mindspore2022/mindspore/nn/optim/adam.py

# Copyright 2020-2021 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.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# See the License for the specific language governing permissions and
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# ============================================================================
"""adam"""
import numpy as np

from mindspore.common import dtype as mstype
from mindspore.common.initializer import initializer
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
from .optimizer import Optimizer
from .optimizer import opt_init_args_register

_adam_opt = C.MultitypeFuncGraph("adam_opt")
_scaler_one = Tensor(1, mstype.int32)
_scaler_ten = Tensor(10, mstype.float32)


@_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor",
                    "Tensor", "Bool", "Bool")
def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, decay_flag, optim_filter):
    """
    Update parameters.

    Args:
        beta1 (Tensor): The exponential decay rate for the 1st moment estimations. Should be in range (0.0, 1.0).
        beta2 (Tensor): The exponential decay rate for the 2nd moment estimations. 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 (numbers.Number): 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.
        decay_flag (bool): Applies weight decay or not.
        optim_filter (bool): Applies parameter update or not.

    Returns:
        Tensor, the new value of v after updating.
    """
    op_cast = P.Cast()
    if optim_filter:
        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 / (eps + op_sqrt(next_v))
        if decay_flag:
            update = op_mul(weight_decay, param_fp32) + update

        update_with_lr = op_mul(lr, update)
        next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))

        next_param = F.depend(next_param, F.assign(param, op_cast(next_param, F.dtype(param))))
        next_param = F.depend(next_param, F.assign(m, op_cast(next_m, F.dtype(m))))
        next_param = F.depend(next_param, F.assign(v, op_cast(next_v, F.dtype(v))))

        return op_cast(next_param, F.dtype(param))
    return op_cast(gradient, F.dtype(param))


@_adam_opt.register("Function", "Function", "Function", "Function", "Bool", "Bool", "Bool", "Tensor", "Tensor",
                    "Tensor", "Tensor", "Tensor", "Tensor", "RowTensor", "Tensor", "Tensor", "Tensor", "Bool", "Bool")
def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov, target, beta1_power,
                         beta2_power, beta1, beta2, eps, lr, gradient, param, m, v, ps_parameter, cache_enable):
    """Apply sparse adam optimizer to the weight parameter when the gradient is sparse."""
    success = True
    indices = gradient.indices
    values = gradient.values
    if ps_parameter and not cache_enable:
        op_shape = P.Shape()
        shapes = (op_shape(param), op_shape(m), op_shape(v),
                  op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
                  op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
        success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2,
                                               eps, values, indices), shapes), param))
        return success

    if not target:
        success = F.depend(success, sparse_opt(param, m, v, beta1_power, beta2_power, lr, beta1, beta2,
                                               eps, values, indices))
    else:
        op_mul = P.Mul()
        op_square = P.Square()
        op_sqrt = P.Sqrt()
        scatter_add = P.ScatterAdd(use_locking)

        success = F.depend(success, F.assign(m, op_mul(beta1, m)))
        success = F.depend(success, F.assign(v, op_mul(beta2, v)))

        grad_indices = gradient.indices
        grad_value = gradient.values

        next_m = scatter_add(m,
                             grad_indices,
                             op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))

        next_v = scatter_add(v,
                             grad_indices,
                             op_mul(F.tuple_to_array((1.0,)) - beta2, op_square(grad_value)))

        if use_nesterov:
            m_temp = next_m * _scaler_ten
            F.assign(m, op_mul(beta1, next_m))
            div_value = scatter_add(m,
                                    op_mul(grad_indices, _scaler_one),
                                    op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
            param_update = div_value / (op_sqrt(next_v) + eps)
            F.assign(m, m_temp / _scaler_ten)
        else:
            param_update = next_m / (op_sqrt(next_v) + eps)

        lr_t = lr * op_sqrt(1 - beta2_power) / (1 - beta1_power)
        next_param = param - lr_t * param_update

        success = F.depend(success, F.assign(param, next_param))
        success = F.depend(success, F.assign(m, next_m))
        success = F.depend(success, F.assign(v, next_v))

    return success


@_adam_opt.register("Function", "Function", "Function", "Function", "Bool", "Bool", "Bool", "Tensor", "Tensor",
                    "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool", "Bool")
def _run_opt_with_one_number(opt, sparse_opt, push, pull, use_locking, use_nesterov, target,
                             beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, param,
                             moment1, moment2, ps_parameter, cache_enable):
    """Apply adam optimizer to the weight parameter using Tensor."""
    success = True
    if ps_parameter and not cache_enable:
        op_shape = P.Shape()
        success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2, eps, gradient),
                                              (op_shape(param), op_shape(moment1), op_shape(moment2))), param))
    else:
        success = F.depend(success, opt(param, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
                                        eps, gradient))
    return success


