()

Parameters:: optimizer¶ (Optimizer) – The optimizer for which grads should be zeroed.
Return type:: None

on_fit_start¶

LightningModule.on_fit_start()

Called at the very beginning of fit.

If on DDP it is called on every process

Return type:: None

on_fit_end¶

LightningModule.on_fit_end()

Called at the very end of fit.

If on DDP it is called on every process

Return type:: None

on_load_checkpoint¶

LightningModule.on_load_checkpoint(checkpoint)

Called by Lightning to restore your model. If you saved something with on_save_checkpoint() this is your chance to restore this.

Parameters:: checkpoint¶ (Dict[str, Any]) – Loaded checkpoint
Return type:: None

Example:

def on_load_checkpoint(self, checkpoint):
    # 99% of the time you don't need to implement this method
    self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']

Note

Lightning auto-restores global step, epoch, and train state including amp scaling. There is no need for you to restore anything regarding training.

on_save_checkpoint¶

LightningModule.on_save_checkpoint(checkpoint)

Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.

Parameters:: checkpoint¶ (Dict[str, Any]) – The full checkpoint dictionary before it gets dumped to a file. Implementations of this hook can insert additional data into this dictionary.
Return type:: None

Example:

def on_save_checkpoint(self, checkpoint):
    # 99% of use cases you don't need to implement this method
    checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object

Note

Lightning saves all aspects of training (epoch, global step, etc…) including amp scaling. There is no need for you to store anything about training.

load_from_checkpoint¶

LightningModule.load_from_checkpoint(map_location=None, hparams_file=None, strict=True, **kwargs)[source]

Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint it stores the arguments passed to __init__ in the checkpoint under "hyper_parameters".

Any arguments specified through **kwargs will override args stored in "hyper_parameters".

Parameters:

checkpoint_path¶ (Union[str, Path, IO]) – Path to checkpoint. This can also be a URL, or file-like object
map_location¶ (Union[device, str, int, Callable[[UntypedStorage, str], Optional[UntypedStorage]], Dict[Union[device, str, int], Union[device, str, int]], None]) – If your checkpoint saved a GPU model and you now load on CPUs or a different number of GPUs, use this to map to the new setup. The behaviour is the same as in torch.load().
hparams_file¶ (Union[str, Path, None]) –
Optional path to a .yaml or .csv file with hierarchical structure as in this example:
```
drop_prob: 0.2
dataloader:
    batch_size: 32
```
You most likely won’t need this since Lightning will always save the hyperparameters to the checkpoint. However, if your checkpoint weights don’t have the hyperparameters saved, use this method to pass in a .yaml file with the hparams you’d like to use. These will be converted into a dict and passed into your LightningModule for use.

If your model’s hparams argument is Namespace and .yaml file has hierarchical structure, you need to refactor your model to treat hparams as dict.
strict¶ (bool) – Whether to strictly enforce that the keys in checkpoint_path match the keys returned by this module’s state dict.
**kwargs¶ (Any) – Any extra keyword args needed to init the model. Can also be used to override saved hyperparameter values.

Return type:

Self

Returns:

LightningModule instance with loaded weights and hyperparameters (if available).

Note

load_from_checkpoint is a class method. You should use your LightningModule class to call it instead of the LightningModule instance, or a TypeError will be raised.

Example:

# load weights without mapping ...
model = MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')

# or load weights mapping all weights from GPU 1 to GPU 0 ...
map_location = {'cuda:1':'cuda:0'}
model = MyLightningModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    map_location=map_location
)

# or load weights and hyperparameters from separate files.
model = MyLightningModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    hparams_file='/path/to/hparams_file.yaml'
)

# override some of the params with new values
model = MyLightningModule.load_from_checkpoint(
    PATH,
    num_layers=128,
    pretrained_ckpt_path=NEW_PATH,
)

# predict
pretrained_model.eval()
pretrained_model.freeze()
y_hat = pretrained_model(x)

on_train_start¶

LightningModule.on_train_start()

Called at the beginning of training after sanity check.

Return type:: None

on_train_end¶

LightningModule.on_train_end()

Called at the end of training before logger experiment is closed.

Return type:: None

on_validation_start¶

LightningModule.on_validation_start()

Called at the beginning of validation.

Return type:: None

on_validation_end¶

LightningModule.on_validation_end()

Called at the end of validation.

Return type:: None

on_test_batch_start¶

LightningModule.on_test_batch_start(batch, batch_idx, dataloader_idx=0)

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

Parameters:

batch¶ (Any) – The batched data as it is returned by the test DataLoader.
batch_idx¶ (int) – the index of the batch
dataloader_idx¶ (int) – the index of the dataloader

Return type:

None

on_test_batch_end¶

LightningModule.on_test_batch_end(outputs, batch, batch_idx, dataloader_idx=0)

Called in the test loop after the batch.

