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 pytest
  
  import mindspore as ms
  from mindspore import context, Tensor, Parameter
  from mindspore.common.api import _executor
  from mindspore.nn import Cell, TrainOneStepCell, Momentum
  from mindspore.ops import operations as P
  
  
  class Net(Cell):
      def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride, pool_kernel_size, pool_strides,
                   strategy1=None, strategy2=None):
          super().__init__()
          self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
                                 pad_mode=pad_mode, stride=stride).shard(strategy1)
          self.conv2d_weight = Parameter(conv2d_weight, "w1")
          self.max_pool = P.MaxPool(kernel_size=pool_kernel_size, strides=pool_strides).shard(strategy2)
  
      def construct(self, x, b):
          out = self.conv2d(x, self.conv2d_weight)
          out = self.max_pool(out)
          return out
  
  
  class Net2(Cell):
      def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride, pool_kernel_size, pool_strides,
                   strategy1=None, strategy2=None):
          super().__init__()
          self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
                                 pad_mode=pad_mode, stride=stride).shard(strategy1)
          self.conv2d_weight = Parameter(conv2d_weight, "w1")
          self.avg_pool = P.AvgPool(kernel_size=pool_kernel_size, strides=pool_strides).shard(strategy2)
  
      def construct(self, x, b):
          out = self.conv2d(x, self.conv2d_weight)
          out = self.avg_pool(out)
          return out
  
  
  _x = Tensor(np.ones([32, 16, 8, 8]), dtype=ms.float32)
  _w1 = Tensor(np.ones([8, 16, 2, 2]), dtype=ms.float32)
  _b = Tensor(np.ones([32, 16, 8, 8]), dtype=ms.float32)
  
  
  def compile_net(net):
      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, _b)
      context.reset_auto_parallel_context()
  
  
  def test_maxpool_data_parallel():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
      strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
      strategy2 = ((8, 1, 1, 1),)
      net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
                strategy1=strategy1, strategy2=strategy2)
      compile_net(net)
  
  
  def test_maxpool_model_parallel1():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
      strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
      strategy2 = ((2, 1, 2, 2),)
      net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
                strategy1=strategy1, strategy2=strategy2)
      compile_net(net)
  
  
  def test_maxpool_model_parallel2():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
      strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
      strategy2 = ((2, 1, 2, 2),)
      net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=4,
                strategy1=strategy1, strategy2=strategy2)
      compile_net(net)
  
  
  def test_maxpool_auto_parallel():
      context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
      net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=4)
      compile_net(net)
  
  
  def test_maxpool_output_can_not_divisible_by_strategy():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
      strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
      strategy2 = ((1, 1, 1, 8),)
      net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
                strategy1=strategy1, strategy2=strategy2)
      with pytest.raises(RuntimeError):
          compile_net(net)
  
  
  def test_avgpool_data_parallel():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
      strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
      strategy2 = ((8, 1, 1, 1),)
      net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
                 strategy1=strategy1, strategy2=strategy2)
      compile_net(net)
  
  
  def test_avgpool_model_parallel1():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
      strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
      strategy2 = ((2, 1, 2, 2),)
      net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
                 strategy1=strategy1, strategy2=strategy2)
      compile_net(net)
  
  
  def test_avgpool_model_parallel2():
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
      strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
      strategy2 = ((2, 1, 2, 2),)
      net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=4,
                 strategy1=strategy1, strategy2=strategy2)
      compile_net(net)
  
  
  def test_avgpool_auto_parallel():
      context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
      net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=4)
      compile_net(net)
  