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LightningModule

nn.Module with additional great features.

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

Bases: abc.ABC, pytorch_lightning.utilities.device_dtype_mixin.DeviceDtypeModuleMixin, 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.

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
  • tensor – 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 all_gather op

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]

Override backward with your own implementation if you need to.

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

  • optimizer (Optimizer) – Current optimizer being used

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

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

Example:

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

None

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 - List of optimizers.

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

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

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

  • None - Fit will run without any optimizer.

Note

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

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

{
    'scheduler': lr_scheduler, # The LR scheduler instance (required)
    'interval': 'epoch', # The unit of the scheduler's step size
    'frequency': 1, # The frequency of the scheduler
    'reduce_on_plateau': False, # For ReduceLROnPlateau scheduler
    'monitor': 'val_loss', # Metric for ReduceLROnPlateau to monitor
    'strict': True, # Whether to crash the training if `monitor` is not found
    'name': None, # Custom name for LearningRateMonitor to use
}

Only the scheduler key is required, the rest will be set to the defaults above.

Examples:

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

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

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

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

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

Note

Some things to know:

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

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

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

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

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

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

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

    {
        'scheduler': lr_scheduler,
        'interval': 'step',  # or 'epoch'
        'monitor': 'val_f1',
        'frequency': x,
    }
    
forward(*args, **kwargs)[source]

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

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

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

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

  • **kwargs – Keyword arguments are also possible.

Returns

Predicted output

Examples:

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

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

    # ...
    return loss

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

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

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

Freeze all params for inference.

Example:

model = MyLightningModule(...)
model.freeze()
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=torch.mean, tbptt_reduce_fx=torch.mean, tbptt_pad_token=0, enable_graph=False, sync_dist=False, sync_dist_op='mean', sync_dist_group=None, add_dataloader_idx=True)[source]

Log a key, value

Example:

self.log('train_loss', loss)

The default behavior per hook is as follows

* also applies to the test loop

LightningMoule Hook

on_step

on_epoch

prog_bar

logger

training_step

T

F

F

T

training_step_end

T

F

F

T

training_epoch_end

F

T

F

T

validation_step*

F

T

F

T

validation_step_end*

F

T

F

T

validation_epoch_end*

F

T

F

T

Parameters
  • name (str) – key name

  • value (Any) – value name

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

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

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

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

  • reduce_fx (Callable) – reduction function over step values for end of epoch. Torch.mean by default

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

  • tbptt_pad_token (int) – token to use for padding

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

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

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

  • 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

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

Log a dictonary of values at once

Example:

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

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

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

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

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

  • reduce_fx (Callable) – reduction function over step values for end of epoch. Torch.mean by default

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

  • tbptt_pad_token (int) – token to use for padding

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

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

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

manual_backward(loss, optimizer=None, *args, **kwargs)[source]

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

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

Tip

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

Tip

In manual mode we still automatically accumulate grad over batches if Trainer(accumulate_grad_batches=x) is set and you use optimizer.step()

Example:

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

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 train_step_and_backward_closure is called within run_training_batch().

Parameters

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 2 steps
    if optimizer_idx == 0:
        if batch_idx % 2 == 0 :
            optimizer.step(closure=optimizer_closure)
            optimizer.zero_grad()

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

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

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

# learning rate warm-up
def optimizer_step(self, 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., float(self.trainer.global_step + 1) / 500.)
        for pg in optimizer.param_groups:
            pg['lr'] = lr_scale * self.learning_rate

    # update params
    optimizer.step(closure=optimizer_closure)
    optimizer.zero_grad()
Return type

None

predict_step(batch, batch_idx, dataloader_idx=None)[source]

Use this function with trainer.predict(…). Override if you need to add any processing logic.

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

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

Save model arguments to hparams attribute.

Parameters
  • args – single object of dict, NameSpace or OmegaConf or string names or arguments from class __init__

  • ignore (Union[Sequence[str], str, None]) – an argument name or a list of argument names from class __init__ to be ignored

  • frame (Optional[frame]) – a frame object. Default is None

Example::
>>> class ManuallyArgsModel(LightningModule):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # manually assign arguments
...         self.save_hyperparameters('arg1', 'arg3')
...     def forward(self, *args, **kwargs):
...         ...
>>> model = ManuallyArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg3": 3.14
>>> class AutomaticArgsModel(LightningModule):
...     def __init__(self, arg1, arg2, arg3):
...         super().__init__()
...         # equivalent automatic
...         self.save_hyperparameters()
...     def forward(self, *args, **kwargs):
...         ...
>>> model = AutomaticArgsModel(1, 'abc', 3.14)
>>> model.hparams
"arg1": 1
"arg2": abc
"arg3": 3.14
>>> class SingleArgModel(LightningModule):
...     def __init__(self, params):
...         super().__init__()
...         # manually assign single argument
...         self.save_hyperparameters(params)
...     def forward(self, *args, **kwargs):
...         ...
>>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))
>>> model.hparams
"p1": 1
"p2": abc
"p3": 3.14
>>> class ManuallyArgsModel(LightningModule):
...     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
Return type

None

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[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).

