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, b):
          predict = self.network(x, y, b)
          return self.loss(predict)
  
  
  class GradWrap(nn.Cell):
      def __init__(self, network):
          super(GradWrap, self).__init__()
          self.network = network
  
      def construct(self, x, y, b):
          return C.grad_all(self.network)(x, y, b)
  
  # model_parallel test
  def test_sum_mul():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_sum = P.ReduceSum(keep_dims=False).set_strategy(strategy2)
              self.mul2 = P.Mul().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_sum(out, (1,))
              out = self.mul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 1, 8), (1, 1, 8))
      strategy2 = ((4, 1, 2), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([128, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  def test_sum_mul2():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_sum = P.ReduceSum(keep_dims=False).set_strategy(strategy2)
              self.mul2 = P.Mul().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_sum(out, (0, 1))
              out = self.mul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 1, 4, 2), (1, 1, 4, 2))
      strategy2 = ((2, 4, 1, 1), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 128, 64, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 128, 64, 64]), dtype=ms.float32)
      b = Tensor(np.ones([64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  def test_sum_mul3():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_sum = P.ReduceSum(keep_dims=False).set_strategy(strategy2)
              self.mul2 = P.Mul().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_sum(out, -1)
              out = self.mul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 2, 1), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([128, 32]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  def test_sum_mul4():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_sum = P.ReduceSum(keep_dims=True).set_strategy(strategy2)
              self.mul2 = P.Mul().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_sum(out, -1)
              out = self.mul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((2, 2, 2), )
      strategy3 = ((4, 2, 1), (4, 2, 1))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([128, 32, 1]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_sum_mul5():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_sum = P.ReduceSum(keep_dims=True).set_strategy(strategy2)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_sum(out, 0)
              return out
  
      context.set_auto_parallel_context(device_num=64, global_rank=0)
      strategy1 = ((1, 8, 8), (1, 8, 8))
      strategy2 = ((2, 4, 1), )
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([1, 32, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_sum_mul6():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_sum = P.ReduceSum(keep_dims=True).set_strategy(strategy2)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_sum(out, 1)
              return out
  
      context.set_auto_parallel_context(device_num=64, global_rank=0)
      strategy1 = ((1, 8, 8), (1, 8, 8))
      strategy2 = ((2, 1, 4), )
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([128, 1, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_sum_mul7():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_sum = P.ReduceSum(keep_dims=True).set_strategy(strategy2)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_sum(out, (0, 1))
              return out
  
      context.set_auto_parallel_context(device_num=64, global_rank=0)
      strategy1 = ((1, 8, 8), (1, 8, 8))
      strategy2 = ((2, 4, 1), )
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([1, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_max_mul():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_max = P.ReduceMax(keep_dims=False).set_strategy(strategy2)
              self.mul2 = P.Mul().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_max(out, -1)
              out = self.mul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 2), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([128, 32]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_min_mul():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_min = P.ReduceMin(keep_dims=False).set_strategy(strategy2)
              self.mul2 = P.Mul().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_min(out, 0)
              out = self.mul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 2), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([32, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_reduce_mean_mul_float32():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_mean = P.ReduceMean(keep_dims=False).set_strategy(strategy2)
              self.mul2 = P.Mul().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_mean(out, 0)
              out = self.mul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 2), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([32, 64]), dtype=ms.float32)
      
      _executor.compile(net, x, y, b)
  
  
  class ArgMaxWithValueNet(nn.Cell):
      def __init__(self, strategy1, strategy2, strategy3):
          super().__init__()
          self.mul1 = P.Mul().set_strategy(strategy1)
          self.arg_max_with_value = P.ArgMaxWithValue(keep_dims=False, axis=-1).set_strategy(strategy2)
          self.mul2 = P.Mul().set_strategy(strategy3)
  
      def construct(self, x, y, b):
          out = self.mul1(x, y)
          index, out = self.arg_max_with_value(out)
          out = self.mul2(out, b)
          return out
  
  
  class ArgMinWithValueNet(nn.Cell):
      def __init__(self, strategy1, strategy2, strategy3):
          super().__init__()
          self.mul1 = P.Mul().set_strategy(strategy1)
          self.arg_min_with_value = P.ArgMinWithValue(keep_dims=False, axis=-1).set_strategy(strategy2)
          self.mul2 = P.Mul().set_strategy(strategy3)
  
      def construct(self, x, y, b):
          out = self.mul1(x, y)
          index, out = self.arg_min_with_value(out)
          out = self.mul2(out, b)
          return out
  
  
  def gen_inputs_and_compile(net):
      x = Tensor(np.ones([128, 64, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 64, 64]), dtype=ms.float32)
      b = Tensor(np.ones([128, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def tobefixed_test_arg_max_with_value_mul_semi_axis_parallel():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 2), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(ArgMaxWithValueNet(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      gen_inputs_and_compile(net)
  
  
  def test_arg_max_with_value_mul_semi():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 1), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(ArgMaxWithValueNet(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      gen_inputs_and_compile(net)
  
  
  def test_arg_max_with_value_mul_auto():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = None
      strategy2 = None
      strategy3 = None
      net = GradWrap(NetWithLoss(ArgMaxWithValueNet(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="auto_parallel")
      gen_inputs_and_compile(net)
  
  
  def test_arg_min_with_value_mul_semi_axis_parallel():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 2), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(ArgMinWithValueNet(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      gen_inputs_and_compile(net)
  
