forked from mindspore-Ecosystem/mindspore
95 lines
3.9 KiB
Python
95 lines
3.9 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 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_gpu_training
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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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@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_gelugrad_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 = GeluNet()
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grad = Grad(net)
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output = grad(Tensor(x_np), Tensor(dy_np))
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expect = [[[8.4045e-02, 3.7817e-01, -6.6748e-01, -3.6914e-01, -1.2415e-01, -4.6362e-01],
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[3.3301e-01, 2.6270e-01, 7.7534e-04, -2.0947e-01, -2.2021e-01, -6.4880e-02],
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[-2.3633e-01, 7.6538e-02, 1.8280e-02, 3.8635e-02, -1.6235e-01, 1.2964e-01]],
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[[-1.4801e-02, 9.6130e-03, -2.1660e+00, -8.5602e-03, 3.3356e-02, -3.1885e-01],
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[-2.0355e-02, 1.7737e-01, 3.8719e-03, -9.1895e-01, 8.4717e-02, 2.0593e-01],
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[5.8350e-02, -1.0020e+00, 6.8652e-01, 1.3428e-01, 6.0352e-01, -2.6270e-01]],
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[[-6.5820e-01, 5.1147e-02, -1.2650e-02, -3.2983e-01, -1.5410e+00, 4.3518e-02],
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[-4.3359e-01, 1.2659e-01, 1.1792e-01, 2.2705e-02, -1.2329e-01, -3.5278e-01],
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[6.2109e-01, 1.3611e-01, 1.7041e-01, 2.7124e-01, -5.5908e-02, 1.7212e-01]],
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[[2.8320e-01, 8.3252e-01, 4.2480e-02, -3.4473e-01, 3.9429e-01, 3.1958e-01],
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[3.6499e-02, 1.2250e-01, 7.1350e-02, -2.7267e-02, 3.0029e-01, -8.0566e-01],
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[8.2617e-01, 5.1367e-01, -9.2480e-01, 3.3203e-02, -7.5684e-01, 8.8623e-01]],
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[[5.4590e-01, -9.2383e-01, -2.8107e-02, 4.2432e-01, 4.6826e-01, 5.0879e-01],
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[-1.4062e-01, 6.6284e-02, -2.9126e-01, -6.3086e-01, -8.6975e-02, 4.1504e-02],
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[-6.3171e-03, 1.0852e-01, 1.3779e-02, 1.0947e+00, -3.0334e-02, 2.3828e+00]]]
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assert np.allclose(output[0].asnumpy(), expect, rtol=1e-2)
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