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.
  
  """
  @File   : test_data_parallel_dense.py
  @Desc   : test data parallel dense
  """
  import numpy as np
  import mindspore.nn as nn
  from mindspore.common.api import _executor
  from mindspore import Tensor
  from mindspore.ops import operations as P
  from mindspore.nn import TrainOneStepCell, WithLossCell
  from mindspore.nn import Momentum
  from mindspore.train.parallel_utils import ParallelMode
  import mindspore.context as context
  
  
  class DenseMMNet(nn.Cell):
      """DenseMMNet definition"""
      def __init__(self):
          super(DenseMMNet, self).__init__()
          self.fc1 = nn.Dense(128, 768, activation='relu')
          self.fc2 = nn.Dense(128, 768, activation='relu')
          self.fc3 = nn.Dense(128, 768, activation='relu')
          self.fc4 = nn.Dense(768, 768, activation='relu')
          self.relu4 = nn.ReLU()
          self.relu5 = nn.ReLU()
          self.transpose = P.Transpose()
          self.matmul1 = P.MatMul()
          self.matmul2 = P.MatMul()
  
      def construct(self, x):
          q = self.fc1(x)
          k = self.fc2(x)
          v = self.fc3(x)
          k = self.transpose(k, (1, 0))
          c = self.relu4(self.matmul1(q, k))
          s = self.relu5(self.matmul2(c, v))
          s = self.fc4(s)
          return s
  
  
  def test_data_parallel_dense():
      """test_data_parallel_dense"""
      context.set_context(mode=context.GRAPH_MODE)
      context.reset_auto_parallel_context()
      context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True, device_num=8)
      inp = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
      label = Tensor(np.zeros([32, 768]).astype(np.float32))
      net = DenseMMNet()
      loss_fn = nn.SoftmaxCrossEntropyWithLogits()
  
      optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
                           learning_rate=0.1,
                           momentum=0.9)
      net = WithLossCell(net, loss_fn)
      net = TrainOneStepCell(net, optimizer)
  
      _executor.compile(net, inp, label)
      context.reset_auto_parallel_context()
  