LightningModule¶
- class pytorch_lightning.core.LightningModule(*args, **kwargs)[source]¶
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)[source]¶
Allows users to call
self.all_gather()
from the LightningModule, thus making theall_gather
operation accelerator agnostic.all_gather
is a function provided by accelerators to gather a tensor from several distributed processes.- Parameters:
data¶ (
Union
[Tensor
,Dict
,List
,Tuple
]) – int, float, tensor of shape (batch, …), or a (possibly nested) collection thereof.group¶ (
Optional
[Any
]) – the process group to gather results from. Defaults to all processes (world)sync_grads¶ (
bool
) – flag that allows users to synchronize gradients for the all_gather operation
- Return type:
- Returns:
A tensor of shape (world_size, batch, …), or if the input was a collection the output will also be a collection with tensors of this shape.
- backward(loss, optimizer, optimizer_idx, *args, **kwargs)[source]¶
Called to perform backward on the loss returned in
training_step()
. Override this hook with your own implementation if you need to.- Parameters:
loss¶ (
Tensor
) – The loss tensor returned bytraining_step()
. If gradient accumulation is used, the loss here holds the normalized value (scaled by 1 / accumulation steps).optimizer¶ (
Optional
[Steppable
]) – Current optimizer being used.None
if using manual optimization.optimizer_idx¶ (
Optional
[int
]) – Index of the current optimizer being used.None
if using manual optimization.
- Return type:
Example:
def backward(self, loss, optimizer, optimizer_idx): loss.backward()
- clip_gradients(optimizer, gradient_clip_val=None, gradient_clip_algorithm=None)[source]¶
Handles gradient clipping internally.
Note
Do not override this method. If you want to customize gradient clipping, consider using
configure_gradient_clipping()
method.
- configure_callbacks()[source]¶
Configure model-specific callbacks. When the model gets attached, e.g., when
.fit()
or.test()
gets called, the list or a callback returned here will be merged with the list of callbacks passed to the Trainer’scallbacks
argument. If a callback returned here has the same type as one or several callbacks already present in the Trainer’s callbacks list, it will take priority and replace them. In addition, Lightning will make sureModelCheckpoint
callbacks run last.- Return type:
- Returns:
A callback or a list of callbacks which will extend the list of callbacks in the Trainer.
Example:
def configure_callbacks(self): early_stop = EarlyStopping(monitor="val_acc", mode="max") checkpoint = ModelCheckpoint(monitor="val_loss") return [early_stop, checkpoint]
- configure_gradient_clipping(optimizer, optimizer_idx, gradient_clip_val=None, gradient_clip_algorithm=None)[source]¶
Perform gradient clipping for the optimizer parameters. Called before
optimizer_step()
.- Parameters:
optimizer_idx¶ (
int
) – Index of the current optimizer being used.gradient_clip_val¶ (
Union
[int
,float
,None
]) – The value at which to clip gradients. By default value passed in Trainer will be available here.gradient_clip_algorithm¶ (
Optional
[str
]) – The gradient clipping algorithm to use. By default value passed in Trainer will be available here.
- Return type:
Example:
# Perform gradient clipping on gradients associated with discriminator (optimizer_idx=1) in GAN def configure_gradient_clipping(self, optimizer, optimizer_idx, gradient_clip_val, gradient_clip_algorithm): if optimizer_idx == 1: # Lightning will handle the gradient clipping self.clip_gradients( optimizer, gradient_clip_val=gradient_clip_val, gradient_clip_algorithm=gradient_clip_algorithm ) else: # implement your own custom logic to clip gradients for generator (optimizer_idx=0)
- configure_optimizers()[source]¶
Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple.
- Return type:
- Returns:
Any of these 6 options.
Single optimizer.
List or Tuple of optimizers.
Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple
lr_scheduler_config
).Dictionary, with an
"optimizer"
key, and (optionally) a"lr_scheduler"
key whose value is a single LR scheduler orlr_scheduler_config
.Tuple of dictionaries as described above, with an optional
"frequency"
key.None - Fit will run without any optimizer.
