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- # Copyright 2021 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.
- # ============================================================================
- """adam"""
-
- import numpy as np
-
- from mindspore.common import dtype as mstype
- from mindspore.common.initializer import initializer
- from mindspore.nn import Optimizer
- from mindspore.ops import operations as P
- from mindspore.ops import composite as C
- from mindspore.ops import functional as F
- from mindspore.common.parameter import Parameter
- from mindspore.common.tensor import Tensor
- from mindspore._checkparam import Validator as validator
- from mindspore._checkparam import Rel
-
- _learning_rate_update_func = ['linear', 'cos', 'sin']
-
- adam_opt = C.MultitypeFuncGraph("adam_opt")
-
-
- @adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool")
- def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
- """
- Update parameters.
-
- Args:
- beta1 (Tensor): The exponential decay rate for the 1st moment estimates. Should be in range (0.0, 1.0).
- beta2 (Tensor): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0).
- eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
- lr (Tensor): Learning rate.
- weight_decay_tensor (Tensor): Weight decay. Should be equal to or greater than 0.
- param (Tensor): Parameters.
- m (Tensor): m value of parameters.
- v (Tensor): v value of parameters.
- gradient (Tensor): Gradient of parameters.
-
- Returns:
- Tensor, the new value of v after updating.
- """
- op_mul = P.Mul()
- op_square = P.Square()
- op_sqrt = P.Sqrt()
- op_cast = P.Cast()
- op_reshape = P.Reshape()
- op_shape = P.Shape()
-
- param_fp32 = op_cast(param, mstype.float32)
- m_fp32 = op_cast(m, mstype.float32)
- v_fp32 = op_cast(v, mstype.float32)
- gradient_fp32 = op_cast(gradient, mstype.float32)
-
- next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
-
- next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
- - beta2, op_square(gradient_fp32))
-
- update = next_m / (op_sqrt(next_v) + eps)
- if decay_flag:
- update = update + op_mul(weight_decay_tensor, param_fp32)
-
- update_with_lr = op_mul(lr, update)
- next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
-
- next_v = F.depend(next_v, F.assign(param, next_param))
- next_v = F.depend(next_v, F.assign(m, next_m))
- next_v = F.depend(next_v, F.assign(v, next_v))
- return next_v
-
-
- 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)
-
-
-
- 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)
-
-
- @adam_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor", "Tensor",
- "Tensor")
- def _run_opt_with_one_number(opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, params, moment1,
- moment2):
- """Apply adam optimizer to the weight parameter using Tensor."""
- success = True
- success = F.depend(success, opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
- eps, gradient))
- return success
-
-
- class Adam(Optimizer):
- r"""
- Updates gradients by Adaptive Moment Estimation (Adam) algorithm.
-
- The Adam algorithm is proposed in `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_.
-
- The updating formulas are as follows,
-
- .. math::
- \begin{array}{ll} \\
- m = \beta_1 * m + (1 - \beta_1) * g \\
- v = \beta_2 * v + (1 - \beta_2) * g * g \\
- l = \alpha * \frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t} \\
- w = w - l * \frac{m}{\sqrt{v} + \epsilon}
- \end{array}
-
- :math:`m` represents the 1st moment vector `moment1`, :math:`v` represents the 2nd moment vector `moment2`,
- :math:`g` represents `gradients`, :math:`l` represents scaling factor `lr`, :math:`\beta_1, \beta_2` represent
- `beta1` and `beta2`, :math:`t` represents updating step while :math:`beta_1^t` and :math:`beta_2^t` represent
- `beta1_power` and `beta2_power`, :math:`\alpha` represents `learning_rate`, :math:`w` represents `params`,
- :math:`\epsilon` represents `eps`.
-
- Note:
- The Adam optimizer supports separating parameter groups. Different parameter groups can set different
- `learning_rate` and `weight_decay`.
-
- When separating parameter groups, the weight decay in each group will be applied on the parameters if the
- value of weight_decay > 0. When not separating parameter groups, the `weight_decay` in the API will be
- applied on the parameters if `weight_decay` > 0 and the 'beta' and 'gamma' are not in the name of parameters.
-
- Args:
- params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
- the element in `params` should be class `Parameter`. When the `params` is a list of `dict`, the "params",
- "lr" and "weight_decay" are the keys can be parsed.
-
- - params: Required. The value should be a list of `Parameter`.
-
- - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used.
- If not, the `learning_rate` in the API will be used.
-
- - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
- will be used. If not, the `weight_decay` in the API will be used.
-
- learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
- Iterable or a Tensor and the dims of the Tensor is 1,
- use dynamic learning rate, then the i-th step will
- take the i-th value as the learning rate.
