text: # Copyright 2019 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.
  # ============================================================================
  import numpy as np
  from mindspore.nn import Dense
  import mindspore.nn as nn
  import datetime
  import mindspore.context as context
  from mindspore.communication.management import init, NCCL_WORLD_COMM_GROUP, get_rank, get_group_size
  from mindspore.nn.optim import Momentum
  from mindspore.nn import TrainOneStepCell, WithLossCell
  from mindspore.ops import operations as P
  from mindspore.common.tensor import Tensor
  
  context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
  init('nccl')
  
  epoch = 2
  total = 50000
  batch_size = 32
  mini_batch = total // batch_size
  
  class LeNet(nn.Cell):
      def __init__(self):
          super(LeNet, self).__init__()
  
          self.relu = P.ReLU()
          self.batch_size = 32
          weight1 = Tensor(np.ones([6, 3, 5, 5]).astype(np.float32) * 0.01)
          weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01)
          self.conv1 = nn.Conv2d(3, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid')
          self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid', stride=1, padding=0)
          self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid")
          self.reshape = P.Reshape()
          
          weight1 = Tensor(np.ones([120, 400]).astype(np.float32) * 0.01)
          self.fc1 = Dense(400, 120, weight_init=weight1)
          
          weight2 = Tensor(np.ones([84, 120]).astype(np.float32) * 0.01)
          self.fc2 = Dense(120, 84, weight_init=weight2)
          
          weight3 = Tensor(np.ones([10, 84]).astype(np.float32) * 0.01)
          self.fc3 = Dense(84, 10, weight_init=weight3)
  
      def construct(self, input_x):
          output = self.conv1(input_x)
          output = self.relu(output)
          output = self.pool(output)
          output = self.conv2(output)
          output = self.relu(output)
          output = self.pool(output)
          output = self.reshape(output, (self.batch_size, -1))
          output = self.fc1(output)
          output = self.fc2(output)
          output = self.fc3(output)
          return output
  
  def test_lenet_nccl():
      net = LeNet()
      net.set_train()
  
      learning_rate = 0.01
      momentum = 0.9
      mom_optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
      criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
      net_with_criterion = WithLossCell(net, criterion)
      context.set_auto_parallel_context(parallel_mode="data_parallel", mirror_mean=True, device_num=get_group_size())
      train_network = TrainOneStepCell(net_with_criterion, mom_optimizer)
      train_network.set_train()
      losses = []
  
      data = Tensor(np.ones([net.batch_size, 3, 32, 32]).astype(np.float32) * 0.01)
      label = Tensor(np.ones([net.batch_size]).astype(np.int32))
      start = datetime.datetime.now()
      for i in range(epoch):
          for step in range(mini_batch):
              loss = train_network(data, label)
              losses.append(loss.asnumpy())
      end = datetime.datetime.now()
      with open("ms_time.txt", "w") as fo1:
          fo1.write("time:")
          fo1.write(str(end - start))
      with open("ms_loss.txt", "w") as fo2:
          fo2.write("loss:")
          fo2.write(str(losses[-5:]))
  