def _check_param_value(decay_steps, warmup_steps, start_learning_rate,
end_learning_rate, power, beta1, beta2, eps, weight_decay, prim_name):
"""Check the type of inputs."""
validator.check_value_type("start_learning_rate", start_learning_rate, [float], prim_name)
validator.check_number_range("start_learning_rate rate", start_learning_rate, 0.0, float("inf"), Rel.INC_LEFT,
prim_name)
validator.check_value_type("end_learning_rate", end_learning_rate, [float], prim_name)
validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT,
prim_name)
validator.check_float_positive('power', power, prim_name)
validator.check_float_legal_value('power', power, prim_name)
validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
validator.check_integer('warmup_steps', warmup_steps, 0, Rel.GE, prim_name)
validator.check_value_type("beta1", beta1, [float], prim_name)
validator.check_value_type("beta2", beta2, [float], prim_name)
validator.check_value_type("eps", eps, [float], prim_name)
validator.check_value_type(
"weight_dacay", weight_decay, [float], prim_name)
validator.check_number_range(
"beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
validator.check_number_range(
"beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
validator.check_number_range(
"eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
validator.check_number_range(
"weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch, is_stair):
validator.check_integer('total_step', total_step, 0, Rel.GT, None)
validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
validator.check_float_positive('learning_rate', learning_rate, None)
validator.check_float_legal_value('learning_rate', learning_rate, None)
validator.check_float_positive('decay_rate', decay_rate, None)
validator.check_float_legal_value('decay_rate', decay_rate, None)
validator.check_value_type('is_stair', is_stair, [bool], None)
def _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, power, prim_name):
"""Check the type of inputs."""
validator.check_value_type("learning_rate", learning_rate, [float], prim_name)
validator.check_number_range("learning_rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
validator.check_value_type("end_learning_rate", end_learning_rate, [float], prim_name)
validator.check_number_range("end_learning_rate", end_learning_rate, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
validator.check_float_positive('power', power, prim_name)
validator.check_float_legal_value('power', power, prim_name)
validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
def _check_inputs(learning_rate, decay_rate, total_step, step_per_epoch,
decay_epoch, is_stair):
validator.check_positive_int(total_step, 'total_step')
validator.check_positive_int(step_per_epoch, 'step_per_epoch')
validator.check_positive_int(decay_epoch, 'decay_epoch')
validator.check_float_positive('learning_rate', learning_rate, None)
validator.check_float_legal_value('learning_rate', learning_rate, None)
validator.check_float_positive('decay_rate', decay_rate, None)
validator.check_float_legal_value('decay_rate', decay_rate, None)
validator.check_value_type('is_stair', is_stair, [bool], None)
def _init_group_params(self, parameters, learning_rate, weight_decay):
"""Init learning rate or weight decay in group params."""
origin_dynamic_lr = self.dynamic_lr
self._parse_group_params(parameters, learning_rate)
if self.dynamic_lr and not origin_dynamic_lr:
self.gather = P.GatherV2()
self.assignadd = P.AssignAdd()
self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step')
params_store = []
for group_param in parameters:
if 'order_params' in group_param.keys():
ordered_parameters = group_param['order_params']
continue
self.group_params += group_param['params']
if 'lr' in group_param.keys():
params_dynamic_lr = isinstance(group_param['lr'], (Iterable, Tensor))
if self.dynamic_lr and not params_dynamic_lr:
lr = Tensor(np.array([group_param['lr']] * self.dynamic_lr_length).astype(np.float32))
else:
lr = self._get_single_lr(group_param['lr'])
else:
if self.dynamic_lr and not origin_dynamic_lr:
lr = Tensor(np.array([self.scalar_lr] * self.dynamic_lr_length).astype(np.float32))
else:
lr = learning_rate
if 'weight_decay' in group_param.keys():
validator.check_float_legal_value('weight_decay', group_param['weight_decay'], None)
validator.check_number_range('weight_decay', group_param['weight_decay'], 0.0, float("inf"),
Rel.INC_LEFT, self.cls_name)
weight_decay_ = group_param['weight_decay'] * self.loss_scale
else:
weight_decay_ = weight_decay * self.loss_scale
for key in group_param.keys():
if key not in ('params', 'lr', 'weight_decay'):
logger.warning(f"The optimizer cannot parse '{key}' when setting parameter groups.")
