text: # Copyright 2020 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.
  # ============================================================================
  """FTRL"""
  from mindspore.ops import functional as F, composite as C, operations as P
  from mindspore.common import Tensor
  import mindspore.common.dtype as mstype
  from mindspore._checkparam import Validator as validator
  from mindspore._checkparam import Rel
  from .optimizer import Optimizer, _apply_decay, _grad_scale
  
  _ftrl_opt = C.MultitypeFuncGraph("ftrl_opt")
  
  
  @_ftrl_opt.register("Function", "Function", "Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor",
                      "RowTensor", "Tensor", "Tensor", "Bool", "Bool")
  def _tensor_run_opt_with_sparse(opt, spars_opt, push, pull, l1, l2, lr_power, learning_rate, linear,
                                  gradient, weight, moment, ps_parameter, cache_enable):
      """Apply sparse ftrl 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(weight), op_shape(moment), op_shape(linear), op_shape(values), op_shape(indices))
          success = F.depend(success, pull(push((values, indices), shapes), weight))
      else:
          success = F.depend(success, spars_opt(weight, moment, linear, values, indices))
      return success
  
  
  @_ftrl_opt.register("Function", "Function", "Function", "Function", "Number", "Number", "Number", "Tensor", "Tensor",
                      "Tensor", "Tensor", "Tensor", "Bool", "Bool")
  def _tensor_run_opt(opt, spars_opt, push, pull, l1, l2, lr_power, learning_rate, linear,
                      gradient, weight, moment, ps_parameter, cache_enable):
      """Apply ftrl optimizer to the weight parameter."""
      success = True
      if ps_parameter and not cache_enable:
          op_shape = P.Shape()
          success = F.depend(success, pull(push((gradient, learning_rate, l1, l2, lr_power),
                                                (op_shape(weight), op_shape(moment), op_shape(linear))), weight))
      else:
          success = F.depend(success, opt(weight, moment, linear, gradient, learning_rate, l1, l2, lr_power))
      return success
  
  
  def _check_param(initial_accum, lr_power, l1, l2, use_locking, prim_name=None):
      """Check param."""
      validator.check_value_type("initial_accum", initial_accum, [float], prim_name)
      validator.check_number("initial_accum", initial_accum, 0.0, Rel.GE, prim_name)
  
      validator.check_value_type("lr_power", lr_power, [float], prim_name)
      validator.check_number("lr_power", lr_power, 0.0, Rel.LE, prim_name)
  
      validator.check_value_type("l1", l1, [float], prim_name)
      validator.check_number("l1", l1, 0.0, Rel.GE, prim_name)
  
      validator.check_value_type("l2", l2, [float], prim_name)
      validator.check_number("l2", l2, 0.0, Rel.GE, prim_name)
  
      validator.check_value_type("use_locking", use_locking, [bool], prim_name)
  
  
  class FTRL(Optimizer):
      r"""
      Implements the FTRL algorithm with ApplyFtrl Operator.
  
      FTRL is an online convex optimization algorithm that adaptively chooses its regularization function
      based on the loss functions. Refer to paper `Adaptive Bound Optimization for Online Convex Optimization
      <https://arxiv.org/abs/1002.4908>`_. Refer to paper `Ad Click Prediction: a View from the Trenches
      <https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf>`_ for engineering document.
  
      The updating formulas are as follows,
  
      .. math::
  
      \begin{array}{ll} \\
          m_{t+1} = m_{t} + g^2 \\
          u_{t+1} = u_{t} + g  - \frac{m_{t+1}^\text{-p} - m_{t}^\text{-p}}{\alpha } * \omega_{t} \\
          \omega_{t+1} =
          \begin{cases}
              \frac{(sign(u_{t+1}) * l1 - u_{t+1})}{\frac{m_{t+1}^\text{-p}}{\alpha } + 2 * l2 }
                  & \text{ if } |u_{t+1}| > l1 \\
              0.0
                  & \text{ otherwise }
          \end{cases}\\
      \end{array}
  
      :math:`m` represents `accum`, :math:`g` represents `grads`, :math:`t` represents updateing step,
      :math:`u` represents `linear`, :math:`p` represents `lr_power`, :math:`\alpha` represents `learning_rate`,
      :math:`\omega` represents `params`.
  
      Note:
          When separating parameter groups, the weight decay in each group will be applied on the parameters if the
          weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied
          on all of the parameters.
  
          To improve parameter groups performance, the customized order of parameters can be supported.
  
          The sparse strategy is applied while the SparseGatherV2 operator being used for forward network.
          The sparse feature is under continuous development. If the sparse strategy wants to be executed on the host,
          set the target to the CPU.
  
