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Running
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Zero
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# Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
# 2022 Ximalaya Inc (Yuguang Yang)
#
# 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.
"""Positionwise feed forward layer definition."""
import torch
class PositionwiseFeedForward(torch.nn.Module):
"""Positionwise feed forward layer.
FeedForward are appied on each position of the sequence.
The output dim is same with the input dim.
Args:
idim (int): Input dimenstion.
hidden_units (int): The number of hidden units.
dropout_rate (float): Dropout rate.
activation (torch.nn.Module): Activation function
"""
def __init__(self,
idim: int,
hidden_units: int,
dropout_rate: float,
activation: torch.nn.Module = torch.nn.ReLU(),
adaptive_scale: bool = False,
init_weights: bool = False):
"""Construct a PositionwiseFeedForward object."""
super(PositionwiseFeedForward, self).__init__()
self.idim = idim
self.hidden_units = hidden_units
self.w_1 = torch.nn.Linear(idim, hidden_units)
self.activation = activation
self.dropout = torch.nn.Dropout(dropout_rate)
self.w_2 = torch.nn.Linear(hidden_units, idim)
self.ada_scale = None
self.ada_bias = None
self.adaptive_scale = adaptive_scale
self.ada_scale = torch.nn.Parameter(torch.ones([1, 1, idim]),
requires_grad=adaptive_scale)
self.ada_bias = torch.nn.Parameter(torch.zeros([1, 1, idim]),
requires_grad=adaptive_scale)
if init_weights:
self.init_weights()
def init_weights(self):
ffn1_max = self.idim**-0.5
ffn2_max = self.hidden_units**-0.5
torch.nn.init.uniform_(self.w_1.weight.data, -ffn1_max, ffn1_max)
torch.nn.init.uniform_(self.w_1.bias.data, -ffn1_max, ffn1_max)
torch.nn.init.uniform_(self.w_2.weight.data, -ffn2_max, ffn2_max)
torch.nn.init.uniform_(self.w_2.bias.data, -ffn2_max, ffn2_max)
def forward(self, xs: torch.Tensor) -> torch.Tensor:
"""Forward function.
Args:
xs: input tensor (B, L, D)
Returns:
output tensor, (B, L, D)
"""
if self.adaptive_scale:
xs = self.ada_scale * xs + self.ada_bias
return self.w_2(self.dropout(self.activation(self.w_1(xs))))
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