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LightningModule

A LightningModule organizes your PyTorch code into 5 sections

  • Computations (init).

  • Train loop (training_step)

  • Validation loop (validation_step)

  • Test loop (test_step)

  • Optimizers (configure_optimizers)



Notice a few things.

  1. It’s the SAME code.

  2. The PyTorch code IS NOT abstracted - just organized.

  3. All the other code that’s not in the LightningModule has been automated for you by the trainer.


net = Net()
trainer = Trainer()
trainer.fit(net)
  1. There are no .cuda() or .to() calls… 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.type_as(x)
  1. There are no samplers for distributed, Lightning also does this for you.


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

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


Minimal Example

Here are the only required methods.

>>> import pytorch_lightning as pl
>>> class LitModel(pl.LightningModule):
...
...     def __init__(self):
...         super().__init__()
...         self.l1 = torch.nn.Linear(28 * 28, 10)
...
...     def forward(self, x):
...         return torch.relu(self.l1(x.view(x.size(0), -1)))
...
...     def training_step(self, batch, batch_idx):
...         x, y = batch
...         y_hat = self(x)
...         loss = F.cross_entropy(y_hat, y)
...         return loss
...
...     def configure_optimizers(self):
...         return torch.optim.Adam(self.parameters(), lr=0.02)

Which you can train by doing:

train_loader = DataLoader(MNIST(os.getcwd(), download=True, transform=transforms.ToTensor()))
trainer = pl.Trainer()
model = LitModel()

trainer.fit(model, train_loader)

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

Name

Description

init

Define computations here

forward

Use for inference only (separate from training_step)

training_step

the full training loop

validation_step

the full validation loop

test_step

the full test loop

configure_optimizers

define optimizers and LR schedulers


Training

Training loop

To add a training loop use the training_step method

class LitClassifier(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

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

# put model in train mode
model.train()
torch.set_grad_enabled(True)

outs = []
for batch in train_dataloader:
    # forward
    out = training_step(val_batch)

    # backward
    loss.backward()

    # apply and clear grads
    optimizer.step()
    optimizer.zero_grad()

Training epoch-level metrics

If you want to calculate epoch-level metrics and log them, use the .log method

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

    # 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 object automatically reduces the requested metrics across the full epoch. Here’s the pseudocode of what it does under the hood:

outs = []
for batch in train_dataloader:
    # forward
    out = training_step(val_batch)

    # backward
    loss.backward()

    # apply and clear grads
    optimizer.step()
    optimizer.zero_grad()

epoch_metric = torch.mean(torch.stack([x['train_loss'] for x in outs]))

Train epoch-level operations

If you need to do something with all the outputs of each training_step, override training_epoch_end yourself.

def training_step(self, batch, batch_idx):
    x, y = batch
    y_hat = self.model(x)
    loss = F.cross_entropy(y_hat, y)
    preds = ...
    return {'loss': loss, 'other_stuff': preds}

def training_epoch_end(self, training_step_outputs):
   for pred in training_step_outputs:
       # do something

The matching pseudocode is:

outs = []
for batch in train_dataloader:
    # forward
    out = training_step(val_batch)

    # backward
    loss.backward()

    # apply and clear grads
    optimizer.step()
    optimizer.zero_grad()

training_epoch_end(outs)

Training with DataParallel

When training using a distributed_backend that splits data from each batch across GPUs, sometimes you might need to aggregate them on the master GPU for processing (dp, or ddp2).

In this case, implement the training_step_end method

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

def training_step_end(self, batch_parts):
    gpu_0_prediction = batch_parts.pred[0]['pred']
    gpu_1_prediction = batch_parts.pred[1]['pred']

    # do something with both outputs
    return (batch_parts[0]['loss'] + batch_parts[1]['loss']) / 2

def training_epoch_end(self, training_step_outputs):
   for out in training_step_outputs:
       # do something with preds

The full pseudocode that lighting does under the hood is:

outs = []
for train_batch in train_dataloader:
    batches = split_batch(train_batch)
    dp_outs = []
    for sub_batch in batches:
        # 1
        dp_out = training_step(sub_batch)
        dp_outs.append(dp_out)

    # 2
    out = training_step_end(dp_outs)
    outs.append(out)

# do something with the outputs for all batches
# 3
training_epoch_end(outs)

Validation loop

To add a validation loop, override the validation_step method of the LightningModule:

class LitModel(pl.LightningModule):
    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.model(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('val_loss', loss)

Under the hood, Lightning does the following:

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

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

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

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

Validation epoch-level metrics

If you need to do something with all the outputs of each validation_step, override validation_epoch_end.

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

def validation_epoch_end(self, validation_step_outputs):
   for pred in validation_step_outputs:
       # do something with a pred

Validating with DataParallel

When training using a distributed_backend that splits data from each batch across GPUs, sometimes you might need to aggregate them on the master GPU for processing (dp, or ddp2).

