lightning¶

Classes

LightningModule

The LightningModule - an nn.Module with many additional features.

class pytorch_lightning.core.lightning.LightningModule(*args, **kwargs)[source]¶

Bases: abc.ABC, pytorch_lightning.core.mixins.device_dtype_mixin.DeviceDtypeModuleMixin, pytorch_lightning.core.mixins.hparams_mixin.HyperparametersMixin, pytorch_lightning.core.grads.GradInformation, pytorch_lightning.core.saving.ModelIO, pytorch_lightning.core.hooks.ModelHooks, pytorch_lightning.core.hooks.DataHooks, pytorch_lightning.core.hooks.CheckpointHooks, torch.nn.

add_to_queue(queue)[source]¶

Appends the trainer.callback_metrics dictionary to the given queue. To avoid issues with memory sharing, we cast the data to numpy.

Parameters: queue¶ (SimpleQueue) – the instance of the queue to append the data.
Return type: None

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

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

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)[source]¶

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

Parameters

loss¶ (Tensor) – The loss tensor returned by training_step(). If gradient accumulation is used, the loss here holds the normalized value (scaled by 1 / accumulation steps).
optimizer¶ (Optional[Optimizer]) – 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.

Example:

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

Return type: None

configure_callbacks()[source]¶

Configure model-specific callbacks. When the model gets attached, e.g., when .fit() or .test() gets called, the list 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.

Returns: 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]

Note

Certain callback methods like on_init_start() will never be invoked on the new callbacks returned here.

configure_optimizers()[source]¶

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

Returns

Any of these 6 options.

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

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

lr_dict = {
    # 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_dict 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",
        },
    }


# 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_dict 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 and learning rate scheduler as needed.
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)[source]¶

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

Parameters

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

Return type

Any

Returns

Your model’s output

freeze()[source]¶

Freeze all params for inference.

Example:

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

Return type: None

get_from_queue(queue)[source]¶

Retrieve the trainer.callback_metrics dictionary from the given queue. To preserve consistency, we cast back the data to torch.Tensor.

Parameters: queue¶ (SimpleQueue) – the instance of the queue from where to get the data.
Return type: None

get_progress_bar_dict()[source]¶

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

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

Here is an example how to override the defaults:

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

Return type: Dict[str, Union[int, str]]
Returns: Dictionary with the items to be displayed in the progress bar.

log(name, value, prog_bar=False, logger=True, on_step=None, on_epoch=None, reduce_fx='default', tbptt_reduce_fx=None, tbptt_pad_token=None, enable_graph=False, sync_dist=False, sync_dist_op=None, sync_dist_group=None, add_dataloader_idx=True, batch_size=None, metric_attribute=None, rank_zero_only=None)[source]¶

Log a key, value pair.

Example:

self.log('train_loss', loss)

The default behavior per hook is as follows:

`*` also applies to the test loop¶
LightningModule Hook	on_step	on_epoch	prog_bar	logger
training_step	T	F	F	T
training_step_end	T	F	F	T
training_epoch_end	F	T	F	T
validation_step*	F	T	F	T
validation_step_end*	F	T	F	T
validation_epoch_end*	F	T	F	T

Parameters

name¶ – key to log
value¶ – value to log. Can be a float, Tensor, Metric, or a dictionary of the former.
prog_bar¶ – if True logs to the progress bar
logger¶ – if True logs to the logger
on_step¶ – if True logs at this step. None auto-logs at the training_step but not validation/test_step
on_epoch¶ – if True logs epoch accumulated metrics. None auto-logs at the val/test step but not training_step
reduce_fx¶ – reduction function over step values for end of epoch. torch.mean() by default.
enable_graph¶ – if True, will not auto detach the graph
sync_dist¶ – if True, reduces the metric across GPUs/TPUs
sync_dist_group¶ – the ddp group to sync across
add_dataloader_idx¶ – 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¶ – Current batch_size. This will be directly inferred from the loaded batch, but some data structures might need to explicitly provide it.
metric_attribute¶ – 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¶ – 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.

log_dict(dictionary, prog_bar=False, logger=True, on_step=None, on_epoch=None, reduce_fx='default', tbptt_reduce_fx=None, tbptt_pad_token=None, enable_graph=False, sync_dist=False, sync_dist_op=None, sync_dist_group=None, add_dataloader_idx=True)[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, Number, Mapping[str, Union[Metric, Tensor, Number]]]]) – 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
on_epoch¶ (Optional[bool]) – if True logs epoch accumulated metrics. None auto-logs for val/test step but not training_step
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
sync_dist_group¶ (Optional[Any]) – the ddp group 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

Return type

None

log_grad_norm(grad_norm_dict)[source]¶

Override this method to change the default behaviour of log_grad_norm.