The
lr_scheduler_config
is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.lr_scheduler_config = { # REQUIRED: The scheduler instance "scheduler": lr_scheduler, # The unit of the scheduler's step size, could also be 'step'. # 'epoch' updates the scheduler on epoch end whereas 'step' # updates it after a optimizer update. "interval": "epoch", # How many epochs/steps should pass between calls to # `scheduler.step()`. 1 corresponds to updating the learning # rate after every epoch/step. "frequency": 1, # Metric to to monitor for schedulers like `ReduceLROnPlateau` "monitor": "val_loss", # If set to `True`, will enforce that the value specified 'monitor' # is available when the scheduler is updated, thus stopping # training if not found. If set to `False`, it will only produce a warning "strict": True, # If using the `LearningRateMonitor` callback to monitor the # learning rate progress, this keyword can be used to specify # a custom logged name "name": None, }
When there are schedulers in which the
.step()
method is conditioned on a value, such as thetorch.optim.lr_scheduler.ReduceLROnPlateau
scheduler, Lightning requires that thelr_scheduler_config
contains the keyword"monitor"
set to the metric name that the scheduler should be conditioned on.# The ReduceLROnPlateau scheduler requires a monitor def configure_optimizers(self): optimizer = Adam(...) return { "optimizer": optimizer, "lr_scheduler": { "scheduler": ReduceLROnPlateau(optimizer, ...), "monitor": "metric_to_track", "frequency": "indicates how often the metric is updated" # If "monitor" references validation metrics, then "frequency" should be set to a # multiple of "trainer.check_val_every_n_epoch". }, } # In the case of two optimizers, only one using the ReduceLROnPlateau scheduler def configure_optimizers(self): optimizer1 = Adam(...) optimizer2 = SGD(...) scheduler1 = ReduceLROnPlateau(optimizer1, ...) scheduler2 = LambdaLR(optimizer2, ...) return ( { "optimizer": optimizer1, "lr_scheduler": { "scheduler": scheduler1, "monitor": "metric_to_track", }, }, {"optimizer": optimizer2, "lr_scheduler": scheduler2}, )
Metrics can be made available to monitor by simply logging it using
self.log('metric_to_track', metric_val)
in yourLightningModule
.Note
The
frequency
value specified in a dict along with theoptimizer
key is an int corresponding to the number of sequential batches optimized with the specific optimizer. It should be given to none or to all of the optimizers. There is a difference between passing multiple optimizers in a list, and passing multiple optimizers in dictionaries with a frequency of 1:In the former case, all optimizers will operate on the given batch in each optimization step.
In the latter, only one optimizer will operate on the given batch at every step.
This is different from the
frequency
value specified in thelr_scheduler_config
mentioned above.def configure_optimizers(self): optimizer_one = torch.optim.SGD(self.model.parameters(), lr=0.01) optimizer_two = torch.optim.SGD(self.model.parameters(), lr=0.01) return [ {"optimizer": optimizer_one, "frequency": 5}, {"optimizer": optimizer_two, "frequency": 10}, ]
In this example, the first optimizer will be used for the first 5 steps, the second optimizer for the next 10 steps and that cycle will continue. If an LR scheduler is specified for an optimizer using the
lr_scheduler
key in the above dict, the scheduler will only be updated when its optimizer is being used.Examples:
# most cases. no learning rate scheduler def configure_optimizers(self): return Adam(self.parameters(), lr=1e-3) # multiple optimizer case (e.g.: GAN) def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) return gen_opt, dis_opt # example with learning rate schedulers def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) dis_sch = CosineAnnealing(dis_opt, T_max=10) return [gen_opt, dis_opt], [dis_sch] # example with step-based learning rate schedulers # each optimizer has its own scheduler def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) gen_sch = { 'scheduler': ExponentialLR(gen_opt, 0.99), 'interval': 'step' # called after each training step } dis_sch = CosineAnnealing(dis_opt, T_max=10) # called every epoch return [gen_opt, dis_opt], [gen_sch, dis_sch] # example with optimizer frequencies # see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1 # https://arxiv.org/abs/1704.00028 def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_dis.parameters(), lr=0.02) n_critic = 5 return ( {'optimizer': dis_opt, 'frequency': n_critic}, {'optimizer': gen_opt, 'frequency': 1} )
Note
Some things to know:
Lightning calls
.backward()
and.step()
on each optimizer as needed.If learning rate scheduler is specified in
configure_optimizers()
with key"interval"
(default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s.step()
method automatically in case of automatic optimization.If you use 16-bit precision (
precision=16
), Lightning will automatically handle the optimizers.If you use multiple optimizers,
training_step()
will have an additionaloptimizer_idx
parameter.If you use
torch.optim.LBFGS
, Lightning handles the closure function automatically for you.If you use multiple optimizers, gradients will be calculated only for the parameters of current optimizer at each training step.
