LightningModule¶
A LightningModule
organizes your PyTorch code into 6 sections:
Computations (init).
Train Loop (training_step)
Validation Loop (validation_step)
Test Loop (test_step)
Prediction Loop (predict_step)
Optimizers and LR Schedulers (configure_optimizers)
Notice a few things.
It is the SAME code.
The PyTorch 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 = Net() 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 atorch.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 pytorch_lightning as pl
import torch.nn as nn
import torch.nn.functional as F
class LitModel(pl.LightningModule):
def __init__(self):
super().__init__()
self.l1 = 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(max_epochs=1)
model = LitModel()
trainer.fit(model, train_dataloaders=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 complete training loop |
validation_step |
the complete validation loop |
test_step |
the complete test loop |
predict_step |
the complete prediction loop |
configure_optimizers |
define optimizers and LR schedulers |
Training¶
Training Loop¶
To activate the training loop, override 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 and enable gradient calculation
model.train()
torch.set_grad_enabled(True)
outs = []
for batch_idx, batch in enumerate(train_dataloader):
loss = training_step(batch, batch_idx)
outs.append(loss.detach())
# 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):
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 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)
# clear gradients
optimizer.zero_grad()
# backward
loss.backward()
# update parameters
optimizer.step()
epoch_metric = torch.mean(torch.stack([x for x in outs]))
Train Epoch-level Operations¶
If you need to do something with all the outputs of each training_step()
,
override the training_epoch_end()
method.
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):
all_preds = torch.stack(training_step_outputs)
...
The matching pseudocode is:
outs = []
for batch_idx, batch in enumerate(train_dataloader):
# forward
loss = training_step(batch, batch_idx)
outs.append(loss)
# clear gradients
optimizer.zero_grad()
# backward
loss.backward()
# update parameters
optimizer.step()
training_epoch_end(outs)
Training with DataParallel¶
When training using a strategy
that splits data from each batch across GPUs, sometimes you might
need to aggregate them on the main GPU for processing (DP).
In this case, implement the training_step_end()
method which will have outputs from all the devices and you can accumulate to get the effective results.
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):
# predictions from each GPU
predictions = batch_parts["pred"]
# losses from each GPU
losses = batch_parts["loss"]
gpu_0_prediction = predictions[0]
gpu_1_prediction = predictions[1]
# do something with both outputs
return (losses[0] + losses[1]) / 2
def training_epoch_end(self, training_step_outputs):
for out in training_step_outputs:
...
Here is the Lightning training pseudo-code for DP:
outs = []
for batch_idx, train_batch in enumerate(train_dataloader):
batches = split_batch(train_batch)
dp_outs = []
for sub_batch in batches:
# 1
dp_out = training_step(sub_batch, batch_idx)
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¶
Validation Loop¶
To activate the validation loop while training, override the validation_step()
method.
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 (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 = Model()
trainer = 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¶
If you need to do something with all the outputs of each validation_step()
,
override the validation_epoch_end()
method. Note that this method is called before training_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):
all_preds = torch.stack(validation_step_outputs)
...
Validating with DataParallel¶
When validating using a strategy
that splits data from each batch across GPUs, sometimes you might
need to aggregate them on the main GPU for processing (DP).
In this case, implement the validation_step_end()
method which will have outputs from all the devices and you can accumulate to get the effective results.
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):
# predictions from each GPU
predictions = batch_parts["pred"]
# losses from each GPU
losses = batch_parts["loss"]
gpu_0_prediction = predictions[0]
gpu_1_prediction = predictions[1]
# do something with both outputs
return (losses[0] + losses[1]) / 2
def validation_epoch_end(self, validation_step_outputs):
for out in validation_step_outputs:
...
