SolverInterface#
- class SolverInterface(models, problem, optimizers, optimizers_kwargs, extra_features=None)[source]#
Bases:
LightningModule
Solver base class. This class inherits is a wrapper of LightningModule class, inheriting all the LightningModule methods.
- Parameters:
models (torch.nn.Module) – A torch neural network model instance.
problem (AbstractProblem) – A problem definition instance.
optimizer (list(torch.optim.Optimizer)) – A list of neural network optimizers to use.
optimizer_kwargs (list(dict)) – A list of optimizer constructor keyword args.
extra_features (list(torch.nn.Module)) – The additional input features to use as augmented input. If
None
no extra features are passed. If it is a list oftorch.nn.Module
, the extra feature list is passed to all models. If it is a list of extra features’ lists, each single list of extra feature is passed to a model.
- abstract 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.
- Returns:
Your model’s output
- abstract training_step()[source]#
Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.
- Parameters:
batch – The output of your data iterable, normally a
DataLoader
.batch_idx – The index of this batch.
dataloader_idx – The index of the dataloader that produced this batch. (only if multiple dataloaders used)
- Returns:
Tensor
- The loss tensordict
- A dictionary which can include any keys, but must include the key'loss'
in the case of automatic optimization.None
- In automatic optimization, this will skip to the next batch (but is not supported for multi-GPU, TPU, or DeepSpeed). For manual optimization, this has no special meaning, as returning the loss is not required.
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
To use multiple optimizers, you can switch to ‘manual optimization’ and control their stepping:
def __init__(self): super().__init__() self.automatic_optimization = False # Multiple optimizers (e.g.: GANs) def training_step(self, batch, batch_idx): opt1, opt2 = self.optimizers() # do training_step with encoder ... opt1.step() # do training_step with decoder ... opt2.step()
Note
When
accumulate_grad_batches
> 1, the loss returned here will be automatically normalized byaccumulate_grad_batches
internally.
- abstract 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. Optimization with multiple optimizers only works in the manual optimization mode.
- 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
.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
Some things to know:
Lightning calls
.backward()
and.step()
automatically in case of automatic optimization.If a 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 optimizer.If you use
torch.optim.LBFGS
, Lightning handles the closure function automatically for you.If you use multiple optimizers, you will have to switch to ‘manual optimization’ mode and step them yourself.
If you need to control how often the optimizer steps, override the
optimizer_step()
hook.
- property models#
The torch model.
- property optimizers#
The torch model.
- property problem#
The problem formulation.