Parameters:

outputs¶ (Union[Tensor, Mapping[str, Any], None]) – The outputs of test_step(x)
batch¶ (Any) – The batched data as it is returned by the test DataLoader.
batch_idx¶ (int) – the index of the batch
dataloader_idx¶ (int) – the index of the dataloader

Return type:

None

on_test_epoch_start¶

LightningModule.on_test_epoch_start()

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

Return type:: None

on_test_epoch_end¶

LightningModule.on_test_epoch_end()

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

Return type:: None

on_test_start¶

LightningModule.on_test_start()

Called at the beginning of testing.

Return type:: None

on_test_end¶

LightningModule.on_test_end()

Called at the end of testing.

Return type:: None

on_predict_batch_start¶

LightningModule.on_predict_batch_start(batch, batch_idx, dataloader_idx=0)

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

Parameters:

batch¶ (Any) – The batched data as it is returned by the test DataLoader.
batch_idx¶ (int) – the index of the batch
dataloader_idx¶ (int) – the index of the dataloader

Return type:

None

on_predict_batch_end¶

LightningModule.on_predict_batch_end(outputs, batch, batch_idx, dataloader_idx=0)

Called in the predict loop after the batch.

Parameters:

outputs¶ (Optional[Any]) – The outputs of predict_step(x)
batch¶ (Any) – The batched data as it is returned by the prediction DataLoader.
batch_idx¶ (int) – the index of the batch
dataloader_idx¶ (int) – the index of the dataloader

Return type:

None

on_predict_epoch_start¶

LightningModule.on_predict_epoch_start()

Called at the beginning of predicting.

Return type:: None

on_predict_epoch_end¶

LightningModule.on_predict_epoch_end()

Called at the end of predicting.

Return type:: None

on_predict_start¶

LightningModule.on_predict_start()

Called at the beginning of predicting.

Return type:: None

on_predict_end¶

LightningModule.on_predict_end()

Called at the end of predicting.

Return type:: None

on_train_batch_start¶

LightningModule.on_train_batch_start(batch, batch_idx)

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

If you return -1 here, you will skip training for the rest of the current epoch.

Parameters:

batch¶ (Any) – The batched data as it is returned by the training DataLoader.
batch_idx¶ (int) – the index of the batch

Return type:

Optional[int]

on_train_batch_end¶

LightningModule.on_train_batch_end(outputs, batch, batch_idx)

Called in the training loop after the batch.

Parameters:

outputs¶ (Union[Tensor, Mapping[str, Any], None]) – The outputs of training_step(x)
batch¶ (Any) – The batched data as it is returned by the training DataLoader.
batch_idx¶ (int) – the index of the batch

Return type:

None

on_train_epoch_start¶

LightningModule.on_train_epoch_start()

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

Return type:: None

on_train_epoch_end¶

LightningModule.on_train_epoch_end()

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

To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the LightningModule and access them in this hook:

class MyLightningModule(L.LightningModule):
    def __init__(self):
        super().__init__()
        self.training_step_outputs = []

    def training_step(self):
        loss = ...
        self.training_step_outputs.append(loss)
        return loss

    def on_train_epoch_end(self):
        # do something with all training_step outputs, for example:
        epoch_mean = torch.stack(self.training_step_outputs).mean()
        self.log("training_epoch_mean", epoch_mean)
        # free up the memory
        self.training_step_outputs.clear()

Return type:: None

on_validation_batch_start¶

LightningModule.on_validation_batch_start(batch, batch_idx, dataloader_idx=0)

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

Parameters:

batch¶ (Any) – The batched data as it is returned by the validation DataLoader.
batch_idx¶ (int) – the index of the batch
dataloader_idx¶ (int) – the index of the dataloader

Return type:

None

on_validation_batch_end¶

LightningModule.on_validation_batch_end(outputs, batch, batch_idx, dataloader_idx=0)

Called in the validation loop after the batch.

Parameters:

outputs¶ (Union[Tensor, Mapping[str, Any], None]) – The outputs of validation_step(x)
batch¶ (Any) – The batched data as it is returned by the validation DataLoader.
batch_idx¶ (int) – the index of the batch
dataloader_idx¶ (int) – the index of the dataloader

Return type:

None

on_validation_epoch_start¶

LightningModule.on_validation_epoch_start()

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

Return type:: None

on_validation_epoch_end¶

LightningModule.on_validation_epoch_end()

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

Return type:: None

configure_model¶

LightningModule.configure_model()

Hook to create modules in a strategy and precision aware context.

This is particularly useful for when using sharded strategies (FSDP and DeepSpeed), where we’d like to shard the model instantly to save memory and initialization time. For non-sharded strategies, you can choose to override this hook or to initialize your model under the init_module() context manager.

