LightningModule

class pytorch_lightning.core.LightningModule(*args, **kwargs)

Bases: lightning_lite.utilities.device_dtype_mixin._DeviceDtypeModuleMixin, pytorch_lightning.core.mixins.hparams_mixin.HyperparametersMixin, pytorch_lightning.core.saving.ModelIO, pytorch_lightning.core.hooks.ModelHooks, pytorch_lightning.core.hooks.DataHooks, pytorch_lightning.core.hooks.CheckpointHooks, torch.nn.modules.module.Module

all_gather(data, group=None, sync_grads=False)

Allows users to call self.all_gather() from the LightningModule, thus making the all_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:

Union[Tensor, Dict, List, Tuple]

Returns:

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

backward(loss, optimizer, optimizer_idx, *args, **kwargs)

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

Parameters:

• loss (Tensor) – The loss tensor returned by training_step(). If gradient accumulation is used, the loss here holds the normalized value (scaled by 1 / accumulation steps).

• 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:

None

Example:

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

clip_gradients(optimizer, gradient_clip_val=None, gradient_clip_algorithm=None)

Handles gradient clipping internally.

Note

Do not override this method. If you want to customize gradient clipping, consider using configure_gradient_clipping() method.

Parameters:

• optimizer (Optimizer) – Current optimizer being used.

• gradient_clip_val (Union[int, float, None]) – The value at which to clip gradients.

• gradient_clip_algorithm (Optional[str]) – The gradient clipping algorithm to use. Pass gradient_clip_algorithm="value" to clip by value, and gradient_clip_algorithm="norm" to clip by norm.

Return type:

None

configure_callbacks()

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

Return type:

Union[Sequence[Callback], Callback]

Returns:

A callback or a list of callbacks which will extend the list of callbacks in the Trainer.

Example:

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

configure_gradient_clipping(optimizer, optimizer_idx, gradient_clip_val=None, gradient_clip_algorithm=None)

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

Parameters:

• optimizer (Optimizer) – Current optimizer being used.

• 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:

None

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)

configure_optimizers()

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:

Any

Returns:

Any of these 6 options.

• Single optimizer.

• List or Tuple of optimizers.

• Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple lr_scheduler_config).

• Dictionary, with an "optimizer" key, and (optionally) a "lr_scheduler" key whose value is a single LR scheduler or lr_scheduler_config.

• 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 the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on.

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


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

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

Note

The frequency value specified in a dict along with the optimizer 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 the lr_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 additional optimizer_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 the optimizer_step() hook.

forward(*args, **kwargs)

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

Parameters:

• *args (Any) – Whatever you decide to pass into the forward method.

• **kwargs (Any) – Keyword arguments are also possible.

Return type:

Any

Returns:

Your model’s output

freeze()

Freeze all params for inference.

Example:

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

Return type:

None

classmethod from_compiled(model)

Returns an instance LightningModule from the output of torch.compile.

The torch.compile function returns a torch._dynamo.OptimizedModule, which wraps the LightningModule passed in as an argument, but doesn’t inherit from it. This means that the output of torch.compile behaves like a LightningModule but it doesn’t inherit from it (i.e. isinstance will fail).

Use this method to obtain a LightningModule that still runs with all the optimizations from torch.compile.

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)

Log a key, value pair.

Example:

self.log('train_loss', loss)

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

Parameters:

• name (str) – key to log.

• value (Union[Metric, Tensor, int, float, Mapping[str, Union[Metric, Tensor, int, float]]]) – value to log. Can be a float, Tensor, Metric, or a dictionary of the former.

• 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. The default value is determined by the hook. See Automatic Logging for details.

• on_epoch (Optional[bool]) – if True logs epoch accumulated metrics. The default value is determined by the hook. See Automatic Logging for details.

• reduce_fx (Union[str, Callable]) – reduction function over step values for end of epoch. torch.mean() by default.

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

• sync_dist (bool) – if True, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.

• sync_dist_group (Optional[Any]) – the DDP group to sync across.

• add_dataloader_idx (bool) – if True, appends the index of the current dataloader to the name (when using multiple dataloaders). If False, user needs to give unique names for each dataloader to not mix the values.

• batch_size (Optional[int]) – Current batch_size. This will be directly inferred from the loaded batch, but for some data structures you might need to explicitly provide it.

• metric_attribute (Optional[str]) – To restore the metric state, Lightning requires the reference of the torchmetrics.Metric in your model. This is found automatically if it is a model attribute.

• rank_zero_only (bool) – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.

Return type:

None

log_dict(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)

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 a float, Tensor, Metric, or a dictionary of the former.

• 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. The default value is determined by the hook. See Automatic Logging for details.

• on_epoch (Optional[bool]) – if True logs epoch accumulated metrics. None auto-logs for val/test step but not training_step. The default value is determined by the hook. See Automatic Logging for details.