In this case, implement the validation_step_end method

def validation_step(self, batch, batch_idx):
    x, y = batch
    y_hat = self.model(x)
    loss = F.cross_entropy(y_hat, y)
    pred = ...
    return {'loss': loss, 'pred': pred}

def validation_step_end(self, batch_parts):
    gpu_0_prediction = batch_parts.pred[0]['pred']
    gpu_1_prediction = batch_parts.pred[1]['pred']

    # do something with both outputs
    return (batch_parts[0]['loss'] + batch_parts[1]['loss']) / 2

def validation_epoch_end(self, validation_step_outputs):
   for out in validation_step_outputs:
       # do something with preds

The full pseudocode that lighting does under the hood is:

outs = []
for batch in dataloader:
    batches = split_batch(batch)
    dp_outs = []
    for sub_batch in batches:
        # 1
        dp_out = validation_step(sub_batch)
        dp_outs.append(dp_out)

    # 2
    out = validation_step_end(dp_outs)
    outs.append(out)

# do something with the outputs for all batches
# 3
validation_epoch_end(outs)

Test loop

The process for adding a test loop is the same as the process for adding a validation loop. Please refer to the section above for details.

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

model = Model()
trainer = Trainer()
trainer.fit()

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

There are two ways to call test():

# call after training
trainer = Trainer()
trainer.fit(model)

# automatically auto-loads the best weights
trainer.test(test_dataloaders=test_dataloader)

# or call with pretrained model
model = MyLightningModule.load_from_checkpoint(PATH)
trainer = Trainer()
trainer.test(model, test_dataloaders=test_dataloader)

Inference

For research, LightningModules are best structured as systems.

import pytorch_lightning as pl
import torch
from torch import nn

class Autoencoder(pl.LightningModule):

     def __init__(self, latent_dim=2):
        super().__init__()
        self.encoder = nn.Sequential(nn.Linear(28 * 28, 256), nn.ReLU(), nn.Linear(256, latent_dim))
        self.decoder = nn.Sequential(nn.Linear(latent_dim, 256), nn.ReLU(), nn.Linear(256, 28 * 28))

     def training_step(self, batch, batch_idx):
        x, _ = batch

        # encode
        x = x.view(x.size(0), -1)
        z = self.encoder(x)

        # decode
        recons = self.decoder(z)

        # reconstruction
        reconstruction_loss = nn.functional.mse_loss(recons, x)
        return reconstruction_loss

     def validation_step(self, batch, batch_idx):
        x, _ = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        recons = self.decoder(z)
        reconstruction_loss = nn.functional.mse_loss(recons, x)
        self.log('val_reconstruction', reconstruction_loss)

     def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.0002)

Which can be trained like this:

autoencoder = Autoencoder()
trainer = pl.Trainer(gpus=1)
trainer.fit(autoencoder, train_dataloader, val_dataloader)

This simple model generates examples that look like this (the encoders and decoders are too weak)

https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/ae_docs.png

The methods above are part of the lightning interface:

  • training_step

  • validation_step

  • test_step

  • configure_optimizers

Note that in this case, the train loop and val loop are exactly the same. We can of course reuse this code.

class Autoencoder(pl.LightningModule):

     def __init__(self, latent_dim=2):
        super().__init__()
        self.encoder = nn.Sequential(nn.Linear(28 * 28, 256), nn.ReLU(), nn.Linear(256, latent_dim))
        self.decoder = nn.Sequential(nn.Linear(latent_dim, 256), nn.ReLU(), nn.Linear(256, 28 * 28))

     def training_step(self, batch, batch_idx):
        loss = self.shared_step(batch)

        return loss

     def validation_step(self, batch, batch_idx):
        loss = self.shared_step(batch)
        self.log('val_loss', loss)

     def shared_step(self, batch):
        x, _ = batch

        # encode
        x = x.view(x.size(0), -1)
        z = self.encoder(x)

        # decode
        recons = self.decoder(z)

        # loss
        return nn.functional.mse_loss(recons, x)

     def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.0002)

We create a new method called shared_step that all loops can use. This method name is arbitrary and NOT reserved.

Inference in research

In the case where we want to perform inference with the system we can add a forward method to the LightningModule.

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

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

Inference in production

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

import pytorch_lightning as pl
from pytorch_lightning.metrics import functional as FM

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):
        x, y = batch
        y_hat = self.model(x)
        loss = F.cross_entropy(y_hat, y)
        acc = FM.accuracy(y_hat, y)

        # loss is tensor. The Checkpoint Callback is monitoring 'checkpoint_on'
        metrics = {'val_acc': acc, 'val_loss': loss}
        self.log_dict(metrics)
        return metrics

     def test_step(self, batch, batch_idx):
        metrics = self.validation_step(batch, batch_idx)
        metrics = {'test_acc': metrics['val_acc'], 'test_loss': metrics['val_loss']}
        self.log_dict(metrics)

     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(gpus=2)
    trainer.fit(task, train_dataloader, 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.

  • You can export to onnx.

  • Or trace using Jit.

  • or run in the python runtime.

task = ClassificationTask(model)

trainer = Trainer(gpus=2)
trainer.fit(task, train_dataloader, val_dataloader)

# use model after training or load weights and drop into the production system
model.eval()
y_hat = model(x)

LightningModule API

Methods

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.

Returns

Any of these 6 options.

  • Single optimizer.

  • List or Tuple - List of optimizers.

  • Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).

  • Dictionary, with an ‘optimizer’ key, and (optionally) a ‘lr_scheduler’ key which value is a single LR scheduler or lr_dict.

  • Tuple of dictionaries as described, with an optional ‘frequency’ key.

  • None - Fit will run without any optimizer.

