forked from mindspore-Ecosystem/mindspore
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
# Copyright 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_control_flow_specialize """
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from mindspore.nn import Cell
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from mindspore.common import Tensor, dtype, Parameter
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import mindspore.ops.functional as F
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import numpy as np
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import pytest
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_renormalization_after_cconv_poly_node():
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"""
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Feature: control flow
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Description: In the renormalization after cconv, there should be no poly node error.
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Expectation: No exception.
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"""
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.w = Parameter(Tensor([(- 1)], dtype.float32), name='w')
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self.b = Parameter(Tensor([(- 1)], dtype.float32), name='b')
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def construct(self, x, y):
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def inner(x):
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if x >= 5:
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return x
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return x
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def outer(x):
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if x >= inner(x):
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return x
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return x
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while self.b == 0:
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if outer(self.b) <= self.b:
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y = self.w + outer(self.w)
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if y > inner(self.b):
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break
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return x + y
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x = np.array([5], np.float32)
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y = np.array([3], np.float32)
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net1 = Net()
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grad_net = F.grad(net1, grad_position=(0, 1))
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expected = np.array([1], np.float32)
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output = grad_net(Tensor(x), Tensor(y))
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assert np.allclose(expected, output[0].asnumpy(), 0.0001)
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assert np.allclose(expected, output[1].asnumpy(), 0.0001)
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