• reduce_fx (Union[str, Callable]) – reduction function over step values for end of epoch. torch.mean() by default.

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

• sync_dist (bool) – if True, reduces the metric across GPUs/TPUs. Use with care as this may lead to a significant communication overhead.

• sync_dist_group (Optional[Any]) – the ddp group to sync across.

• add_dataloader_idx (bool) – if True, appends the index of the current dataloader to the name (when using multiple). If False, user needs to give unique names for each dataloader to not mix values.

• batch_size (Optional[int]) – Current batch size. This will be directly inferred from the loaded batch, but some data structures might need to explicitly provide it.

• rank_zero_only (bool) – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.

Return type:

None

log_grad_norm(grad_norm_dict)

Override this method to change the default behaviour of log_grad_norm.

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

Parameters:

grad_norm_dict (Dict[str, float]) – Dictionary containing current grad norm metrics

Return type:

None

Example:

# DEFAULT
def log_grad_norm(self, grad_norm_dict):
    self.log_dict(grad_norm_dict, on_step=True, on_epoch=True, prog_bar=False, logger=True)

lr_scheduler_step(scheduler, optimizer_idx, metric)

Override this method to adjust the default way the Trainer calls each scheduler. By default, Lightning calls step() and as shown in the example for each scheduler based on its interval.

Parameters:

• scheduler (Union[_LRScheduler, ReduceLROnPlateau]) – Learning rate scheduler.

• optimizer_idx (int) – Index of the optimizer associated with this scheduler.

• metric (Optional[Any]) – Value of the monitor used for schedulers like ReduceLROnPlateau.

Return type:

None

Examples:

# DEFAULT
def lr_scheduler_step(self, scheduler, optimizer_idx, metric):
    if metric is None:
        scheduler.step()
    else:
        scheduler.step(metric)

# Alternative way to update schedulers if it requires an epoch value
def lr_scheduler_step(self, scheduler, optimizer_idx, metric):
    scheduler.step(epoch=self.current_epoch)

lr_schedulers()

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

Return type:

Union[None, List[Union[_LRScheduler, ReduceLROnPlateau]], _LRScheduler, ReduceLROnPlateau]

Returns:

A single scheduler, or a list of schedulers in case multiple ones are present, or None if no schedulers were returned in configure_optimizers().

manual_backward(loss, *args, **kwargs)

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

See manual optimization for more examples.

Example:

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

Parameters:

• loss (Tensor) – The tensor on which to compute gradients. Must have a graph attached.

• *args (Any) – Additional positional arguments to be forwarded to backward()

• **kwargs (Any) – Additional keyword arguments to be forwarded to backward()

Return type:

None

optimizer_step(epoch, batch_idx, optimizer, optimizer_idx=0, optimizer_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False)

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. This method (and zero_grad()) won’t be called during the accumulation phase when Trainer(accumulate_grad_batches != 1). Overriding this hook has no benefit with manual optimization.

Parameters:

• epoch (int) – Current epoch

• batch_idx (int) – Index of current batch

• optimizer (Union[Optimizer, LightningOptimizer]) – A PyTorch optimizer

• optimizer_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 to training_step(), optimizer.zero_grad(), and backward().

• 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 torch.optim.LBFGS

Return type:

None

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(epoch, batch_idx, optimizer, optimizer_idx)

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

Parameters:

• epoch (int) – Current epoch

• batch_idx (int) – Index of current batch

• optimizer (Optimizer) – A PyTorch optimizer

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

Return type:

None

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.

optimizers(use_pl_optimizer=True)

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

Parameters:

use_pl_optimizer (bool) – If True, will wrap the optimizer(s) in a LightningOptimizer for automatic handling of precision 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.

predict_step(batch, batch_idx, dataloader_idx=0)

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

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

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

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

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)

Parameters:

• batch (Any) – Current batch.

• batch_idx (int) – Index of current batch.

• dataloader_idx (int) – Index of the current dataloader.

Return type:

Any

Returns:

Predicted output

print(*args, **kwargs)

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

Parameters:

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

• **kwargs (Any) – The same as for Python’s built-in print function.

Return type:

None

Example:

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

tbptt_split_batch(batch, split_size)

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 (Any) – Current batch

• split_size (int) – The size of the split

Return type:

List[Any]

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() if truncated_bptt_steps > 0. Each returned batch split is passed separately to training_step().

test_epoch_end(outputs)

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 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)

test_step(*args, **kwargs)

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:

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

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(*args, **kwargs)

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:

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

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.

to_onnx(file_path, input_sample=None, **kwargs)

Saves the model in ONNX format.

Parameters:

• file_path (Union[str, Path]) – The path of the file the onnx model should be saved to.

