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64 lines
2.1 KiB
64 lines
2.1 KiB
# 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 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
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class GeluNet(nn.Cell):
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def __init__(self):
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super(GeluNet, self).__init__()
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self.gelu = P.GeLU()
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def construct(self, x):
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return self.gelu(x)
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = C.GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, input_data, sens):
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gout = self.grad(self.network)(input_data, sens)
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return gout
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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_gelugrad():
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x_ms = Tensor(np.array([0.58401114, 0.68800163, 0.9760397, 0.14702141, 0.46563736, 0.9607501,
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0.14567593, 0.12261796, 0.37054458, 0.46421242]).astype(np.float32))
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dy_ms = Tensor(np.array([0.5559598, 0.96994054, 0.24770357, 0.34646875, 0.2984393, 0.03287048,
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0.55681044, 0.966908, 0.06015943, 0.6099489]).astype(np.float32))
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net = GeluNet()
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grad = Grad(net)
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output = grad(x_ms, dy_ms)
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expect = [0.50963277, 0.9414753, 0.2667653, 0.21358444, 0.25243032, 0.0352667,
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0.34266686, 0.57757664, 0.04707306, 0.51536125]
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assert np.allclose(output[0].asnumpy(), expect)
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