def __init__(self,
is_grad=True,
sparse=False,
reduction=None,
smooth_factor=0,
num_classes=2):
super(SoftmaxCrossEntropyWithLogits, self).__init__(reduction)
self.is_grad = is_grad
self.sparse = sparse
validator.check_integer("num_classes", num_classes, 1, Rel.GT,
self.cls_name)
validator.check_number_range("smooth_factor", smooth_factor, 0, 1,
Rel.INC_BOTH, self.cls_name)
self.smooth_factor = smooth_factor
self.num_classes = num_classes
self.softmax_cross_entropy = P.SoftmaxCrossEntropyWithLogits()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0 - self.smooth_factor, mstype.float32)
self.off_value = Tensor(
1.0 * self.smooth_factor / (self.num_classes - 1), mstype.float32)
self.is_cpugpu = context.get_context('device_target') in ["CPU", "GPU"]
if self.is_cpugpu:
self.sparse_softmax_cross_entropy = P.SparseSoftmaxCrossEntropyWithLogits(
is_grad=self.is_grad)
def _preprocess_single_lr(self, learning_rate):
"""Check lr value, and convert lr to a float, a Tensor or a LearningRateSchedule."""
if isinstance(learning_rate, (float, int)):
learning_rate = float(learning_rate)
validator.check_number_range("learning rate", learning_rate, 0.0,
float("inf"), Rel.INC_LEFT,
self.cls_name)
return learning_rate
if isinstance(learning_rate, Tensor) and learning_rate.dim() == 0:
return learning_rate
self.dynamic_lr = True
if isinstance(learning_rate, Iterable):
return Tensor(np.array(list(learning_rate)).astype(np.float32))
if isinstance(learning_rate, Tensor):
if learning_rate.dim() > 1:
raise ValueError(
"The dim of `Tensor` type Learning rate should be a 0 or 1,"
f"but got {learning_rate.dim()}.")
if learning_rate.dim() == 1 and learning_rate.size() < 2:
logger.warning(
"If use `Tensor` type dynamic learning rate, please make sure that the number"
"of elements in the tensor passed is greater than 1.")
return learning_rate
if isinstance(learning_rate, LearningRateSchedule):
return learning_rate
raise TypeError(
"Learning rate should be int, float, Tensor, Iterable or LearningRateSchedule."
)
def _preprocess_weight_decay(self, weight_decay):
"""Check weight decay, and convert int to float."""
if isinstance(weight_decay, (float, int)):
weight_decay = float(weight_decay)
validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
return weight_decay
raise TypeError("Weight decay should be int or float.")
def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
use_nesterov=False, weight_decay=0.0, loss_scale=1.0):
super(Adam, self).__init__(learning_rate, params, weight_decay, loss_scale)
_check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
validator.check_value_type("use_nesterov", use_nesterov, [bool], self.cls_name)
validator.check_value_type("loss_scale", loss_scale, [float], self.cls_name)
validator.check_number_range("loss_scale", loss_scale, 1.0, float("inf"), Rel.INC_LEFT, self.cls_name)
self.beta1 = Tensor(beta1, mstype.float32)
self.beta2 = Tensor(beta2, mstype.float32)
self.beta1_power = Parameter(initializer(1, [1], mstype.float32), name="beta1_power")
self.beta2_power = Parameter(initializer(1, [1], mstype.float32), name="beta2_power")
self.eps = eps
self.moment1 = self.parameters.clone(prefix="moment1", init='zeros')
self.moment2 = self.parameters.clone(prefix="moment2", init='zeros')
self.hyper_map = C.HyperMap()
self.opt = P.Adam(use_locking, use_nesterov)
self.pow = P.Pow()
self.sqrt = P.Sqrt()
self.one = Tensor(np.array([1.0]).astype(np.float32))
self.realdiv = P.RealDiv()
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("decay_steps", decay_steps, [int], prim_name)
validator.check_value_type("warmup_steps", warmup_steps, [int], prim_name)
validator.check_value_type("start_learning_rate", start_learning_rate,
[float], prim_name)
validator.check_value_type("end_learning_rate", end_learning_rate, [float],
prim_name)
validator.check_value_type("power", power, [float], 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("decay_steps", decay_steps, 1, float("inf"),
Rel.INC_LEFT, 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_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 __init__(self, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
super(SSIM, self).__init__()
validator.check_value_type('max_val', max_val, [int, float], self.cls_name)
validator.check_number('max_val', max_val, 0.0, Rel.GT, self.cls_name)
self.max_val = max_val
self.filter_size = validator.check_integer('filter_size', filter_size, 1, Rel.GE, self.cls_name)
self.filter_sigma = validator.check_float_positive('filter_sigma', filter_sigma, self.cls_name)
validator.check_value_type('k1', k1, [float], self.cls_name)
self.k1 = validator.check_number_range('k1', k1, 0.0, 1.0, Rel.INC_NEITHER, self.cls_name)
validator.check_value_type('k2', k2, [float], self.cls_name)
self.k2 = validator.check_number_range('k2', k2, 0.0, 1.0, Rel.INC_NEITHER, self.cls_name)
self.mean = P.DepthwiseConv2dNative(channel_multiplier=1, kernel_size=filter_size)
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 _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."""