If you need to control how often those optimizers step or override the default
.step()
schedule, override theoptimizer_step()
hook.
- forward(*args, **kwargs)[source]¶
Same as
torch.nn.Module.forward()
.
- freeze()[source]¶
Freeze all params for inference.
Example:
model = MyLightningModule(...) model.freeze()
- Return type:
- log(name, value, prog_bar=False, logger=True, on_step=None, on_epoch=None, reduce_fx='mean', enable_graph=False, sync_dist=False, sync_dist_group=None, add_dataloader_idx=True, batch_size=None, metric_attribute=None, rank_zero_only=False)[source]¶
Log a key, value pair.
Example:
self.log('train_loss', loss)
The default behavior per hook is documented here: Automatic Logging.
- Parameters:
value¶ (
Union
[Metric
,Tensor
,int
,float
,Mapping
[str
,Union
[Metric
,Tensor
,int
,float
]]]) – value to log. Can be afloat
,Tensor
,Metric
, or a dictionary of the former.on_step¶ (
Optional
[bool
]) – ifTrue
logs at this step. The default value is determined by the hook. See Automatic Logging for details.on_epoch¶ (
Optional
[bool
]) – ifTrue
logs epoch accumulated metrics. The default value is determined by the hook. See Automatic Logging for details.reduce_fx¶ (
Union
[str
,Callable
]) – reduction function over step values for end of epoch.torch.mean()
by default.enable_graph¶ (
bool
) – ifTrue
, will not auto detach the graph.sync_dist¶ (
bool
) – ifTrue
, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.sync_dist_group¶ (
Optional
[Any
]) – the DDP group to sync across.add_dataloader_idx¶ (
bool
) – ifTrue
, appends the index of the current dataloader to the name (when using multiple dataloaders). If False, user needs to give unique names for each dataloader to not mix the values.batch_size¶ (
Optional
[int
]) – Current batch_size. This will be directly inferred from the loaded batch, but for some data structures you might need to explicitly provide it.metric_attribute¶ (
Optional
[str
]) – To restore the metric state, Lightning requires the reference of thetorchmetrics.Metric
in your model. This is found automatically if it is a model attribute.rank_zero_only¶ (
bool
) – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.
- Return type:
- log_dict(dictionary, prog_bar=False, logger=True, on_step=None, on_epoch=None, reduce_fx='mean', enable_graph=False, sync_dist=False, sync_dist_group=None, add_dataloader_idx=True, batch_size=None, rank_zero_only=False)[source]¶
Log a dictionary of values at once.