for param in group_param['params']:
validator.check_value_type("parameter", param, [Parameter], self.cls_name)
if param.name in params_store:
raise RuntimeError(f"The {param.name} parameter has appeared in parameter groups.")
params_store.append(param.name)
self.group_lr.append(Parameter(lr, name="lr_" + param.name))
self.group_weight_decay.append(weight_decay_)
if self.is_group_params_ordered:
self._order_and_adjust_group_params(ordered_parameters, learning_rate, weight_decay)
def cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch):
r"""
Calculate 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\_epoch}{decay\_epoch}\pi))
Where :math:`current\_epoch=floor(\frac{i}{step\_per\_epoch})`.
Args:
min_lr (float): The minimum value of learning rate.
max_lr (float): The maximum value of learning rate.
total_step (int): The total number of steps.
step_per_epoch (int): The number of steps in per epoch.
decay_epoch (int): A value used to calculate decayed learning rate.
Returns:
list[float]. The size of list is `total_step`.
Examples:
>>> min_lr = 0.01
>>> max_lr = 0.1
>>> total_step = 6
>>> step_per_epoch = 2
>>> decay_epoch = 2
>>> cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
[0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]
"""
if not isinstance(min_lr, float):
raise TypeError("min_lr must be float.")
validator.check_number_range("min_lr", min_lr, 0.0, float("inf"),
Rel.INC_LEFT, None)
validator.check_float_positive('max_lr', max_lr, None)
validator.check_float_legal_value('max_lr', max_lr, None)
validator.check_integer('total_step', total_step, 0, Rel.GT, None)
validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
if min_lr >= max_lr:
raise ValueError('`max_lr` should be greater than `min_lr`.')
delta = 0.5 * (max_lr - min_lr)
lr = []
for i in range(total_step):
tmp_epoch = min(math.floor(i / step_per_epoch), decay_epoch)
lr.append(min_lr + delta *
(1 + math.cos(math.pi * tmp_epoch / decay_epoch)))
return lr
def piecewise_constant_lr(milestone, learning_rates):
r"""
Get piecewise constant learning rate.
Calculate learning rate by given `milestone` and `learning_rates`. Let the value of `milestone` be
:math:`(M_1, M_2, ..., M_N)` and the value of `learning_rates` be :math:`(x_1, x_2, ..., x_N)`. N is the length of
`milestone`. Let the output learning rate be `y`.
.. math::
y[i] = x_t,\ for\ i \in [M_{t-1}, M_t)
Args:
milestone (Union[list[int], tuple[int]]): A list of milestone. This list is a monotone increasing list.
Every element is a milestone step, and must be greater than 0.
learning_rates (Union[list[float], tuple[float]]): A list of learning rates.
Returns:
list[float]. The size of list is :math:`M_N`.
Examples:
>>> milestone = [2, 5, 10]
>>> learning_rates = [0.1, 0.05, 0.01]
>>> piecewise_constant_lr(milestone, learning_rates)
[0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
"""
validator.check_value_type('milestone', milestone, (tuple, list), None)
validator.check_value_type('learning_rates', learning_rates, (tuple, list),
None)
if len(milestone) != len(learning_rates):
raise ValueError(
'The size of `milestone` must be same with the size of `learning_rates`.'