@_adam_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
                    "Tensor", "Tensor")
def _run_off_load_opt(opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, param, moment1, moment2):
    """Apply AdamOffload optimizer to the weight parameter using Tensor."""
    success = True
    delat_param = opt(moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2, eps, gradient)
    success = F.depend(success, F.assign_add(param, delat_param))
    return success


def _check_param_value(beta1, beta2, eps, 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_float_range(beta1, 0.0, 1.0, Rel.INC_NEITHER, "beta1", prim_name)
    validator.check_float_range(beta2, 0.0, 1.0, Rel.INC_NEITHER, "beta2", prim_name)
    validator.check_positive_float(eps, "eps", prim_name)


class Adam(Optimizer):
    r"""
    Updates gradients by the Adaptive Moment Estimation (Adam) algorithm.

    The Adam optimizer can dynamically adjust the learning rate of each parameter using the first-order
    moment estimation and the second-order moment estimation of the gradient.
    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{gather*}
            m_{t+1} = \beta_1 * m_{t} + (1 - \beta_1) * g \\
            v_{t+1} = \beta_2 * v_{t} + (1 - \beta_2) * g * g \\
            l_{t+1} = l_{t} * \frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t} \\
            w_{t+1} = w_{t} - l * \frac{m_{t+1}}{\sqrt{v_{t+1}} + \epsilon}
        \end{gather*}

    :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, :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 sparse strategy is applied while the SparseGatherV2 operator is used for forward network. If the sparse
        strategy wants to be executed on the host, set the target to the CPU.
        The sparse feature is under continuous development.

        If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without
        'beta' or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When
        parameters are grouped, each group can set `weight_decay`, if not, the `weight_decay` in optimizer will be
        applied.

    Args:
        params (Union[list[Parameter], list[dict]]): Must be list of `Parameter` or list of `dict`. When the
            `params` is a list of `dict`, the string "params", "lr", "weight_decay", "grad_centralization" and
            "order_params" are the keys can be parsed.

            - params: Required. Parameters in current group. The value must 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 optimizer will be used. Fixed and dynamic learning rate are supported.

            - 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 optimizer will be used.

            - grad_centralization: Optional. Must be Boolean. If "grad_centralization" is in the keys, the set value
              will be used. If not, the `grad_centralization` is False by default. This configuration only works on the
              convolution layer.

            - order_params: Optional. When parameters is grouped, this usually is used to maintain the order of
              parameters that appeared in the network to improve performance. The value should be parameters whose
              order will be followed in optimizer.
              If `order_params` in the keys, other keys will be ignored and the element of 'order_params' must be in
              one group of `params`.

        learning_rate (Union[float, int, Tensor, Iterable, LearningRateSchedule]): Default: 1e-3.

            - float: The fixed learning rate value. Must be equal to or greater than 0.

            - int: The fixed learning rate value. Must be equal to or greater than 0. It will be converted to float.

            - Tensor: Its value should be a scalar or a 1-D vector. For scalar, fixed learning rate will be applied.
              For vector, learning rate is dynamic, then the i-th step will take the i-th value as the learning rate.

            - Iterable: Learning rate is dynamic. The i-th step will take the i-th value as the learning rate.

            - LearningRateSchedule: Learning rate is dynamic. During training, the optimizer calls the instance of
              LearningRateSchedule with step as the input to get the learning rate of current step.

        beta1 (float): The exponential decay rate for the 1st moment estimations. Should be in range (0.0, 1.0).
                       Default: 0.9.
        beta2 (float): The exponential decay rate for the 2nd moment estimations. 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 variable tensors from being updated.
            If true, updates of the `w`, `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, update the gradients using NAG.
            If false, update the gradients without using NAG. Default: False.
        weight_decay (float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0.
        loss_scale (float): A floating point value for the loss scale. Should be greater than 0. In general, use the
            default value. Only when `FixedLossScaleManager` is used for training and the `drop_overflow_update` in
            `FixedLossScaleManager` is set to False, then this value needs to be the same as the `loss_scale` in
            `FixedLossScaleManager`. Refer to class :class:`mindspore.FixedLossScaleManager` for more details.
            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.