This hook is called during each of fit/val/test/predict stages in the same process, so ensure that implementation of this hook is idempotent.

Return type:: None

on_validation_model_eval¶

LightningModule.on_validation_model_eval()

Sets the model to eval during the val loop.

Return type:: None

on_validation_model_train¶

LightningModule.on_validation_model_train()

Sets the model to train during the val loop.

Return type:: None

on_test_model_eval¶

LightningModule.on_test_model_eval()

Sets the model to eval during the test loop.

Return type:: None

on_test_model_train¶

LightningModule.on_test_model_train()

Sets the model to train during the test loop.

Return type:: None

on_before_optimizer_step¶

LightningModule.on_before_optimizer_step(optimizer)

Called before optimizer.step().

If using gradient accumulation, the hook is called once the gradients have been accumulated. See: accumulate_grad_batches.

If using AMP, the loss will be unscaled before calling this hook. See these docs for more information on the scaling of gradients.

If clipping gradients, the gradients will not have been clipped yet.

Parameters:: optimizer¶ (Optimizer) – Current optimizer being used.
Return type:: None

Example:

def on_before_optimizer_step(self, optimizer):
    # example to inspect gradient information in tensorboard
    if self.trainer.global_step % 25 == 0:  # don't make the tf file huge
        for k, v in self.named_parameters():
            self.logger.experiment.add_histogram(
                tag=k, values=v.grad, global_step=self.trainer.global_step
            )

configure_gradient_clipping¶

LightningModule.configure_gradient_clipping(optimizer, gradient_clip_val=None, gradient_clip_algorithm=None)[source]

Perform gradient clipping for the optimizer parameters. Called before optimizer_step().

Parameters:

optimizer¶ (Optimizer) – Current optimizer being used.
gradient_clip_val¶ (Union[int, float, None]) – The value at which to clip gradients. By default, value passed in Trainer will be available here.
gradient_clip_algorithm¶ (Optional[str]) – The gradient clipping algorithm to use. By default, value passed in Trainer will be available here.

Return type:

None

Example:

def configure_gradient_clipping(self, optimizer, gradient_clip_val, gradient_clip_algorithm):
    # Implement your own custom logic to clip gradients
    # You can call `self.clip_gradients` with your settings:
    self.clip_gradients(
        optimizer,
        gradient_clip_val=gradient_clip_val,
        gradient_clip_algorithm=gradient_clip_algorithm
    )

optimizer_step¶

LightningModule.optimizer_step(epoch, batch_idx, optimizer, optimizer_closure=None)[source]

Override this method to adjust the default way the Trainer calls the optimizer.

By default, Lightning calls step() and zero_grad() as shown in the example. This method (and zero_grad()) won’t be called during the accumulation phase when Trainer(accumulate_grad_batches != 1). Overriding this hook has no benefit with manual optimization.

Parameters:

epoch¶ (int) – Current epoch
batch_idx¶ (int) – Index of current batch
optimizer¶ (Union[Optimizer, LightningOptimizer]) – A PyTorch optimizer
optimizer_closure¶ (Optional[Callable[[], Any]]) – The optimizer closure. This closure must be executed as it includes the calls to training_step(), optimizer.zero_grad(), and backward().

Return type:

None

Examples:

# DEFAULT
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_closure):
    optimizer.step(closure=optimizer_closure)

# Learning rate warm-up
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_closure):
    # update params
    optimizer.step(closure=optimizer_closure)

    # manually warm up lr without a scheduler
    if self.trainer.global_step < 500:
        lr_scale = min(1.0, float(self.trainer.global_step + 1) / 500.0)
        for pg in optimizer.param_groups:
            pg["lr"] = lr_scale * self.learning_rate

optimizer_zero_grad¶

LightningModule.optimizer_zero_grad(epoch, batch_idx, optimizer)[source]

Override this method to change the default behaviour of optimizer.zero_grad().

Parameters:

epoch¶ (int) – Current epoch
batch_idx¶ (int) – Index of current batch
optimizer¶ (Optimizer) – A PyTorch optimizer

Return type:

None

Examples:

# DEFAULT
def optimizer_zero_grad(self, epoch, batch_idx, optimizer):
    optimizer.zero_grad()

# Set gradients to `None` instead of zero to improve performance (not required on `torch>=2.0.0`).
def optimizer_zero_grad(self, epoch, batch_idx, optimizer):
    optimizer.zero_grad(set_to_none=True)

See torch.optim.Optimizer.zero_grad() for the explanation of the above example.

prepare_data¶

LightningModule.prepare_data()

Use this to download and prepare data. Downloading and saving data with multiple processes (distributed settings) will result in corrupted data. Lightning ensures this method is called only within a single process, so you can safely add your downloading logic within. :rtype: None

Warning

DO NOT set state to the model (use setup instead) since this is NOT called on every device

Example:

def prepare_data(self):
    # good
    download_data()
    tokenize()
    etc()

    # bad
    self.split = data_split
    self.some_state = some_other_state()

In a distributed environment, prepare_data can be called in two ways (using prepare_data_per_node)

Once per node. This is the default and is only called on LOCAL_RANK=0.
Once in total. Only called on GLOBAL_RANK=0.

