scvi.train.TrainingPlan#

class scvi.train.TrainingPlan(module, lr=0.001, weight_decay=1e-06, eps=0.01, optimizer='Adam', n_steps_kl_warmup=None, n_epochs_kl_warmup=400, reduce_lr_on_plateau=False, lr_factor=0.6, lr_patience=30, lr_threshold=0.0, lr_scheduler_metric='elbo_validation', lr_min=0, **loss_kwargs)[source]#

Bases: pytorch_lightning.core.lightning.LightningModule

Lightning module task to train scvi-tools modules.

The training plan is a PyTorch Lightning Module that is initialized with a scvi-tools module object. It configures the optimizers, defines the training step and validation step, and computes metrics to be recorded during training. The training step and validation step are functions that take data, run it through the model and return the loss, which will then be used to optimize the model parameters in the Trainer. Overall, custom training plans can be used to develop complex inference schemes on top of modules. The following developer tutorial will familiarize you more with training plans and how to use them: Constructing a high-level model.

Parameters
module : BaseModuleClass

A module instance from class BaseModuleClass.

lr : float (default: 0.001)

Learning rate used for optimization.

weight_decay : float (default: 1e-06)

Weight decay used in optimizatoin.

eps : float (default: 0.01)

eps used for optimization.

optimizer : {‘Adam’, ‘AdamW’}Literal[‘Adam’, ‘AdamW’] (default: 'Adam')

One of “Adam” (Adam), “AdamW” (AdamW).

n_steps_kl_warmup : int | NoneOptional[int] (default: None)

Number of training steps (minibatches) to scale weight on KL divergences from 0 to 1. Only activated when n_epochs_kl_warmup is set to None.

n_epochs_kl_warmup : int | NoneOptional[int] (default: 400)

Number of epochs to scale weight on KL divergences from 0 to 1. Overrides n_steps_kl_warmup when both are not None.

reduce_lr_on_plateau : bool (default: False)

Whether to monitor validation loss and reduce learning rate when validation set lr_scheduler_metric plateaus.

lr_factor : float (default: 0.6)

Factor to reduce learning rate.

lr_patience : int (default: 30)

Number of epochs with no improvement after which learning rate will be reduced.

lr_threshold : float (default: 0.0)

Threshold for measuring the new optimum.

lr_scheduler_metric : {‘elbo_validation’, ‘reconstruction_loss_validation’, ‘kl_local_validation’}Literal[‘elbo_validation’, ‘reconstruction_loss_validation’, ‘kl_local_validation’] (default: 'elbo_validation')

Which metric to track for learning rate reduction.

lr_min : float (default: 0)

Minimum learning rate allowed

**loss_kwargs

Keyword args to pass to the loss method of the module. kl_weight should not be passed here and is handled automatically.

Attributes table#

kl_weight

Scaling factor on KL divergence during training.

n_obs_training

Number of observations in the training set.

n_obs_validation

Number of observations in the validation set.

Methods table#

compute_and_log_metrics(loss_recorder, ...)

Computes and logs metrics.

configure_optimizers()

Choose what optimizers and learning-rate schedulers to use in your optimization.

forward(*args, **kwargs)

Passthrough to model.forward().

initialize_train_metrics()

Initialize train related metrics.

initialize_val_metrics()

Initialize train related metrics.

training_step(batch, batch_idx[, optimizer_idx])

Here you compute and return the training loss and some additional metrics for e.g.

validation_step(batch, batch_idx)

Operates on a single batch of data from the validation set.

Attributes#

CHECKPOINT_HYPER_PARAMS_KEY#

TrainingPlan.CHECKPOINT_HYPER_PARAMS_KEY = 'hyper_parameters'#

CHECKPOINT_HYPER_PARAMS_NAME#

TrainingPlan.CHECKPOINT_HYPER_PARAMS_NAME = 'hparams_name'#

CHECKPOINT_HYPER_PARAMS_TYPE#

TrainingPlan.CHECKPOINT_HYPER_PARAMS_TYPE = 'hparams_type'#

T_destination#

TrainingPlan.T_destination#

alias of TypeVar(‘T_destination’, bound=Mapping[str, torch.Tensor])

alias of TypeVar(‘T_destination’, bound=Mapping[str, torch.Tensor]) .. autoattribute:: TrainingPlan.T_destination automatic_optimization ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

TrainingPlan.automatic_optimization#

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

Return type

bool

current_epoch#

TrainingPlan.current_epoch#

The current epoch in the Trainer.

If no Trainer is attached, this propery is 0.

Return type

int

device#

TrainingPlan.device#
Return type

str | deviceUnion[str, device]

dtype#

TrainingPlan.dtype#
Return type

str | dtypeUnion[str, dtype]

dump_patches#

TrainingPlan.dump_patches: bool = False#

This allows better BC support for load_state_dict(). In state_dict(), the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See _load_from_state_dict on how to use this information in loading.

If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.

example_input_array#

TrainingPlan.example_input_array#

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)

Return type

Any

global_rank#

TrainingPlan.global_rank#

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

Return type

int

global_step#

TrainingPlan.global_step#

Total training batches seen across all epochs.

If no Trainer is attached, this propery is 0.

Return type

int

hparams#

TrainingPlan.hparams#

The collection of hyperparameters saved with save_hyperparameters(). It is mutable by the user. For the frozen set of initial hyperparameters, use hparams_initial.

Returns

mutable hyperparameters dicionary

Return type

Union[AttributeDict, dict, Namespace]

hparams_initial#

TrainingPlan.hparams_initial#

The collection of hyperparameters saved with save_hyperparameters(). These contents are read-only. Manual updates to the saved hyperparameters can instead be performed through hparams.

