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| # Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe) | |
| # 2020 Northwestern Polytechnical University (Pengcheng Guo) | |
| # 2020 Mobvoi Inc (Binbin Zhang) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Swish() activation function for Conformer.""" | |
| import math | |
| import torch | |
| class Swish(torch.nn.Module): | |
| """Construct an Swish object.""" | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| """Return Swish activation function.""" | |
| return x * torch.sigmoid(x) | |
| class New_gelu4npu(torch.nn.Module): | |
| """Construct an Swish object.""" | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| """Return Swish activation function.""" | |
| return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) | |
| def new_gelu_func(x: torch.Tensor): | |
| return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) | |