mirror of
https://github.com/hpcaitech/ColossalAI.git
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0fedef4f3c
* integrated parallel layers for ease of building models * integrated 2.5d layers * cleaned codes and unit tests * added log metric by step hook; updated imagenet benchmark; fixed some bugs * reworked initialization; cleaned codes Co-authored-by: BoxiangW <45734921+BoxiangW@users.noreply.github.com>
45 lines
1.7 KiB
Python
45 lines
1.7 KiB
Python
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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"""Build LR scheduler
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:param trainer: Trainer attached with current hook
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:type trainer: Trainer
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:param lr_scheduler_cfg: The config of LR scheduler
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:type lr_scheduler_cfg: dict
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:param by_epoch: If `True`, the LR will be scheduled every epoch. Else, the LR will be scheduled every batch. Defaults to `True`.
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:type by_epoch: bool
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:param priority: Priority in the printing, hooks with small priority will be printed in front
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:type priority: int, optional
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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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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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