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61 lines
1.8 KiB
61 lines
1.8 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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from mindspore.common import dtype as mstype
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from mindspore import nn
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from mindspore import Tensor
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from mindspore.ops import composite as C
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from mindspore import context
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context.set_context(mode=context.GRAPH_MODE, save_graphs=True, device_target="Ascend")
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class ForwardNet(nn.Cell):
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def construct(self, x, y):
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y = y + 10
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while x < y:
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x = (x + 2) * (y - 9)
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y = y + 2
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x = x + 5
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return x
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class BackwardNet(nn.Cell):
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def __init__(self, forward_net):
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super(BackwardNet, self).__init__()
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self.forward_net = forward_net
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self.grad = C.GradOperation()
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def construct(self, *inputs):
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grads = self.grad(self.forward_net)(*inputs)
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return grads
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def test_forward():
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c1 = Tensor([0], mstype.int32)
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c2 = Tensor([0], mstype.int32)
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expect = Tensor([75], mstype.int32)
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forward_net = ForwardNet()
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output = forward_net(c1, c2)
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assert expect == output
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def test_backward():
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c1 = Tensor([0], mstype.int32)
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c2 = Tensor([0], mstype.int32)
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expect = Tensor([75], mstype.int32)
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forward_net = ForwardNet()
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output = forward_net(c1, c2)
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assert expect == output
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