mindspore2022/mindspore/nn/optim/asgd.py

# 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
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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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# ============================================================================
"""asgd"""
from mindspore.ops import functional as F, operations as P
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
import mindspore
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer
from .optimizer import opt_init_args_register

class ASGD(Optimizer):
    r"""
    Implements Average Stochastic Gradient Descent.

    Introduction to ASGD can be found at `Acceleration of stochastic approximation by average
    <http://dl.acm.org/citation.cfm?id=131098>`_.

    The updating formulas are as follows:

    .. math::
        \begin{gather*}
            w_{t} = w_{t-1} * (1 - \lambda * \eta_{t-1}) - \eta_{t-1} * g_{t} \\
            ax_{t} = (w_t - ax_{t-1}) * \mu_{t-1} \\
            \eta_{t} = \frac{1.}{(1 + \lambda * lr * t)^\alpha} \\
            \mu_{t} = \frac{1}{\max(1, t - t0)}
        \end{gather*}

    :math:`\lambda` represents the decay term, :math:`\mu` and :math:`\eta` are tracked to
    update :math:`ax` and :math:`w`, :math:`t0` represents the point of starting averaging,
    :math:`\alpha` represents the power for eta update, :math:`ax` represents the averaged
    parameter value, :math:`t` represents the current step, :math:`g` represents `gradients`,
    :math:`w` represents `params`.

    Note:
        If parameters are not grouped, the `weight_decay` in optimizer will be applied on the 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
            `parameters` is a list of `dict`, the "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]):

            - 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.

        lambd (float): The decay term. Default: 1e-4.
        alpha (float): The power for eta update. Default: 0.75.
        t0 (float): The point of starting averaging. Default: 1e6.
        weight_decay (int, 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:
        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 `lambd`, `alpha` or `t0` is not a float.
        TypeError: If `weight_decay` is neither float nor int.
        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.ASGD(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,'grad_centralization':True},
        ...                 {'params': no_conv_params, 'lr': 0.01},
        ...                 {'order_params': net.trainable_params()}]
        >>> optim = nn.ASGD(group_params, learning_rate=0.1, weight_decay=0.0)
        >>> # The conv_params's parameters will use default learning rate of 0.1 default weight decay of 0.0 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=0.1, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0.):

        super(ASGD, self).__init__(learning_rate, params, weight_decay)

        validator.check_value_type("lambd", lambd, [float], self.cls_name)
        validator.check_value_type("alpha", alpha, [float], self.cls_name)
        validator.check_value_type("t0", t0, [float], self.cls_name)

        self.lambd = lambd
        self.alpha = alpha
        self.t0 = Tensor([t0], dtype=mstype.float32)
        mu, eta = [], []
        for param in self.parameters:
            mu.append(Parameter(Tensor(1., dtype=mstype.float32), name='mu_'+param.name))
            eta.append(Parameter(Tensor(0., dtype=mstype.float32), name='eta_'+param.name))
        self.lens = len(self.parameters)
        self.mu = mindspore.ParameterTuple(mu)
        self.eta = mindspore.ParameterTuple(eta)
        self.step = Parameter(Tensor(1., dtype=mstype.float32), name='step')
        self.ax = self.parameters.clone(prefix="ax_", init='zeros')
        self.pow = P.Pow()
        self.maximum = P.Maximum()
        self.assign = P.Assign()
        self.assignadd = P.AssignAdd()
        self.assignsub = P.AssignSub()
        self.cast = P.Cast()
        self.squeeze = P.Squeeze()

    def construct(self, gradients):
        gradients = self.decay_weight(gradients)
        gradients = self.gradients_centralization(gradients)
        gradients = self.scale_grad(gradients)
        lrs = self.get_lr()
        success = True

        for index, (grad, param, mu, eta, ax) in enumerate(zip(gradients, self.parameters, self.mu, self.eta, self.ax)):
            lr = lrs[index] if self.is_group_lr else lrs
            lr = self.squeeze(lr)

            if self.step == 1.:
                self.assign(eta, lr)

            param_fp32 = self.cast(param, mstype.float32)
            gradient_fp32 = self.cast(grad, mstype.float32)
            ax_fp32 = self.cast(ax, mstype.float32)
            param_fp32 = param_fp32 * (1. - self.lambd * eta) - eta * gradient_fp32

            self.assign(param, self.cast(param_fp32, param.dtype))

            if mu != 1:
                self.assignadd(ax, self.cast((param_fp32 - ax_fp32) * mu, ax.dtype))
            else:
                self.assign(ax, param)

            self.assign(eta, lr / (self.pow((1. + (self.lambd * lr * self.step)), self.alpha)))
            self.assign(mu, 1. / self.squeeze(self.maximum(1., self.step - self.t0)))

        success = F.depend(success, self.assignadd(self.step, 1.))
        return success