_ = warmup_steps
validator.check_float_positive('start_learning_rate', start_learning_rate,
prim_name)
validator.check_float_legal_value('start_learning_rate',
start_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)
validator.check_integer('warmup_steps', decay_steps, 0, Rel.GT, 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 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 __init__(self, margin=0.0, reduction="mean"):
super(CosineEmbeddingLoss, self).__init__(reduction)
self.reduce_sum = P.ReduceSum()
self.maximum = P.Maximum()
validator.check_value_type("margin", margin, [float], self.cls_name)
self.margin = validator.check_number_range("margin", margin, -1.0, 1.0,
Rel.INC_BOTH, self.cls_name)
def __init__(self, central_fraction):
super(CentralCrop, self).__init__()
validator.check_value_type("central_fraction", central_fraction, [float], self.cls_name)
self.central_fraction = validator.check_number_range('central_fraction', central_fraction,
0.0, 1.0, Rel.INC_RIGHT, self.cls_name)
self.central_fraction_tensor = Tensor(np.array([central_fraction]).astype(np.float64))
self.slice = P.Slice()
def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
"""Check the type of inputs."""
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 warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch):
r"""
Get 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\_epoch / tmp\_warmup\_epoch
Where :math:`tmp\_epoch=min(current\_epoch, warmup\_epoch),\ current\_epoch=floor(\frac{i}{step\_per\_epoch})`
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.
Returns:
Tensor. The learning rate value for the current step.
Examples:
>>> learning_rate = 0.1
>>> total_step = 6
>>> step_per_epoch = 2
>>> warmup_epoch = 2
>>> warmup_lr(learning_rate, total_step, step_per_epoch, warmup_epoch)
[0.0, 0.0, 0.05, 0.05, 0.1, 0.1]
"""
if not isinstance(learning_rate, float):
raise TypeError("learning_rate must be float.")
validator.check_number_range("learning_rate", learning_rate, 0.0,
float("inf"), Rel.INC_LEFT, None)
validator.check_integer('warmup_epoch', warmup_epoch, 0, Rel.GT, 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)
function = lambda x, y: (x, min(x, y))
lr = []
for i in range(total_step):
current_epoch = math.floor(i / step_per_epoch)
warmup_epoch, tmp_epoch = function(warmup_epoch, current_epoch)
lr.append(learning_rate * tmp_epoch / warmup_epoch)
return lr
def _get_single_lr(self, learning_rate):
"""Get learning rate in Tensor type."""
if isinstance(learning_rate, float):
validator.check_number_range("learning rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
lr = Tensor(learning_rate, mstype.float32)
elif isinstance(learning_rate, Iterable):
lr = 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.")
lr = learning_rate
else:
raise TypeError("Learning rate should be float, Tensor or Iterable.")