Note

The ‘frequency’ value is an int corresponding to the number of sequential batches optimized with the specific optimizer. It should be given to none or to all of the optimizers. There is a difference between passing multiple optimizers in a list, and passing multiple optimizers in dictionaries with a frequency of 1: In the former case, all optimizers will operate on the given batch in each optimization step. In the latter, only one optimizer will operate on the given batch at every step.

The lr_dict is a dictionary which contains scheduler and its associated configuration. It has five keys. The default configuration is shown below.

{
    'scheduler': lr_scheduler, # The LR schduler
    'interval': 'epoch', # The unit of the scheduler's step size
    'frequency': 1, # The frequency of the scheduler
    'reduce_on_plateau': False, # For ReduceLROnPlateau scheduler
    'monitor': 'val_loss' # Metric to monitor
}

If user only provides LR schedulers, then their configuration will set to default as shown above.

Examples

# most cases
def configure_optimizers(self):
    opt = Adam(self.parameters(), lr=1e-3)
    return opt

# multiple optimizer case (e.g.: GAN)
def configure_optimizers(self):
    generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
    disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
    return generator_opt, disriminator_opt

# example with learning rate schedulers
def configure_optimizers(self):
    generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
    disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
    discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10)
    return [generator_opt, disriminator_opt], [discriminator_sched]

# example with step-based learning rate schedulers
def configure_optimizers(self):
    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
    dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
    gen_sched = {'scheduler': ExponentialLR(gen_opt, 0.99),
                 'interval': 'step'}  # called after each training step
    dis_sched = CosineAnnealing(discriminator_opt, T_max=10) # called every epoch
    return [gen_opt, dis_opt], [gen_sched, dis_sched]

# example with optimizer frequencies
# see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1
# https://arxiv.org/abs/1704.00028
def configure_optimizers(self):
    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
    dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
    n_critic = 5
    return (
        {'optimizer': dis_opt, 'frequency': n_critic},
        {'optimizer': gen_opt, 'frequency': 1}
    )

Note

Some things to know:

  • Lightning calls .backward() and .step() on each optimizer and learning rate scheduler as needed.

  • If you use 16-bit precision (precision=16), Lightning will automatically handle the optimizers for you.

  • If you use multiple optimizers, training_step() will have an additional optimizer_idx parameter.

  • If you use LBFGS Lightning handles the closure function automatically for you.

  • If you use multiple optimizers, gradients will be calculated only for the parameters of current optimizer at each training step.

  • If you need to control how often those optimizers step or override the default .step() schedule, override the optimizer_step() hook.

  • If you only want to call a learning rate scheduler every x step or epoch, or want to monitor a custom metric, you can specify these in a lr_dict:

    {
        'scheduler': lr_scheduler,
        'interval': 'step',  # or 'epoch'
        'monitor': 'val_f1',
        'frequency': x,
    }
    

forward

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

Same as torch.nn.Module.forward(), however in Lightning you want this to define the operations you want to use for prediction (i.e.: on a server or as a feature extractor).

Normally you’d call self() from your training_step() method. This makes it easy to write a complex system for training with the outputs you’d want in a prediction setting.

You may also find the auto_move_data() decorator useful when using the module outside Lightning in a production setting.

Parameters
  • *args – Whatever you decide to pass into the forward method.

  • **kwargs – Keyword arguments are also possible.

Returns

Predicted output

Examples

# example if we were using this model as a feature extractor
def forward(self, x):
    feature_maps = self.convnet(x)
    return feature_maps

def training_step(self, batch, batch_idx):
    x, y = batch
    feature_maps = self(x)
    logits = self.classifier(feature_maps)

    # ...
    return loss

# splitting it this way allows model to be used a feature extractor
model = MyModelAbove()

inputs = server.get_request()
results = model(inputs)
server.write_results(results)

# -------------
# This is in stark contrast to torch.nn.Module where normally you would have this:
def forward(self, batch):
    x, y = batch
    feature_maps = self.convnet(x)
    logits = self.classifier(feature_maps)
    return logits

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=True, on_step=None, on_epoch=None, reduce_fx=torch.mean, tbptt_reduce_fx=torch.mean, tbptt_pad_token=0, enable_graph=False, sync_dist=False, sync_dist_op='mean', sync_dist_group=None)[source]

Log a key, value

Example:

self.log('train_loss', loss)

The default behavior per hook is as follows

* also applies to the test loop

LightningMoule Hook

on_step

on_epoch

prog_bar

logger

training_step

T

F

F

T

training_step_end

T

F

F

T

training_epoch_end

F

T

F

T

validation_step*

F

T

F

T

validation_step_end*

F

T

F

T

validation_epoch_end*

F

T

F

T

Parameters
  • name (str) – key name

  • value (Any) – value name

  • prog_bar (bool) – if True logs to the progress bar

  • logger (bool) – if True logs to the logger

  • on_step (Optional[bool]) – if True logs at this step. None auto-logs at the training_step but not validation/test_step

  • on_epoch (Optional[bool]) – if True logs epoch accumulated metrics. None auto-logs at the val/test step but not training_step

