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.common.api import _executor
  from mindspore.ops import operations as P
  from mindspore.ops import composite as C
  from mindspore import Tensor, context
  from tests.ut.python.ops.test_math_ops import VirtualLoss
  
  
  grad_all = C.GradOperation(get_all=True)
  
  
  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)
  
  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 Net(nn.Cell):
      def __init__(self, shape, offset, strategy1=None, strategy2=None, target="Device"):
          super().__init__()
          self.index = Tensor(np.ones(shape), dtype=ms.int32)
          self.offset = offset
          self.elu = P.EmbeddingLookup().set_strategy(strategy1).add_prim_attr("primitive_target", target)
          self.mm = P.BatchMatMul().set_strategy(strategy2)
  
      def construct(self, x, y):
          out = self.elu(x, self.index, self.offset)
          out = self.mm(out, y)
          return out
  
  
  def test_embeddinglookup_reducescatter_false():
      shape = [8, 8]
      offset = 8
      net = NetWithLoss(Net(shape, offset))
      net.set_auto_parallel()
  
      x = Tensor(np.ones([64, 32]), dtype=ms.float32)
      y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
      _executor.compile(net, x, y)
  
  
  def test_embeddinglookup_reducescatter_true():
      shape = [8, 8]
      offset = 8
      net = NetWithLoss(Net(shape, offset))
      net.set_auto_parallel()
  
      x = Tensor(np.ones([64, 32]), dtype=ms.float32)
      y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
      _executor.compile(net, x, y)
  
  
  def test_embeddinglookup_reducescatter_false_grad():
      shape = [8, 8]
      offset = 8
      net = GradWrap(NetWithLoss(Net(shape, offset)))
      net.set_auto_parallel()
  
      x = Tensor(np.ones([64, 32]), dtype=ms.float32)
      y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
      _executor.compile(net, x, y)
  
  
  def test_embeddinglookup_reducescatter_true_grad():
      context.set_context(save_graphs=True)
      shape = [8, 8]
      offset = 8
      net = GradWrap(NetWithLoss(Net(shape, offset)))
      net.set_auto_parallel()
  
      x = Tensor(np.ones([64, 32]), dtype=ms.float32)
      y = Tensor(np.ones([8, 32, 8]), dtype=ms.float32)
      _executor.compile(net, x, y)
  
  
  def test_embeddinglookup_semi_auto1():
      context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
      shape = [64, 32]
      offset = 0
      strategy1 = ((8, 1), (1, 1))
      strategy2 = ((4, 1, 2), (4, 2, 1))
      net = GradWrap(NetWithLoss(Net(shape, offset, strategy1, strategy2, "CPU")))
  
      net.set_auto_parallel()
      x = Tensor(np.ones([64, 64]), dtype=ms.float32)
      y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y)
  
  
  def test_embeddinglookup_semi_auto2():
      context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
      shape = [64, 32]
      offset = 0
      strategy1 = ((1, 8), (1, 1))
      strategy2 = ((4, 1, 2), (4, 2, 1))
      net = GradWrap(NetWithLoss(Net(shape, offset, strategy1, strategy2, "CPU")))
  
      net.set_auto_parallel()
      x = Tensor(np.ones([64, 64]), dtype=ms.float32)
      y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y)
  