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# Copyright 2021 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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import pytest |
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from mindspore.ops import composite as C |
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import mindspore.common.dtype as mstype |
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import mindspore.nn as nn |
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import mindspore.context as context |
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from mindspore.common.tensor import Tensor |
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class Net(nn.Cell): |
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def construct(self, x, y): |
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while x < y: |
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x = x * x + 1 |
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return x |
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class GradNet(nn.Cell): |
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def __init__(self, net): |
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super().__init__() |
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self.net = net |
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self.grad_op = C.GradOperation(get_all=True) |
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def construct(self, x, y): |
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gradient_function = self.grad_op(self.net) |
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return gradient_function(x, y) |
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@pytest.mark.level0 |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.env_onecard |
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def test_while_grad(): |
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True) |
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x = Tensor([2.0], dtype=mstype.float32) |
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y = Tensor([2.0], dtype=mstype.float32) |
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GradNet(Net())(x, y) |