Here is the Lightning validation pseudo-code for DP:
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)
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 = Model()
trainer = Trainer()
trainer.fit(model)
# 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 from the previous run
trainer.test(dataloaders=test_dataloader)
# or call with pretrained model
model = MyLightningModule.load_from_checkpoint(PATH)
trainer = Trainer()
trainer.test(model, dataloaders=test_dataloader)
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
and try out Monte Carlo Dropout:
class LitMCdropoutModel(pl.LightningModule):
def __init__(self, model, mc_iteration):
super().__init__()
self.model = model
self.dropout = nn.Dropout()
self.mc_iteration = mc_iteration
def predict_step(self, batch, batch_idx):
# enable Monte Carlo Dropout
self.dropout.train()
# take average of `self.mc_iteration` iterations
pred = torch.vstack([self.dropout(self.model(x)).unsqueeze(0) for _ in range(self.mc_iteration)]).mean(dim=0)
return pred
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 = Trainer()
trainer.fit(model)
# automatically auto-loads the best weights from the previous run
predictions = trainer.predict(dataloaders=predict_dataloader)
# or call with pretrained model
model = MyLightningModule.load_from_checkpoint(PATH)
trainer = 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 Autoencoder(pl.LightningModule):
def forward(self, x):
return self.decoder(x)
model = Autoencoder()
model.eval()
with torch.no_grad():
reconstruction = model(embedding)
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.
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]
Allows users to call
self.all_gather()
from the LightningModule, thus making theall_gather
operation accelerator agnostic.all_gather
is a function provided by accelerators to gather a tensor from several distributed processes.- 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:
- 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’scallbacks
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 sureModelCheckpoint
callbacks run last.- Return type:
- 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.
- Return type:
- 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 orlr_scheduler_config
.Tuple of dictionaries as described above, with an optional
"frequency"
key.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 thetorch.optim.lr_scheduler.ReduceLROnPlateau
scheduler, Lightning requires that thelr_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 yourLightningModule
.Note
The
frequency
value specified in a dict along with theoptimizer
key 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.
This is different from the
frequency
value specified in thelr_scheduler_config
mentioned above.def configure_optimizers(self): optimizer_one = torch.optim.SGD(self.model.parameters(), lr=0.01) optimizer_two = torch.optim.SGD(self.model.parameters(), lr=0.01) return [ {"optimizer": optimizer_one, "frequency": 5}, {"optimizer": optimizer_two, "frequency": 10}, ]
In this example, the first optimizer will be used for the first 5 steps, the second optimizer for the next 10 steps and that cycle will continue. If an LR scheduler is specified for an optimizer using the
lr_scheduler
key in the above dict, the scheduler will only be updated when its optimizer is being used.Examples:
# most cases. no learning rate scheduler def configure_optimizers(self): return Adam(self.parameters(), lr=1e-3) # multiple optimizer case (e.g.: GAN) def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) return gen_opt, dis_opt # example with learning rate schedulers def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) dis_sch = CosineAnnealing(dis_opt, T_max=10) return [gen_opt, dis_opt], [dis_sch] # example with step-based learning rate schedulers # each optimizer has its own scheduler def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) gen_sch = { 'scheduler': ExponentialLR(gen_opt, 0.99), 'interval': 'step' # called after each training step } dis_sch = CosineAnnealing(dis_opt, T_max=10) # called every epoch return [gen_opt, dis_opt], [gen_sch, dis_sch] # 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_dis.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 as needed.If 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 optimizers.If you use multiple optimizers,
training_step()
will have an additionaloptimizer_idx
parameter.If you use
torch.optim.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 theoptimizer_step()
hook.
forward¶
freeze¶
log¶
- LightningModule.log(name, value, prog_bar=False, logger=True, 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:
value¶ (
Union
[Metric
,Tensor
,int
,float
,Mapping
[str
,Union
[Metric
,Tensor
,int
,float
]]]) – value to log. Can be afloat
,Tensor
,Metric
, or a dictionary of the former.on_step¶ (
Optional
[bool
]) – ifTrue
logs at this step. The default value is determined by the hook. See Automatic Logging for details.on_epoch¶ (
Optional
[bool
]) – ifTrue
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
) – ifTrue
, will not auto detach the graph.sync_dist¶ (
bool
) – ifTrue
, 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
) – ifTrue
, 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 thetorchmetrics.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:
log_dict¶
- LightningModule.log_dict(dictionary, prog_bar=False, logger=True, 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¶ (
Mapping
[str
,Union
[Metric
,Tensor
,int
,float
,Mapping
[str
,Union
[Metric
,Tensor
,int
,float
]]]]) – key value pairs. The values can be afloat
,Tensor
,Metric
, or a dictionary of the former.on_step¶ (
Optional
[bool
]) – ifTrue
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
]) – ifTrue
logs epoch accumulated metrics.None
auto-logs for val/test step but nottraining_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
) – ifTrue
, will not auto-detach the graphsync_dist¶ (
bool
) – ifTrue
, 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
) – ifTrue
, appends the index of the current dataloader to the name (when using multiple). IfFalse
, 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:
lr_schedulers¶
- LightningModule.lr_schedulers()[source]
Returns the learning rate scheduler(s) that are being used during training. Useful for manual optimization.