- When the learning_rate is float or learning_rate is a Tensor
- but the dims of the Tensor is 0, use fixed learning rate.
- Other cases are not supported. Default: 1e-3.
- beta1 (float): The exponential decay rate for the 1st moment estimates. Should be in range (0.0, 1.0). Default:
- 0.9.
- beta2 (float): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0). Default:
- 0.999.
- eps (float): Term added to the denominator to improve numerical stability. Should be greater than 0. Default:
- 1e-8.
- use_locking (bool): Whether to enable a lock to protect updating variable tensors.
- If True, updating of the var, m, and v tensors will be protected by a lock.
- If False, the result is unpredictable. Default: False.
- use_nesterov (bool): Whether to use Nesterov Accelerated Gradient (NAG) algorithm to update the gradients.
- If True, updates the gradients using NAG.
- If False, updates the gradients without using NAG. Default: False.
- weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
- loss_scale (float): A floating point value for the loss scale. Should be equal to or greater than 1. Default:
- 1.0.
-
- Inputs:
- - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
-
- Outputs:
- Tensor[bool], the value is True.
-
- Examples:
- >>> net = Net()
- >>> #1) All parameters use the same learning rate and weight decay
- >>> optim = nn.Adam(params=net.trainable_params())
- >>>
- >>> #2) Use parameter groups and set different values
- >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
- >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
- >>> group_params = [{'params': conv_params, 'weight_decay': 0.01, 'lr': 0.01},
- >>> {'params': no_conv_params}]
- >>> opt = nn.Adam(group_params, learning_rate=0.1, weight_decay=0.0)
- >>> # the conv_params's parameters will use a learning rate of 0.01 and a weight decay of 0.01
- >>> # the no_cov_params's parameters don't set learning and weight decay. So they will use a
- >>> # learning rate of 0.1 and a weight decay of 0.0.
- >>>
- >>> loss = nn.SoftmaxCrossEntropyWithLogits()
- >>> model = Model(net, loss_fn=loss, optimizer=optim)
- """
-
- 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="beta2")
- self.beta2_power = Parameter(initializer(1, [1], mstype.float32), name="beta1")
- 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()
-
- self.lr_scalar = P.ScalarSummary()
-
- def construct(self, gradients):
- """Adam optimizer."""
- params = self.parameters
- moment1 = self.moment1
- moment2 = self.moment2
- gradients = self.decay_weight(gradients)
- gradients = self.scale_grad(gradients)
- lr = self.get_lr()
-
- self.lr_scalar("learning_rate", lr)
-
- beta1_power = self.beta1_power * self.beta1
- self.beta1_power = beta1_power
- beta2_power = self.beta2_power * self.beta2
- self.beta2_power = beta2_power
- if self.is_group_lr:
- success = self.hyper_map(F.partial(adam_opt, self.opt, beta1_power, beta2_power, self.beta1,
- self.beta2, self.eps),
- lr, gradients, params, moment1, moment2)
- else:
- success = self.hyper_map(F.partial(adam_opt, self.opt, beta1_power, beta2_power, self.beta1,
- self.beta2, self.eps, lr),
- gradients, params, moment1, moment2)
- return success
-
-
- class AdamWeightDecay(Optimizer):
- """
- Implements Adam algorithm weight decay fix.
-
- Args:
- params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
- should be class mindspore.Parameter.
- learning_rate (Union[float, Tensor, Iterable]): A value for the learning rate. When the learning_rate is
- Iterable or a Tensor and the dims of the Tensor is 1,
- use dynamic learning rate, then the i-th step will
- take the i-th value as the learning rate.
- When the learning_rate is float or learning_rate is a Tensor
- but the dims of the Tensor is 0, use fixed learning rate.
- Other cases are not supported. Default: 1e-3.
- beta1 (float): The exponential decay rate for the 1st moment estimates. Default: 0.9.
- Should be in range (0.0, 1.0).
- beta2 (float): The exponential decay rate for the 2nd moment estimates. Default: 0.999.
- Should be in range (0.0, 1.0).
- eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
- Should be greater than 0.
- weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
- decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
- lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name.
-
- Inputs:
- - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
-
- Outputs:
- tuple[Parameter], the updated velocity value, the shape is the same as `params`.
