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.
  # ============================================================================
  """Automatic differentiation with grad clip."""
  import numpy as np
  from mindspore.parallel._utils import (_get_device_num, _get_mirror_mean,
                                         _get_parallel_mode)
  from mindspore.train.parallel_utils import ParallelMode
  from mindspore.common import dtype as mstype
  from mindspore.ops import composite as C
  from mindspore.ops import functional as F
  from mindspore.ops import operations as P
  from mindspore.nn.cell import Cell
  from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
  import mindspore.nn as nn
  from mindspore.common.tensor import Tensor
  
  compute_norm = C.MultitypeFuncGraph("compute_norm")
  
  
  @compute_norm.register("Tensor")
  def _compute_norm(grad):
      norm = nn.Norm()
      norm = norm(F.cast(grad, mstype.float32))
      ret = F.expand_dims(F.cast(norm, mstype.float32), 0)
      return ret
  
  
  grad_div = C.MultitypeFuncGraph("grad_div")
  
  
  @grad_div.register("Tensor", "Tensor")
  def _grad_div(val, grad):
      div = P.RealDiv()
      mul = P.Mul()
      grad = mul(grad, 10.0)
      ret = div(grad, val)
      return ret
  
  
  class TrainOneStepCellWithGradClip(Cell):
      """
      Network training package class.
  
      Wraps the network with an optimizer. The resulting Cell be trained with input data and label.
      Backward graph with grad clip will be created in the construct function to do parameter updating.
      Different parallel modes are available to run the training.
  
      Args:
          network (Cell): The training network.
          optimizer (Cell): Optimizer for updating the weights.
          sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
  
      Inputs:
          - data (Tensor) - Tensor of shape :(N, ...).
          - label (Tensor) - Tensor of shape :(N, ...).
  
      Outputs:
          Tensor, a scalar Tensor with shape :math:`()`.
      """
  
      def __init__(self, network, optimizer, sens=1.0):
          super(TrainOneStepCellWithGradClip, self).__init__(auto_prefix=False)
          self.network = network
          self.network.set_grad()
          self.network.add_flags(defer_inline=True)
          self.weights = optimizer.parameters
          self.optimizer = optimizer
          self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
          self.sens = sens
          self.reducer_flag = False
          self.grad_reducer = None
          self.hyper_map = C.HyperMap()
          self.greater = P.Greater()
          self.select = P.Select()
          self.norm = nn.Norm(keep_dims=True)
          self.dtype = P.DType()
          self.cast = P.Cast()
          self.concat = P.Concat(axis=0)
          self.ten = Tensor(np.array([10.0]).astype(np.float32))
          parallel_mode = _get_parallel_mode()
          if parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL):
              self.reducer_flag = True
          if self.reducer_flag:
              mean = _get_mirror_mean()
              degree = _get_device_num()
              self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
  
      def construct(self, data, label):
          weights = self.weights
          loss = self.network(data, label)
          sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
          grads = self.grad(self.network, weights)(data, label, sens)
          norm = self.hyper_map(F.partial(compute_norm), grads)
          norm = self.concat(norm)
          norm = self.norm(norm)
          cond = self.greater(norm, self.cast(self.ten, self.dtype(norm)))
          clip_val = self.select(cond, norm, self.cast(self.ten, self.dtype(norm)))
          grads = self.hyper_map(F.partial(grad_div, clip_val), grads)
          if self.reducer_flag:
              # apply grad reducer on grads
              grads = self.grad_reducer(grads)
          return F.depend(loss, self.optimizer(grads))
  