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 tobackward()
**kwargs¶ (
Any
) – Additional keyword arguments to be forwarded tobackward()
- Return type:
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
) – IfTrue
, will wrap the optimizer(s) in aLightningOptimizer
for automatic handling of precision and profiling.- Return type:
Union
[Optimizer
,LightningOptimizer
,List
[Optimizer
],List
[LightningOptimizer
]]- 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:
- Return type:
Example:
def forward(self, x): self.print(x, 'in forward')
predict_step¶
- LightningModule.predict_step(batch, batch_idx, dataloader_idx=0)[source]
Step function called during
predict()
. By default, it callsforward()
. 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 forTrainer(strategy="ddp_spawn")
or training on 8 TPU cores withTrainer(accelerator="tpu", devices=8)
as predictions won’t be returned.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 ignoredframe¶ (
Optional
[FrameType
]) – a frame object. Default is Nonelogger¶ (
bool
) – Whether to send the hyperparameters to the logger. Default: True
- Return type:
- Example::
>>> 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
>>> 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
>>> 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
>>> 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, optimizer_idx)[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.
This is only called automatically when automatic optimization is enabled and multiple optimizers are used. It works with
untoggle_optimizer()
to make sureparam_requires_grad_state
is properly reset.
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¶ – The output of your
DataLoader
.batch_idx¶ – The index of this batch.
dataloader_id¶ – The index of the dataloader that produced this batch. (only if multiple test dataloaders used).
- Return type:
- 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=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.
test_step_end¶
- LightningModule.test_step_end(*args, **kwargs)[source]
Use this when testing with DP 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) step_output = [test_step(sub_batch) for sub_batch in sub_batches] test_step_end(step_output)
- Parameters:
step_output¶ – What you return in
test_step()
for each batch part.- Return type:
- Returns:
None or anything
# WITHOUT test_step_end # if used in DP, 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_step_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¶ (
Union
[List
[Union
[Tensor
,Dict
[str
,Any
]]],List
[List
[Union
[Tensor
,Dict
[str
,Any
]]]]]) – List of outputs you defined intest_step_end()
, or if there are multiple dataloaders, a list containing a list of outputs for each dataloader- Return type:
- 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:
- Return type:
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 argumentmethod='trace'
and make sure that either the example_inputs argument is provided, or the model hasexample_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 (usesexample_input_array
)**kwargs¶ (
Any
) – Additional arguments that will be passed to thetorch.jit.script()
ortorch.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") >>> os.path.isfile("model.pt") >>> torch.jit.save(model.to_torchscript(file_path="model_trace.pt", method='trace', ... example_inputs=torch.randn(1, 64))) >>> os.path.isfile("model_trace.pt") True
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 yourDataLoader
. A tensor, tuple or list.batch_idx¶ (
int
) – Integer displaying index of this batchoptimizer_idx¶ (
int
) – When using multiple optimizers, this argument will also be present.hiddens¶ (
Any
) – Passed in iftruncated_bptt_steps
> 0.
- Return type:
- Returns:
Any of.
Tensor
- The loss tensordict
- A dictionary. Can include any keys, but must include the key'loss'
None
- Training will skip to the next batch. This is only 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
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) loss = ... 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.
Note
When
accumulate_grad_batches
> 1, the loss returned here will be automatically normalized byaccumulate_grad_batches
internally.
training_step_end¶
- LightningModule.training_step_end(step_output)[source]
Use this when training with dp 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) step_output = [training_step(sub_batch) for sub_batch in sub_batches] training_step_end(step_output)
- Parameters:
step_output¶ (
Union
[Tensor
,Dict
[str
,Any
]]) – What you return in training_step for each batch part.- Return type:
- Returns:
Anything
When using the DP strategy, 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 denominator 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 returned by
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
[Union
[Tensor
,Dict
[str
,Any
]]]) – List of outputs you defined intraining_step()
. If there are multiple optimizers or when usingtruncated_bptt_steps > 0
, the lists have the dimensions (n_batches, tbptt_steps, n_optimizers). Dimensions of length 1 are squeezed.- Return type:
- Returns:
None
Note
If this method is not overridden, this won’t be called.