Parameters: grad_norm_dict¶ (Dict[str, Tensor]) – Dictionary containing current grad norm metrics

Example:

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

Return type: None

lr_schedulers()[source]¶

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

Return type: Union[Any, List[Any], None]
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)[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¶ – Additional positional arguments to be forwarded to backward()
**kwargs¶ – Additional keyword arguments to be forwarded to backward()

Return type

None

optimizer_step(epoch=None, batch_idx=None, optimizer=None, optimizer_idx=None, optimizer_closure=None, on_tpu=None, using_native_amp=None, using_lbfgs=None)[source]¶

Override this method to adjust the default way the Trainer calls each optimizer. By default, Lightning calls step() and zero_grad() as shown in the example once per optimizer. This method (and zero_grad()) won’t be called during the accumulation phase when Trainer(accumulate_grad_batches != 1).

Warning

If you are overriding this method, make sure that you pass the optimizer_closure parameter to optimizer.step() function as shown in the examples. This ensures that training_step(), optimizer.zero_grad(), backward() are called within the training loop.

Parameters

epoch¶ (Optional[int]) – Current epoch
batch_idx¶ (Optional[int]) – Index of current batch
optimizer¶ (Optional[Optimizer]) – A PyTorch optimizer
optimizer_idx¶ (Optional[int]) – If you used multiple optimizers, this indexes into that list.
optimizer_closure¶ (Optional[Callable]) – Closure for all optimizers
on_tpu¶ (Optional[bool]) – True if TPU backward is required
using_native_amp¶ (Optional[bool]) – True if using native amp
using_lbfgs¶ (Optional[bool]) – True if the matching optimizer is torch.optim.LBFGS

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)

    # ...
    # 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,
):
    # warm up lr
    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

    # update params
    optimizer.step(closure=optimizer_closure)

Return type: None

optimizer_zero_grad(epoch, batch_idx, optimizer, optimizer_idx)[source]¶

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

Parameters

epoch¶ (int) – Current epoch
batch_idx¶ (int) – Index of current batch
optimizer¶ (Optimizer) – A PyTorch optimizer
optimizer_idx¶ (int) – If you used multiple optimizers this indexes into that list.

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)[source]¶

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

Parameters: use_pl_optimizer¶ (bool) – If True, will wrap the optimizer(s) in a LightningOptimizer for automatic handling of precision 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=None)[source]¶

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

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

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

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

Example

class MyModel(LightningModule):

    def predicts_step(self, batch, batch_idx, dataloader_idx):
        return self(batch)

dm = ...
model = MyModel()
trainer = Trainer(gpus=2)
predictions = trainer.predict(model, dm)

Parameters

batch¶ (Any) – Current batch
batch_idx¶ (int) – Index of current batch
dataloader_idx¶ (Optional[int]) – Index of the current dataloader

Return type

Any

Returns

Predicted output

print(*args, **kwargs)[source]¶

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

Parameters

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

Example:

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

Return type: None

summarize(mode='top', max_depth=None)[source]¶

Summarize this LightningModule.

Parameters

mode¶ (Optional[str]) –
Can be either 'top' (summarize only direct submodules) or 'full' (summarize all layers).

Deprecated since version v1.4: This parameter was deprecated in v1.4 in favor of max_depth and will be removed in v1.6.
max_depth¶ (Optional[int]) – The maximum depth of layer nesting that the summary will include. A value of 0 turns the layer summary off. Default: 1.

Return type

Optional[ModelSummary]

Returns

The model summary object

tbptt_split_batch(batch, split_size)[source]¶

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

Parameters

batch¶ (Tensor) – Current batch
split_size¶ (int) – The size of the split

Return type

list

Returns

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

Examples:

def tbptt_split_batch(self, batch, split_size):
  splits = []
  for t in range(0, time_dims[0], split_size):
      batch_split = []
      for i, x in enumerate(batch):
          if isinstance(x, torch.Tensor):
              split_x = x[:, t:t + split_size]
          elif isinstance(x, collections.Sequence):
              split_x = [None] * len(x)
              for batch_idx in range(len(x)):
                  split_x[batch_idx] = x[batch_idx][t:t + split_size]

          batch_split.append(split_x)

      splits.append(batch_split)

  return splits

Note

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

test_epoch_end(outputs)[source]¶

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

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

Parameters: outputs¶ (List[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)[source]¶

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

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

Parameters

batch¶ (Tensor | (Tensor, …) | [Tensor, …]) – The output of your DataLoader. A tensor, tuple or list.
batch_idx¶ (int) – The index of this batch.
dataloader_idx¶ (int) – The index of the dataloader that produced this batch (only if multiple test 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):
    ...

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.

# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx):
    # 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)[source]¶

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

Note

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

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

Parameters: batch_parts_outputs¶ – What you return in test_step() for each batch part.
Return type: Union[Tensor, Dict[str, Any], None]
Returns: None or anything

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

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


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

    out = self.encoder(x)
    return out


def test_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)