mirror of
https://github.com/hpcaitech/ColossalAI.git
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554aa9592e
* [legacy] move communication to legacy (#4640) * [legacy] refactor logger and clean up legacy codes (#4654) * [legacy] make logger independent to gpc * [legacy] make optim independent to registry * [legacy] move test engine to legacy * [legacy] move nn to legacy (#4656) * [legacy] move nn to legacy * [checkpointio] fix save hf config * [test] remove useledd rpc pp test * [legacy] fix nn init * [example] skip tutorial hybriad parallel example * [devops] test doc check * [devops] test doc check
72 lines
3.4 KiB
Python
72 lines
3.4 KiB
Python
from torch.optim.lr_scheduler import ExponentialLR as _ExponentialLR
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from torch.optim.lr_scheduler import LambdaLR as _LambdaLR
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from torch.optim.lr_scheduler import MultiplicativeLR as _MultiplicativeLR
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from torch.optim.lr_scheduler import StepLR as _StepLR
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class LambdaLR(_LambdaLR):
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"""Sets the learning rate of each parameter group to the initial lr
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times a given function. When last_epoch=-1, sets initial lr as lr.
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Args:
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optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
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total_steps (int): Number of total training steps.
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lr_lambda (Union[``function``, ``list[function]``]): A function which computes a multiplicative
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factor given an integer parameter epoch, or a list of such functions,
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one for each group in optimizer.param_groups, defaults to None.
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last_epoch (int, optional): The index of last epoch, defaults to -1.
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"""
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def __init__(self, optimizer, total_steps, lr_lambda=None, last_epoch: int = -1) -> None:
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super().__init__(optimizer, lr_lambda, last_epoch=last_epoch)
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class MultiplicativeLR(_MultiplicativeLR):
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"""Multiply the learning rate of each parameter group by the factor given
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in the specified function. When last_epoch=-1, sets initial lr as lr.
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Args:
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optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
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total_steps (int): Number of total training steps.
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lr_lambda (Union[``function``, ``list[function]``]): A function which computes a multiplicative
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factor given an integer parameter epoch, or a list of such functions,
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one for each group in optimizer.param_groups, defaults to None.
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last_epoch (int, optional): The index of last epoch, defaults to -1.
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"""
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def __init__(self, optimizer, total_steps, lr_lambda=None, last_epoch: int = -1) -> None:
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super().__init__(optimizer, lr_lambda, last_epoch=last_epoch)
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class StepLR(_StepLR):
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"""Decays the learning rate of each parameter group by gamma every
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step_size epochs. Notice that such decay can happen simultaneously with
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other changes to the learning rate from outside this scheduler. When
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last_epoch=-1, sets initial lr as lr.
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Args:
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optimizer (:class:`torch.optim.Optimizer`): Wrapped optimizer.
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total_steps (int): Number of total training steps.
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step_size (int, optional): Period of learning rate decay, defaults to 1.
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gamma (float, optional): Multiplicative factor of learning rate decay, defaults to 0.1.
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last_epoch (int, optional): The index of last epoch, defaults to -1.
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"""
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def __init__(self, optimizer, total_steps, step_size: int = 1, gamma: float = 0.1, last_epoch: int = -1) -> None:
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super().__init__(optimizer, step_size, gamma=gamma, last_epoch=last_epoch)
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class ExponentialLR(_ExponentialLR):
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"""Decays the learning rate of each parameter group by gamma every epoch.
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When last_epoch=-1, sets initial lr as lr
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Args:
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optimizer (Union[:class:`torch.optim.Optimizer`, :class:`colossalai.nn.optimizer`]): Wrapped optimizer.
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total_steps (int): Number of total training steps.
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gamma (float, optional): Multiplicative factor of learning rate decay, defaults to 1.0.
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last_epoch (int, optional): The index of last epoch, defaults to -1.
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"""
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def __init__(self, optimizer, total_steps, gamma: float = 1.0, last_epoch: int = -1) -> None:
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super().__init__(optimizer, gamma, last_epoch=last_epoch)
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