Example:
values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n} self.log_dict(values)
- Parameters:
dictionary¶ (
Mapping
[str
,Union
[Metric
,Tensor
,int
,float
,Mapping
[str
,Union
[Metric
,Tensor
,int
,float
]]]]) – key value pairs. The values can be afloat
,Tensor
,Metric
, or a dictionary of the former.on_step¶ (
Optional
[bool
]) – ifTrue
logs at this step.None
auto-logs for training_step but not validation/test_step. The default value is determined by the hook. See Automatic Logging for details.on_epoch¶ (
Optional
[bool
]) – ifTrue
logs epoch accumulated metrics.None
auto-logs for val/test step but nottraining_step
. The default value is determined by the hook. See Automatic Logging for details.reduce_fx¶ (
Union
[str
,Callable
]) – reduction function over step values for end of epoch.torch.mean()
by default.enable_graph¶ (
bool
) – ifTrue
, will not auto-detach the graphsync_dist¶ (
bool
) – ifTrue
, reduces the metric across GPUs/TPUs. Use with care as this may lead to a significant communication overhead.sync_dist_group¶ (
Optional
[Any
]) – the ddp group to sync across.add_dataloader_idx¶ (
bool
) – ifTrue
, appends the index of the current dataloader to the name (when using multiple). IfFalse
, user needs to give unique names for each dataloader to not mix values.batch_size¶ (
Optional
[int
]) – Current batch size. This will be directly inferred from the loaded batch, but some data structures might need to explicitly provide it.rank_zero_only¶ (
bool
) – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.
- Return type:
- log_grad_norm(grad_norm_dict)[source]¶
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:
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)[source]¶
Override this method to adjust the default way the
Trainer
calls each scheduler. By default, Lightning callsstep()
and as shown in the example for each scheduler based on itsinterval
.- Parameters:
- Return type:
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()[source]¶
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 inconfigure_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¶ (
Any
) – Additional positional arguments to be forwarded tobackward()
**kwargs¶ (
Any
) – Additional keyword arguments to be forwarded tobackward()
- Return type:
- optimizer_step(epoch, batch_idx, optimizer, optimizer_idx=0, optimizer_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False)[source]¶
Override this method to adjust the default way the
Trainer
calls each optimizer.By default, Lightning calls
step()
andzero_grad()
as shown in the example once per optimizer. This method (andzero_grad()
) won’t be called during the accumulation phase whenTrainer(accumulate_grad_batches != 1)
. Overriding this hook has no benefit with manual optimization.- Parameters:
optimizer¶ (
Union
[Optimizer
,LightningOptimizer
]) – A PyTorch optimizeroptimizer_idx¶ (
int
) – If you used multiple optimizers, this indexes into that list.optimizer_closure¶ (
Optional
[Callable
[[],Any
]]) – The optimizer closure. This closure must be executed as it includes the calls totraining_step()
,optimizer.zero_grad()
, andbackward()
.using_lbfgs¶ (
bool
) – True if the matching optimizer istorch.optim.LBFGS
- Return type:
Examples:
# DEFAULT def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs): optimizer.step(closure=optimizer_closure) # Alternating schedule for optimizer steps (i.e.: GANs) def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs): # update generator opt every step if optimizer_idx == 0: optimizer.step(closure=optimizer_closure) # update discriminator opt every 2 steps if optimizer_idx == 1: if (batch_idx + 1) % 2 == 0 : optimizer.step(closure=optimizer_closure) else: # call the closure by itself to run `training_step` + `backward` without an optimizer step optimizer_closure() # ... # add as many optimizers as you want
Here’s another example showing how to use this for more advanced things such as learning rate warm-up:
# learning rate warm-up def optimizer_step( self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs, ): # update params optimizer.step(closure=optimizer_closure) # manually warm up lr without a scheduler if self.trainer.global_step < 500: lr_scale = min(1.0, float(self.trainer.global_step + 1) / 500.0) for pg in optimizer.param_groups: pg["lr"] = lr_scale * self.learning_rate
- optimizer_zero_grad(epoch, batch_idx, optimizer, optimizer_idx)[source]¶
Override this method to change the default behaviour of
optimizer.zero_grad()
.- Parameters:
- Return type:
Examples:
# DEFAULT def optimizer_zero_grad(self, epoch, batch_idx, optimizer, optimizer_idx): optimizer.zero_grad() # Set gradients to `None` instead of zero to improve performance. def optimizer_zero_grad(self, epoch, batch_idx, optimizer, optimizer_idx): optimizer.zero_grad(set_to_none=True)
See
torch.optim.Optimizer.zero_grad()
for the explanation of the above example.
- optimizers(use_pl_optimizer=True)[source]¶
Returns the optimizer(s) that are being used during training. Useful for manual optimization.
