```# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import math

__all__ = [
"LinearDecayWithWarmup",
"ConstScheduleWithWarmup",
"CosineDecayWithWarmup",
"PolyDecayWithWarmup",
"CosineAnnealingWithWarmupDecay",
"LinearAnnealingWithWarmupDecay",
]

def is_integer(number):
return isinstance(number, int)

[文档]class CosineAnnealingWithWarmupDecay(LRScheduler):
def __init__(self, max_lr, min_lr, warmup_step, decay_step, last_epoch=-1, verbose=False):
self.decay_step = decay_step
self.warmup_step = warmup_step
self.max_lr = max_lr
self.min_lr = min_lr
super(CosineAnnealingWithWarmupDecay, self).__init__(max_lr, last_epoch, verbose)

[文档]    def get_lr(self):
if self.warmup_step > 0 and self.last_epoch <= self.warmup_step:
return float(self.max_lr) * (self.last_epoch) / self.warmup_step

if self.last_epoch > self.decay_step:
return self.min_lr

num_step_ = self.last_epoch - self.warmup_step
decay_step_ = self.decay_step - self.warmup_step
decay_ratio = float(num_step_) / float(decay_step_)
coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
return self.min_lr + coeff * (self.max_lr - self.min_lr)

[文档]class LinearAnnealingWithWarmupDecay(LRScheduler):
def __init__(self, max_lr, min_lr, warmup_step, decay_step, last_epoch=-1, verbose=False):

self.decay_step = decay_step
self.warmup_step = warmup_step
self.max_lr = max_lr
self.min_lr = min_lr
super(LinearAnnealingWithWarmupDecay, self).__init__(max_lr, last_epoch, verbose)

[文档]    def get_lr(self):
if self.warmup_step > 0 and self.last_epoch <= self.warmup_step:
return float(self.max_lr) * (self.last_epoch) / self.warmup_step

if self.last_epoch > self.decay_step:
return self.min_lr

num_step_ = self.last_epoch - self.warmup_step
decay_step_ = self.decay_step - self.warmup_step
decay_ratio = float(num_step_) / float(decay_step_)
coeff = 1.0 - decay_ratio
return self.min_lr + coeff * (self.max_lr - self.min_lr)

"""
Creates a learning rate scheduler, which increases learning rate linearly
from 0 to given `learning_rate`, after this warmup period learning rate
would be decreased linearly from the base learning rate to 0.

Args:
learning_rate (float):
The base learning rate. It is a python float number.
total_steps (int):
The number of training steps.
warmup (int or float):
If int, it means the number of steps for warmup. If float, it means
the proportion of warmup in total training steps.
last_epoch (int, optional):
The index of last epoch. It can be set to restart training. If
None, it means initial learning rate.
Defaults to -1.
verbose (bool, optional):
If True, prints a message to stdout for each update.
Defaults to False.

Examples:

.. code-block:: python

lr, warmup_steps, max_steps = 0.1, 100, 1000
lr_scheduler = LinearDecayWithWarmup(lr, max_steps, warmup_steps)

"""

def __init__(self, learning_rate, total_steps, warmup, last_epoch=-1, verbose=False):
warmup_steps = warmup if is_integer(warmup) else int(math.floor(warmup * total_steps))

def lr_lambda(current_step):
if current_step < warmup_steps:
return float(current_step) / float(max(1, warmup_steps))
return max(0.0, float(total_steps - current_step) / float(max(1, total_steps - warmup_steps)))

super(LinearDecayWithWarmup, self).__init__(learning_rate, lr_lambda, last_epoch, verbose)

"""
Creates a learning rate scheduler, which increases learning rate linearly
from 0 to given `learning_rate` during warmup periods and keeps learning
rate a constant after that.

Args:
learning_rate (float):
The base learning rate. It is a python float number.
warmup (int or float):
If int, it means the number of steps for warmup. If float, it means
the proportion of warmup in total training steps.
total_steps (int, optional):
The number of training steps. If `warmup` is a float number,
`total_steps` must be provided.
Defaults to None.
last_epoch (int, optional):
The index of last epoch. It can be set to restart training. If
None, it means initial learning rate.
Defaults to -1.

Examples:

.. code-block:: python

lr, warmup_steps = 0.1, 100
lr_scheduler = ConstScheduleWithWarmup(lr, warmup_steps)

"""

def __init__(self, learning_rate, warmup, total_steps=None, last_epoch=-1, verbose=False):
warmup_steps = warmup if is_integer(warmup) else int(math.floor(warmup * total_steps))
if is_integer(warmup):
warmup_steps = warmup
elif total_steps:
warmup_steps = int(math.floor(warmup * total_steps))
else:
raise ValueError(
"Please provide total steps if `warmup` is a float number , or provide integer for argument `warmup`."