  • reduce_fx (Callable) – Torch.mean by default

  • tbptt_reduce_fx (Callable) – function to reduce on truncated back prop

  • tbptt_pad_token (int) – token to use for padding

  • enable_graph (bool) – if True, will not auto detach the graph

  • sync_dist (bool) – if True, reduces the metric across GPUs/TPUs

  • sync_dist_op (Union[Any, str]) – the op to sync across

  • sync_dist_group (Optional[Any]) – the ddp group

log_dict

LightningModule.log_dict(dictionary, prog_bar=False, logger=True, on_step=None, on_epoch=None, reduce_fx=torch.mean, tbptt_reduce_fx=torch.mean, tbptt_pad_token=0, enable_graph=False, sync_dist=False, sync_dist_op='mean', sync_dist_group=None)[source]

Log a dictonary of values at once

Example:

values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n}
self.log_dict(values)
Parameters
  • dictionary (dict) – key value pairs (str, tensors)

  • prog_bar (bool) – if True logs to the progress base

  • logger (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

  • on_epoch (Optional[bool]) – if True logs epoch accumulated metrics. None auto-logs for val/test step but not training_step

  • reduce_fx (Callable) – Torch.mean by default

  • tbptt_reduce_fx (Callable) – function to reduce on truncated back prop

  • tbptt_pad_token (int) – token to use for padding

  • enable_graph (bool) – if True, will not auto detach the graph

  • sync_dist (bool) – if True, reduces the metric across GPUs/TPUs

  • sync_dist_op (Union[Any, str]) – the op to sync across

  • sync_dist_group (Optional[Any]) – the ddp group:

print

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

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

Parameters
  • *args – The thing to print. Will be passed to Python’s built-in print function.

  • **kwargs – Will be passed to Python’s built-in print function.

Example

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

None

save_hyperparameters

LightningModule.save_hyperparameters(*args, frame=None)[source]

Save all model arguments.

Parameters

args – single object of dict, NameSpace or OmegaConf or string names or argumenst from class __init__

>>> from collections import OrderedDict
>>> class ManuallyArgsModel(LightningModule):
...     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
>>> class AutomaticArgsModel(LightningModule):
...     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
>>> class SingleArgModel(LightningModule):
...     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
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.

# the pseudocode for these calls
test_outs = []
for test_batch in test_data:
    out = test_step(test_batch)
    test_outs.append(out)
test_epoch_end(test_outs)
Parameters
  • batch (Tensor | (Tensor, …) | [Tensor, …]) – The output of your DataLoader. A tensor, tuple or list.

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

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

Returns

Any of.

  • Any object or value

  • None - Testing will 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)

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 validation datasets, test_step() will have an additional argument.

# CASE 2: multiple test datasets
def test_step(self, batch, batch_idx, dataloader_idx):
    # 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 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.

test_step_end

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

Use this when testing with dp or ddp2 because test_step() will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss.

Note

If you later switch to ddp or some other mode, this will still be called so that you don’t have to change your code.

# pseudocode
sub_batches = split_batches_for_dp(batch)
batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]
test_step_end(batch_parts_outputs)
Parameters

batch_parts_outputs – What you return in test_step() for each batch part.

Returns

None or anything

# WITHOUT test_step_end
# if used in DP or DDP2, this batch is 1/num_gpus large
def test_step(self, batch, batch_idx):
    # batch is 1/num_gpus big
    x, y = batch

    out = self(x)
    loss = self.softmax(out)
    self.log('test_loss', loss)

# --------------
# with test_step_end to do softmax over the full batch
def test_step(self, batch, batch_idx):
    # batch is 1/num_gpus big
    x, y = batch

    out = self.encoder(x)
    return out

def test_epoch_end(self, output_results):
    # this out is now the full size of the batch
    all_test_step_outs = output_results.out
    loss = nce_loss(all_test_step_outs)
    self.log('test_loss', loss)

See also

See the Multi-GPU training guide for more details.

test_epoch_end

LightningModule.test_epoch_end(outputs)[source]

Called at the end of a test epoch with the output of all test steps.

# the pseudocode for these calls
test_outs = []
for test_batch in test_data:
    out = test_step(test_batch)
    test_outs.append(out)
test_epoch_end(test_outs)
Parameters

outputs (List[Any]) – List of outputs you defined in test_step_end(), or if there are multiple dataloaders, a list containing a list of outputs for each dataloader

Return type

None

Returns

None

Note

If you didn’t define a test_step(), this won’t be called.

Examples

With a single dataloader:

def test_epoch_end(self, outputs):
    # do something with the outputs of all test batches
    all_test_preds = test_step_outputs.predictions

    some_result = calc_all_results(all_test_preds)
    self.log(some_result)

With multiple dataloaders, outputs will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each test step for that dataloader.

def test_epoch_end(self, outputs):
    final_value = 0
    for dataloader_outputs in outputs:
        for test_step_out in dataloader_outputs:
            # do something
            final_value += test_step_out

    self.log('final_metric', final_value)

to_onnx

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

Saves the model in ONNX format

Parameters
  • file_path (str) – The path of the file the model should be saved to.

  • input_sample (Optional[Tensor]) – A sample of an input tensor for tracing.

  • **kwargs – Will be passed to torch.onnx.export function.

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)))
>>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:
...     model = SimpleModel()
...     input_sample = torch.randn((1, 64))
...     model.to_onnx(tmpfile.name, input_sample, export_params=True)
...     os.path.isfile(tmpfile.name)
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 self.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 (Optional[str]) – 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[Tensor]) – Tensor to be used to do tracing when method is set to ‘trace’. Default: None (Use self.example_input_array)

  • **kwargs – 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()
>>> torch.jit.save(model.to_torchscript(), "model.pt")  
>>> os.path.isfile("model.pt")  
True
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 (Tensor | (Tensor, …) | [Tensor, …]) – The output of your DataLoader. A tensor, tuple or list.

