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48 lines
2.1 KiB
48 lines
2.1 KiB
from colossalai.registry import HOOKS
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from torch import Tensor
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from ._metric_hook import LearningRateMetric, MetricHook
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@HOOKS.register_module
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class LRSchedulerHook(MetricHook):
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r"""Build LR scheduler for trainer.
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Args:
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lr_scheduler (:class:`colossalai.nn.lr_scheduler`): The specific LR scheduler
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in range of ``colossalai.nn.lr_scheduler``, more details about ``lr_scheduler`` could be found in
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`lr_scheduler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/nn/lr_scheduler>`_.
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by_epoch (bool): If `True`, the LR will be scheduled every epoch. Else, the LR will be scheduled every batch.
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store_lr_in_state (bool, optional): If `True`, store the learning rate in each state, defaults to `True`.
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priority (int, optional): Priority in the printing, hooks with small priority will be printed in front
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defaults to 1. If different hooks share same priority, the order of printing would
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depend on the hooks order in the hook list.
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"""
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def __init__(
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self,
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lr_scheduler,
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by_epoch: bool,
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store_lr_in_state: bool = True,
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priority: int = 1,
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):
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super().__init__(priority=priority)
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self.by_epoch = by_epoch
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self.lr_scheduler = lr_scheduler
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self.store_lr_in_state = store_lr_in_state
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def after_hook_is_attached(self, trainer):
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self._check_metric_states_initialization(trainer)
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trainer.states['metrics']['train']['LR'] = LearningRateMetric(epoch_only=self.by_epoch,
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initial_lr=self.lr_scheduler.get_last_lr()[0])
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def after_train_epoch(self, trainer):
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if self.by_epoch:
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self.lr_scheduler.step()
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trainer.states['metrics']['train']['LR'].update(self.lr_scheduler.get_last_lr()[0])
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def after_train_iter(self, trainer, output: Tensor, label: Tensor, loss: Tensor):
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if not self.by_epoch:
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self.lr_scheduler.step()
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trainer.states['metrics']['train']['LR'].update(self.lr_scheduler.get_last_lr()[0])
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