)
lr = []
last_item = 0
for i, item in enumerate(milestone):
validator.check_integer(f'milestone[{i}]', item, 0, Rel.GT, None)
validator.check_float_legal_value(f'learning_rates[{i}]',
learning_rates[i], None)
if item < last_item:
raise ValueError(
f'The value of milestone[{i}] must be greater than milestone[{i - 1}]'
)
lr += [learning_rates[i]] * (item - last_item)
last_item = item
return lr
def polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
update_decay_epoch=False):
r"""
Calculate 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\_epoch / tmp\_decay\_epoch)^{power} + end\_learning\_rate
Where :math:`tmp\_epoch=min(current\_epoch, decay\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`, and
:math:`tmp\_decay\_epoch = decay\_epoch`. If `update_decay_epoch` is true, update the value of `tmp_decay_epoch`
every epoch. The formula is :math:`tmp\_decay\_epoch = decay\_epoch * ceil(current\_epoch / decay\_epoch)`
Args:
learning_rate (float): The initial value of learning rate.
end_learning_rate (float): The end value of learning rate.
total_step (int): The total number of steps.
step_per_epoch (int): The number of steps in per epoch.
decay_epoch (int): A value used to calculate decayed learning rate.
power (float): A value used to calculate decayed learning rate. This parameter should be greater than 0.
update_decay_epoch (bool): If true, update `decay_epoch`. Default: False.
Returns:
list[float]. The size of list is `total_step`.
Examples:
>>> learning_rate = 0.1
>>> end_learning_rate = 0.01
>>> total_step = 6
>>> step_per_epoch = 2
>>> decay_epoch = 2
>>> power = 0.5
>>> polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
[0.1, 0.1, 0.07363961030678928, 0.07363961030678928, 0.01, 0.01]
"""
validator.check_float_positive('learning_rate', learning_rate, None)
validator.check_float_legal_value('learning_rate', learning_rate, None)
validator.check_float_positive('end_learning_rate', end_learning_rate, None)
validator.check_float_legal_value('end_learning_rate', end_learning_rate, None)
validator.check_float_positive('power', power, None)
validator.check_float_legal_value('power', power, None)
validator.check_integer('total_step', total_step, 0, Rel.GT, None)
validator.check_integer('step_per_epoch', step_per_epoch, 0, Rel.GT, None)
validator.check_integer('decay_epoch', decay_epoch, 0, Rel.GT, None)
validator.check_value_type('update_decay_epoch', update_decay_epoch, [bool], None)
origin_decay_epoch = decay_epoch
function = lambda x, y: (x, min(x, y))
if update_decay_epoch:
function = lambda x, y: (origin_decay_epoch * max(math.ceil(y / origin_decay_epoch), 1), y)
lr = []
delta = learning_rate - end_learning_rate
for i in range(total_step):
current_epoch = math.floor(i / step_per_epoch)
decay_epoch, tmp_epoch = function(decay_epoch, current_epoch)
lr.append(delta * (1 - tmp_epoch / decay_epoch) ** power + end_learning_rate)
return lr
def _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, power, prim_name):
"""Check the type of inputs."""
validator.check_float_positive('learning_rate', learning_rate, prim_name)
validator.check_float_legal_value('learning_rate', learning_rate, prim_name)
validator.check_float_positive('end_learning_rate', end_learning_rate, prim_name)
validator.check_float_legal_value('end_learning_rate', end_learning_rate, prim_name)
validator.check_float_positive('power', power, prim_name)
validator.check_float_legal_value('power', power, prim_name)
validator.check_integer('decay_steps', decay_steps, 0, Rel.GT, prim_name)
def _init_group_params(self, parameters, learning_rate, weight_decay):
"""Init learning rate or weight decay in group params."""
origin_dynamic_lr = self.dynamic_lr
if self.dynamic_lr:
dynamic_lr_length = learning_rate.size()
else:
dynamic_lr_length = 0
for group_param in parameters:
lr_length = dynamic_lr_length
if 'lr' in group_param.keys():
self._get_single_lr(group_param['lr'])
if isinstance(group_param['lr'], Iterable):
lr_length = len(group_param['lr'])
self.dynamic_lr = True
elif isinstance(group_param['lr'], Tensor):
lr_length = group_param['lr'].size()
self.dynamic_lr = True
if dynamic_lr_length not in (lr_length, 0):
raise ValueError(
"The dynamic learning rate in group should be the same size."