- Parameters:
use_pl_optimizer¶ (
bool
) – IfTrue
, will wrap the optimizer(s) in aLightningOptimizer
for automatic handling of precision and profiling.- Return type:
Union
[Optimizer
,LightningOptimizer
,List
[Optimizer
],List
[LightningOptimizer
]]- Returns:
A single optimizer, or a list of optimizers in case multiple ones are present.
- predict_step(batch, batch_idx, dataloader_idx=0)[source]¶
Step function called during
predict()
. By default, it callsforward()
. Override to add any processing logic.The
predict_step()
is used to scale inference on multi-devices.To prevent an OOM error, it is possible to use
BasePredictionWriter
callback to write the predictions to disk or database after each batch or on epoch end.The
BasePredictionWriter
should be used while using a spawn based accelerator. This happens forTrainer(strategy="ddp_spawn")
or training on 8 TPU cores withTrainer(accelerator="tpu", devices=8)
as predictions won’t be returned.Example
class MyModel(LightningModule): def predict_step(self, batch, batch_idx, dataloader_idx=0): return self(batch) dm = ... model = MyModel() trainer = Trainer(accelerator="gpu", devices=2) predictions = trainer.predict(model, dm)
- print(*args, **kwargs)[source]¶
Prints only from process 0. Use this in any distributed mode to log only once.
- Parameters:
- Return type:
Example:
def forward(self, x): self.print(x, 'in forward')
- tbptt_split_batch(batch, split_size)[source]¶
When using truncated backpropagation through time, each batch must be split along the time dimension. Lightning handles this by default, but for custom behavior override this function.
- Parameters:
- Return type:
- Returns:
List of batch splits. Each split will be passed to
training_step()
to enable truncated back propagation through time. The default implementation splits root level Tensors and Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.
Examples:
def tbptt_split_batch(self, batch, split_size): splits = [] for t in range(0, time_dims[0], split_size): batch_split = [] for i, x in enumerate(batch): if isinstance(x, torch.Tensor): split_x = x[:, t:t + split_size] elif isinstance(x, collections.abc.Sequence): split_x = [None] * len(x) for batch_idx in range(len(x)): split_x[batch_idx] = x[batch_idx][t:t + split_size] batch_split.append(split_x) splits.append(batch_split) return splits
Note
Called in the training loop after
on_train_batch_start()
iftruncated_bptt_steps
> 0. Each returned batch split is passed separately totraining_step()
.
- test_epoch_end(outputs)[source]¶
Called at the end of a test epoch with the output of all test steps.
# the pseudocode for these calls test_outs = [] for test_batch in test_data: out = test_step(test_batch) test_outs.append(out) test_epoch_end(test_outs)
- Parameters:
outputs¶ (
Union
[List
[Union
[Tensor
,Dict
[str
,Any
]]],List
[List
[Union
[Tensor
,Dict
[str
,Any
]]]]]) – List of outputs you defined intest_step_end()
, or if there are multiple dataloaders, a list containing a list of outputs for each dataloader- Return type:
- Returns:
None
Note
If you didn’t define a
test_step()
, this won’t be called.Examples
With a single dataloader:
def test_epoch_end(self, outputs): # do something with the outputs of all test batches all_test_preds = test_step_outputs.predictions some_result = calc_all_results(all_test_preds) self.log(some_result)
With multiple dataloaders, outputs will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each test step for that dataloader.
def test_epoch_end(self, outputs): final_value = 0 for dataloader_outputs in outputs: for test_step_out in dataloader_outputs: # do something final_value += test_step_out self.log("final_metric", final_value)
- test_step(*args, **kwargs)[source]¶
Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.
# the pseudocode for these calls test_outs = [] for test_batch in test_data: out = test_step(test_batch) test_outs.append(out) test_epoch_end(test_outs)
- Parameters:
batch¶ – The output of your
DataLoader
.batch_idx¶ – The index of this batch.
dataloader_id¶ – The index of the dataloader that produced this batch. (only if multiple test dataloaders used).
