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
  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
  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)
  
  
  def test_matmul_pow():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.pow = P.Pow().set_strategy(strategy2)
              self.matmul2 = P.MatMul().set_strategy(strategy1)
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              out = self.pow(out, 2.0)
              out = self.matmul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((2, 2), (2, 2))
      strategy2 = ((4, 2), )
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_matmul_exp():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.exp = P.Exp().set_strategy(strategy2)
              self.matmul2 = P.MatMul().set_strategy(strategy1)
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              out = self.exp(out)
              out = self.matmul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((2, 2), (2, 2))
      strategy2 = ((4, 2), )
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_matmul_log():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.log = P.Log().set_strategy(strategy2)
              self.matmul2 = P.MatMul().set_strategy(strategy1)
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              out = self.log(out)
              out = self.matmul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((2, 2), (2, 2))
      strategy2 = ((4, 2), )
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_matmul_logical_not():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.logicalnot = P.LogicalNot().set_strategy(strategy2)
              self.equal = P.Equal().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              out = self.equal(out, b)
              out = self.logicalnot(out)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((2, 2), (2, 2))
      strategy2 = ((4, 2), )
      strategy3 = ((4, 2), (4, 2))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([128, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  def test_matmul_cast():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.cast = P.Cast().set_strategy(strategy2)
              self.matmul2 = P.MatMul().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              b = self.cast(b, ms.float32)
              out = self.matmul2(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((2, 2), (2, 2))
      strategy2 = ((4, 2), )
      strategy3 = ((1, 4), (4, 2))
      net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([64, 64]), dtype=ms.int32)
      _executor.compile(net, x, y, b)
  
  
  def test_cast_before_mirror():
      class Net(nn.Cell):
          def __init__(self, strategy1):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.cast = P.Cast()
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              b = self.cast(b, ms.float32)
              out = self.matmul(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=True)
      strategy1 = ((2, 2), (2, 2))
      net = GradWrap(NetWithLoss(Net(strategy1)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([32, 64]), dtype=ms.float32)
      b = Tensor(np.ones([64, 64]), dtype=ms.float16)
      _executor.compile(net, x, y, b)
  
  
  def test_cast_before_mirror1():
      class Net(nn.Cell):
          def __init__(self, strategy1):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.cast = P.Cast()
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              b = self.cast(b, ms.float16)
              out = self.matmul(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=True)
      strategy1 = ((2, 2), (2, 2))
      net = GradWrap(NetWithLoss(Net(strategy1)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      
      x = Tensor(np.ones([128, 32]), dtype=ms.float16)
      y = Tensor(np.ones([32, 64]), dtype=ms.float16)
      b = Tensor(np.ones([64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_cast_before_mirror2():
      class Net(nn.Cell):
          def __init__(self, strategy1):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.cast = P.Cast()
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              b = self.cast(b, ms.float16)
              out = self.matmul(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0, cast_before_mirror=False)
      strategy1 = ((2, 2), (2, 2))
      net = GradWrap(NetWithLoss(Net(strategy1)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      
      x = Tensor(np.ones([128, 32]), dtype=ms.float16)
      y = Tensor(np.ones([32, 64]), dtype=ms.float16)
      b = Tensor(np.ones([64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_cast_before_mirror3():
      class Net(nn.Cell):
          def __init__(self, strategy1):
              super().__init__()
              self.matmul = P.MatMul().set_strategy(strategy1)
              self.cast = P.Cast()
  
          def construct(self, x, y, b):
              out = self.matmul(x, y)
              b = self.cast(b, ms.float16)
              out = self.matmul(out, b)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((2, 2), (2, 2))
      net = GradWrap(NetWithLoss(Net(strategy1)))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
      
      x = Tensor(np.ones([128, 32]), dtype=ms.float16)
      y = Tensor(np.ones([32, 64]), dtype=ms.float16)
      b = Tensor(np.ones([64, 64]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  
  
  def test_mul_two_cast():
      class Net(nn.Cell):
          def __init__(self, strategy1, strategy2, strategy3):
              super().__init__()
              self.mul = P.Mul().set_strategy(strategy1)
              self.mul2 = P.Mul().set_strategy(strategy2)
              self.cast = P.Cast().set_strategy(strategy3)
              self.cast2 = P.Cast().set_strategy(strategy3)
  
          def construct(self, x, y, b):
              out = self.mul(x, y)
              out = self.mul2(out, b)
              out = self.cast(out, ms.int32)
              out = self.cast2(out, ms.bool_)
              return out
  
      context.set_auto_parallel_context(device_num=8, global_rank=0)
      strategy1 = ((2, 2), (2, 2))
      strategy2 = ((8, 1), (8, 1))
      strategy3 = ((8, 1), )
      net = GradWrap(Net(strategy1, strategy2, strategy3))
      context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
  
      x = Tensor(np.ones([128, 32]), dtype=ms.float32)
      y = Tensor(np.ones([128, 32]), dtype=ms.float32)
      b = Tensor(np.ones([128, 32]), dtype=ms.float32)
      _executor.compile(net, x, y, b)
  