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):
          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 C.grad_all(self.network)(x, y)
  
  
  def test_prelu_single_success1():
      class Net(nn.Cell):
          def __init__(self):
              super().__init__()
              self.prelu = P.PReLU()
  
          def construct(self, x, y):
              out = self.prelu(x, y)
              return out
  
      context.reset_auto_parallel_context()
      net = GradWrap(NetWithLoss(Net()))
      x = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
      w = Tensor(np.random.rand(33), ms.float32)
      _executor.compile(net, x, w)
  
  def test_prelu_single_success2():
      class Net(nn.Cell):
          def __init__(self):
              super().__init__()
              self.prelu = P.PReLU()
  
          def construct(self, x, y):
              out = self.prelu(x, y)
              return out
  
      context.reset_auto_parallel_context()
      net = GradWrap(NetWithLoss(Net()))
      x = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
      w = Tensor([0.1], ms.float32)
      _executor.compile(net, x, w)
  
  def test_prelu_parallel_success1():
      class Net(nn.Cell):
          def __init__(self, strategy):
              super().__init__()
              self.prelu = P.PReLU().set_strategy(strategy)
          def construct(self, x, y):
              out = self.prelu(x, y)
              return out
      context.reset_auto_parallel_context()
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      strategy = ((1, 1, 1, 1), (1, ))
      x = Tensor(np.random.rand(4, 4, 32, 64),dtype=ms.float32)
      w = Tensor(np.random.rand(4),dtype=ms.float32)
      net = GradWrap(NetWithLoss(Net(strategy)))
      _executor.compile(net, x, w)
  
  def test_prelu_parallel_success2():
      class Net(nn.Cell):
          def __init__(self, strategy):
              super().__init__()
              self.prelu = P.PReLU().set_strategy(strategy)
          def construct(self, x, y):
              out = self.prelu(x, y)
              return out
      context.reset_auto_parallel_context()
      context.set_auto_parallel_context(device_num=64, global_rank=0)
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      strategy = ((2, 1, 4, 8), (1, ))
      x = Tensor(np.random.rand(4, 4, 32, 64),dtype=ms.float32)
      w = Tensor(np.random.rand(4),dtype=ms.float32)
      net = GradWrap(NetWithLoss(Net(strategy)))
      _executor.compile(net, x, w)
  
  def test_prelu_parallel_success3():
      class NetWithLoss(nn.Cell):
          def __init__(self, network):
              super(NetWithLoss, self).__init__()
              self.loss = VirtualLoss()
              self.network = network
  
          def construct(self, x, y, w):
              predict = self.network(x, y, w)
              return self.loss(predict)
  
  
      class GradWrap(nn.Cell):
          def __init__(self, network):
              super(GradWrap, self).__init__()
              self.network = network
  
          def construct(self, x, y, w):
              return C.grad_all(self.network)(x, y, w)
  
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.prelu = P.PReLU().set_strategy(strategy2)
          def construct(self, x, y, w):
              out = self.matmul(x, y)
              out = self.prelu(out, w)
              return out
  
      context.reset_auto_parallel_context()
      context.set_auto_parallel_context(device_num=64, global_rank=0)
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      strategy1 = ((2, 4), (4, 2))
      strategy2 = ((32, 1), (1, ))
      x = Tensor(np.random.rand(128, 64),dtype=ms.float32)
      y = Tensor(np.random.rand(64, 16),dtype=ms.float32)
      w = Tensor(np.random.rand(16),dtype=ms.float32)
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
      _executor.compile(net, x, y, w)
  
  def test_prelu_parallel_success4():
      class Net(nn.Cell):
          def __init__(self, strategy):
              super().__init__()
              self.prelu = P.PReLU().set_strategy(strategy)
          def construct(self, x, y):
              out = self.prelu(x, y)
              return out
      context.reset_auto_parallel_context()
      context.set_auto_parallel_context(device_num=64, global_rank=0)
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      strategy = ((2, 4, 4, 2), (4, ))
      x = Tensor(np.random.rand(4, 16, 32, 64),dtype=ms.float32)
      w = Tensor(np.random.rand(16),dtype=ms.float32)
      net = GradWrap(NetWithLoss(Net(strategy)))
      _executor.compile(net, x, w)
  
  def test_prelu_parallel_success5():
      class Net(nn.Cell):
          def __init__(self, strategy):
              super().__init__()
              self.prelu = P.PReLU().set_strategy(strategy)
          def construct(self, x, y):
              out = self.prelu(x, y)
              return out
      context.reset_auto_parallel_context()
      context.set_auto_parallel_context(device_num=64, global_rank=0)
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      strategy = ((2, 4, 4, 2), (1, ))
      x = Tensor(np.random.rand(4, 16, 32, 64),dtype=ms.float32)
      w = Tensor(np.random.rand(1),dtype=ms.float32)
      net = GradWrap(NetWithLoss(Net(strategy)))
      _executor.compile(net, x, w)
  
  