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152 lines
5.1 KiB
152 lines
5.1 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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"""Lightweight Convolution Module."""
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import numpy
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddlespeech.t2s.modules.activation import get_activation
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from paddlespeech.t2s.modules.masked_fill import masked_fill
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MIN_VALUE = float(numpy.finfo(numpy.float32).min)
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class LightweightConvolution(nn.Layer):
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"""Lightweight Convolution layer.
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This implementation is based on
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https://github.com/pytorch/fairseq/tree/master/fairseq
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Args:
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wshare (int):
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the number of kernel of convolution
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n_feat (int):
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the number of features
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dropout_rate (float):
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dropout_rate
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kernel_size (int):
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kernel size (length)
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use_kernel_mask (bool):
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Use causal mask or not for convolution kernel
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use_bias (bool):
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Use bias term or not.
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"""
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def __init__(
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self,
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wshare,
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n_feat,
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dropout_rate,
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kernel_size,
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use_kernel_mask=False,
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use_bias=False, ):
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"""Construct Lightweight Convolution layer."""
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super().__init__()
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assert n_feat % wshare == 0
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self.wshare = wshare
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self.use_kernel_mask = use_kernel_mask
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self.dropout_rate = dropout_rate
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self.kernel_size = kernel_size
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self.padding_size = int(kernel_size / 2)
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# linear -> GLU -> lightconv -> linear
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self.linear1 = nn.Linear(n_feat, n_feat * 2)
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self.linear2 = nn.Linear(n_feat, n_feat)
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self.act = get_activation("glu")
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# lightconv related
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self.uniform_ = nn.initializer.Uniform()
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self.weight = paddle.to_tensor(
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numpy.random.uniform(0, 1, size=[self.wshare, 1, kernel_size]),
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dtype="float32")
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self.uniform_(self.weight)
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self.weight = paddle.create_parameter(
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shape=self.weight.shape,
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dtype=str(self.weight.numpy().dtype),
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default_initializer=paddle.nn.initializer.Assign(self.weight))
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self.use_bias = use_bias
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if self.use_bias:
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self.bias = paddle.Tensor(n_feat)
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self.bias = paddle.create_parameter(
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shape=self.bias.shape,
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dtype=str(self.bias.numpy().dtype),
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default_initializer=paddle.nn.initializer.Assign(self.bias))
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# mask of kernel
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kernel_mask0 = paddle.zeros([self.wshare, int(kernel_size / 2)])
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kernel_mask1 = paddle.ones([self.wshare, int(kernel_size / 2 + 1)])
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self.kernel_mask = paddle.concat(
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(kernel_mask1, kernel_mask0), axis=-1).unsqueeze(1)
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def forward(self, query, key, value, mask):
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"""Forward of 'Lightweight Convolution'.
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This function takes query, key and value but uses only query.
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This is just for compatibility with self-attention layer (attention.py)
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Args:
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query (Tensor):
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input tensor. (batch, time1, d_model)
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key (Tensor):
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NOT USED. (batch, time2, d_model)
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value (Tensor):
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NOT USED. (batch, time2, d_model)
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mask : (Tensor):
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(batch, time1, time2) mask
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Return:
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Tensor: output. (batch, time1, d_model)
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"""
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# linear -> GLU -> lightconv -> linear
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x = query
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B, T, C = x.shape
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H = self.wshare
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# first liner layer
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x = self.linear1(x)
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# GLU activation
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x = self.act(x)
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# lightconv
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# B x C x T
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x = x.transpose([0, 2, 1]).reshape([-1, H, T])
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weight = F.dropout(
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self.weight, self.dropout_rate, training=self.training)
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if self.use_kernel_mask:
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weight = masked_fill(weight, self.kernel_mask == 0.0, float("-inf"))
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# weight = weight.masked_fill(self.kernel_mask == 0.0, float("-inf"))
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weight = F.softmax(weight, axis=-1)
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x = F.conv1d(
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x, weight, padding=self.padding_size,
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groups=self.wshare).reshape([B, C, T])
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if self.use_bias:
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x = x + self.bias.reshape([1, -1, 1])
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# B x T x C
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x = x.transpose([0, 2, 1])
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if mask is not None and not self.use_kernel_mask:
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mask = mask.transpose([0, 2, 1])
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# x = x.masked_fill(mask == 0, 0.0)
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x = masked_fill(x, mask == 0, 0.0)
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# second linear layer
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x = self.linear2(x)
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return x
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