def training_epoch_end(self, training_step_outputs): # do something with all training_step outputs for out in training_step_outputs: ...
unfreeze¶
untoggle_optimizer¶
- LightningModule.untoggle_optimizer(optimizer_idx)[source]
Resets the state of required gradients that were toggled with
toggle_optimizer()
.This is only called automatically when automatic optimization is enabled and multiple optimizers are used.
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(val_batch) val_outs.append(out) validation_epoch_end(val_outs)
- Parameters:
batch¶ – The output of your
DataLoader
.batch_idx¶ – The index of this batch.
dataloader_idx¶ – The index of the dataloader that produced this batch. (only if multiple val dataloaders used)
- Return type:
- Returns:
Any object or value
None
- Validation will skip to the next batch
# pseudocode of order val_outs = [] for val_batch in val_data: out = validation_step(val_batch) if defined("validation_step_end"): out = validation_step_end(out) val_outs.append(out) val_outs = validation_epoch_end(val_outs)
# 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.
validation_step_end¶
- LightningModule.validation_step_end(*args, **kwargs)[source]
Use this when validating with dp 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) step_output = [validation_step(sub_batch) for sub_batch in sub_batches] validation_step_end(step_output)
- Parameters:
step_output¶ – What you return in
validation_step()
for each batch part.- Return type:
- Returns:
None or anything
# WITHOUT validation_step_end # if used in DP, 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_step_end(self, val_step_outputs): for out in val_step_outputs: ...
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¶ (
Union
[List
[Union
[Tensor
,Dict
[str
,Any
]]],List
[List
[Union
[Tensor
,Dict
[str
,Any
]]]]]) – List of outputs you defined invalidation_step()
, or if there are multiple dataloaders, a list containing a list of outputs for each dataloader.- Return type:
- 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: ...
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 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 and TBPTT steps (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:
...
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()
This is recommended only if using 2+ optimizers AND if you know how to perform the optimization procedure properly. Note
that automatic optimization can still be used with multiple optimizers by relying on the optimizer_idx
parameter.
Manual optimization is most useful for research topics like reinforcement learning, sparse coding, and GAN research.
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)
truncated_bptt_steps¶
Truncated Backpropagation Through Time (TBPTT) performs perform backpropogation every k steps of
a much longer sequence. This is made possible by passing training batches
split along the time-dimensions into splits of size k to the
training_step
. In order to keep the same forward propagation behavior, all
hidden states should be kept in-between each time-dimension split.
If this is enabled, your batches will automatically get truncated and the Trainer will apply Truncated Backprop to it.
from pytorch_lightning import LightningModule
class MyModel(LightningModule):
def __init__(self, input_size, hidden_size, num_layers):
super().__init__()
# batch_first has to be set to True
self.lstm = nn.LSTM(
input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
batch_first=True,
)
...
# Important: This property activates truncated backpropagation through time
# Setting this value to 2 splits the batch into sequences of size 2
self.truncated_bptt_steps = 2
# Truncated back-propagation through time
def training_step(self, batch, batch_idx, hiddens):
x, y = batch
# the training step must be updated to accept a ``hiddens`` argument
# hiddens are the hiddens from the previous truncated backprop step
out, hiddens = self.lstm(x, hiddens)
...
return {"loss": ..., "hiddens": hiddens}
Lightning takes care of splitting your batch along the time-dimension. It is
assumed to be the second dimension of your batches. Therefore, in the
example above, we have set batch_first=True
.
# we use the second as the time dimension
# (batch, time, ...)
sub_batch = batch[0, 0:t, ...]
