Spaces:
Running
on
Zero
Running
on
Zero
# 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))) | |