forked from mindspore-Ecosystem/mindspore
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
# Copyright 2020 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="GPU")
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class SoftplusNet(nn.Cell):
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def __init__(self):
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super(SoftplusNet, self).__init__()
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self.softplus = P.Softplus()
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def construct(self, x):
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return self.softplus(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_gpu_training
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@pytest.mark.env_onecard
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def test_softplusgrad():
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x = 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 = 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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x_ms = Tensor(x)
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dy_ms = Tensor(dy)
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net = SoftplusNet()
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grad = Grad(net)
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output = grad(x_ms, dy_ms)
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expect = dy * np.exp(x) / (1 + np.exp(x))
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assert np.allclose(output[0].asnumpy(), expect, rtol=1e-3)
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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_softplusgrad_fp16():
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np.random.seed(42)
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x_np = np.random.randn(5, 3, 6).astype(np.float16)
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dy_np = np.random.randn(5, 3, 6).astype(np.float16)
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net = SoftplusNet()
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grad = Grad(net)
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output = grad(Tensor(x_np), Tensor(dy_np))
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expect = dy_np * np.exp(x_np) / (1 + np.exp(x_np))
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assert np.allclose(output[0].asnumpy(), expect, rtol=1e-2)
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