  • batch_idx (int) – Integer displaying index of this batch

  • optimizer_idx (int) – When using multiple optimizers, this argument will also be present.

  • hiddens (Tensor) – Passed in if truncated_bptt_steps > 0.

Returns

Any of.

  • Tensor - The loss tensor

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

  • None - Training will skip to the next batch

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

If you define multiple optimizers, this step will be called with an additional optimizer_idx parameter.

# Multiple optimizers (e.g.: GANs)
def training_step(self, batch, batch_idx, optimizer_idx):
    if optimizer_idx == 0:
        # do training_step with encoder
    if optimizer_idx == 1:
        # do training_step with decoder

If you add truncated back propagation through time you will also get an additional argument with the hidden states of the previous step.

# Truncated back-propagation through time
def training_step(self, batch, batch_idx, hiddens):
    # hiddens are the hidden states from the previous truncated backprop step
    ...
    out, hiddens = self.lstm(data, hiddens)
    ...
    return {'loss': loss, 'hiddens': hiddens}

Note

The loss value shown in the progress bar is smoothed (averaged) over the last values, so it differs from the actual loss returned in train/validation step.

training_step_end

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

Use this when training with dp or ddp2 because training_step() will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss.

Note

If you later switch to ddp or some other mode, this will still be called so that you don’t have to change your code

# pseudocode
sub_batches = split_batches_for_dp(batch)
batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]
training_step_end(batch_parts_outputs)
Parameters

batch_parts_outputs – What you return in training_step for each batch part.

Returns

Anything

When using dp/ddp2 distributed backends, only a portion of the batch is inside the training_step:

def training_step(self, batch, batch_idx):
    # batch is 1/num_gpus big
    x, y = batch

    out = self(x)

    # softmax uses only a portion of the batch in the denomintaor
    loss = self.softmax(out)
    loss = nce_loss(loss)
    return loss

If you wish to do something with all the parts of the batch, then use this method to do it:

def training_step(self, batch, batch_idx):
    # batch is 1/num_gpus big
    x, y = batch

    out = self.encoder(x)
    return {'pred': out}

def training_step_end(self, training_step_outputs):
    gpu_0_pred = training_step_outputs[0]['pred']
    gpu_1_pred = training_step_outputs[1]['pred']
    gpu_n_pred = training_step_outputs[n]['pred']

    # this softmax now uses the full batch
    loss = nce_loss([gpu_0_pred, gpu_1_pred, gpu_n_pred])
    return loss

See also

See the Multi-GPU training guide for more details.

training_epoch_end

LightningModule.training_epoch_end(outputs)[source]

Called at the end of the training epoch with the outputs of all training steps. Use this in case you need to do something with all the outputs for every training_step.

# the pseudocode for these calls
train_outs = []
for train_batch in train_data:
    out = training_step(train_batch)
    train_outs.append(out)
training_epoch_end(train_outs)
Parameters

outputs (List[Any]) – List of outputs you defined in training_step(), or if there are multiple dataloaders, a list containing a list of outputs for each dataloader.

Return type

None

Returns

None

Note

If this method is not overridden, this won’t be called.

Example:

def training_epoch_end(self, training_step_outputs):
    # do something with all training_step outputs
    return result

With multiple dataloaders, outputs will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each training step for that dataloader.

def training_epoch_end(self, training_step_outputs):
    for out in training_step_outputs:
        # do something here

unfreeze

LightningModule.unfreeze()[source]

Unfreeze all parameters for training.

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

# the pseudocode for these calls
val_outs = []
for val_batch in val_data:
    out = validation_step(train_batch)
    val_outs.append(out)
    validation_epoch_end(val_outs)
Parameters
  • batch (Tensor | (Tensor, …) | [Tensor, …]) – The output of your DataLoader. A tensor, tuple or list.

  • batch_idx (int) – The index of this batch

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

Returns

Any of.

  • Any object or value

  • None - Validation will skip to the next batch

# pseudocode of order
out = validation_step()
if defined('validation_step_end'):
    out = validation_step_end(out)
out = validation_epoch_end(out)
# 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)

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 datasets, validation_step will have an additional argument.

# CASE 2: multiple validation datasets
def validation_step(self, batch, batch_idx, dataloader_idx):
    # 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.

validation_step_end

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

Use this when validating with dp or ddp2 because validation_step() will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss.

Note

If you later switch to ddp or some other mode, this will still be called so that you don’t have to change your code.

# pseudocode
sub_batches = split_batches_for_dp(batch)
batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]
validation_step_end(batch_parts_outputs)
Parameters

batch_parts_outputs – What you return in validation_step() for each batch part.