Example:

# DEFAULT
# called once per node on LOCAL_RANK=0 of that node
class LitDataModule(LightningDataModule):
    def __init__(self):
        super().__init__()
        self.prepare_data_per_node = True


# call on GLOBAL_RANK=0 (great for shared file systems)
class LitDataModule(LightningDataModule):
    def __init__(self):
        super().__init__()
        self.prepare_data_per_node = False

This is called before requesting the dataloaders:

model.prepare_data()
initialize_distributed()
model.setup(stage)
model.train_dataloader()
model.val_dataloader()
model.test_dataloader()
model.predict_dataloader()

setup¶

LightningModule.setup(stage)

Called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when you need to build models dynamically or adjust something about them. This hook is called on every process when using DDP.

Parameters:: stage¶ (str) – either 'fit', 'validate', 'test', or 'predict'
Return type:: None

Example:

class LitModel(...):
    def __init__(self):
        self.l1 = None

    def prepare_data(self):
        download_data()
        tokenize()

        # don't do this
        self.something = else

    def setup(self, stage):
        data = load_data(...)
        self.l1 = nn.Linear(28, data.num_classes)

teardown¶

LightningModule.teardown(stage)

Called at the end of fit (train + validate), validate, test, or predict.

Parameters:: stage¶ (str) – either 'fit', 'validate', 'test', or 'predict'
Return type:: None

train_dataloader¶

LightningModule.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: :rtype: Any

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.

val_dataloader¶

LightningModule.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(). :rtype: Any

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.

test_dataloader¶

LightningModule.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: :rtype: Any

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.

predict_dataloader¶

LightningModule.predict_dataloader()

An iterable or collection of iterables specifying prediction samples.

For more information about multiple dataloaders, see this section.

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

predict()
prepare_data()
setup()

Note

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

Return type:: Any
Returns:: A torch.utils.data.DataLoader or a sequence of them specifying prediction samples.

transfer_batch_to_device¶

LightningModule.transfer_batch_to_device(batch, device, dataloader_idx)

Override this hook if your DataLoader returns tensors wrapped in a custom data structure.

The data types listed below (and any arbitrary nesting of them) are supported out of the box:

torch.Tensor or anything that implements .to(…)
list
dict
tuple

For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, …).

Note

This hook should only transfer the data and not modify it, nor should it move the data to any other device than the one passed in as argument (unless you know what you are doing). To check the current state of execution of this hook you can use self.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

batch¶ (Any) – A batch of data that needs to be transferred to a new device.
device¶ (device) – The target device as defined in PyTorch.
dataloader_idx¶ (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A reference to the data on the new device.

Example:

def transfer_batch_to_device(self, batch, device, dataloader_idx):
    if isinstance(batch, CustomBatch):
        # move all tensors in your custom data structure to the device
        batch.samples = batch.samples.to(device)
        batch.targets = batch.targets.to(device)
    elif dataloader_idx == 0:
        # skip device transfer for the first dataloader or anything you wish
        pass
    else:
        batch = super().transfer_batch_to_device(batch, device, dataloader_idx)
    return batch

Raises:: MisconfigurationException – If using IPUs, Trainer(accelerator='ipu').

on_before_batch_transfer¶

LightningModule.on_before_batch_transfer(batch, dataloader_idx)

Override to alter or apply batch augmentations to your batch before it is transferred to the device.

Note

To check the current state of execution of this hook you can use self.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

batch¶ (Any) – A batch of data that needs to be altered or augmented.
dataloader_idx¶ (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A batch of data

Example:

def on_before_batch_transfer(self, batch, dataloader_idx):
    batch['x'] = transforms(batch['x'])
    return batch

on_after_batch_transfer¶

LightningModule.on_after_batch_transfer(batch, dataloader_idx)

Override to alter or apply batch augmentations to your batch after it is transferred to the device.

Note

To check the current state of execution of this hook you can use self.trainer.training/testing/validating/predicting so that you can add different logic as per your requirement.

Parameters:

batch¶ (Any) – A batch of data that needs to be altered or augmented.
dataloader_idx¶ (int) – The index of the dataloader to which the batch belongs.

Return type:

Any

Returns:

A batch of data

Example:

def on_after_batch_transfer(self, batch, dataloader_idx):
    batch['x'] = gpu_transforms(batch['x'])
    return batch

Raises:: MisconfigurationException – If using IPUs, Trainer(accelerator='ipu').