Source code for pytorch_lightning.tuner.tuning
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Dict, Optional, Union
import pytorch_lightning as pl
from pytorch_lightning.trainer.states import TrainerStatus
from pytorch_lightning.tuner.batch_size_scaling import scale_batch_size
from pytorch_lightning.tuner.lr_finder import _LRFinder, lr_find
from pytorch_lightning.utilities.types import EVAL_DATALOADERS, TRAIN_DATALOADERS
[docs]class Tuner:
"""Tuner class to tune your model"""
def __init__(self, trainer: "pl.Trainer") -> None:
self.trainer = trainer
def on_trainer_init(self, auto_lr_find: Union[str, bool], auto_scale_batch_size: Union[str, bool]) -> None:
self.trainer.auto_lr_find = auto_lr_find
self.trainer.auto_scale_batch_size = auto_scale_batch_size
def _tune(
self,
model: "pl.LightningModule",
scale_batch_size_kwargs: Optional[Dict[str, Any]] = None,
lr_find_kwargs: Optional[Dict[str, Any]] = None,
) -> Dict[str, Optional[Union[int, _LRFinder]]]:
scale_batch_size_kwargs = scale_batch_size_kwargs or {}
lr_find_kwargs = lr_find_kwargs or {}
# return a dict instead of a tuple so BC is not broken if a new tuning procedure is added
result = {}
# Run auto batch size scaling
if self.trainer.auto_scale_batch_size:
if isinstance(self.trainer.auto_scale_batch_size, str):
scale_batch_size_kwargs.setdefault("mode", self.trainer.auto_scale_batch_size)
result["scale_batch_size"] = scale_batch_size(self.trainer, model, **scale_batch_size_kwargs)
# Run learning rate finder:
if self.trainer.auto_lr_find:
lr_find_kwargs.setdefault("update_attr", True)
result["lr_find"] = lr_find(self.trainer, model, **lr_find_kwargs)
self.trainer.state.status = TrainerStatus.FINISHED
return result
def _run(self, *args: Any, **kwargs: Any) -> None:
"""`_run` wrapper to set the proper state during tuning, as this can be called multiple times"""
self.trainer.state.status = TrainerStatus.RUNNING # last `_run` call might have set it to `FINISHED`
self.trainer.training = True
self.trainer._run(*args, **kwargs)
self.trainer.tuning = True
[docs] def scale_batch_size(
self,
model: "pl.LightningModule",
train_dataloaders: Optional[Union[TRAIN_DATALOADERS, "pl.LightningDataModule"]] = None,
val_dataloaders: Optional[EVAL_DATALOADERS] = None,
datamodule: Optional["pl.LightningDataModule"] = None,
mode: str = "power",
steps_per_trial: int = 3,
init_val: int = 2,
max_trials: int = 25,
batch_arg_name: str = "batch_size",
train_dataloader=None, # noqa TODO: remove with 1.6
) -> Optional[int]:
"""
Iteratively try to find the largest batch size for a given model
that does not give an out of memory (OOM) error.
Args:
model: Model to tune.
train_dataloaders: A collection of :class:`torch.utils.data.DataLoader` or a
:class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying training samples.
In the case of multiple dataloaders, please see this :ref:`page <multiple-training-dataloaders>`.
val_dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
mode: Search strategy to update the batch size:
- ``'power'`` (default): Keep multiplying the batch size by 2, until we get an OOM error.
- ``'binsearch'``: Initially keep multiplying by 2 and after encountering an OOM error
do a binary search between the last successful batch size and the batch size that failed.
steps_per_trial: number of steps to run with a given batch size.
Ideally 1 should be enough to test if a OOM error occurs,
however in practise a few are needed
init_val: initial batch size to start the search with
max_trials: max number of increase in batch size done before
algorithm is terminated
batch_arg_name: name of the attribute that stores the batch size.
It is expected that the user has provided a model or datamodule that has a hyperparameter
with that name. We will look for this attribute name in the following places
- ``model``
- ``model.hparams``
- ``model.datamodule``
- ``trainer.datamodule`` (the datamodule passed to the tune method)
"""
self.trainer.auto_scale_batch_size = True
result = self.trainer.tune(
model,
train_dataloaders=train_dataloaders,
train_dataloader=train_dataloader, # TODO: deprecated - remove with 1.6
val_dataloaders=val_dataloaders,
datamodule=datamodule,
scale_batch_size_kwargs={
"mode": mode,
"steps_per_trial": steps_per_trial,
"init_val": init_val,
"max_trials": max_trials,
"batch_arg_name": batch_arg_name,
},
)
self.trainer.auto_scale_batch_size = False
return result["scale_batch_size"]
[docs] def lr_find(
self,
model: "pl.LightningModule",
train_dataloaders: Optional[Union[TRAIN_DATALOADERS, "pl.LightningDataModule"]] = None,
val_dataloaders: Optional[EVAL_DATALOADERS] = None,
datamodule: Optional["pl.LightningDataModule"] = None,
min_lr: float = 1e-8,
max_lr: float = 1,
num_training: int = 100,
mode: str = "exponential",
early_stop_threshold: float = 4.0,
update_attr: bool = False,
train_dataloader=None, # noqa TODO: remove with 1.6
) -> Optional[_LRFinder]:
"""
Enables the user to do a range test of good initial learning rates,
to reduce the amount of guesswork in picking a good starting learning rate.
Args:
model: Model to tune.
train_dataloaders: A collection of :class:`torch.utils.data.DataLoader` or a
:class:`~pytorch_lightning.core.datamodule.LightningDataModule` specifying training samples.
In the case of multiple dataloaders, please see this :ref:`page <multiple-training-dataloaders>`.
val_dataloaders: A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
min_lr: minimum learning rate to investigate
max_lr: maximum learning rate to investigate
num_training: number of learning rates to test
mode: Search strategy to update learning rate after each batch:
- ``'exponential'`` (default): Will increase the learning rate exponentially.
- ``'linear'``: Will increase the learning rate linearly.
early_stop_threshold: threshold for stopping the search. If the
loss at any point is larger than early_stop_threshold*best_loss
then the search is stopped. To disable, set to None.
update_attr: Whether to update the learning rate attribute or not.
Raises:
MisconfigurationException:
If learning rate/lr in ``model`` or ``model.hparams`` isn't overridden when ``auto_lr_find=True``,
or if you are using more than one optimizer.
"""
self.trainer.auto_lr_find = True
result = self.trainer.tune(
model,
train_dataloaders=train_dataloaders,
train_dataloader=train_dataloader, # TODO: deprecated - remove with 1.6
val_dataloaders=val_dataloaders,
datamodule=datamodule,
lr_find_kwargs={
"min_lr": min_lr,
"max_lr": max_lr,
"num_training": num_training,
"mode": mode,
"early_stop_threshold": early_stop_threshold,
"update_attr": update_attr,
},
)
self.trainer.auto_lr_find = False
return result["lr_find"]
