ModelPruning¶

class pytorch_lightning.callbacks.ModelPruning(pruning_fn, parameters_to_prune=(), parameter_names=None, use_global_unstructured=True, amount=0.5, apply_pruning=True, make_pruning_permanent=True, use_lottery_ticket_hypothesis=True, resample_parameters=False, pruning_dim=None, pruning_norm=None, verbose=0, prune_on_train_epoch_end=True)[source]¶

Bases: pytorch_lightning.callbacks.callback.Callback

Model pruning Callback, using PyTorch’s prune utilities. This callback is responsible of pruning networks parameters during training.

To learn more about pruning with PyTorch, please take a look at this tutorial.

Warning

ModelPruning is in beta and subject to change.

parameters_to_prune = [(model.mlp_1, "weight"), (model.mlp_2, "weight")]

trainer = Trainer(
    callbacks=[
        ModelPruning(
            pruning_fn="l1_unstructured",
            parameters_to_prune=parameters_to_prune,
            amount=0.01,
            use_global_unstructured=True,
        )
    ]
)

When parameters_to_prune is None, parameters_to_prune will contain all parameters from the model. The user can override filter_parameters_to_prune to filter any nn.Module to be pruned.

Parameters:

pruning_fn¶ (Union[Callable, str]) – Function from torch.nn.utils.prune module or your own PyTorch BasePruningMethod subclass. Can also be string e.g. “l1_unstructured”. See pytorch docs for more details.
parameters_to_prune¶ (Sequence[Tuple[Module, str]]) – List of tuples (nn.Module, "parameter_name_string").
parameter_names¶ (Optional[List[str]]) – List of parameter names to be pruned from the nn.Module. Can either be "weight" or "bias".
use_global_unstructured¶ (bool) – Whether to apply pruning globally on the model. If parameters_to_prune is provided, global unstructured will be restricted on them.
amount¶ (Union[int, float, Callable[[int], Union[int, float]]]) –
Quantity of parameters to prune:
- float. Between 0.0 and 1.0. Represents the fraction of parameters to prune.
- int. Represents the absolute number of parameters to prune.
- Callable. For dynamic values. Will be called every epoch. Should return a value.
apply_pruning¶ (Union[bool, Callable[[int], bool]]) –
Whether to apply pruning.
- bool. Always apply it or not.
- Callable[[epoch], bool]. For dynamic values. Will be called every epoch.
make_pruning_permanent¶ (bool) – Whether to remove all reparametrization pre-hooks and apply masks when training ends or the model is saved.
use_lottery_ticket_hypothesis¶ (Union[bool, Callable[[int], bool]]) –
See The lottery ticket hypothesis:
- bool. Whether to apply it or not.
- Callable[[epoch], bool]. For dynamic values. Will be called every epoch.
resample_parameters¶ (bool) – Used with use_lottery_ticket_hypothesis. If True, the model parameters will be resampled, otherwise, the exact original parameters will be used.
pruning_dim¶ (Optional[int]) – If you are using a structured pruning method you need to specify the dimension.
pruning_norm¶ (Optional[int]) – If you are using ln_structured you need to specify the norm.
verbose¶ (int) – Verbosity level. 0 to disable, 1 to log overall sparsity, 2 to log per-layer sparsity
prune_on_train_epoch_end¶ (bool) – whether to apply pruning at the end of the training epoch. If this is False, then the check runs at the end of the validation epoch.

Raises:

MisconfigurationException – If parameter_names is neither "weight" nor "bias", if the provided pruning_fn is not supported, if pruning_dim is not provided when "unstructured", if pruning_norm is not provided when "ln_structured", if pruning_fn is neither str nor torch.nn.utils.prune.BasePruningMethod, or if amount is none of int, float and Callable.

apply_lottery_ticket_hypothesis()[source]¶

Lottery ticket hypothesis algorithm (see page 2 of the paper):

Randomly initialize a neural network $f(x; \theta_0)$ (where $\theta_0 \sim \mathcal{D}_\theta$ ).

Train the network for $j$ iterations, arriving at parameters $\theta_j$ .

Prune $p\%$ of the parameters in $\theta_j$ , creating a mask $m$ .

Reset the remaining parameters to their values in $\theta_0$ , creating the winning ticket $f(x; m \odot \theta_0)$ .

This function implements the step 4.

The resample_parameters argument can be used to reset the parameters with a new $\theta_z \sim \mathcal{D}_\theta$

Return type:: None

apply_pruning(amount)[source]¶

Applies pruning to parameters_to_prune.

Return type:: None

filter_parameters_to_prune(parameters_to_prune=())[source]¶

This function can be overridden to control which module to prune.

Return type:: Sequence[Tuple[Module, str]]

make_pruning_permanent(module)[source]¶

Removes pruning buffers from any pruned modules.

Adapted from https://github.com/pytorch/pytorch/blob/1.7.1/torch/nn/utils/prune.py#L1176-L1180

Return type:: None

on_save_checkpoint(trainer, pl_module, checkpoint)[source]¶

Called when saving a checkpoint to give you a chance to store anything else you might want to save.

Parameters:

trainer¶ (Trainer) – the current Trainer instance.
pl_module¶ (LightningModule) – the current LightningModule instance.
checkpoint¶ (Dict[str, Any]) – the checkpoint dictionary that will be saved.

Return type:

Optional[dict]

Returns:

None or the callback state. Support for returning callback state will be removed in v1.8.

Deprecated since version v1.6: Returning a value from this method was deprecated in v1.6 and will be removed in v1.8. Implement Callback.state_dict instead to return state. In v1.8 Callback.on_save_checkpoint can only return None.

on_train_end(trainer, pl_module)[source]¶

Called when the train ends.

Return type:: None

on_train_epoch_end(trainer, pl_module)[source]¶

Called when the train epoch ends.

To access all batch outputs at the end of the epoch, either:

Implement training_epoch_end in the LightningModule and access outputs via the module OR
Cache data across train batch hooks inside the callback implementation to post-process in this hook.

Return type:: None

on_validation_epoch_end(trainer, pl_module)[source]¶

Called when the val epoch ends.

Return type:: None

static sanitize_parameters_to_prune(pl_module, parameters_to_prune=(), parameter_names=())[source]¶

This function is responsible of sanitizing parameters_to_prune and parameter_names. If parameters_to_prune is None, it will be generated with all parameters of the model.

Raises:: MisconfigurationException – If parameters_to_prune doesn’t exist in the model, or if parameters_to_prune is neither a list nor a tuple.
Return type:: Sequence[Tuple[Module, str]]

setup(trainer, pl_module, stage=None)[source]¶

Called when fit, validate, test, predict, or tune begins.

Return type:: None