text: # Copyright 2020 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.
  # ============================================================================
  """ test sparse feature bprop """
  import numpy as np
  
  import mindspore as ms
  import mindspore.nn as nn
  from mindspore import context
  from mindspore.common.parameter import Parameter
  from mindspore.common.tensor import Tensor
  from mindspore.ops import composite as C, operations as P
  from mindspore.ops.operations.comm_ops import AllReduce
  from mindspore.common.api import _executor
  from mindspore.nn import TrainOneStepCell, Adam
  
  
  class GradWrap(nn.Cell):
      def __init__(self, network):
          super(GradWrap, self).__init__()
          self.network = network
  
      def construct(self, x):
          return C.grad_all(self.network)(x)
  
  def test_bprop_with_sparse_feature_allreduce():
      context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="hybrid_parallel")
      context.set_context(enable_sparse=True)
  
      class Net(nn.Cell):
          def __init__(self, axis=0, shape=None):
              super(Net, self).__init__()
              if shape is None:
                  shape = [8, 8]
              self.all_reduce = AllReduce()
              self.gatherv2 = P.SparseGatherV2()
              self.index = Tensor(np.ones(shape), dtype=ms.int32)
              self.axis = axis
  
          def construct(self, x):
              out = self.all_reduce(x)
              out = self.gatherv2(out, self.index, self.axis)
  
              return out
  
      net = GradWrap(Net())
      x = Tensor(np.ones([64, 64]), dtype=ms.float32)
  
      _executor.compile(net, x)
  
  
  def test_bprop_with_sparse_feature_mirror():
      context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
      context.set_context(enable_sparse=True)
  
      class Net(nn.Cell):
          def __init__(self, shape=None):
              super(Net, self).__init__()
              if shape is None:
                  shape = [8, 8]
              weight = Tensor(np.ones([64, 64]), dtype=ms.float32)
              self.weight = Parameter(weight, "w")
              self.index = Tensor(np.ones(shape), dtype=ms.int32)
              self.embeddinglookup = nn.EmbeddingLookup()
              self.embeddinglookup.embeddinglookup.set_strategy(((1, 1), (8, 1)))
  
          def construct(self, x, b):
              out = self.embeddinglookup(self.weight, self.index)
  
              return out
  
      _x = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
      _b = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
  
      def compile_net(net):
          optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
          train_net = TrainOneStepCell(net, optimizer)
          _executor.compile(train_net, _x, _b)
  
      net = Net()
      compile_net(net)
  
  
  def test_bprop_with_sparse_feature_dataparallel():
      context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="data_parallel")
      context.set_context(enable_sparse=True)
  
      class Net(nn.Cell):
          def __init__(self, axis=0, shape=None):
              super(Net, self).__init__()
              if shape is None:
                  shape = [8, 8]
              weight = Tensor(np.ones([64, 64]), dtype=ms.float32)
              self.weight = Parameter(weight, "w")
              self.index = Tensor(np.ones(shape), dtype=ms.int32)
              self.axis = axis
              self.gatherv2 = P.SparseGatherV2()
  
          def construct(self, x, b):
              out = self.gatherv2(self.weight, self.index, self.axis)
  
              return out
  
      _x = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
      _b = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
  
      def compile_net(net):
          optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
          train_net = TrainOneStepCell(net, optimizer)
          _executor.compile(train_net, _x, _b)
  
      net = Net()
      compile_net(net)
  