To modify how the batch is split,
override the pytorch_lightning.core.module.LightningModule.tbptt_split_batch()
method:
class LitMNIST(LightningModule):
def tbptt_split_batch(self, batch, split_size):
# do your own splitting on the batch
return splits
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
on_fit_start()
setup("fit")
configure_optimizers()
# 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():
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()
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()
if should_check_val:
val_loop()
# end training epoch
training_epoch_end()
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()
val_outs = []
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(batch, batch_idx)
val_outs.append(out)
validation_epoch_end(val_outs)
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, optimizer, optimizer_idx, *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 bytraining_step()
. If gradient accumulation is used, the loss here holds the normalized value (scaled by 1 / accumulation steps).optimizer¶ (
Optional
[Steppable
]) – Current optimizer being used.None
if using manual optimization.optimizer_idx¶ (
Optional
[int
]) – Index of the current optimizer being used.None
if using manual optimization.
- Return type:
Example:
def backward(self, loss, optimizer, optimizer_idx): loss.backward()
on_before_backward¶
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 beforeoptimizer.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()
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:
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:
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.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:
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¶
- classmethod LightningModule.load_from_checkpoint(checkpoint_path, map_location=None, hparams_file=None, strict=True, **kwargs)
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
,IO
]) – Path to checkpoint. This can also be a URL, or file-like objectmap_location¶ (
Union
[device
,str
,int
,Callable
[[Union
[device
,str
,int
]],Union
[device
,str
,int
]],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 intorch.load()
.hparams_file¶ (
Optional
[str
]) –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 adict
and passed into yourLightningModule
for use.If your model’s
hparams
argument isNamespace
and.yaml
file has hierarchical structure, you need to refactor your model to treathparams
asdict
.strict¶ (
bool
) – Whether to strictly enforce that the keys incheckpoint_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:
- Returns:
LightningModule
instance with loaded weights and hyperparameters (if available).
Note
load_from_checkpoint
is a class method. You should use yourLightningModule
class to call it instead of theLightningModule
instance.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:
on_train_end¶
- LightningModule.on_train_end()
Called at the end of training before logger experiment is closed.
- Return type:
on_validation_start¶
- LightningModule.on_validation_start()
Called at the beginning of validation.
- Return type:
on_validation_end¶
- LightningModule.on_validation_end()
Called at the end of validation.
- Return type:
on_test_batch_start¶
- LightningModule.on_test_batch_start(batch, batch_idx, dataloader_idx)
Called in the test loop before anything happens for that batch.
on_test_batch_end¶
- LightningModule.on_test_batch_end(outputs, batch, batch_idx, dataloader_idx)
Called in the test loop after the batch.
on_test_epoch_start¶
- LightningModule.on_test_epoch_start()
Called in the test loop at the very beginning of the epoch.
- Return type:
on_test_epoch_end¶
- LightningModule.on_test_epoch_end()
Called in the test loop at the very end of the epoch.
- Return type:
on_test_start¶
- LightningModule.on_test_start()
Called at the beginning of testing.
- Return type:
on_test_end¶
- LightningModule.on_test_end()
Called at the end of testing.
- Return type:
on_predict_batch_start¶
- LightningModule.on_predict_batch_start(batch, batch_idx, dataloader_idx)
Called in the predict loop before anything happens for that batch.
on_predict_batch_end¶
- LightningModule.on_predict_batch_end(outputs, batch, batch_idx, dataloader_idx)
Called in the predict loop after the batch.
on_predict_epoch_start¶
- LightningModule.on_predict_epoch_start()
Called at the beginning of predicting.
- Return type:
on_predict_epoch_end¶
- LightningModule.on_predict_epoch_end(results)
Called at the end of predicting.
- Return type:
on_predict_start¶
- LightningModule.on_predict_start()
Called at the beginning of predicting.
- Return type:
on_predict_end¶
- LightningModule.on_predict_end()
Called at the end of predicting.
- Return type:
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.
on_train_batch_end¶
- LightningModule.on_train_batch_end(outputs, batch, batch_idx)
Called in the training loop after the batch.
on_train_epoch_start¶
- LightningModule.on_train_epoch_start()
Called in the training loop at the very beginning of the epoch.
- Return type:
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, either: :rtype:
None
Implement training_epoch_end in the LightningModule OR
Cache data across steps on the attribute(s) of the LightningModule and access them in this hook
on_validation_batch_start¶
- LightningModule.on_validation_batch_start(batch, batch_idx, dataloader_idx)
Called in the validation loop before anything happens for that batch.
on_validation_batch_end¶
- LightningModule.on_validation_batch_end(outputs, batch, batch_idx, dataloader_idx)
Called in the validation loop after the batch.