    Raises:
        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
        TypeError: If element of `parameters` is neither Parameter nor dict.
        TypeError: If `beta1`, `beta2`, `eps` or `loss_scale` is not a float.
        TypeError: If `weight_decay` is neither float nor int.
        TypeError: If `use_locking` or `use_nesterov` is not a bool.
        ValueError: If `loss_scale` or `eps` is less than or equal to 0.
        ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0).
        ValueError: If `weight_decay` is less than 0.

    Supported Platforms:
        ``Ascend`` ``GPU``  ``CPU``

    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, 'grad_centralization':True},
        ...                 {'params': no_conv_params, 'lr': 0.01},
        ...                 {'order_params': net.trainable_params()}]
        >>> optim = nn.Adam(group_params, learning_rate=0.1, weight_decay=0.0)
        >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01 and grad
        >>> # centralization of True.
        >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0 and grad
        >>> # centralization of False.
        >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
        >>>
        >>> loss = nn.SoftmaxCrossEntropyWithLogits()
        >>> model = Model(net, loss_fn=loss, optimizer=optim)
    """

    @opt_init_args_register
    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, 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)

        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 = Tensor(eps, mstype.float32)
        self.use_nesterov = use_nesterov
        self.use_locking = use_locking
        self.moment1 = self.parameters.clone(prefix="moment1", init='zeros')
        self.moment2 = self.parameters.clone(prefix="moment2", init='zeros')

        self._is_device = True
        self.opt = P.Adam(use_locking, use_nesterov)
        self.sparse_opt = P.FusedSparseAdam(use_locking, use_nesterov)
        self.sparse_opt.add_prim_attr("primitive_target", "CPU")
        self._ps_pull = P.Pull()
        self._ps_push = P.Push("Adam", [0, 1, 2])
        self._ps_push.add_prim_attr("use_nesterov", use_nesterov)

    def construct(self, gradients):
        params = self.parameters
        moment1 = self.moment1
        moment2 = self.moment2
        gradients = self.decay_weight(gradients)
        gradients = self.gradients_centralization(gradients)
        gradients = self.scale_grad(gradients)
        gradients = self._grad_sparse_indices_deduplicate(gradients)
        lr = self.get_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.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
                                          self.use_locking, self.use_nesterov, self._is_device,
                                          beta1_power, beta2_power, self.beta1, self.beta2, self.eps),
                                lr, gradients, params, moment1, moment2, self.ps_parameters, self.cache_enable)
        else:
            success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
                                          self.use_locking, self.use_nesterov, self._is_device,
                                          beta1_power, beta2_power, self.beta1, self.beta2, self.eps, lr),
                                gradients, params, moment1, moment2, self.ps_parameters, self.cache_enable)
        return success

    @Optimizer.target.setter
    def target(self, value):
        """
        If the input value is set to "CPU", the parameters will be updated on the host using the Fused
        optimizer operation.
        """
        self._set_base_target(value)


class AdamWeightDecay(Optimizer):
    r"""
    Implements the Adam algorithm to fix the weight decay.

    .. math::
        \begin{array}{ll} \\
            m_{t+1} = \beta_1 * m_{t} + (1 - \beta_1) * g \\
            v_{t+1} = \beta_2 * v_{t} + (1 - \beta_2) * g * g \\
            update = \frac{m_{t+1}}{\sqrt{v_{t+1}} + eps} \\
            update =
            \begin{cases}
                update + weight\_decay * w_{t}
                    & \text{ if } weight\_decay > 0 \\
                update
                    & \text{ otherwise }
            \end{cases} \\
            w_{t+1}  = w_{t} - lr * update
        \end{array}

    :math:`m` represents the 1st moment vector `moment1`, :math:`v` represents the 2nd moment vector `moment2`,
    :math:`g` represents `gradients`, :math:`lr` represents `learning_rate`,
    :math:`\beta_1, \beta_2` represent `beta1` and `beta2`, :math:`t` represents updating step while
    :math:`w` represents `params`.

    Note:
        There is usually no connection between a optimizer and mixed precision. But when `FixedLossScaleManager` is used
        and `drop_overflow_update` in `FixedLossScaleManager` is set to False, optimizer needs to set the 'loss_scale'.
        As this optimizer has no argument of `loss_scale`, so `loss_scale` needs to be processed by other means, refer
        document `LossScale <https://www.mindspore.cn/docs/programming_guide/zh-CN/master/lossscale.html>`_ to process
        `loss_scale` correctly.

