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learning_rate_scheduler.py
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learning_rate_scheduler.py
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from typing import Any, Dict, List, Union
from overrides import overrides
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.common.registrable import Registrable
from allennlp.training.scheduler import Scheduler
from allennlp.training.optimizers import Optimizer
from transformers.optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
)
class LearningRateScheduler(Scheduler, Registrable):
def __init__(self, optimizer: torch.optim.Optimizer, last_epoch: int = -1) -> None:
super().__init__(optimizer, "lr", last_epoch)
@overrides
def get_values(self):
raise NotImplementedError
class _PyTorchLearningRateSchedulerWrapper(LearningRateScheduler):
def __init__(self, lr_scheduler: torch.optim.lr_scheduler._LRScheduler) -> None:
self.lr_scheduler = lr_scheduler
def get_values(self):
return self.lr_scheduler.get_last_lr()
@overrides
def step(self, metric: float = None) -> None:
self.lr_scheduler.step()
@overrides
def state_dict(self) -> Dict[str, Any]:
return self.lr_scheduler.state_dict()
@overrides
def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
self.lr_scheduler.load_state_dict(state_dict)
class _PyTorchLearningRateSchedulerWithMetricsWrapper(_PyTorchLearningRateSchedulerWrapper):
@overrides
def step(self, metric: float = None) -> None:
if metric is None:
raise ConfigurationError(
"This learning rate scheduler requires "
"a validation metric to compute the schedule and therefore "
"must be used with a validation dataset."
)
self.lr_scheduler.step(metric)
@LearningRateScheduler.register("step")
class StepLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
"""
Registered as a `LearningRateScheduler` with name "step". The "optimizer" argument does not get
an entry in a configuration file for the object.
"""
def __init__(
self, optimizer: Optimizer, step_size: int, gamma: float = 0.1, last_epoch: int = -1
) -> None:
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer=optimizer, step_size=step_size, gamma=gamma, last_epoch=last_epoch
)
super().__init__(lr_scheduler)
@LearningRateScheduler.register("multi_step")
class MultiStepLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
"""
Registered as a `LearningRateScheduler` with name "multi_step". The "optimizer" argument does
not get an entry in a configuration file for the object.
"""
def __init__(
self, optimizer: Optimizer, milestones: List[int], gamma: float = 0.1, last_epoch: int = -1
) -> None:
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer=optimizer, milestones=milestones, gamma=gamma, last_epoch=last_epoch
)
super().__init__(lr_scheduler)
@LearningRateScheduler.register("exponential")
class ExponentialLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
"""
Registered as a `LearningRateScheduler` with name "exponential". The "optimizer" argument does
not get an entry in a configuration file for the object.
"""
def __init__(self, optimizer: Optimizer, gamma: float = 0.1, last_epoch: int = -1) -> None:
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(
optimizer=optimizer, gamma=gamma, last_epoch=last_epoch
)
super().__init__(lr_scheduler)
@LearningRateScheduler.register("reduce_on_plateau")
class ReduceOnPlateauLearningRateScheduler(_PyTorchLearningRateSchedulerWithMetricsWrapper):
"""
Registered as a `LearningRateScheduler` with name "reduce_on_plateau". The "optimizer" argument
does not get an entry in a configuration file for the object.
"""
def __init__(
self,
optimizer: Optimizer,
mode: str = "min",
factor: float = 0.1,
patience: int = 10,
verbose: bool = False,
threshold_mode: str = "rel",
threshold: float = 1e-4,
cooldown: int = 0,
min_lr: Union[float, List[float]] = 0,
eps: float = 1e-8,
) -> None:
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer=optimizer,
mode=mode,
factor=factor,
patience=patience,
verbose=verbose,
threshold_mode=threshold_mode,
threshold=threshold,
cooldown=cooldown,
min_lr=min_lr,
eps=eps,
)
super().__init__(lr_scheduler)
@LearningRateScheduler.register("constant")
class ConstantLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
"""
Registered as a `LearningRateScheduler` with name "constant". The "optimizer" argument does
not get an entry in a configuration file for the object.
"""
def __init__(self, optimizer: Optimizer, last_epoch: int = -1) -> None:
lr_scheduler = get_constant_schedule(optimizer=optimizer, last_epoch=last_epoch)
super().__init__(lr_scheduler)
@LearningRateScheduler.register("constant_with_warmup")
class ConstantWithWarmupLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
"""
Registered as a `LearningRateScheduler` with name "constant_with_warmup". The "optimizer" argument does
not get an entry in a configuration file for the object.
"""
def __init__(self, optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1) -> None:
lr_scheduler = get_constant_schedule_with_warmup(
optimizer=optimizer, num_warmup_steps=num_warmup_steps, last_epoch=last_epoch
)
super().__init__(lr_scheduler)
@LearningRateScheduler.register("cosine_with_warmup")
class CosineWithWarmupLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
"""
Registered as a `LearningRateScheduler` with name "cosine_with_warmup". The "optimizer" argument does
not get an entry in a configuration file for the object.
"""
def __init__(
self,
optimizer: Optimizer,
num_warmup_steps: int,
num_training_steps: int,
num_cycles: float = 0.5,
last_epoch: int = -1,
) -> None:
lr_scheduler = get_cosine_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
num_cycles=num_cycles,
last_epoch=last_epoch,
)
super().__init__(lr_scheduler)
@LearningRateScheduler.register("cosine_hard_restarts_with_warmup")
class CosineHardRestartsWithWarmupLearningRateScheduler(_PyTorchLearningRateSchedulerWrapper):
"""
Registered as a `LearningRateScheduler` with name "cosine_hard_restarts_with_warmup".
The "optimizer" argument does not get an entry in a configuration file for the object.
"""
def __init__(
self,
optimizer: Optimizer,
num_warmup_steps: int,
num_training_steps: int,
num_cycles: int = 1,
last_epoch: int = -1,
) -> None:
lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
num_cycles=num_cycles,
last_epoch=last_epoch,
)
super().__init__(lr_scheduler)