# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Learning rate schedule."""
import math
from ..common import dtype as mstype
from ..ops import operations as P
from .cell import Cell
from .._checkparam import Validator as validator
class LearningRateSchedule(Cell):
"""Basic class of learning rate schedule."""
def __init__(self):
super(LearningRateSchedule, self).__init__()
def construct(self, global_step):
"""
Defines the computation to get the current learning rate.
This method must be overridden by all subclasses.
Note:
The output must be a Tensor of scalar.
Inputs:
Tensor. The current step number.
"""
raise NotImplementedError
def _check_inputs(learning_rate, decay_rate, decay_steps, is_stair, cls_name):
validator.check_positive_int(decay_steps, 'decay_steps', cls_name)
validator.check_positive_float(learning_rate, 'learning_rate', cls_name)
validator.check_is_float(learning_rate, 'learning_rate', cls_name)
validator.check_positive_float(decay_rate, 'decay_rate', cls_name)
validator.check_is_float(decay_rate, 'decay_rate', cls_name)
validator.check_value_type('is_stair', is_stair, [bool], cls_name)
[docs]class ExponentialDecayLR(LearningRateSchedule):
r"""
Calculates learning rate base on exponential decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{p}
Where :
.. math::
p = \frac{current\_step}{decay\_steps}
If `is_stair` is True, the formula is :
.. math::
p = floor(\frac{current\_step}{decay\_steps})
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay rate.
decay_steps (int): A value used to calculate decayed learning rate.
is_stair (bool): If true, learning rate is decayed once every `decay_steps` time. Default: False.
Inputs:
Tensor. The current step number.
Outputs:
Tensor. The learning rate value for the current step.
Examples:
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tensor(2, mstype.int32)
>>> exponential_decay_lr = nn.ExponentialDecayLR(learning_rate, decay_rate, decay_steps)
>>> result = exponential_decay_lr(global_step)
>>> print(result)
0.09486833
"""
def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
super(ExponentialDecayLR, self).__init__()
_check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
self.learning_rate = learning_rate
self.decay_rate = decay_rate
self.decay_steps = decay_steps
self.is_stair = is_stair
self.pow = P.Pow()
self.cast = P.Cast()
def construct(self, global_step):
p = self.cast(global_step, mstype.float32) / self.decay_steps
if self.is_stair:
p = P.Floor()(p)
return self.learning_rate * self.pow(self.decay_rate, p)
[docs]class NaturalExpDecayLR(LearningRateSchedule):
r"""
Calculates learning rate base on natural exponential decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * p}
Where :
.. math::
p = \frac{current\_step}{decay\_steps}
If `is_stair` is True, the formula is :
.. math::
p = floor(\frac{current\_step}{decay\_steps})
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay rate.
decay_steps (int): A value used to calculate decayed learning rate.
is_stair (bool): If true, learning rate is decayed once every `decay_steps` time. Default: False.
Inputs:
Tensor. The current step number.
Outputs:
Tensor. The learning rate value for the current step.
Examples:
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tensor(2, mstype.int32)
>>> natural_exp_decay_lr = nn.NaturalExpDecayLR(learning_rate, decay_rate, decay_steps, True)
>>> result = natural_exp_decay_lr(global_step)
>>> print(result)
0.1
"""
def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
super(NaturalExpDecayLR, self).__init__()
_check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
self.learning_rate = learning_rate
self.decay_rate = decay_rate
self.decay_steps = decay_steps
self.is_stair = is_stair
self.math_e = math.e
self.pow = P.Pow()
self.cast = P.Cast()
def construct(self, global_step):
p = self.cast(global_step, mstype.float32)
if self.is_stair:
p = P.FloorDiv()(p, self.decay_steps) * self.decay_steps
return self.learning_rate * self.pow(self.math_e, -self.decay_rate * p)
[docs]class InverseDecayLR(LearningRateSchedule):
r"""
Calculates learning rate base on inverse-time decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * p)
Where :
.. math::
p = \frac{current\_step}{decay\_steps}
If `is_stair` is True, The formula is :
.. math::
p = floor(\frac{current\_step}{decay\_steps})
Args:
learning_rate (float): The initial value of learning rate.
decay_rate (float): The decay rate.
decay_steps (int): A value used to calculate decayed learning rate.
is_stair (bool): If true, learning rate decay once every `decay_steps` times. Default: False.
