forked from mindspore-Ecosystem/mindspore
63 lines
2.4 KiB
Python
63 lines
2.4 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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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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import mindspore.ops as ops
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.apply_power_sign = ops.ApplyPowerSign()
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self.var = Parameter(Tensor(np.array([[0.6, 0.4],
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[0.1, 0.5]]).astype(np.float32)), name="var")
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self.m = Parameter(Tensor(np.array([[0.6, 0.5],
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[0.2, 0.6]]).astype(np.float32)), name="m")
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self.lr = 0.001
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self.logbase = np.e
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self.sign_decay = 0.99
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self.beta = 0.9
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def construct(self, grad):
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out = self.apply_power_sign(self.var, self.m, self.lr, self.logbase,
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self.sign_decay, self.beta, grad)
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return out
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_apply_power_assign():
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"""
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Feature: test ops ApplyPowerSign.
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Description: Update var and m by ApplyPowerSign op.
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Expectation: match to expected benchmark output.
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"""
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grad = Tensor(np.array([[0.3, 0.7], [0.1, 0.8]]).astype(np.float32))
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net = Net()
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net(grad)
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expect_var = [[5.95575690e-01, 3.89676481e-01],
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[9.85252112e-02, 4.88201708e-01]]
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expect_m = [[5.70000052e-01, 5.19999981e-01],
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[1.89999998e-01, 6.20000064e-01]]
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assert np.allclose(net.var.asnumpy(), expect_var, atol=0.0001, rtol=0.0001, equal_nan=True)
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assert np.allclose(net.m.asnumpy(), expect_m, atol=0.0001, rtol=0.0001, equal_nan=True)
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