        If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without
        'beta' or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When
        parameters are grouped, each group can set `weight_decay`, if not, the `weight_decay` in optimizer will be
        applied.

    Args:
        params (Union[list[Parameter], list[dict]]): Must be list of `Parameter` or list of `dict`. When the
            `params` is a list of `dict`, the string "params", "lr", "weight_decay", and "order_params"
            are the keys can be parsed.

            - params: Required. Parameters in current group. The value must 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 optimizer will be used. Fixed and dynamic learning rate are supported.

            - 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 optimizer will be used.

            - order_params: Optional. When parameters is grouped, this usually is used to maintain the order of
              parameters that appeared in the network to improve performance. The value should be parameters whose
              order will be followed in optimizer.
              If `order_params` in the keys, other keys will be ignored and the element of 'order_params' must be in
              one group of `params`.

        learning_rate (Union[float, int, Tensor, Iterable, LearningRateSchedule]): Default: 1e-3.

            - float: The fixed learning rate value. Must be equal to or greater than 0.

            - int: The fixed learning rate value. Must be equal to or greater than 0. It will be converted to float.

            - Tensor: Its value should be a scalar or a 1-D vector. For scalar, fixed learning rate will be applied.
              For vector, learning rate is dynamic, then the i-th step will take the i-th value as the learning rate.

            - Iterable: Learning rate is dynamic. The i-th step will take the i-th value as the learning rate.

            - LearningRateSchedule: Learning rate is dynamic. During training, the optimizer calls the instance of
              LearningRateSchedule with step as the input to get the learning rate of current step.

        beta1 (float): The exponential decay rate for the 1st moment estimations. Default: 0.9.
            Should be in range (0.0, 1.0).
        beta2 (float): The exponential decay rate for the 2nd moment estimations. 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). It must be equal to or greater than 0. Default: 0.0.

    Inputs:
        - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.

    Outputs:
        tuple[bool], all elements are True.

    Raises:
        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
        TypeError: If element of `parameters` is neither Parameter nor dict.
        TypeError: If `beta1`, `beta2` or `eps` is not a float.
        TypeError: If `weight_decay` is neither float nor int.
        ValueError: If `eps` is less than or equal to 0.
        ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0).
        ValueError: If `weight_decay` is less than 0.

    Supported Platforms:
        ``Ascend`` ``GPU`` ``CPU``

    Examples:
        >>> net = Net()
        >>> #1) All parameters use the same learning rate and weight decay
        >>> optim = nn.AdamWeightDecay(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},
        ...                 {'params': no_conv_params, 'lr': 0.01},
        ...                 {'order_params': net.trainable_params()}]
        >>> optim = nn.AdamWeightDecay(group_params, learning_rate=0.1, weight_decay=0.0)
        >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
        >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0.
        >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
        >>>
        >>> 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-6, weight_decay=0.0):
        super(AdamWeightDecay, self).__init__(learning_rate, params, weight_decay)
        _check_param_value(beta1, beta2, eps, 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.moments1 = self.parameters.clone(prefix="adam_m", init='zeros')
        self.moments2 = self.parameters.clone(prefix="adam_v", init='zeros')

    def construct(self, gradients):
        lr = self.get_lr()
        if self.is_group:
            if self.is_group_lr:
                optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps),
                                              lr, self.weight_decay, self.parameters, self.moments1,
                                              self.moments2, gradients, self.decay_flags, self.optim_filter)
            else:
                optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr),
                                              self.weight_decay, self.parameters, self.moments1, self.moments2,
                                              gradients, self.decay_flags, self.optim_filter)
        else:
            optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr,
                                                    self.weight_decay),
                                          self.parameters, self.moments1, self.moments2,
                                          gradients, self.decay_flags, self.optim_filter)
        if self.use_parallel:
            self.broadcast_params(optim_result)
        return optim_result


class AdamOffload(Optimizer):
    r"""
    This optimizer will offload Adam optimizer to host CPU and keep parameters being updated on the device,
    to minimize the memory cost. Although that would bring about an increase of performance overhead,
    the optimizer could be used to run a larger model.

    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_{t+1} = \beta_1 * m_{t} + (1 - \beta_1) * g \\
            v_{t+1} = \beta_2 * v_{t} + (1 - \beta_2) * g * g \\
            l = \alpha * \frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t} \\
            w_{t+1} = w_{t} - l * \frac{m_{t+1}}{\sqrt{v_{t+1}} + \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, :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:
        This optimizer only supports `GRAPH_MODE` currently.