Returns

immutable initial hyperparameters

Return type

AttributeDict

kl_weight#

TrainingPlan.kl_weight#

Scaling factor on KL divergence during training.

loaded_optimizer_states_dict#

TrainingPlan.loaded_optimizer_states_dict#
Return type

dict

local_rank#

TrainingPlan.local_rank#

The index of the current process within a single node.

Return type

int

logger#

TrainingPlan.logger#

Reference to the logger object in the Trainer.

model_size#

TrainingPlan.model_size#

Returns the model size in MegaBytes (MB)

Note

This property will not return correct value for Deepspeed (stage 3) and fully-sharded training.

Return type

float

n_obs_training#

TrainingPlan.n_obs_training#

Number of observations in the training set.

This will update the loss kwargs for loss rescaling.

Notes

This can get set after initialization

n_obs_validation#

TrainingPlan.n_obs_validation#

Number of observations in the validation set.

This will update the loss kwargs for loss rescaling.

Notes

This can get set after initialization

on_gpu#

TrainingPlan.on_gpu#

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

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

truncated_bptt_steps#

TrainingPlan.truncated_bptt_steps#

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

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

Return type

int

training#

TrainingPlan.training: bool#

Methods#

compute_and_log_metrics#

TrainingPlan.compute_and_log_metrics(loss_recorder, elbo_metric)[source]#

Computes and logs metrics.

Parameters
loss_recorder : LossRecorder

LossRecorder object from scvi-tools module

metric_attr_name

The name of the torch metric object to use

configure_optimizers#

TrainingPlan.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_scheduler_config).

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

  • Tuple of dictionaries as described above, with an optional "frequency" key.

  • None - Fit will run without any optimizer.

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

lr_scheduler_config = {
    # REQUIRED: The scheduler instance
    "scheduler": lr_scheduler,
    # The unit of the scheduler's step size, could also be 'step'.
    # 'epoch' updates the scheduler on epoch end whereas 'step'
    # updates it after a optimizer update.
    "interval": "epoch",
    # How many epochs/steps should pass between calls to
    # `scheduler.step()`. 1 corresponds to updating the learning
    # rate after every epoch/step.
    "frequency": 1,
    # Metric to to monitor for schedulers like `ReduceLROnPlateau`
    "monitor": "val_loss",
    # If set to `True`, will enforce that the value specified 'monitor'
    # is available when the scheduler is updated, thus stopping
    # training if not found. If set to `False`, it will only produce a warning
    "strict": True,
    # If using the `LearningRateMonitor` callback to monitor the
    # learning rate progress, this keyword can be used to specify
    # a custom logged name
    "name": None,
}

When there are schedulers in which the .step() method is conditioned on a value, such as the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on.

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

Note

The frequency value specified in a dict along with the optimizer key is an int corresponding to the number of sequential batches optimized with the specific optimizer. It should be given to none or to all of the optimizers. There is a difference between passing multiple optimizers in a list, and passing multiple optimizers in dictionaries with a frequency of 1:

  • In the former case, all optimizers will operate on the given batch in each optimization step.

  • In the latter, only one optimizer will operate on the given batch at every step.

This is different from the frequency value specified in the lr_scheduler_config mentioned above.

def configure_optimizers(self):
    optimizer_one = torch.optim.SGD(self.model.parameters(), lr=0.01)
    optimizer_two = torch.optim.SGD(self.model.parameters(), lr=0.01)
    return [
        {"optimizer": optimizer_one, "frequency": 5},
        {"optimizer": optimizer_two, "frequency": 10},
    ]

In this example, the first optimizer will be used for the first 5 steps, the second optimizer for the next 10 steps and that cycle will continue. If an LR scheduler is specified for an optimizer using the lr_scheduler key in the above dict, the scheduler will only be updated when its optimizer is being used.

Examples:

# most cases. no learning rate scheduler
def configure_optimizers(self):
    return Adam(self.parameters(), lr=1e-3)

# multiple optimizer case (e.g.: GAN)
def configure_optimizers(self):
    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
    dis_opt = Adam(self.model_dis.parameters(), lr=0.02)
    return gen_opt, dis_opt

# example with learning rate schedulers
def configure_optimizers(self):
    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
    dis_opt = Adam(self.model_dis.parameters(), lr=0.02)
    dis_sch = CosineAnnealing(dis_opt, T_max=10)
    return [gen_opt, dis_opt], [dis_sch]

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

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

Note

Some things to know:

  • Lightning calls .backward() and .step() on each optimizer 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#

TrainingPlan.forward(*args, **kwargs)[source]#

Passthrough to model.forward().

initialize_train_metrics#

TrainingPlan.initialize_train_metrics()[source]#

Initialize train related metrics.

initialize_val_metrics#

TrainingPlan.initialize_val_metrics()[source]#

Initialize train related metrics.

training_step#

TrainingPlan.training_step(batch, batch_idx, optimizer_idx=0)[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 your DataLoader. A tensor, tuple or list.

batch_idx : int

Integer displaying index of this batch

optimizer_idx : int

When using multiple optimizers, this argument will also be present.

hiddens : Any

Passed in if :paramref:`~pytorch_lightning.core.lightning.LightningModule.truncated_bptt_steps` > 0.

Returns

Any of.

  • Tensor - The loss tensor

  • dict - A dictionary. Can include any keys, but must include the key 'loss'

  • None - Training will skip to the next batch. This is only for automatic optimization.

    This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.

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

Example:

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

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

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

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

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

Note

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

validation_step#

TrainingPlan.validation_step(batch, batch_idx)[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 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.