- Return type:
- Returns:
Any of.
Any object or value
None
- Testing will skip to the next batch
# if you have one test dataloader: def test_step(self, batch, batch_idx): ... # if you have multiple test dataloaders: def test_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single test dataset def test_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'test_loss': loss, 'test_acc': test_acc})
If you pass in multiple test dataloaders,
test_step()
will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.# CASE 2: multiple test dataloaders def test_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
Note
If you don’t need to test you don’t need to implement this method.
Note
When the
test_step()
is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.
- test_step_end(*args, **kwargs)[source]¶
Use this when testing with DP because
test_step()
will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss.Note
If you later switch to ddp or some other mode, this will still be called so that you don’t have to change your code.
# pseudocode sub_batches = split_batches_for_dp(batch) step_output = [test_step(sub_batch) for sub_batch in sub_batches] test_step_end(step_output)
- Parameters:
step_output¶ – What you return in
test_step()
for each batch part.- Return type:
- Returns:
None or anything
# WITHOUT test_step_end # if used in DP, this batch is 1/num_gpus large def test_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) loss = self.softmax(out) self.log("test_loss", loss) # -------------- # with test_step_end to do softmax over the full batch def test_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self.encoder(x) return out def test_step_end(self, output_results): # this out is now the full size of the batch all_test_step_outs = output_results.out loss = nce_loss(all_test_step_outs) self.log("test_loss", loss)
See also
See the Multi GPU Training guide for more details.
- to_onnx(file_path, input_sample=None, **kwargs)[source]¶
Saves the model in ONNX format.
- Parameters:
- Return type:
Example
>>> class SimpleModel(LightningModule): ... def __init__(self): ... super().__init__() ... self.l1 = torch.nn.Linear(in_features=64, out_features=4) ... ... def forward(self, x): ... return torch.relu(self.l1(x.view(x.size(0), -1)))
>>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile: ... model = SimpleModel() ... input_sample = torch.randn((1, 64)) ... model.to_onnx(tmpfile.name, input_sample, export_params=True) ... os.path.isfile(tmpfile.name) True
- to_torchscript(file_path=None, method='script', example_inputs=None, **kwargs)[source]¶
By default compiles the whole model to a
ScriptModule
. If you want to use tracing, please provided the argumentmethod='trace'
and make sure that either the example_inputs argument is provided, or the model hasexample_input_array
set. If you would like to customize the modules that are scripted you should override this method. In case you want to return multiple modules, we recommend using a dictionary.- Parameters:
file_path¶ (
Union
[str
,Path
,None
]) – Path where to save the torchscript. Default: None (no file saved).method¶ (
Optional
[str
]) – Whether to use TorchScript’s script or trace method. Default: ‘script’example_inputs¶ (
Optional
[Any
]) – An input to be used to do tracing when method is set to ‘trace’. Default: None (usesexample_input_array
)**kwargs¶ (
Any
) – Additional arguments that will be passed to thetorch.jit.script()
ortorch.jit.trace()
function.
Note
Requires the implementation of the
forward()
method.The exported script will be set to evaluation mode.
It is recommended that you install the latest supported version of PyTorch to use this feature without limitations. See also the
torch.jit
documentation for supported features.
Example
>>> class SimpleModel(LightningModule): ... def __init__(self): ... super().__init__() ... self.l1 = torch.nn.Linear(in_features=64, out_features=4) ... ... def forward(self, x): ... return torch.relu(self.l1(x.view(x.size(0), -1))) ... >>> model = SimpleModel() >>> model.to_torchscript(file_path="model.pt") >>> os.path.isfile("model.pt") >>> torch.jit.save(model.to_torchscript(file_path="model_trace.pt", method='trace', ... example_inputs=torch.randn(1, 64))) >>> os.path.isfile("model_trace.pt") True
- toggle_optimizer(optimizer, optimizer_idx)[source]¶
Makes sure only the gradients of the current optimizer’s parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.
This is only called automatically when automatic optimization is enabled and multiple optimizers are used. It works with
untoggle_optimizer()
to make sureparam_requires_grad_state
is properly reset.