return lr
def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0):
super(RMSProp, self).__init__(learning_rate, params, weight_decay, loss_scale)
validator.check_value_type("decay", decay, [float], self.cls_name)
validator.check_number_range("decay", decay, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
validator.check_value_type("momentum", momentum, [float], self.cls_name)
validator.check_number_range("momentum", momentum, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name)
validator.check_value_type("epsilon", epsilon, [float], self.cls_name)
validator.check_number_range("epsilon", epsilon, 0.0, float("inf"), Rel.INC_NEITHER, self.cls_name)
validator.check_value_type("use_locking", use_locking, [bool], self.cls_name)
validator.check_value_type("centered", centered, [bool], self.cls_name)
self.centered = centered
if centered:
self.opt = P.ApplyCenteredRMSProp(use_locking)
self.mg = self.parameters.clone(prefix="mean_grad", init='zeros')
else:
self.opt = P.ApplyRMSProp(use_locking)
self.momentum = momentum
self.ms = self.parameters.clone(prefix="mean_square", init='zeros')
self.moment = self.parameters.clone(prefix="moment", init='zeros')
self.hyper_map = C.HyperMap()
self.epsilon = epsilon
self.decay = decay
def _check_param_value(accum, l1, l2, use_locking, prim_name=None):
"""Check inputs param."""
validator.check_value_type("accum", accum, [float], prim_name)
validator.check_value_type("l1", l1, [float], prim_name)
validator.check_value_type("l2", l2, [float], prim_name)
validator.check_value_type("use_locking", use_locking, [bool], prim_name)
validator.check_number_range("accum", accum, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
validator.check_number_range("l1", l1, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
validator.check_number_range("l2", l2, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
def _check_param_value(beta1, beta2, eps, 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_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)
def __init__(self,
learning_rate,
parameters,
weight_decay=0.0,
loss_scale=1.0):
super(Optimizer, self).__init__(auto_prefix=False)
if parameters and not isinstance(parameters, list):
parameters = list(parameters)
if not parameters:
raise ValueError("Optimizer got an empty parameter list.")
if not isinstance(parameters[0], (dict, Parameter)):
raise TypeError(
"Only a list of Parameter or dict can be supported.")
if isinstance(loss_scale, int):
loss_scale = float(loss_scale)
validator.check_value_type("loss_scale", loss_scale, [float], None)
validator.check_number_range("loss_scale", loss_scale, 0.0,
float("inf"), Rel.INC_NEITHER, None)
if isinstance(weight_decay, int):
weight_decay = float(weight_decay)
validator.check_value_type("weight_decay", weight_decay, [float], None)
validator.check_number_range("weight_decay", weight_decay, 0.0,
float("inf"), Rel.INC_LEFT, None)
self.is_group = False
self.is_group_lr = False
self.loss_scale = loss_scale
if isinstance(learning_rate, float):
self.dynamic_lr = False
self.gather = None
self.assignadd = None
self.global_step = None
self.scalar_lr = learning_rate
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')
self.scalar_lr = None
learning_rate = self._get_single_lr(learning_rate)
if isinstance(parameters[0], dict):
self.is_group = True
self.params = []
self.group_lr = []
self.group_weight_decay = []
self._init_group_params(parameters, learning_rate, weight_decay)
if self.is_group_lr:
self.learning_rate = ParameterTuple(self.group_lr)
else:
self.learning_rate = Parameter(learning_rate, name="learning_rate")
if self.is_group:
self.parameters = ParameterTuple(self.params)
self.weight_decay = tuple(self.group_weight_decay)
decay_filter = lambda x: x > 0
self.decay_flags = tuple(
decay_filter(x) for x in self.weight_decay)
else:
self.parameters = ParameterTuple(parameters)
self.weight_decay = weight_decay * loss_scale
decay_filter = lambda x: 'beta' not in x.name and 'gamma' not in x.name
self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
self.reciprocal_scale = 1.0 / loss_scale
self.exec_weight_decay = any(self.decay_flags)
self.param_length = len(self.parameters)
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_value_type("weight_decay", weight_decay, [float], None)
validator.check_number_range("weight_decay", weight_decay, 0.0,
float("inf"), Rel.INC_LEFT, None)
if isinstance(loss_scale, int):
loss_scale = float(loss_scale)
validator.check_value_type("loss_scale", loss_scale, [float], None)
validator.check_number_range("loss_scale", loss_scale, 0.0,
float("inf"), Rel.INC_NEITHER, None)
self.loss_scale = loss_scale
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.")
