forked from mindspore-Ecosystem/mindspore
85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
# Copyright 2021-2022 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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""" test high order control flow """
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import pytest
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from mindspore.nn import Cell
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from mindspore.common import Tensor, dtype
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import mindspore.ops.functional as F
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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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_high_control_while():
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"""
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Feature: High-order differential function.
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Description: Infer of the high-order differential function.
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Expectation: Null.
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"""
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class Net(Cell):
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def construct(self, x):
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while x < 10:
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x = (x * 2)
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return x
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net = Net()
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x = Tensor(1, dtype.float32)
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grad_net = F.grad(net)
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order_grad_net = F.grad(grad_net)
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order_grad = order_grad_net(x)
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assert order_grad == 0.0
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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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_high_control_for_while():
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"""
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Feature: High-order differential function.
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Description: Infer of the complex high-order differential function.
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Expectation: Null.
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"""
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class Net(Cell):
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def construct(self, x):
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for _ in [2]:
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for _ in [2]:
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while x > 1:
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x = (x / 3)
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x = (x / 2)
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for _ in [2]:
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x = (x / 1)
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x = (x + 1)
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for _ in [3]:
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for _ in [4]:
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x = (x / 1)
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x = (x + 3)
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for _ in [5]:
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x = (x / 3)
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x = (x / 2)
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return x
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net = Net()
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x = Tensor(4, dtype.float32)
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grad_net = F.grad(net)
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grad_grad_net = F.grad(grad_net)
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result = grad_grad_net(x)
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assert result == 0.0
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