Inputs:
Tensor. The current step number.
Outputs:
Tensor. The learning rate value for the current step.
Examples:
>>> learning_rate = 0.1
>>> decay_rate = 0.9
>>> decay_steps = 4
>>> global_step = Tensor(2, mstype.int32)
>>> inverse_decay_lr = nn.InverseDecayLR(learning_rate, decay_rate, decay_steps, True)
>>> result = inverse_decay_lr(global_step)
>>> print(result)
0.1
"""
def __init__(self, learning_rate, decay_rate, decay_steps, is_stair=False):
super(InverseDecayLR, self).__init__()
_check_inputs(learning_rate, decay_rate, decay_steps, is_stair, self.cls_name)
self.learning_rate = learning_rate
self.decay_rate = decay_rate
self.decay_steps = decay_steps
self.is_stair = is_stair
self.cast = P.Cast()
def construct(self, global_step):
p = self.cast(global_step, mstype.float32) / self.decay_steps
if self.is_stair:
p = P.Floor()(p)
return self.learning_rate / (1 + self.decay_rate * p)
[docs]class CosineDecayLR(LearningRateSchedule):
r"""
Calculates learning rate base on cosine decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = min\_learning\_rate + 0.5 * (max\_learning\_rate - min\_learning\_rate) *
(1 + cos(\frac{current\_step}{decay\_steps}\pi))
Args:
min_lr (float): The minimum value of learning rate.
max_lr (float): The maximum value of learning rate.
decay_steps (int): A value used to calculate decayed learning rate.
Inputs:
Tensor. The current step number.
Outputs:
Tensor. The learning rate value for the current step.
Examples:
>>> min_lr = 0.01
>>> max_lr = 0.1
>>> decay_steps = 4
>>> global_steps = Tensor(2, mstype.int32)
>>> cosine_decay_lr = nn.CosineDecayLR(min_lr, max_lr, decay_steps)
>>> result = cosine_decay_lr(global_steps)
>>> print(result)
0.055
"""
def __init__(self, min_lr, max_lr, decay_steps):
super(CosineDecayLR, self).__init__()
if not isinstance(min_lr, float):
raise TypeError("min_lr must be float.")
validator.check_non_negative_float(min_lr, "min_lr", self.cls_name)
validator.check_positive_float(max_lr, 'max_lr', self.cls_name)
validator.check_is_float(max_lr, 'max_lr', self.cls_name)
validator.check_positive_int(decay_steps, "decay_steps", self.cls_name)
if min_lr >= max_lr:
raise ValueError('`max_lr` should be greater than `min_lr`.')
self.min_lr = min_lr
self.max_lr = max_lr
self.decay_steps = decay_steps
self.math_pi = math.pi
self.delta = 0.5 * (max_lr - min_lr)
self.cos = P.Cos()
self.min = P.Minimum()
self.cast = P.Cast()
def construct(self, global_step):
p = self.cast(self.min(global_step, self.decay_steps), mstype.float32)
return self.min_lr + self.delta * (1.0 + self.cos(self.math_pi * p / self.decay_steps))
[docs]class PolynomialDecayLR(LearningRateSchedule):
r"""
Calculates learning rate base on polynomial decay function.
For the i-th step, the formula of computing decayed_learning_rate[i] is:
.. math::
decayed\_learning\_rate[i] = (learning\_rate - end\_learning\_rate) *
(1 - tmp\_step / tmp\_decay\_steps)^{power} + end\_learning\_rate
Where :
.. math::
tmp\_step=min(current\_step, decay\_steps)
If `update_decay_steps` is true, update the value of `tmp_decay_step` every `decay_steps`. The formula is :
.. math::
tmp\_decay\_steps = decay\_steps * ceil(current\_step / decay\_steps)
Args:
learning_rate (float): The initial value of learning rate.
end_learning_rate (float): The end value of learning rate.
decay_steps (int): A value used to calculate decayed learning rate.
power (float): A value used to calculate decayed learning rate. This parameter must be greater than 0.
update_decay_steps (bool): If true, learning rate is decayed once every `decay_steps` time. Default: False.