)
dynamic_lr_length = lr_length
if self.dynamic_lr and not origin_dynamic_lr:
self.gather = P.GatherV2()
self.assignadd = P.AssignAdd()
self.global_step = Parameter(initializer(0, [1], mindspore.int32),
name='global_step')
params_store = []
for group_param in parameters:
self.params += group_param['params']
if 'lr' in group_param.keys():
params_dynamic_lr = isinstance(group_param['lr'],
(Iterable, Tensor))
if self.dynamic_lr and not params_dynamic_lr:
lr = Tensor(
np.array([group_param['lr']] *
dynamic_lr_length).astype(np.float32))
else:
lr = self._get_single_lr(group_param['lr'])
else:
if self.dynamic_lr and not origin_dynamic_lr:
lr = Tensor(
np.array([self.scalar_lr] * dynamic_lr_length).astype(
np.float32))
else:
lr = learning_rate
if 'weight_decay' in group_param.keys():
validator.check_float_legal_value('weight_decay',
group_param['weight_decay'],
None)
validator.check_number_range('weight_decay',
group_param['weight_decay'], 0.0,
float("inf"), Rel.INC_LEFT,
self.cls_name)
weight_decay_ = group_param['weight_decay'] * self.loss_scale
else:
weight_decay_ = weight_decay * self.loss_scale
for param in group_param['params']:
if param in params_store:
raise RuntimeError(
f"The {param.name} parameter has appeared in parameter groups."
)
params_store.append(param)
self.group_lr.append(Parameter(lr, name="lr_" + param.name))
self.group_weight_decay.append(weight_decay_)
def __init__(self,
learning_rate,
parameters,
weight_decay=0.0,
loss_scale=1.0,
decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in
x.name):
super(Optimizer, self).__init__(auto_prefix=False)
if isinstance(learning_rate, float):
self.dynamic_lr = False
self.gather = None
self.assignadd = None
self.global_step = None
validator.check_number_range("learning rate", learning_rate, 0.0,
float("inf"), Rel.INC_LEFT,
self.cls_name)
learning_rate = Tensor(learning_rate, mstype.float32)
else:
self.dynamic_lr = True
self.gather = P.GatherV2()
self.assignadd = P.AssignAdd()
self.global_step = Parameter(initializer(0, [1], mindspore.int32),
name='global_step')
if isinstance(learning_rate, Iterable):
learning_rate = Tensor(
np.array(list(learning_rate)).astype(np.float32))
elif isinstance(learning_rate, Tensor):
if learning_rate.dim() > 1:
raise ValueError(
"Learning rate should be a 0 or 1 dim `Tensor`,"
f"but got {learning_rate.dim()}.")
if learning_rate.dim() == 1 and learning_rate.size() < 2:
logger.warning(
"If want to use the dynamic learning rate, please make sure that the number "
"of elements in the list, tuple or tensor passed is greater than 1."
)
else:
raise TypeError(
"Learning rate should be float, Tensor or Iterable.")
if isinstance(weight_decay, int):
weight_decay = float(weight_decay)
validator.check_float_legal_value('weight_decay', weight_decay, None)
if isinstance(loss_scale, int):
loss_scale = float(loss_scale)
validator.check_float_legal_value('loss_scale', loss_scale, None)
if loss_scale <= 0.0:
raise ValueError(
"Loss scale should be greater than 0, but got {}".format(
loss_scale))
self.loss_scale = loss_scale
if weight_decay < 0.0:
raise ValueError(
"Weight decay should be equal or greater than 0, but got {}".
format(weight_decay))
self.learning_rate = Parameter(learning_rate, name="learning_rate")
self.parameters = ParameterTuple(parameters)
self.reciprocal_scale = 1.0 / loss_scale
self.weight_decay = weight_decay * loss_scale
self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
if not self.parameters:
raise ValueError("optimizer got an empty parameter list.")
