text: # 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
  #
  # 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
  import mindspore as ms
  import mindspore.context as context
  from mindspore import Tensor, Parameter
  import mindspore.nn as nn
  from mindspore.common.api import _executor
  from mindspore.nn import TrainOneStepCell, Momentum
  from mindspore.ops import operations as P
  
  class Net(nn.Cell):
      def __init__(self, wi, wo, stra1=None, stra2=None, stra3=None, stra4=None,
                   stra5=None, stra6=None):
          super(Net, self).__init__()
          self.relu = P.ReLU().shard(stra1)
          self.transpose = P.Transpose().shard(stra2)
          self.wi = Parameter(wi, "wi")
          self.batch_mm = P.BatchMatMul().shard(stra3)
          self.wo = Parameter(wo, "wo")
          self.batch_mm2 = P.BatchMatMul().shard(stra4)
          self.transpose2 = P.Transpose().shard(stra5)
          self.relu2 = P.ReLU().shard(stra6)
          self.reshape = P.Reshape()
          self.reshape2 = P.Reshape()
  
      def construct(self, x):
          output = self.relu(x)
          trans_out = self.transpose(output, (2, 0, 3, 1))
          output = self.reshape(trans_out,
                                (trans_out.shape[0], trans_out.shape[1]*trans_out.shape[2], trans_out.shape[3]))
          output = self.batch_mm(output, self.wi)
          output = self.batch_mm2(output, self.wo)
          output = self.reshape2(output, trans_out.shape)
          output = self.transpose2(output, (1, 3, 0, 2))
          output = self.relu2(output)
          return output
  
  _x = Tensor(np.ones([32, 16, 48, 128]), dtype=ms.float32)
  _wi = Tensor(np.ones([48, 16, 64]), dtype=ms.float32)
  _wo = Tensor(np.ones([48, 64, 16]), dtype=ms.float32)
  
  
  def compile_net(net):
      context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
      optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
      train_net = TrainOneStepCell(net, optimizer)
      train_net.set_auto_parallel()
      train_net.set_train()
      _executor.compile(train_net, _x)
      context.reset_auto_parallel_context()
  
  
  def test_batchmm():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, enable_alltoall=True,
                                        global_rank=0)
      stra1 = ((8, 1, 1, 1),)
      stra2 = ((8, 1, 1, 1),)
      stra3 = ((8, 1, 1), (8, 1, 1))
      stra4 = ((8, 1, 1), (8, 1, 1))
      stra5 = ((8, 1, 1, 1),)
      stra6 = ((8, 1, 1, 1),)
      net = Net(_wi, _wo, stra1=stra1, stra2=stra2, stra3=stra3, stra4=stra4, stra5=stra5, stra6=stra6)
      compile_net(net)
  
  
  def test_batchmm2():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", enable_alltoall=True,
                                        device_num=32, global_rank=0)
      stra1 = ((4, 1, 1, 1),)
      stra2 = ((4, 1, 1, 1),)
      stra3 = ((4, 1, 1), (4, 1, 8))
      stra4 = ((4, 1, 8), (4, 8, 1))
      stra5 = ((4, 1, 1, 1),)
      stra6 = ((4, 1, 1, 1),)
      net = Net(_wi, _wo, stra1=stra1, stra2=stra2, stra3=stra3, stra4=stra4, stra5=stra5, stra6=stra6)
      compile_net(net)
  