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- # 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.
- # ============================================================================
- import pytest
- from mindspore.common import dtype as mstype
- from mindspore import nn
- from mindspore import Tensor
- from mindspore.ops import composite as C
- from mindspore import context
-
- context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
-
-
- class ForwardNet(nn.Cell):
- def construct(self, x, y):
- y = y + 10
- while x < y:
- x = (x + 2) * (y - 9)
- y = y + 2
- x = x + 5
- return x
-
-
- class BackwardNet(nn.Cell):
- def __init__(self, forward_net):
- super(BackwardNet, self).__init__()
- self.forward_net = forward_net
- self.grad = C.GradOperation()
-
- def construct(self, *inputs):
- grads = self.grad(self.forward_net)(*inputs)
- return grads
-
- @pytest.mark.level1
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.platform_arm_ascend_training
- @pytest.mark.platform_x86_ascend_training
- @pytest.mark.env_onecard
- def test_forward():
- c1 = Tensor([0], mstype.int32)
- c2 = Tensor([0], mstype.int32)
- expect = Tensor([75], mstype.int32)
- forward_net = ForwardNet()
- output = forward_net(c1, c2)
- assert expect == output
-
- @pytest.mark.level1
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.platform_arm_ascend_training
- @pytest.mark.platform_x86_ascend_training
- @pytest.mark.env_onecard
- def test_backward():
- c1 = Tensor([0], mstype.int32)
- c2 = Tensor([0], mstype.int32)
- expect = Tensor([75], mstype.int32)
- forward_net = ForwardNet()
- output = forward_net(c1, c2)
- assert expect == output
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