text: # Copyright 2022 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 pytest
  
  import mindspore as ms
  import mindspore.nn as nn
  from mindspore import Tensor
  from mindspore import context
  from mindspore.common.api import _cell_graph_executor
  from mindspore.ops import composite as C
  from mindspore.ops import operations as P
  from tests.ut.python.ops.test_math_ops import VirtualLoss
  
  grad_all = C.GradOperation(get_all=True)
  
  
  class NetWithLoss(nn.Cell):
      def __init__(self, network):
          super(NetWithLoss, self).__init__()
          self.loss = VirtualLoss()
          self.network = network
  
      def construct(self, x, y):
          predict = self.network(x, y)
          return self.loss(predict)
  
  
  class GradWrap(nn.Cell):
      def __init__(self, network):
          super(GradWrap, self).__init__()
          self.network = network
  
      def construct(self, x, y):
          return grad_all(self.network)(x, y)
  
  
  def compile_net(net, x, y):
      net.set_auto_parallel()
      net.set_train()
      _cell_graph_executor.compile(net, x, y)
  
  
  def test_cumprod_semi():
      """
      Feature: CumProd operatorInfo in parallel.
      Description: MatMul->CumProd
      Expectation: Currently, CumProd does not support the axis dimension split. compile done without error.
      """
      class Net(nn.Cell):
          def __init__(self):
              super().__init__()
              self.matmul1 = P.MatMul().shard(((16, 1), (1, 1)))
              self.cumprod = P.CumProd().shard(((16, 1),))
  
          def construct(self, x, y):
              out = self.matmul1(x, y)
              out = self.cumprod(out, 0)
              return out
  
      size = 16
      context.set_auto_parallel_context(device_num=size, global_rank=0)
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
  
      net = GradWrap(NetWithLoss(Net()))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      with pytest.raises(RuntimeError):
          compile_net(net, x, y)
  
  
  def test_cumprod_semi2():
      """
      Feature: CumProd operatorInfo in parallel.
      Description: MatMul->CumProd
      Expectation: Compile done without error.
      """
      class Net(nn.Cell):
          def __init__(self):
              super().__init__()
              self.matmul1 = P.MatMul().shard(((16, 1), (1, 1)))
              self.cumprod = P.CumProd().shard(((1, 16),))
  
          def construct(self, x, y):
              out = self.matmul1(x, y)
              out = self.cumprod(out, 0)
              return out
  
      size = 16
      context.set_auto_parallel_context(device_num=size, global_rank=0)
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
  
      net = GradWrap(NetWithLoss(Net()))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      compile_net(net, x, y)
  
  
  def test_cumprod_semi3():
      """
      Feature: CumProd operatorInfo in parallel.
      Description: MatMul->CumProd
      Expectation: Compile done without error.
      """
      class Net(nn.Cell):
          def __init__(self):
              super().__init__()
              self.matmul1 = P.MatMul().shard(((16, 1), (1, 1)))
              self.cumprod = P.CumProd().shard(((2, 1),))
  
          def construct(self, x, y):
              out = self.matmul1(x, y)
              out = self.cumprod(out, 1)
              return out
  
      size = 16
      context.set_auto_parallel_context(device_num=size, global_rank=0)
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
  
      net = GradWrap(NetWithLoss(Net()))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      compile_net(net, x, y)
  
  
  def test_cumprod_auto():
      """
      Feature: CumProd operatorInfo in parallel.
      Description: MatMul->CumProd
      Expectation: Compile done without error.
      """
      class Net(nn.Cell):
          def __init__(self):
              super().__init__()
              self.matmul1 = P.MatMul().shard(((16, 1), (1, 1)))
              self.cumprod = P.CumProd()
  
          def construct(self, x, y):
              out = self.matmul1(x, y)
              out = self.cumprod(out, -1)
              return out
  
      size = 16
      context.set_auto_parallel_context(device_num=size, global_rank=0)
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
  
      net = GradWrap(NetWithLoss(Net()))
      context.set_auto_parallel_context(parallel_mode="auto_parallel")
      compile_net(net, x, y)
  