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55 lines
1.9 KiB
55 lines
1.9 KiB
# Copyright 2019 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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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore.common.api import _executor
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from mindspore.ops import operations as P
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from ....train_step_wrap import train_step_with_loss_warp
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class DenseMutMulNet(nn.Cell):
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def __init__(self):
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super(DenseMutMulNet, self).__init__()
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self.fc1 = nn.Dense(128, 768, activation='relu')
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self.fc2 = nn.Dense(128, 768, activation='relu')
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self.fc3 = nn.Dense(128, 768, activation='relu')
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self.fc4 = nn.Dense(768, 768, activation='relu')
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self.relu4 = nn.ReLU()
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self.relu5 = nn.ReLU()
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self.transpose = P.Transpose()
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self.matmul1 = P.MatMul()
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self.matmul2 = P.MatMul()
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def construct(self, x):
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q = self.fc1(x)
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k = self.fc2(x)
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v = self.fc3(x)
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k = self.transpose(k, (1, 0))
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c = self.relu4(self.matmul1(q, k))
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s = self.relu5(self.matmul2(c, v))
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s = self.fc4(s)
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return s
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def test_dmnet_train_step():
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context.reset_auto_parallel_context()
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input_ = Tensor(np.ones([32, 128]).astype(np.float32) * 0.01)
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label = Tensor(np.zeros([32, 768]).astype(np.float32))
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net = DenseMutMulNet()
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net = train_step_with_loss_warp(DenseMutMulNet())
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_executor.compile(net, input_, label)
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