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PaddleSpeech/paddlespeech/t2s/modules/transformer/lightconv.py

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# Copyright (c) 2021 PaddlePaddle Authors. 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.
3 years ago
# Modified from espnet(https://github.com/espnet/espnet)
"""Lightweight Convolution Module."""
import numpy
import paddle
import paddle.nn.functional as F
from paddle import nn
from paddlespeech.t2s.modules.glu import GLU
from paddlespeech.t2s.modules.masked_fill import masked_fill
MIN_VALUE = float(numpy.finfo(numpy.float32).min)
class LightweightConvolution(nn.Layer):
"""Lightweight Convolution layer.
This implementation is based on
https://github.com/pytorch/fairseq/tree/master/fairseq
Parameters
----------
wshare : int
the number of kernel of convolution
n_feat : int
the number of features
dropout_rate : float
dropout_rate
kernel_size : int
kernel size (length)
use_kernel_mask : bool
Use causal mask or not for convolution kernel
use_bias : bool
Use bias term or not.
"""
def __init__(
self,
wshare,
n_feat,
dropout_rate,
kernel_size,
use_kernel_mask=False,
use_bias=False, ):
"""Construct Lightweight Convolution layer."""
super(LightweightConvolution, self).__init__()
assert n_feat % wshare == 0
self.wshare = wshare
self.use_kernel_mask = use_kernel_mask
self.dropout_rate = dropout_rate
self.kernel_size = kernel_size
self.padding_size = int(kernel_size / 2)
# linear -> GLU -> lightconv -> linear
self.linear1 = nn.Linear(n_feat, n_feat * 2)
self.linear2 = nn.Linear(n_feat, n_feat)
self.act = GLU()
# lightconv related
self.uniform_ = nn.initializer.Uniform()
self.weight = paddle.to_tensor(
numpy.random.uniform(0, 1, size=[self.wshare, 1, kernel_size]),
dtype="float32")
self.uniform_(self.weight)
self.weight = paddle.create_parameter(
shape=self.weight.shape,
dtype=str(self.weight.numpy().dtype),
default_initializer=paddle.nn.initializer.Assign(self.weight))
self.use_bias = use_bias
if self.use_bias:
self.bias = paddle.Tensor(n_feat)
self.bias = paddle.create_parameter(
shape=self.bias.shape,
dtype=str(self.bias.numpy().dtype),
default_initializer=paddle.nn.initializer.Assign(self.bias))
# mask of kernel
kernel_mask0 = paddle.zeros([self.wshare, int(kernel_size / 2)])
kernel_mask1 = paddle.ones([self.wshare, int(kernel_size / 2 + 1)])
self.kernel_mask = paddle.concat(
(kernel_mask1, kernel_mask0), axis=-1).unsqueeze(1)
def forward(self, query, key, value, mask):
"""Forward of 'Lightweight Convolution'.
This function takes query, key and value but uses only query.
This is just for compatibility with self-attention layer (attention.py)
Parameters
----------
query : paddle.Tensor
(batch, time1, d_model) input tensor
key : paddle.Tensor
(batch, time2, d_model) NOT USED
value : paddle.Tensor
(batch, time2, d_model) NOT USED
mask : paddle.Tensor
(batch, time1, time2) mask
Return
----------
x : paddle.Tensor
(batch, time1, d_model) ouput
"""
# linear -> GLU -> lightconv -> linear
x = query
B, T, C = x.shape
H = self.wshare
# first liner layer
x = self.linear1(x)
# GLU activation
x = self.act(x)
# lightconv
# B x C x T
x = x.transpose([0, 2, 1]).reshape([-1, H, T])
weight = F.dropout(
self.weight, self.dropout_rate, training=self.training)
if self.use_kernel_mask:
weight = masked_fill(weight, self.kernel_mask == 0.0, float("-inf"))
# weight = weight.masked_fill(self.kernel_mask == 0.0, float("-inf"))
weight = F.softmax(weight, axis=-1)
x = F.conv1d(
x, weight, padding=self.padding_size,
groups=self.wshare).reshape([B, C, T])
if self.use_bias:
x = x + self.bias.reshape([1, -1, 1])
# B x T x C
x = x.transpose([0, 2, 1])
if mask is not None and not self.use_kernel_mask:
mask = mask.transpose([0, 2, 1])
# x = x.masked_fill(mask == 0, 0.0)
x = masked_fill(x, mask == 0, 0.0)
# second linear layer
x = self.linear2(x)
return x