- training_epoch_end(outputs)[source]¶
Called at the end of the training epoch with the outputs of all training steps. Use this in case you need to do something with all the outputs returned by
training_step()
.# the pseudocode for these calls train_outs = [] for train_batch in train_data: out = training_step(train_batch) train_outs.append(out) training_epoch_end(train_outs)
- Parameters:
outputs¶ (
List
[Union
[Tensor
,Dict
[str
,Any
]]]) – List of outputs you defined intraining_step()
. If there are multiple optimizers or when usingtruncated_bptt_steps > 0
, the lists have the dimensions (n_batches, tbptt_steps, n_optimizers). Dimensions of length 1 are squeezed.- Return type:
- Returns:
None
Note
If this method is not overridden, this won’t be called.
def training_epoch_end(self, training_step_outputs): # do something with all training_step outputs for out in training_step_outputs: ...
- training_step(*args, **kwargs)[source]¶
Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
- Parameters:
batch¶ (
Tensor
| (Tensor
, …) | [Tensor
, …]) – The output of yourDataLoader
. A tensor, tuple or list.batch_idx¶ (
int
) – Integer displaying index of this batchoptimizer_idx¶ (
int
) – When using multiple optimizers, this argument will also be present.hiddens¶ (
Any
) – Passed in iftruncated_bptt_steps
> 0.
- Return type:
- Returns:
Any of.
Tensor
- The loss tensordict
- A dictionary. Can include any keys, but must include the key'loss'
None
- Training will skip to the next batch. This is only for automatic optimization.This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.
In this step you’d normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific.
Example:
def training_step(self, batch, batch_idx): x, y, z = batch out = self.encoder(x) loss = self.loss(out, x) return loss
If you define multiple optimizers, this step will be called with an additional
optimizer_idx
parameter.# Multiple optimizers (e.g.: GANs) def training_step(self, batch, batch_idx, optimizer_idx): if optimizer_idx == 0: # do training_step with encoder ... if optimizer_idx == 1: # do training_step with decoder ...
If you add truncated back propagation through time you will also get an additional argument with the hidden states of the previous step.
# Truncated back-propagation through time def training_step(self, batch, batch_idx, hiddens): # hiddens are the hidden states from the previous truncated backprop step out, hiddens = self.lstm(data, hiddens) loss = ... return {"loss": loss, "hiddens": hiddens}
Note
The loss value shown in the progress bar is smoothed (averaged) over the last values, so it differs from the actual loss returned in train/validation step.
Note
When
accumulate_grad_batches
> 1, the loss returned here will be automatically normalized byaccumulate_grad_batches
internally.
- training_step_end(step_output)[source]¶
Use this when training with dp because
training_step()
will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss.Note
If you later switch to ddp or some other mode, this will still be called so that you don’t have to change your code
# pseudocode sub_batches = split_batches_for_dp(batch) step_output = [training_step(sub_batch) for sub_batch in sub_batches] training_step_end(step_output)
- Parameters:
step_output¶ (
Union
[Tensor
,Dict
[str
,Any
]]) – What you return in training_step for each batch part.- Return type:
- Returns:
Anything
When using the DP strategy, only a portion of the batch is inside the training_step:
def training_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) # softmax uses only a portion of the batch in the denominator loss = self.softmax(out) loss = nce_loss(loss) return loss
If you wish to do something with all the parts of the batch, then use this method to do it:
def training_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self.encoder(x) return {"pred": out} def training_step_end(self, training_step_outputs): gpu_0_pred = training_step_outputs[0]["pred"] gpu_1_pred = training_step_outputs[1]["pred"] gpu_n_pred = training_step_outputs[n]["pred"] # this softmax now uses the full batch loss = nce_loss([gpu_0_pred, gpu_1_pred, gpu_n_pred]) return loss
See also
See the Multi GPU Training guide for more details.