)

def lr_lambda(current_step):
if current_step < warmup_steps:
return float(current_step) / float(max(1.0, warmup_steps))
return 1.0

super(ConstScheduleWithWarmup, self).__init__(learning_rate, lr_lambda, last_epoch, verbose)

"""
Creates a learning rate scheduler, which increases learning rate linearly
from 0 to given `learning_rate`, after this warmup period learning rate
would be decreased following the values of the cosine function. If
`with_hard_restarts` is True, the cosine function could have serveral hard
restarts.

Args:
learning_rate (float):
The base learning rate. It is a python float number.
total_steps (int):
The number of training steps.
warmup (int or float):
If int, it means the number of steps for warmup. If float, it means
the proportion of warmup in total training steps.
with_hard_restarts (bool):
Whether cosine function has several hard restarts.
Defaults to False.
num_cycles (int or float, optional):
If `with_hard_restarts` is False, it means the number of waves in
cosine scheduler and should be an integer number and defaults to 1.
If `with_hard_restarts` is True, it means the number of hard
restarts to use and should be a float number and defaults to be 0.5.
Defaults to None.
last_epoch (int, optional):
The index of last epoch. It can be set to restart training. If
None, it means initial learning rate.
Defaults to -1.

Examples:

.. code-block:: python

lr, warmup_steps, max_steps = 0.1, 100, 1000
lr_scheduler = CosineDecayWithWarmup(lr, max_steps, warmup_steps)

"""

def __init__(
self,
learning_rate,
total_steps,
warmup,
with_hard_restarts=False,
num_cycles=None,
last_epoch=-1,
verbose=False,
):
warmup_steps = warmup if is_integer(warmup) else int(math.floor(warmup * total_steps))
# Input check
if num_cycles is not None:
assert (
not with_hard_restarts
and isinstance(num_cycles, int)
or with_hard_restarts
and isinstance(num_cycles, float)
), "`num_circles` should be an integer while `with_hard_restarts` is False, an float while `with_hard_restarts` is True."
else:
num_cycles = 1 if not with_hard_restarts else 0.5

def lr_lambda(current_step):
if current_step < warmup_steps:
return float(current_step) / float(max(1, warmup_steps))

progress = float(current_step - warmup_steps) / float(max(1, total_steps - warmup_steps))

if with_hard_restarts:
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(num_cycles) * progress) % 1.0))))

return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))

super(CosineDecayWithWarmup, self).__init__(learning_rate, lr_lambda, last_epoch, verbose)

"""
Creates a learning rate scheduler, which increases learning rate linearly
from 0 to given `lr_init`, after this warmup period learning rate would
be decreased as a polynomial decay from the base learning rate to the end
learning rate `lr_end`.

Args:
learning_rate (float):
The base learning rate. It is a python float number.
total_steps (int):
The number of training steps.
warmup (int or float):
If int, it means the number of steps for warmup. If float, it means
the proportion of warmup in total training steps.
lr_end (float, optional):
The end learning rate.
Defaults to 1e-7.
power (float, optional):
Power factor.
Defaults to 1.0.
last_epoch (int, optional):
The index of last epoch. It can be set to restart training. If
None, it means initial learning rate.
Defaults to -1.

Examples:

.. code-block:: python

lr, lr_end, warmup_steps, max_steps = 0.1, 1e-6, 100, 1000
lr_scheduler = PolyDecayWithWarmup(lr, max_steps, warmup_steps, lr_end)

"""

def __init__(self, learning_rate, total_steps, warmup, lr_end=1e-7, power=1.0, last_epoch=-1, verbose=False):
lr_init = learning_rate
assert (
lr_init > lr_end
), f"`lr_end` must be be smaller than `learning_rate`. But `lr_end` is {lr_end} while `learning_rate` is {lr_init}."
warmup_steps = warmup if is_integer(warmup) else int(math.floor(warmup * total_steps))

def lr_lambda(current_step):
if current_step < warmup_steps:
return float(current_step) / float(max(1, warmup_steps))
elif current_step > total_steps:
return lr_end / lr_init  # it multiplies by lr_init equals to lr_end
else:
lr_range = lr_init - lr_end
decay_steps = total_steps - warmup_steps
pct_remaining = 1 - (current_step - warmup_steps) / decay_steps
decay = lr_range * pct_remaining**power + lr_end
return decay / lr_init  # it multiplies by lr_init equals to decay

super(PolyDecayWithWarmup, self).__init__(lr_init, lr_lambda, last_epoch, verbose)
```