• input_sample (Optional[Any]) – An input for tracing. Default: None (Use self.example_input_array)

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

Return type:

None

Example

>>> class SimpleModel(LightningModule):
...     def __init__(self):
...         super().__init__()
...         self.l1 = torch.nn.Linear(in_features=64, out_features=4)
...
...     def forward(self, x):
...         return torch.relu(self.l1(x.view(x.size(0), -1)))

>>> 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(file_path=None, method='script', example_inputs=None, **kwargs)

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

Parameters:

• file_path (Union[str, Path, None]) – Path where to save the torchscript. Default: None (no file saved).

• method (Optional[str]) – Whether to use TorchScript’s script or trace method. Default: ‘script’

• example_inputs (Optional[Any]) – An input to be used to do tracing when method is set to ‘trace’. Default: None (uses example_input_array)

• **kwargs (Any) – Additional arguments that will be passed to the torch.jit.script() or torch.jit.trace() function.

Note

• Requires the implementation of the forward() method.

• The exported script will be set to evaluation mode.

• It is recommended that you install the latest supported version of PyTorch to use this feature without limitations. See also the torch.jit documentation for supported features.

Example

>>> class SimpleModel(LightningModule):
...     def __init__(self):
...         super().__init__()
...         self.l1 = torch.nn.Linear(in_features=64, out_features=4)
...
...     def forward(self, x):
...         return torch.relu(self.l1(x.view(x.size(0), -1)))
...
>>> model = SimpleModel()
>>> model.to_torchscript(file_path="model.pt")  
>>> 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

Return type:

Union[ScriptModule, Dict[str, ScriptModule]]

Returns:

This LightningModule as a torchscript, regardless of whether file_path is defined or not.

classmethod to_uncompiled(model)

Returns an instance of LightningModule without any compilation optimizations from a compiled model.

This takes either a torch._dynamo.OptimizedModule returned by torch.compile() or a LightningModule returned by LightningModule.from_compiled.

Note: this method will in-place modify the LightningModule that is passed in.

toggle_optimizer(optimizer, optimizer_idx)

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 sure param_requires_grad_state is properly reset.

Parameters:

• optimizer (Union[Optimizer, LightningOptimizer]) – The optimizer to toggle.

• optimizer_idx (int) – The index of the optimizer to toggle.

Return type:

None

training_epoch_end(outputs)

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 in training_step(). If there are multiple optimizers or when using truncated_bptt_steps > 0, the lists have the dimensions (n_batches, tbptt_steps, n_optimizers). Dimensions of length 1 are squeezed.

Return type:

None

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:
        ...

training_step(*args, **kwargs)

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 (Any) – Passed in if truncated_bptt_steps > 0.

Return type:

Union[Tensor, Dict[str, Any]]

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. 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 by accumulate_grad_batches internally.

training_step_end(step_output)

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:

Union[Tensor, Dict[str, Any]]

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.

unfreeze()

Unfreeze all parameters for training.

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

Return type:

None

untoggle_optimizer(optimizer_idx)

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.

Parameters:

optimizer_idx (int) – The index of the optimizer to untoggle.

Return type:

None

validation_epoch_end(outputs)

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 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:
        ...

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)

validation_step(*args, **kwargs)

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:

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

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(*args, **kwargs)

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:

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

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.

property automatic_optimization: bool

If set to False you are responsible for calling .backward(), .step(), .zero_grad().

property current_epoch: int

The current epoch in the Trainer, or 0 if not attached.

property example_input_array: Optional[Union[torch.Tensor, Tuple, Dict]]

The example input array is a specification of what the module can consume in the forward() method. The return type is interpreted as follows:

• Single tensor: It is assumed the model takes a single argument, i.e., model.forward(model.example_input_array)

• Tuple: The input array should be interpreted as a sequence of positional arguments, i.e., model.forward(*model.example_input_array)

• Dict: The input array represents named keyword arguments, i.e., model.forward(**model.example_input_array)

property global_rank: int

The index of the current process across all nodes and devices.

property global_step: int

Total training batches seen across all epochs.

If no Trainer is attached, this propery is 0.

property local_rank: int

The index of the current process within a single node.

property logger: Optional[pytorch_lightning.loggers.logger.Logger]

Reference to the logger object in the Trainer.

property loggers: List[pytorch_lightning.loggers.logger.Logger]

Reference to the list of loggers in the Trainer.

property on_gpu: bool

Returns True if this model is currently located on a GPU.

Useful to set flags around the LightningModule for different CPU vs GPU behavior.

property truncated_bptt_steps: int

Enables Truncated Backpropagation Through Time in the Trainer when set to a positive integer.

It represents the number of times training_step() gets called before backpropagation. If this is > 0, the training_step() receives an additional argument hiddens and is expected to return a hidden state.