- unfreeze()[source]¶
Unfreeze all parameters for training.
model = MyLightningModule(...) model.unfreeze()
- Return type:
- untoggle_optimizer(optimizer_idx)[source]¶
Resets the state of required gradients that were toggled with
toggle_optimizer()
.This is only called automatically when automatic optimization is enabled and multiple optimizers are used.
- validation_epoch_end(outputs)[source]¶
Called at the end of the validation epoch with the outputs of all validation steps.
# the pseudocode for these calls val_outs = [] for val_batch in val_data: out = validation_step(val_batch) val_outs.append(out) validation_epoch_end(val_outs)
- Parameters:
outputs¶ (
Union
[List
[Union
[Tensor
,Dict
[str
,Any
]]],List
[List
[Union
[Tensor
,Dict
[str
,Any
]]]]]) – List of outputs you defined invalidation_step()
, or if there are multiple dataloaders, a list containing a list of outputs for each dataloader.- Return type:
- Returns:
None
Note
If you didn’t define a
validation_step()
, this won’t be called.Examples
With a single dataloader:
def validation_epoch_end(self, val_step_outputs): for out in val_step_outputs: ...
With multiple dataloaders, outputs will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each validation step for that dataloader.
def validation_epoch_end(self, outputs): for dataloader_output_result in outputs: dataloader_outs = dataloader_output_result.dataloader_i_outputs self.log("final_metric", final_value)
- validation_step(*args, **kwargs)[source]¶
Operates on a single batch of data from the validation set. In this step you’d might generate examples or calculate anything of interest like accuracy.
# the pseudocode for these calls val_outs = [] for val_batch in val_data: out = validation_step(val_batch) val_outs.append(out) validation_epoch_end(val_outs)
- Parameters:
batch¶ – The output of your
DataLoader
.batch_idx¶ – The index of this batch.
dataloader_idx¶ – The index of the dataloader that produced this batch. (only if multiple val dataloaders used)
- Return type:
- Returns:
Any object or value
None
- Validation will skip to the next batch
# pseudocode of order val_outs = [] for val_batch in val_data: out = validation_step(val_batch) if defined("validation_step_end"): out = validation_step_end(out) val_outs.append(out) val_outs = validation_epoch_end(val_outs)
# if you have one val dataloader: def validation_step(self, batch, batch_idx): ... # if you have multiple val dataloaders: def validation_step(self, batch, batch_idx, dataloader_idx=0): ...
Examples:
# CASE 1: A single validation dataset def validation_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # log the outputs! self.log_dict({'val_loss': loss, 'val_acc': val_acc})
If you pass in multiple val dataloaders,
validation_step()
will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.# CASE 2: multiple validation dataloaders def validation_step(self, batch, batch_idx, dataloader_idx=0): # dataloader_idx tells you which dataset this is. ...
Note
If you don’t need to validate you don’t need to implement this method.
Note
When the
validation_step()
is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled.
- validation_step_end(*args, **kwargs)[source]¶
Use this when validating with dp because
validation_step()
will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss.Note
If you later switch to ddp or some other mode, this will still be called so that you don’t have to change your code.
# pseudocode sub_batches = split_batches_for_dp(batch) step_output = [validation_step(sub_batch) for sub_batch in sub_batches] validation_step_end(step_output)
- Parameters:
step_output¶ – What you return in
validation_step()
for each batch part.- Return type:
- Returns:
None or anything
# WITHOUT validation_step_end # if used in DP, this batch is 1/num_gpus large def validation_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self.encoder(x) loss = self.softmax(out) loss = nce_loss(loss) self.log("val_loss", loss) # -------------- # with validation_step_end to do softmax over the full batch def validation_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) return out def validation_step_end(self, val_step_outputs): for out in val_step_outputs: ...
See also
See the Multi GPU Training guide for more details.
- property automatic_optimization: bool¶
If set to
False
you are responsible for calling.backward()
,.step()
,.zero_grad()
.
- property example_input_array: Any¶
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_step: int¶
Total training batches seen across all epochs.
If no Trainer is attached, this propery is 0.
- 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, thetraining_step()
receives an additional argumenthiddens
and is expected to return a hidden state.