  
  
  def test_arg_min_with_value_mul_semi():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 1), )
      strategy3 = ((2, 4), (2, 4))
      net = GradWrap(NetWithLoss(ArgMinWithValueNet(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      gen_inputs_and_compile(net)
  
  
  def test_arg_min_with_value_mul_auto():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = None
      strategy2 = None
      strategy3 = None
      net = GradWrap(NetWithLoss(ArgMinWithValueNet(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="auto_parallel")
      gen_inputs_and_compile(net)
  
  
  class ArgMinWithValueNet2(nn.Cell):
      def __init__(self, strategy1, strategy2, strategy3):
          super().__init__()
          self.mul1 = P.Mul().set_strategy(strategy1)
          self.arg_min_with_value = P.ArgMinWithValue(keep_dims=True, axis=-1).set_strategy(strategy2)
          self.relu = P.ReLU().set_strategy(strategy3)
  
      def construct(self, x, y, b):
          out = self.mul1(x, y)
          index, out = self.arg_min_with_value(out)
          out = self.relu(out)
          return out
  
  
  def tobefixed_test_arg_min_with_value_mul_semi_axis_parallel2():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 2), )
      strategy3 = ((2, 4, 1), )
      net = GradWrap(NetWithLoss(ArgMinWithValueNet2(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      gen_inputs_and_compile(net)
  
  
  def test_arg_min_with_value_mul_semi2():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 1), )
      strategy3 = ((2, 4, 1), )
      net = GradWrap(NetWithLoss(ArgMinWithValueNet2(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      gen_inputs_and_compile(net)
  
  
  def test_arg_min_with_value_mul_auto2():
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = None
      strategy2 = None
      strategy3 = None
      net = GradWrap(NetWithLoss(ArgMinWithValueNet2(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="auto_parallel")
      gen_inputs_and_compile(net)
  
  
  def test_cross_batch():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_sum = P.ReduceSum(keep_dims=False).set_strategy(strategy2)
              self.reduce_mean = P.ReduceMean(keep_dims=False).set_strategy(strategy3).add_prim_attr("cross_batch", True)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_sum(out, -1)
              out = self.reduce_mean(out, 0)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((4, 2), (4, 2))
      strategy2 = ((2, 1), )
      strategy3 = ((8, ), )
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      
  
      x = Tensor(np.ones([32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([32, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_cross_batch2():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul1 = P.Mul().set_strategy(strategy1)
              self.reduce_mean = P.ReduceMean(keep_dims=False).set_strategy(strategy2)
              self.reduce_sum = P.ReduceSum(keep_dims=False).set_strategy(strategy3).add_prim_attr("cross_batch", True)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_mean(out, -1)
              out = self.reduce_sum(out, 0)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((4, 2), (4, 2))
      strategy2 = ((2, 1), )
      strategy3 = ((8, ), )
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      
  
      x = Tensor(np.ones([32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([32, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_cross_batch_auto():
      class Net(nn.Cell):
          def __init__(self):
              super().__init__()
              self.mul1 = P.Mul()
              self.reduce_mean = P.ReduceMean(keep_dims=False)
              self.reduce_sum = P.ReduceSum(keep_dims=False).add_prim_attr("cross_batch", True)
  
          def construct(self, x, y, b):
              out = self.mul1(x, y)
              out = self.reduce_mean(out, -1)
              out = self.reduce_sum(out, 0)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      net = GradWrap(NetWithLoss(Net()))
      context.set_auto_parallel_context(parallel_mode="auto_parallel")
      
  
      x = Tensor(np.ones([32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([32, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_max_empty_tuple():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul = P.Mul().set_strategy(strategy1)
              self.reduce_max = P.ReduceMax(keep_dims=False).set_strategy(strategy2)
              self.add = P.TensorAdd().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul(x, y)
              out = self.reduce_max(out)
              out = self.add(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((1, 4, 2), (1, 4, 2))
      strategy2 = ((4, 1, 2), )
      strategy3 = ((), (1, 1))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([128, 32]), dtype=ms.float32)
      
      _executor.compile(net, x, y, b)
  