        If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without
        'beta' or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When
        parameters are grouped, each group can set `weight_decay`, if not, the `weight_decay` in optimizer will be
        applied.

    Args:
        params (Union[list[Parameter], list[dict]]): Must be list of `Parameter` or list of `dict`. When the
            `params` is a list of `dict`, the string "params", "lr", "weight_decay", and "order_params"
            are the keys can be parsed.

            - params: Required. Parameters in current group. The value must 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 optimizer will be used. Fixed and dynamic learning rate are supported.

            - 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 optimizer will be used.

            - order_params: Optional. When parameters is grouped, this usually is used to maintain the order of
              parameters that appeared in the network to improve performance. The value should be parameters whose
              order will be followed in optimizer.
              If `order_params` in the keys, other keys will be ignored and the element of 'order_params' must be in
              one group of `params`.

        learning_rate (Union[float, int, Tensor, Iterable, LearningRateSchedule]): Default: 1e-3.

            - float: The fixed learning rate value. Must be equal to or greater than 0.

            - int: The fixed learning rate value. Must be equal to or greater than 0. It will be converted to float.

            - Tensor: Its value should be a scalar or a 1-D vector. For scalar, fixed learning rate will be applied.
              For vector, learning rate is dynamic, then the i-th step will take the i-th value as the learning rate.

            - Iterable: Learning rate is dynamic. The i-th step will take the i-th value as the learning rate.

            - LearningRateSchedule: Learning rate is dynamic. During training, the optimizer calls the instance of
              LearningRateSchedule with step as the input to get the learning rate of current step.

        beta1 (float): The exponential decay rate for the 1st moment estimations. Should be in range (0.0, 1.0).
                       Default: 0.9.
        beta2 (float): The exponential decay rate for the 2nd moment estimations. 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 variable tensors from being updated.
            If true, updates of the `w`, `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, update the gradients using NAG.
            If false, update the gradients without using NAG. Default: False.
        weight_decay (float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0.
        loss_scale (float): A floating point value for the loss scale. Should be greater than 0. In general, use the
            default value. Only when `FixedLossScaleManager` is used for training and the `drop_overflow_update` in
            `FixedLossScaleManager` is set to False, then this value needs to be the same as the `loss_scale` in
            `FixedLossScaleManager`. Refer to class :class:`mindspore.FixedLossScaleManager` for more details.
            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.

    Raises:
        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
        TypeError: If element of `parameters` is neither Parameter nor dict.
        TypeError: If `beta1`, `beta2`, `eps` or `loss_scale` is not a float.
        TypeError: If `weight_decay` is neither float nor int.
        TypeError: If `use_locking` or `use_nesterov` is not a bool.
        ValueError: If `loss_scale` or `eps` is less than or equal to 0.
        ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0).
        ValueError: If `weight_decay` is less than 0.

    Supported Platforms:
        ``Ascend`` ``GPU`` ``CPU``

    Examples:
        >>> net = Net()
        >>> #1) All parameters use the same learning rate and weight decay
        >>> optim = nn.AdamOffload(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},
        ...                 {'params': no_conv_params, 'lr': 0.01},
        ...                 {'order_params': net.trainable_params()}]
        >>> optim = nn.AdamOffload(group_params, learning_rate=0.1, weight_decay=0.0)
        >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01.
        >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0.
        >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
        >>>
        >>> 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(AdamOffload, self).__init__(learning_rate, params, weight_decay, loss_scale)
        _check_param_value(beta1, beta2, eps, 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)

        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 = Tensor(eps, mstype.float32)
        self.moment1 = self.parameters.clone(prefix="moment1", init='zeros')
        self.moment2 = self.parameters.clone(prefix="moment2", init='zeros')
        self.opt = P.AdamNoUpdateParam(use_locking, use_nesterov)
        self.opt.add_prim_attr("primitive_target", "CPU")

    def construct(self, gradients):
        params = self.parameters
        moment1 = self.moment1
        moment2 = self.moment2
        gradients = self.decay_weight(gradients)
        gradients = self.scale_grad(gradients)
        lr = self.get_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.map_reverse(F.partial(_adam_opt, self.opt,
                                                 beta1_power, beta2_power, self.beta1, self.beta2, self.eps),
                                       lr, gradients, params, moment1, moment2)
        else:
            success = self.map_reverse(F.partial(_adam_opt, self.opt,
                                                 beta1_power, beta2_power, self.beta1, self.beta2, self.eps, lr),
                                       gradients, params, moment1, moment2)
        return success