Returns

None or anything

# WITHOUT validation_step_end
# if used in DP or DDP2, this batch is 1/num_gpus large
def validation_step(self, batch, batch_idx):
    # batch is 1/num_gpus big
    x, y = batch

    out = self.encoder(x)
    loss = self.softmax(out)
    loss = nce_loss(loss)
    self.log('val_loss', loss)

# --------------
# with validation_step_end to do softmax over the full batch
def validation_step(self, batch, batch_idx):
    # batch is 1/num_gpus big
    x, y = batch

    out = self(x)
    return out

def validation_epoch_end(self, val_step_outputs):
    for out in val_step_outputs:
        # do something with these

See also

See the Multi-GPU training guide for more details.

validation_epoch_end

LightningModule.validation_epoch_end(outputs)[source]

Called at the end of the validation epoch with the outputs of all validation steps.

# the pseudocode for these calls
val_outs = []
for val_batch in val_data:
    out = validation_step(val_batch)
    val_outs.append(out)
validation_epoch_end(val_outs)
Parameters

outputs (List[Any]) – List of outputs you defined in validation_step(), or if there are multiple dataloaders, a list containing a list of outputs for each dataloader.

Return type

None

Returns

None

Note

If you didn’t define a validation_step(), this won’t be called.

Examples

With a single dataloader:

def validation_epoch_end(self, val_step_outputs):
    for out in val_step_outputs:
        # do something

With multiple dataloaders, outputs will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each validation step for that dataloader.

def validation_epoch_end(self, outputs):
    for dataloader_output_result in outputs:
        dataloader_outs = dataloader_output_result.dataloader_i_outputs

    self.log('final_metric', final_value)

Properties

These are properties available in a LightningModule.


current_epoch

The current epoch

def training_step(...):
    if self.current_epoch == 0:

device

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

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

global_rank

The global_rank of this LightningModule. Lightning saves logs, weights etc only from global_rank = 0. You normally do not need to use this property

Global rank refers to the index of that GPU across ALL GPUs. For example, if using 10 machines, each with 4 GPUs, the 4th GPU on the 10th machine has global_rank = 39


global_step

The current step (does not reset each epoch)

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

hparams

After calling save_hyperparameters anything passed to init() is available via hparams.

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(...):
    # the generic logger (same no matter if tensorboard or other supported logger)
    self.logger

    # the particular logger
    tensorboard_logger = self.logger.experiment

local_rank

The local_rank of this LightningModule. Lightning saves logs, weights etc only from global_rank = 0. You normally do not need to use this property

Local rank refers to the rank on that machine. For example, if using 10 machines, the GPU at index 0 on each machine has local_rank = 0.


precision

The type of precision used:

def training_step(...):
    if self.precision == 16:

trainer

Pointer to the trainer

def training_step(...):
    max_steps = self.trainer.max_steps
    any_flag = self.trainer.any_flag

use_amp

True if using Automatic Mixed Precision (AMP)


use_ddp

True if using ddp


use_ddp2

True if using ddp2


use_dp

True if using dp


use_tpu

True if using TPUs


Hooks

This is the pseudocode to describe how all the hooks are called during a call to .fit()

def fit(...):
    on_fit_start()

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

    for gpu/tpu in gpu/tpus:
        train_on_device(model.copy())

    on_fit_end()

def train_on_device(model):
    # setup is called PER DEVICE
    setup()
    configure_optimizers()
    on_pretrain_routine_start()

    for epoch in epochs:
        train_loop()

    teardown()

def train_loop():
    on_train_epoch_start()
    train_outs = []
    for train_batch in train_dataloader():
        on_train_batch_start()

        # ----- train_step methods -------
        out = training_step(batch)
        train_outs.append(out)

        loss = out.loss

        backward()
        on_after_backward()
        optimizer_step()
        on_before_zero_grad()
        optimizer_zero_grad()

        on_train_batch_end(out)

        if should_check_val:
            val_loop()

    # end training epoch
    logs = training_epoch_end(outs)

def val_loop():
    model.eval()
    torch.set_grad_enabled(False)

    on_validation_epoch_start()
    val_outs = []
    for val_batch in val_dataloader():
        on_validation_batch_start()

        # -------- val step methods -------
        out = validation_step(val_batch)
        val_outs.append(out)

        on_validation_batch_end(out)

    validation_epoch_end(val_outs)
    on_validation_epoch_end()

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

backward

LightningModule.backward(loss, optimizer, optimizer_idx)[source]

Override backward with your own implementation if you need to.

Parameters
  • loss (Tensor) – Loss is already scaled by accumulated grads

  • optimizer (Optimizer) – Current optimizer being used

  • optimizer_idx (int) – Index of the current optimizer being used

Called to perform backward step. Feel free to override as needed. The loss passed in has already been scaled for accumulated gradients if requested.

Example:

def backward(self, loss, optimizer, optimizer_idx):
    loss.backward()
Return type

None

get_progress_bar_dict

LightningModule.get_progress_bar_dict()[source]

Implement this to override the default items displayed in the progress bar. By default it includes the average loss value, split index of BPTT (if used) and the version of the experiment when using a logger.

Epoch 1:   4%|▎         | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]

Here is an example how to override the defaults:

def get_progress_bar_dict(self):
    # don't show the version number
    items = super().get_progress_bar_dict()
    items.pop("v_num", None)
    return items
Return type

Dict[str, Union[int, str]]

Returns

Dictionary with the items to be displayed in the progress bar.

manual_backward

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

Call this directly from your training_step when doing optimizations manually. By using this we can ensure that all the proper scaling when using 16-bit etc has been done for you

This function forwards all args to the .backward() call as well.