- Parameters:
- Return type:
on_validation_epoch_start¶
- LightningModule.on_validation_epoch_start()
Called in the validation loop at the very beginning of the epoch.
- Return type:
on_validation_epoch_end¶
- LightningModule.on_validation_epoch_end()
Called in the validation loop at the very end of the epoch.
- Return type:
configure_sharded_model¶
- LightningModule.configure_sharded_model()
Hook to create modules in a distributed aware context. This is useful for when using sharded plugins, where we’d like to shard the model instantly, which is useful for extremely large models which can save memory and initialization time.
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:
on_validation_model_eval¶
- LightningModule.on_validation_model_eval()
Sets the model to eval during the val loop.
- Return type:
on_validation_model_train¶
- LightningModule.on_validation_model_train()
Sets the model to train during the val loop.
- Return type:
on_test_model_eval¶
- LightningModule.on_test_model_eval()
Sets the model to eval during the test loop.
- Return type:
on_test_model_train¶
- LightningModule.on_test_model_train()
Sets the model to train during the test loop.
- Return type:
on_before_optimizer_step¶
- LightningModule.on_before_optimizer_step(optimizer, optimizer_idx)
Called before
optimizer.step()
.If using gradient accumulation, the hook is called once the gradients have been accumulated. See:
accumulate_grad_batches
.If using native 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:
- Return type:
Example:
def on_before_optimizer_step(self, optimizer, optimizer_idx): # 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, optimizer_idx, gradient_clip_val=None, gradient_clip_algorithm=None)[source]
Perform gradient clipping for the optimizer parameters. Called before
optimizer_step()
.- Parameters:
optimizer_idx¶ (
int
) – Index of the 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:
Example:
# Perform gradient clipping on gradients associated with discriminator (optimizer_idx=1) in GAN def configure_gradient_clipping(self, optimizer, optimizer_idx, gradient_clip_val, gradient_clip_algorithm): if optimizer_idx == 1: # Lightning will handle the gradient clipping self.clip_gradients( optimizer, gradient_clip_val=gradient_clip_val, gradient_clip_algorithm=gradient_clip_algorithm ) else: # implement your own custom logic to clip gradients for generator (optimizer_idx=0)
optimizer_step¶
- LightningModule.optimizer_step(epoch, batch_idx, optimizer, optimizer_idx=0, optimizer_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()
andzero_grad()
as shown in the example once per optimizer. This method (andzero_grad()
) won’t be called during the accumulation phase whenTrainer(accumulate_grad_batches != 1)
. Overriding this hook has no benefit with manual optimization.- Parameters:
optimizer¶ (
Union
[Optimizer
,LightningOptimizer
]) – A PyTorch optimizeroptimizer_idx¶ (
int
) – If you used multiple optimizers, this indexes into that list.optimizer_closure¶ (
Optional
[Callable
[[],Any
]]) – The optimizer closure. This closure must be executed as it includes the calls totraining_step()
,optimizer.zero_grad()
, andbackward()
.using_lbfgs¶ (
bool
) – True if the matching optimizer istorch.optim.LBFGS
- Return type:
Examples:
# DEFAULT def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs): optimizer.step(closure=optimizer_closure) # Alternating schedule for optimizer steps (i.e.: GANs) def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs): # update generator opt every step if optimizer_idx == 0: optimizer.step(closure=optimizer_closure) # update discriminator opt every 2 steps if optimizer_idx == 1: if (batch_idx + 1) % 2 == 0 : optimizer.step(closure=optimizer_closure) else: # call the closure by itself to run `training_step` + `backward` without an optimizer step optimizer_closure() # ... # 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, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs, ): # 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, optimizer_idx)[source]
Override this method to change the default behaviour of
optimizer.zero_grad()
.- Parameters:
- Return type:
Examples:
# DEFAULT def optimizer_zero_grad(self, epoch, batch_idx, optimizer, optimizer_idx): optimizer.zero_grad() # Set gradients to `None` instead of zero to improve performance. def optimizer_zero_grad(self, epoch, batch_idx, optimizer, optimizer_idx): 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 deviceExample:
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.
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)
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:
- Return type:
- 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.abc.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_train_batch_start()
iftruncated_bptt_steps
> 0. Each returned batch split is passed separately totraining_step()
.
teardown¶
train_dataloader¶
- LightningModule.train_dataloader()
Implement one or more PyTorch DataLoaders for training.