      Args:
          params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated,
              the element in `params` must be class `Parameter`. When the `params` is a list of `dict`, the "params",
              "lr", "weight_decay" and "order_params" are the keys can be parsed.
  
              - params: Required. The value must be a list of `Parameter`.
  
              - lr: Using different learning rate by separating parameters is currently not 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 API will be used.
  
              - order_params: Optional. If "order_params" in the keys, the value must be the order of parameters and
                the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which
                in the value of 'order_params' must be in one of group parameters.
  
          initial_accum (float): The starting value for accumulators, must be zero or positive values. Default: 0.1.
          learning_rate (float): The learning rate value, must be zero or positive, dynamic learning rate is currently
              not supported. Default: 0.001.
          lr_power (float): Learning rate power controls how the learning rate decreases during training, must be less
              than or equal to zero. Use fixed learning rate if lr_power is zero. Default: -0.5.
          l1 (float): l1 regularization strength, must be greater than or equal to zero. Default: 0.0.
          l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
          use_locking (bool): If true, use locks for updating operation. Default: False.
          loss_scale (float): Value for the loss scale. It must be equal to or greater than 1.0. Default: 1.0.
          weight_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
  
      Inputs:
          - **grads** (tuple[Tensor]) - The gradients of `params` in the optimizer, the shape is the same as the `params`
            in optimizer.
  
      Outputs:
          tuple[Parameter], the updated parameters, the shape is the same as `params`.
  
      Supported Platforms:
          ``Ascend`` ``GPU``
  
      Examples:
          >>> net = Net()
          >>> #1) All parameters use the same learning rate and weight decay
          >>> optim = nn.FTRL(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},
          ...                 {'order_params': net.trainable_params()}]
          >>> optim = nn.FTRL(group_params, learning_rate=0.1, weight_decay=0.0)
          >>> # The conv_params's parameters will use weight decay of 0.01.
          >>> # The no_conv_params's parameters will use 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, initial_accum=0.1, learning_rate=0.001, lr_power=-0.5, l1=0.0, l2=0.0,
                   use_locking=False, loss_scale=1.0, weight_decay=0.0):
          super(FTRL, self).__init__(learning_rate, params, weight_decay, loss_scale=loss_scale)
          if self.dynamic_lr or self.is_group_lr:
              raise ValueError('Dynamic learning rate or group learning rate is currently not supported.')
          _check_param(initial_accum, lr_power, l1, l2, use_locking, self.cls_name)
          self.moments = self.parameters.clone(prefix="moments", init=initial_accum)
          self.linear = self.parameters.clone(prefix="linear", init='zeros')
          self.l1 = l1
          self.l2 = l2
          self.lr = learning_rate
          self.lr_power = lr_power
          if not self.is_group:
              self.decay_flags = tuple((lambda: True)() for x in self.parameters)
          self.hyper_map = C.HyperMap()
          self.opt = P.ApplyFtrl(use_locking=use_locking)
          self.use_locking = use_locking
          self.sparse_opt = P.SparseApplyFtrl(learning_rate, l1, l2, lr_power, use_locking=use_locking)
          self._ps_pull = P.Pull()
          self._ps_push = P.Push("Ftrl", [0, 1, 2])
          self._ps_push.add_prim_attr("init_accum", initial_accum)
          self._ps_push.add_prim_attr("lr", learning_rate)
          self._ps_push.add_prim_attr("l1", l1)
          self._ps_push.add_prim_attr("l2", l2)
          self._ps_push.add_prim_attr("lr_power", lr_power)
  
      def construct(self, grads):
          params = self.parameters
          moments = self.moments
          linear = self.linear
          grads = self.decay_weight(grads)
          grads = self.scale_grad(grads)
          grads = self._grad_sparse_indices_deduplicate(grads)
          lr = self.get_lr()
  
          success = self.map_(F.partial(_ftrl_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
                                        self.l1, self.l2, self.lr_power, lr),
                              linear, grads, params, moments, 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."""
          if not isinstance(value, str):
              raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
  
          if value not in ('CPU', 'Ascend', 'GPU'):
              raise ValueError("The value must be 'CPU', 'Ascend' or 'GPU', but got value {}".format(value))
  
          if value == 'CPU':
              self.sparse_opt = P.FusedSparseFtrl(self.lr, self.l1, self.l2, self.lr_power, self.use_locking)
              self.sparse_opt.add_prim_attr("primitive_target", "CPU")
          else:
              self.sparse_opt = P.SparseApplyFtrl(self.lr, self.l1, self.l2, self.lr_power, self.use_locking)
  
          self._target = value
  