Tip

In manual mode we still automatically clip grads if Trainer(gradient_clip_val=x) is set

Example:

def training_step(...):
    (opt_a, opt_b) = self.optimizers()
    loss = ...
    # automatically applies scaling, etc...
    self.manual_backward(loss, opt_a)
Return type

None

on_after_backward

LightningModule.on_after_backward()

Called in the training loop after loss.backward() and before optimizers do anything. This is the ideal place to inspect or log gradient information.

Example:

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)
Return type

None

on_before_zero_grad

LightningModule.on_before_zero_grad(optimizer)

Called after optimizer.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:
    optimizer.step()
    model.on_before_zero_grad(optimizer) # < ---- called here
    optimizer.zero_grad()
Parameters

optimizer (Optimizer) – The optimizer for which grads should be zeroed.

Return type

None

on_fit_start

ModelHooks.on_fit_start()[source]

Called at the very beginning of fit. If on DDP it is called on every process

on_fit_end

ModelHooks.on_fit_end()[source]

Called at the very end of fit. If on DDP it is called on every process

on_load_checkpoint

LightningModule.on_load_checkpoint(checkpoint)

Do something with the checkpoint. Gives model a chance to load something before state_dict is restored.

Parameters

checkpoint (Dict[str, Any]) – A dictionary with variables from the checkpoint.

Return type

None

on_save_checkpoint

LightningModule.on_save_checkpoint(checkpoint)

Give the model a chance to add something to the checkpoint. state_dict is already there.

Parameters

checkpoint (Dict[str, Any]) – A dictionary in which you can save variables to save in a checkpoint. Contents need to be pickleable.

Return type

None

on_pretrain_routine_start

ModelHooks.on_pretrain_routine_start()[source]

Called at the beginning of the pretrain routine (between fit and train start).

  • fit

  • pretrain_routine start

  • pretrain_routine end

  • training_start

Return type

None

on_pretrain_routine_end

ModelHooks.on_pretrain_routine_end()[source]

Called at the end of the pretrain routine (between fit and train start).

  • fit

  • pretrain_routine start

  • pretrain_routine end

  • training_start

Return type

None

on_test_batch_start

ModelHooks.on_test_batch_start(batch, batch_idx, dataloader_idx)[source]

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

Parameters
  • batch (Any) – The batched data as it is returned by the training DataLoader.

  • batch_idx (int) – the index of the batch

  • dataloader_idx (int) – the index of the dataloader

Return type

None

on_test_batch_end

ModelHooks.on_test_batch_end(outputs, batch, batch_idx, dataloader_idx)[source]

Called in the test loop after the batch.

Parameters
  • outputs (Any) – The outputs of test_step_end(test_step(x))

  • batch (Any) – The batched data as it is returned by the training DataLoader.

  • batch_idx (int) – the index of the batch

  • dataloader_idx (int) – the index of the dataloader

Return type

None

on_test_epoch_start

ModelHooks.on_test_epoch_start()[source]

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

Return type

None

on_test_epoch_end

ModelHooks.on_test_epoch_end()[source]

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

Return type

None

on_train_batch_start

ModelHooks.on_train_batch_start(batch, batch_idx, dataloader_idx)[source]

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

  • dataloader_idx (int) – the index of the dataloader

Return type

None

on_train_batch_end

ModelHooks.on_train_batch_end(outputs, batch, batch_idx, dataloader_idx)[source]

Called in the training loop after the batch.

Parameters
  • outputs (Any) – The outputs of validation_step_end(validation_step(x))

  • batch (Any) – The batched data as it is returned by the training DataLoader.

  • batch_idx (int) – the index of the batch

  • dataloader_idx (int) – the index of the dataloader

Return type

None

on_train_epoch_start

ModelHooks.on_train_epoch_start()[source]

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

Return type

None

on_train_epoch_end

ModelHooks.on_train_epoch_end(outputs)[source]

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

Return type

None

on_validation_batch_start

ModelHooks.on_validation_batch_start(batch, batch_idx, dataloader_idx)[source]

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

Parameters
  • batch (Any) – The batched data as it is returned by the training DataLoader.

  • batch_idx (int) – the index of the batch

  • dataloader_idx (int) – the index of the dataloader

Return type

None

on_validation_batch_end

ModelHooks.on_validation_batch_end(outputs, batch, batch_idx, dataloader_idx)[source]

Called in the validation loop after the batch.

Parameters
  • outputs (Any) – The outputs of validation_step_end(validation_step(x))

  • batch (Any) – The batched data as it is returned by the training DataLoader.

  • batch_idx (int) – the index of the batch

  • dataloader_idx (int) – the index of the dataloader

Return type

None

on_validation_epoch_start

ModelHooks.on_validation_epoch_start()[source]

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

Return type

None

on_validation_epoch_end

ModelHooks.on_validation_epoch_end()[source]

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

Return type

None

optimizer_step

LightningModule.optimizer_step(epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False)[source]

Override this method to adjust the default way the Trainer calls each optimizer. By default, Lightning calls step() and zero_grad() as shown in the example once per optimizer.

Parameters
  • epoch (int) – Current epoch

  • batch_idx (int) – Index of current batch

  • optimizer (Optimizer) – A PyTorch optimizer

  • optimizer_idx (int) – If you used multiple optimizers this indexes into that list.