-
- Examples:
- >>> net = Net()
- >>> loss = nn.SoftmaxCrossEntropyWithLogits()
- >>> optim = nn.AdamWeightDecay(params=net.trainable_params())
- >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
- """
-
- def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0,
- decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
- super(AdamWeightDecay, self).__init__(learning_rate, params)
- if self.is_group:
- raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
- _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
- self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
- self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
- self.eps = Tensor(np.array([eps]).astype(np.float32))
- self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))
-
- self.params = self.parameters
- self.moments1 = self.params.clone(prefix="adam_m", init='zeros')
- self.moments2 = self.params.clone(prefix="adam_v", init='zeros')
- self.decay_flag = tuple(decay_filter(x) for x in self.params)
-
- self.hyper_map = C.HyperMap()
-
- def construct(self, gradients):
- """Adam Weight Decay"""
- lr = self.get_lr()
- updated_velocity = self.hyper_map(F.partial(adam_opt, self.beta1, self.beta2, self.eps, lr,
- self.weight_decay_tensor),
- self.params, self.moments1, self.moments2, gradients, self.decay_flag)
-
- return updated_velocity
-
-
- class AdamWeightDecayDynamicLR(Optimizer):
- """
- Adam Weight Decay Dynamic Learning Rate (LR).
-
- Args:
- params (list[Parameter]): A list of parameter, which will be updated. The element in `params`
- should be class mindspore.Parameter.
- decay_steps (int): The steps of the decay.
- learning_rate (float): A floating point value for the learning rate. Default: 0.001.
- end_learning_rate (float): A floating point value for the end learning rate. Default: 0.0001.
- power (float): Power. Default: 10.0.
- beta1 (float): The exponential decay rate for the 1st moment estimates. Default: 0.9.
- Should be in range (0.0, 1.0).
- beta2 (float): The exponential decay rate for the 2nd moment estimates. Default: 0.999.
- Should be in range (0.0, 1.0).
- eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6.
- Should be greater than 0.
- weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
- decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
- lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name.
-
- Inputs:
- - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
-
- Outputs:
- tuple[Parameter], the updated velocity value, the shape is the same as `params`.
-
- Examples:
- >>> net = Net()
- >>> loss = nn.SoftmaxCrossEntropyWithLogits()
- >>> optim = nn.AdamWeightDecayDynamicLR(params=net.trainable_params(), decay_steps=10)
- >>> model = Model(net, loss_fn=loss, optimizer=optim, metrics=None)
- """
-
- def __init__(self,
- params,
- decay_steps,
- learning_rate=0.001,
- end_learning_rate=0.0001,
- power=10.0,
- beta1=0.9,
- beta2=0.999,
- eps=1e-6,
- weight_decay=0.0,
- decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name,
- warmup_steps=0):
- super(AdamWeightDecayDynamicLR, self).__init__(learning_rate, params)
- if self.is_group:
- raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
- _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
- _check_learning_rate_value(learning_rate, end_learning_rate, decay_steps, power, self.cls_name)
- # turn them to scalar when me support scalar/tensor mix operations
- self.global_step = Parameter(initializer(0, [1]), name="global_step")
- self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32))
- self.warmup_flag = False
- if warmup_steps > 0:
- self.warmup_flag = True
- self.decay_steps = Tensor(np.array([decay_steps]).astype(np.float32))
- self.end_learning_rate = Tensor(np.array([end_learning_rate]).astype(np.float32))
- self.diff_learning_rate = Tensor(np.array([learning_rate - end_learning_rate]).astype(np.float32))
- self.power = power
- self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
- self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
- self.eps = Tensor(np.array([eps]).astype(np.float32))
- self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))
- self.params = self.parameters
- self.moments1 = self.params.clone(prefix="adam_m", init='zeros')
- self.moments2 = self.params.clone(prefix="adam_v", init='zeros')
- self.decay_flag = tuple(decay_filter(x) for x in self.params)
- self.hyper_map = C.HyperMap()
- self.min = P.Minimum()
- self.pow = P.Pow()
- self.greater = P.Greater()
- self.one = Tensor(np.array([1.0]).astype(np.float32))
- self.cast = P.Cast()
- self.start_learning_rate = Tensor(np.array([learning_rate]).astype(np.float32))
-
- def construct(self, gradients):
- """Adam Weight Decay Dynamic LR."""
- step = self.min(self.global_step, self.decay_steps)
- p = step / self.decay_steps
- lr = self.diff_learning_rate * self.pow(self.one - p, self.power) + self.end_learning_rate
- if self.warmup_flag:
- warmup_percent = self.global_step / self.warmup_steps
- warmup_lr = self.start_learning_rate * warmup_percent
- is_warmup = self.cast(self.greater(self.warmup_steps, self.global_step), mstype.float32)
- lr = (self.one - is_warmup) * lr + is_warmup * warmup_lr
- updated_velocity = self.hyper_map(F.partial(adam_opt, self.beta1, self.beta2, self.eps, lr,
- self.weight_decay_tensor),
- self.params, self.moments1, self.moments2, gradients, self.decay_flag)
-
- added_global_step = self.global_step + self.one
- F.control_depend(lr, added_global_step)
- self.global_step = added_global_step
-
- return updated_velocity
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