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
  
  import mindspore as ms
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore import context
  from mindspore.common.api import _executor
  from mindspore.ops import composite as C
  from mindspore.ops import operations as P
  
  
  grad_all = C.GradOperation(get_all=True)
  
  
  class NetWithLoss(nn.Cell):
      def __init__(self, network, strategy3):
          super(NetWithLoss, self).__init__()
          self.loss = P.SoftmaxCrossEntropyWithLogits().shard(strategy3)
          self.network = network
  
      def construct(self, x, y, bias, label):
          predict = self.network(x, y, bias)
          return self.loss(predict, label)[0]
  
  
  class GradWrap(nn.Cell):
      def __init__(self, network):
          super(GradWrap, self).__init__()
          self.network = network
  
      def construct(self, x, y, bias, label):
          return grad_all(self.network)(x, y, bias, label)
  
  
  def test_linear():
      class Net(nn.Cell):
          def __init__(self, strategy0, strategy1, strategy2):
              super().__init__()
              self.fc_nobias = P.MatMul(transpose_b=True).shard(strategy0)
              self.add = P.Add().shard(strategy1)
              self.gelu = P.GeLU().shard(strategy2)
  
          def construct(self, x, y, bias):
              out = self.fc_nobias(x, y)
              out = self.add(out, bias)
              out = self.gelu(out)
              return out
  
      context.set_auto_parallel_context(device_num=16, global_rank=0)
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      strategy0 = ((2, 4), (2, 4))
      strategy1 = ((2, 4), (4,))
      strategy2 = ((2, 8),)
      strategy3 = ((16, 1), (16, 1))
      net = GradWrap(NetWithLoss(Net(strategy0, strategy1, strategy2), strategy3))
      net.set_auto_parallel()
  
      x = Tensor(np.ones([64, 32]), dtype=ms.float32)
      y = Tensor(np.ones([64, 32]), dtype=ms.float32)
      bias = Tensor(np.ones([64]), dtype=ms.float32)
      label = Tensor(np.ones([64, 64]), dtype=ms.float32)
      net.set_train()
      _executor.compile(net, x, y, bias, label)
  