Inputs:
Tensor. The current step number.
Outputs:
Tensor. The learning rate value for the current step.
Examples:
>>> learning_rate = 0.1
>>> end_learning_rate = 0.01
>>> decay_steps = 4
>>> power = 0.5
>>> global_step = Tensor(2, mstype.int32)
>>> polynomial_decay_lr = nn.PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power)
>>> result = polynomial_decay_lr(global_step)
>>> print(result)
0.07363961
"""
def __init__(self, learning_rate, end_learning_rate, decay_steps, power, update_decay_steps=False):
super(PolynomialDecayLR, self).__init__()
validator.check_positive_float(learning_rate, 'learning_rate')
validator.check_is_float(learning_rate, 'learning_rate')
if not isinstance(end_learning_rate, float):
raise TypeError("end_learning_rate must be float.")
validator.check_non_negative_float(end_learning_rate, "end_learning_rate", self.cls_name)
validator.check_positive_int(decay_steps, 'decay_steps', self.cls_name)
validator.check_value_type('update_decay_steps', update_decay_steps, [bool], self.cls_name)
validator.check_positive_float(power, 'power', self.cls_name)
validator.check_is_float(power, 'power', self.cls_name)
self.decay_steps = decay_steps
self.start_learning_rate = learning_rate
self.end_learning_rate = end_learning_rate
self.diff_learning_rate = learning_rate - end_learning_rate
self.power = power
self.update_decay_steps = update_decay_steps
self.pow = P.Pow()
self.ceil = P.Ceil()
self.min = P.Minimum()
self.max = P.Maximum()
def construct(self, global_step):
tmp_global_step = P.Cast()(global_step, mstype.float32)
tmp_decay_step = self.decay_steps
if self.update_decay_steps:
tmp_decay_step = tmp_decay_step * self.max(self.ceil(tmp_global_step / tmp_decay_step), 1)
else:
tmp_global_step = self.min(tmp_global_step, tmp_decay_step)
p = tmp_global_step / tmp_decay_step
lr = self.diff_learning_rate * self.pow(1.0 - p, self.power) + self.end_learning_rate
return lr
[docs]class WarmUpLR(LearningRateSchedule):
r"""
Gets learning rate warming up.
For the i-th step, the formula of computing warmup_learning_rate[i] is:
.. math::
warmup\_learning\_rate[i] = learning\_rate * tmp\_step / warmup\_steps
Where :
.. math:
tmp\_step=min(current\_step, warmup\_steps)
Args:
learning_rate (float): The initial value of learning rate.
warmup_steps (int): The warm up steps of learning rate.
Inputs:
Tensor. The current step number.
Outputs:
Tensor. The learning rate value for the current step.
Examples:
>>> learning_rate = 0.1
>>> warmup_steps = 2
>>> global_step = Tensor(2, mstype.int32)
>>> warmup_lr = nn.WarmUpLR(learning_rate, warmup_steps)
>>> result = warmup_lr(global_step)
>>> print(result)
0.1
"""
def __init__(self, learning_rate, warmup_steps):
super(WarmUpLR, self).__init__()
if not isinstance(learning_rate, float):
raise TypeError("learning_rate must be float.")
validator.check_non_negative_float(learning_rate, "learning_rate", self.cls_name)
validator.check_positive_int(warmup_steps, 'warmup_steps', self.cls_name)
self.warmup_steps = warmup_steps
self.learning_rate = learning_rate
self.min = P.Minimum()
self.cast = P.Cast()
def construct(self, global_step):
warmup_percent = self.cast(self.min(global_step, self.warmup_steps), mstype.float32)/ self.warmup_steps
return self.learning_rate * warmup_percent
__all__ = [
'ExponentialDecayLR',
'NaturalExpDecayLR',
'InverseDecayLR',
'CosineDecayLR',
'PolynomialDecayLR',
'WarmUpLR'
]