# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
#
# 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.
# Modified from wenet(https://github.com/wenet-e2e/wenet)
"""Positionwise feed forward layer definition."""
import paddle
from paddle import nn
from paddle.nn import initializer as I
from paddlespeech.s2t.modules.align import Linear
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = ["PositionwiseFeedForward"]
[docs]class PositionwiseFeedForward(nn.Layer):
"""Positionwise feed forward layer."""
def __init__(self,
idim: int,
hidden_units: int,
dropout_rate: float,
activation: nn.Layer=nn.ReLU(),
adaptive_scale: bool=False,
init_weights: bool=False):
"""Construct a PositionwiseFeedForward object.
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 (paddle.nn.Layer): Activation function
"""
super().__init__()
self.idim = idim
self.hidden_units = hidden_units
self.w_1 = Linear(idim, hidden_units)
self.activation = activation
self.dropout = nn.Dropout(dropout_rate)
self.w_2 = Linear(hidden_units, idim)
self.adaptive_scale = adaptive_scale
if self.adaptive_scale:
ada_scale = self.create_parameter(
[1, 1, idim], default_initializer=I.XavierUniform())
self.add_parameter('ada_scale', ada_scale)
ada_bias = self.create_parameter(
[1, 1, idim], default_initializer=I.XavierUniform())
self.add_parameter('ada_bias', ada_bias)
if init_weights:
self.init_weights()
[docs] def init_weights(self):
ffn1_max = self.idim**-0.5
ffn2_max = self.hidden_units**-0.5
self.w_1._param_attr = paddle.nn.initializer.Uniform(
low=-ffn1_max, high=ffn1_max)
self.w_1._bias_attr = paddle.nn.initializer.Uniform(
low=-ffn1_max, high=ffn1_max)
self.w_2._param_attr = paddle.nn.initializer.Uniform(
low=-ffn2_max, high=ffn2_max)
self.w_2._bias_attr = paddle.nn.initializer.Uniform(
low=-ffn2_max, high=ffn2_max)
[docs] def forward(self, xs: paddle.Tensor) -> paddle.Tensor:
"""Forward function.
Args:
xs: input tensor (B, Lmax, D)
Returns:
output tensor, (B, Lmax, 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))))