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.

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)

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 – Will be passed to torch.onnx.export function.

Example

>>> class SimpleModel(LightningModule):
...     def __init__(self):
...         super().__init__()
...         self.l1 = torch.nn.Linear(in_features=64, out_features=4)
...
...     def forward(self, x):
...         return torch.relu(self.l1(x.view(x.size(0), -1)))
>>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:
...     model = SimpleModel()
...     input_sample = torch.randn((1, 64))
...     model.to_onnx(tmpfile.name, input_sample, export_params=True)
...     os.path.isfile(tmpfile.name)
True
to_torchscript(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 self.example_input_array set. If you would like to customize the modules that are scripted you should override this method. In case you want to return multiple modules, we recommend using a dictionary.

Parameters
  • file_path (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 (Use self.example_input_array)

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

Note

  • Requires the implementation of the forward() method.

  • The exported script will be set to evaluation mode.

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

Example

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

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.

Note

Only called when using multiple optimizers

Override for your own behavior

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

Parameters
  • optimizer (Optimizer) – Current optimizer used in training_loop

  • optimizer_idx (int) – Current optimizer idx in training_loop

training_epoch_end(outputs)[source]

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

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

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

Return type

None

Returns

None

Note

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

Example:

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

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

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

Note

Returning None is currently not supported for multi-GPU or TPU, or with 16-bit precision enabled.

In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.

Example:

def training_step(self, batch, batch_idx):
    x, y, z = batch
    out = self.encoder(x)
    loss = self.loss(out, x)
    return loss

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

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

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

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

Note

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

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

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

Note

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

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

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

Returns

Anything

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

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

    out = self(x)

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

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

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

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

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

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

See also

See the Multi-GPU training guide for more details.

unfreeze()[source]

Unfreeze all parameters for training.

model = MyLightningModule(...)
model.unfreeze()
Return type

None

untoggle_optimizer(optimizer_idx)[source]

Note

Only called when using multiple optimizers

Override for your own behavior

Parameters

optimizer_idx (int) – Current optimizer idx in training_loop

validation_epoch_end(outputs)[source]

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

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

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

Return type

None

Returns

None

Note

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

Examples

With a single dataloader:

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

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

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

    self.log('final_metric', final_value)
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 (Tensor | (Tensor, …) | [Tensor, …]) – The output of your DataLoader. A tensor, tuple or list.

  • batch_idx (int) – The index of this batch

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

Returns

Any of.

  • 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)

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.

# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx):
    # dataloader_idx tells you which dataset this is.

Note

If you don’t need to validate you don’t need to implement this method.

Note

When the validation_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.

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

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

Note

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

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

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

Returns

None or anything

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

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

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

    out = self(x)
    return out

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

See also

See the Multi-GPU training guide for more details.

write_prediction(name, value, filename='predictions.pt')[source]

Write predictions to disk using torch.save

Example:

self.write_prediction('pred', torch.tensor(...), filename='my_predictions.pt')
Parameters
  • name (str) – a string indicating the name to save the predictions under

  • value (Union[Tensor, List[Tensor]]) – the predictions, either a single Tensor or a list of them

  • filename (str) – name of the file to save the predictions to

Note

when running in distributed mode, calling write_prediction will create a file for each device with respective names: filename_rank_0.pt, filename_rank_1.pt, …

write_prediction_dict(predictions_dict, filename='predictions.pt')[source]

Write a dictonary of predictions to disk at once using torch.save

Example:

pred_dict = {'pred1': torch.tensor(...), 'pred2': torch.tensor(...)}
self.write_prediction_dict(pred_dict)
Parameters

predictions_dict (Dict[str, Any]) – dict containing predictions, where each prediction should either be single Tensor or a list of them

Note

when running in distributed mode, calling write_prediction_dict will create a file for each device with respective names: filename_rank_0.pt, filename_rank_1.pt, …

property automatic_optimization

If False you are responsible for calling .backward, .step, zero_grad.

Return type

bool

property current_epoch

The current epoch

Return type

int

property global_rank

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

Return type

int

property global_step

Total training batches seen across all epochs

Return type

int

property local_rank

The index of the current process within a single node.

Return type

int

property logger

Reference to the logger object in the Trainer.

property on_gpu

True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior.

precision

The precision used

trainer

Pointer to the trainer object

use_amp

True if using amp

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