Bases: GenericDecay
A non-decaying learning rate scheduler with optional linear warmup.
This scheduler is a convenience wrapper around minnt.schedulers.GenericDecay
with the decay parameter set to "none" and total_steps being optional.
Source code in minnt/schedulers/no_decay.py
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48 | class NoDecay(GenericDecay):
"""A non-decaying learning rate scheduler with optional linear warmup.
This scheduler is a convenience wrapper around [minnt.schedulers.GenericDecay][]
with the `decay` parameter set to `"none"` and `total_steps` being optional.
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
total_steps: int | None = None,
*,
warmup: int | float = 0,
last_epoch: int = -1,
) -> None:
"""Creates a new NoDecay scheduler instance.
Parameters:
optimizer: The optimizer for which to schedule the learning rate.
total_steps: An optional number of training steps. Useful only when `warmup` is
specified as a fraction.
warmup: Specifies the warmup phase. If a number smaller than 1 is given,
it is treated as a fraction of `total_steps`; otherwise, it is treated as
an absolute number of steps. Default is 0 (no warmup).
last_epoch: The index of the last epoch when resuming training. Default is -1.
"""
if total_steps is None:
if 0 < warmup < 1:
raise ValueError("If total_steps is None, warmup must be zero or a absolute number of steps.")
total_steps = warmup
super().__init__(
optimizer,
total_steps,
decay="none",
warmup=warmup,
last_epoch=last_epoch,
warn_about_exceeding_steps=False,
)
|
__init__(
optimizer: Optimizer,
total_steps: int | None = None,
*,
warmup: int | float = 0,
last_epoch: int = -1
) -> None
Creates a new NoDecay scheduler instance.
Parameters:
-
optimizer
(Optimizer)
–
The optimizer for which to schedule the learning rate.
-
total_steps
(int | None, default:
None
)
–
An optional number of training steps. Useful only when warmup is
specified as a fraction.
-
warmup
(int | float, default:
0
)
–
Specifies the warmup phase. If a number smaller than 1 is given,
it is treated as a fraction of total_steps; otherwise, it is treated as
an absolute number of steps. Default is 0 (no warmup).
-
last_epoch
(int, default:
-1
)
–
The index of the last epoch when resuming training. Default is -1.
Source code in minnt/schedulers/no_decay.py
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48 | def __init__(
self,
optimizer: torch.optim.Optimizer,
total_steps: int | None = None,
*,
warmup: int | float = 0,
last_epoch: int = -1,
) -> None:
"""Creates a new NoDecay scheduler instance.
Parameters:
optimizer: The optimizer for which to schedule the learning rate.
total_steps: An optional number of training steps. Useful only when `warmup` is
specified as a fraction.
warmup: Specifies the warmup phase. If a number smaller than 1 is given,
it is treated as a fraction of `total_steps`; otherwise, it is treated as
an absolute number of steps. Default is 0 (no warmup).
last_epoch: The index of the last epoch when resuming training. Default is -1.
"""
if total_steps is None:
if 0 < warmup < 1:
raise ValueError("If total_steps is None, warmup must be zero or a absolute number of steps.")
total_steps = warmup
super().__init__(
optimizer,
total_steps,
decay="none",
warmup=warmup,
last_epoch=last_epoch,
warn_about_exceeding_steps=False,
)
|