  • second_order_closure (Optional[Callable]) – closure for second order methods

  • on_tpu (bool) – true if TPU backward is required

  • using_native_amp (bool) – True if using native amp

  • using_lbfgs (bool) – True if the matching optimizer is lbfgs

Examples

# DEFAULT
def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
                   second_order_closure, on_tpu, using_native_amp, using_lbfgs):
    optimizer.step()

# Alternating schedule for optimizer steps (i.e.: GANs)
def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
                   second_order_closure, on_tpu, using_native_amp, using_lbfgs):
    # update generator opt every 2 steps
    if optimizer_idx == 0:
        if batch_idx % 2 == 0 :
            optimizer.step()
            optimizer.zero_grad()

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

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

Here’s another example showing how to use this for more advanced things such as learning rate warm-up:

# learning rate warm-up
def optimizer_step(self, current_epoch, batch_idx, optimizer,
                    optimizer_idx, second_order_closure, on_tpu, using_native_amp, using_lbfgs):
    # warm up lr
    if self.trainer.global_step < 500:
        lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
        for pg in optimizer.param_groups:
            pg['lr'] = lr_scale * self.learning_rate

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

Note

If you also override the on_before_zero_grad() model hook don’t forget to add the call to it before optimizer.zero_grad() yourself.

Return type

None

optimizer_zero_grad

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

prepare_data

LightningModule.prepare_data()

Use this to download and prepare data.

Warning

DO NOT set state to the model (use setup instead) since this is NOT called on every GPU in DDP/TPU

Example:

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

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

In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):

  1. Once per node. This is the default and is only called on LOCAL_RANK=0.

  2. Once in total. Only called on GLOBAL_RANK=0.

Example:

# DEFAULT
# called once per node on LOCAL_RANK=0 of that node
Trainer(prepare_data_per_node=True)

# call on GLOBAL_RANK=0 (great for shared file systems)
Trainer(prepare_data_per_node=False)

This is called before requesting the dataloaders:

model.prepare_data()
    if ddp/tpu: init()
model.setup(stage)
model.train_dataloader()
model.val_dataloader()
model.test_dataloader()
Return type

None

setup

ModelHooks.setup(stage)[source]

Called at the beginning of fit and test. 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’ or ‘test’

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(stage):
        data = Load_data(...)
        self.l1 = nn.Linear(28, data.num_classes)

tbptt_split_batch

LightningModule.tbptt_split_batch(batch, split_size)[source]

When using truncated backpropagation through time, each batch must be split along the time dimension. Lightning handles this by default, but for custom behavior override this function.

Parameters
  • batch (Tensor) – Current batch

  • split_size (int) – The size of the split

Return type

list

Returns

List of batch splits. Each split will be passed to training_step() to enable truncated back propagation through time. The default implementation splits root level Tensors and Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.

Examples

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

Note

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

teardown

ModelHooks.teardown(stage)[source]

Called at the end of fit and test.

Parameters

stage (str) – either ‘fit’ or ‘test’

train_dataloader

LightningModule.train_dataloader()

Implement a PyTorch DataLoader for training.

Return type

DataLoader

Returns

Single PyTorch DataLoader.

The dataloader you return will not be called every epoch unless you set reload_dataloaders_every_epoch to True.

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()

  • train_dataloader()

Note

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

Example

def train_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,
                    download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=True
    )
    return loader

val_dataloader

LightningModule.val_dataloader()

Implement one or multiple PyTorch DataLoaders for validation.

The dataloader you return will not be called every epoch unless you set reload_dataloaders_every_epoch to True.

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

  • fit()

  • prepare_data()

  • train_dataloader()

  • val_dataloader()

  • test_dataloader()

Note

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

Return type

Union[DataLoader, List[DataLoader]]

Returns

Single or multiple PyTorch DataLoaders.

Examples

def val_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False,
                    transform=transform, download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=False
    )

    return loader

# can also return multiple dataloaders
def val_dataloader(self):
    return [loader_a, loader_b, ..., loader_n]

Note

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

Note

In the case where you return multiple validation dataloaders, the validation_step() will have an argument dataloader_idx which matches the order here.

test_dataloader

LightningModule.test_dataloader()

Implement one or multiple PyTorch DataLoaders for testing.

The dataloader you return will not be called every epoch unless you set reload_dataloaders_every_epoch to True.

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()

  • train_dataloader()

  • val_dataloader()

  • test_dataloader()

Note

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

Return type

Union[DataLoader, List[DataLoader]]

Returns

Single or multiple PyTorch DataLoaders.

Example

def test_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
                    download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=False
    )

    return loader

# can also return multiple dataloaders
def test_dataloader(self):
    return [loader_a, loader_b, ..., loader_n]

Note

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

Note

In the case where you return multiple test dataloaders, the test_step() will have an argument dataloader_idx which matches the order here.

transfer_batch_to_device

DataHooks.transfer_batch_to_device(batch, device)[source]

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:

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

Example:

def transfer_batch_to_device(self, batch, device)
    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)
    else:
        batch = super().transfer_batch_to_device(data, device)
    return batch
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.

Return type

Any

Returns

A reference to the data on the new device.

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).

Note

This hook only runs on single GPU training (no data-parallel). If you need multi-GPU support for your custom batch objects, you need to define your custom DistributedDataParallel or LightningDistributedDataParallel and override configure_ddp().

See also

  • move_data_to_device()

  • apply_to_collection()

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