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63 lines
2.8 KiB
63 lines
2.8 KiB
from typing import List
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from torch.optim.lr_scheduler import MultiStepLR as _MultiStepLR
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from colossalai.registry import LR_SCHEDULERS
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from .delayed import WarmupScheduler
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@LR_SCHEDULERS.register_module
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class MultiStepLR(_MultiStepLR):
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"""Decays the learning rate of each parameter group by gamma once the
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number of epoch reaches one of the milestones. Notice that such decay can
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happen simultaneously with other changes to the learning rate from outside
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this scheduler. When last_epoch=-1, sets initial lr as lr.
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:param optimizer: Wrapped optimizer
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:type optimizer: torch.optim.Optimizer
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:param total_steps: Number of total training steps
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:type total_steps: int
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:param milestones: List of epoch indices. Must be increasing, defaults to None
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:type milestones: List[int], optional
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:param gamma: Multiplicative factor of learning rate decay, defaults to 0.1
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:type gamma: float, optional
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:param num_steps_per_epoch: Number of steps per epoch, defaults to -1
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:type num_steps_per_epoch: int, optional
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:param last_epoch: The index of last epoch, defaults to -1
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:type last_epoch: int, optional
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"""
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def __init__(self, optimizer, total_steps: int, milestones: List[int] = None, gamma: float = 0.1, last_epoch: int = -1, **kwargs):
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super().__init__(optimizer, milestones, gamma=gamma, last_epoch=last_epoch)
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@LR_SCHEDULERS.register_module
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class MultiStepWarmupLR(WarmupScheduler):
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"""Multi-step laerning rate scheduler with warmup.
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:param optimizer: Wrapped optimizer
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:type optimizer: torch.optim.Optimizer
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:param total_steps: Number of total training steps
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:type total_steps: int
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:param warmup_steps: Number of warmup steps, defaults to 0
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:type warmup_steps: int, optional
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:param milestones: List of epoch indices. Must be increasing, defaults to None
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:type milestones: List[int], optional
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:param gamma: Multiplicative factor of learning rate decay, defaults to 0.1
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:type gamma: float, optional
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:param num_steps_per_epoch: Number of steps per epoch, defaults to -1
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:type num_steps_per_epoch: int, optional
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:param last_epoch: The index of last epoch, defaults to -1
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:type last_epoch: int, optional
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"""
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def __init__(self, optimizer, total_steps: int, warmup_steps: int = 0, milestones: List[int] = None,
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gamma: float = 0.1, last_epoch: int = -1, **kwargs):
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if len(milestones) == 0:
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raise ValueError('milestones cannot be empty')
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milestones = [
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v - warmup_steps for v in milestones if v >= warmup_steps]
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base_scheduler = _MultiStepLR(optimizer, milestones=milestones,
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gamma=gamma)
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super().__init__(optimizer, warmup_steps, base_scheduler, last_epoch=last_epoch)
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