def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0):
super(Optimizer, self).__init__(auto_prefix=False)
if parameters is not None and not isinstance(parameters, list):
parameters = list(parameters)
if not parameters:
raise ValueError("Optimizer got an empty parameter list.")
if not isinstance(parameters[0], (dict, Parameter)):
raise TypeError("Only a list of Parameter or dict can be supported.")
if isinstance(loss_scale, int):
loss_scale = float(loss_scale)
validator.check_value_type("loss_scale", loss_scale, [float], self.cls_name)
validator.check_number_range("loss_scale", loss_scale, 0.0, float("inf"), Rel.INC_NEITHER, self.cls_name)
self.loss_scale = loss_scale
weight_decay = self._preprocess_weight_decay(weight_decay)
self.dynamic_lr = False
self.assignadd = None
self.global_step = None
self.is_group = False
self.is_group_lr = False
self.is_group_params_ordered = False
learning_rate = self._preprocess_single_lr(learning_rate)
if isinstance(parameters[0], dict):
self.is_group = True
self.group_params = []
self.group_lr = []
self.group_weight_decay = []
self._init_group_params(parameters, learning_rate, weight_decay)
# The final value of dynamic_lr can be determined after the process of parse_single_lr and init_group_params
if self.dynamic_lr:
self.assignadd = P.AssignAdd()
self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step')
if self.is_group_lr:
if self.dynamic_lr:
self.learning_rate = CellList(self.group_lr)
else:
self.learning_rate = ParameterTuple(self.group_lr)
else:
self.learning_rate = self._build_single_lr(learning_rate, 'learning_rate')
if self.is_group:
self.parameters = ParameterTuple(self.group_params)
self.weight_decay = tuple(self.group_weight_decay)
decay_filter = lambda x: x > 0
self.decay_flags = tuple(decay_filter(x) for x in self.weight_decay)
self.exec_weight_decay = any(self.decay_flags)
else:
self.parameters = ParameterTuple(parameters)
self.weight_decay = weight_decay * loss_scale
decay_filter = lambda x: 'beta' not in x.name and 'gamma' not in x.name
self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
self.exec_weight_decay = self.weight_decay > 0
ps_filter = lambda x: x.is_param_ps
self.ps_parameters = tuple(ps_filter(x) for x in self.parameters)
self.reciprocal_scale = 1.0 / loss_scale
self.param_length = len(self.parameters)
self.map_ = C.Map()
use_parallel = context.get_auto_parallel_context("enable_parallel_optimizer")
self.use_parallel = use_parallel
if use_parallel:
if self.cls_name not in ["Lamb", "AdamWeightDecay"]:
raise RuntimeError("Optimizer segmentation does not support optimizer {}".format(self.cls_name))
if _get_parallel_mode() != ParallelMode.DATA_PARALLEL:
raise RuntimeError("Optimizer segmentation does not support parallel mode {}".format
(_get_parallel_mode()))
self.dev_num = _get_device_num()
if self.dev_num > self.param_length:
raise RuntimeError("Optimizer segmentation can not be applied when the number of parameters {} is"
" less than the number of devices {}".format(self.param_length, self.dev_num))
self.param_rank = self._get_parameter_group_id()
self.optim_filter = tuple(map(lambda x: x == _get_global_rank(), self.param_rank))
self.param_names = []
for param in self.parameters:
self.param_names.append(param.name)
else:
self.optim_filter = (True,) * self.param_length
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 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})`,
.. 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)
if not isinstance(end_learning_rate, float):
raise TypeError("end_learning_rate must be float.")
validator.check_number_range("end_learning_rate", end_learning_rate, 0.0,
float("inf"), Rel.INC_LEFT, 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