- Return type:
Union
[DataLoader
,Sequence
[DataLoader
],Sequence
[Sequence
[DataLoader
]],Sequence
[Dict
[str
,DataLoader
]],Dict
[str
,DataLoader
],Dict
[str
,Dict
[str
,DataLoader
]],Dict
[str
,Sequence
[DataLoader
]]]- Returns:
A collection of
torch.utils.data.DataLoader
specifying training samples. In the case of multiple dataloaders, please see this section.
The dataloader you return will not be reloaded unless you set
reload_dataloaders_every_n_epochs
to a positive integer.For data processing use the following pattern:
download in
prepare_data()
process and split in
setup()
However, the above are only necessary for distributed processing.
Warning
do not assign state in prepare_data
prepare_data()
setup()
Note
Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.
Example:
# single dataloader 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 # multiple dataloaders, return as list def train_dataloader(self): mnist = MNIST(...) cifar = CIFAR(...) mnist_loader = torch.utils.data.DataLoader( dataset=mnist, batch_size=self.batch_size, shuffle=True ) cifar_loader = torch.utils.data.DataLoader( dataset=cifar, batch_size=self.batch_size, shuffle=True ) # each batch will be a list of tensors: [batch_mnist, batch_cifar] return [mnist_loader, cifar_loader] # multiple dataloader, return as dict def train_dataloader(self): mnist = MNIST(...) cifar = CIFAR(...) mnist_loader = torch.utils.data.DataLoader( dataset=mnist, batch_size=self.batch_size, shuffle=True ) cifar_loader = torch.utils.data.DataLoader( dataset=cifar, batch_size=self.batch_size, shuffle=True ) # each batch will be a dict of tensors: {'mnist': batch_mnist, 'cifar': batch_cifar} return {'mnist': mnist_loader, 'cifar': cifar_loader}
val_dataloader¶
- LightningModule.val_dataloader()
Implement one or multiple PyTorch DataLoaders for validation.
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()
.prepare_data()
setup()
Note
Lightning adds the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.
- Return type:
- Returns:
A
torch.utils.data.DataLoader
or a sequence of them specifying validation samples.
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 argumentdataloader_idx
which matches the order here.
test_dataloader¶
- LightningModule.test_dataloader()
Implement one or multiple PyTorch DataLoaders for testing.
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
prepare_data()
setup()
Note
Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.
- Return type:
- Returns:
A
torch.utils.data.DataLoader
or a sequence of them specifying testing samples.
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 argumentdataloader_idx
which matches the order here.
predict_dataloader¶
- LightningModule.predict_dataloader()
Implement one or multiple PyTorch DataLoaders for prediction.
It’s recommended that all data downloads and preparation happen in
prepare_data()
.prepare_data()
setup()
Note
Lightning adds the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.
- Return type:
- Returns:
A
torch.utils.data.DataLoader
or a sequence of them specifying prediction samples.
Note
In the case where you return multiple prediction dataloaders, the
predict_step()
will have an argumentdataloader_idx
which matches the order here.
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(…)
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.Note
This hook only runs on single GPU training and DDP (no data-parallel). Data-Parallel support will come in near future.
- Parameters:
- Return type:
- 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(data, device, dataloader_idx) return batch
- Raises:
MisconfigurationException – If using data-parallel,
Trainer(strategy='dp')
.MisconfigurationException – If using IPUs,
Trainer(accelerator='ipu')
.
See also
move_data_to_device()
apply_to_collection()
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.Note
This hook only runs on single GPU training and DDP (no data-parallel). Data-Parallel support will come in near future.
- Parameters:
- Return type:
- Returns:
A batch of data
Example:
def on_before_batch_transfer(self, batch, dataloader_idx): batch['x'] = transforms(batch['x']) return batch
See also
on_after_batch_transfer()
transfer_batch_to_device()
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.Note
This hook only runs on single GPU training and DDP (no data-parallel). Data-Parallel support will come in near future.
- Parameters:
- Return type:
- 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 data-parallel,
Trainer(strategy='dp')
.MisconfigurationException – If using IPUs,
Trainer(accelerator='ipu')
.
See also
on_before_batch_transfer()
transfer_batch_to_device()