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 import context
  import mindspore.nn as nn
  from mindspore.ops import operations as P
  from mindspore import Tensor
  from tests.ut.python.ops.test_math_ops import VirtualLoss
  import mindspore as ms
  from mindspore.common.api import _executor
  from mindspore.ops import composite as C
  
  
  class NetWithLoss(nn.Cell):
      def __init__(self, network):
          super(NetWithLoss, self).__init__()
          self.loss = VirtualLoss()
          self.network = network
  
      def construct(self, x, y, b):
          predict = self.network(x, y, b)
          return self.loss(predict)
  
  
  class GradWrap(nn.Cell):
      def __init__(self, network):
          super(GradWrap, self).__init__()
          self.network = network
  
      def construct(self, x, y, b):
          return C.grad_all(self.network)(x, y, b)
  
  def test_matmul_add():
      class Net(nn.Cell):
          def __init__(self):
              super().__init__()
              self.matmul = P.MatMul()
              self.add = P.TensorAdd()
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              out = self.add(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      
      net = GradWrap(NetWithLoss(Net()))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      net.set_auto_parallel()
  
      x = Tensor(np.ones([64, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  