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PaddleSpeech/paddlespeech/t2s/modules/transformer/encoder.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.
# Modified from espnet(https://github.com/espnet/espnet)
from typing import List
from typing import Union
from paddle import nn
from paddlespeech.t2s.modules.activation import get_activation
from paddlespeech.t2s.modules.conformer.convolution import ConvolutionModule
from paddlespeech.t2s.modules.conformer.encoder_layer import EncoderLayer as ConformerEncoderLayer
from paddlespeech.t2s.modules.layer_norm import LayerNorm
from paddlespeech.t2s.modules.transformer.attention import MultiHeadedAttention
from paddlespeech.t2s.modules.transformer.attention import RelPositionMultiHeadedAttention
from paddlespeech.t2s.modules.transformer.embedding import PositionalEncoding
from paddlespeech.t2s.modules.transformer.embedding import RelPositionalEncoding
from paddlespeech.t2s.modules.transformer.embedding import ScaledPositionalEncoding
from paddlespeech.t2s.modules.transformer.encoder_layer import EncoderLayer
from paddlespeech.t2s.modules.transformer.multi_layer_conv import Conv1dLinear
from paddlespeech.t2s.modules.transformer.multi_layer_conv import MultiLayeredConv1d
from paddlespeech.t2s.modules.transformer.positionwise_feed_forward import PositionwiseFeedForward
from paddlespeech.t2s.modules.transformer.repeat import repeat
from paddlespeech.t2s.modules.transformer.subsampling import Conv2dSubsampling
class BaseEncoder(nn.Layer):
"""Base Encoder module.
Args:
idim (int): Input dimension.
attention_dim (int): Dimention of attention.
attention_heads (int): The number of heads of multi head attention.
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of decoder blocks.
dropout_rate (float): Dropout rate.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
attention_dropout_rate (float): Dropout rate in attention.
input_layer (Union[str, nn.Layer]): Input layer type.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
macaron_style (bool): Whether to use macaron style for positionwise layer.
pos_enc_layer_type (str): Encoder positional encoding layer type.
selfattention_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
use_cnn_module (bool): Whether to use convolution module.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
cnn_module_kernel (int): Kernerl size of convolution module.
padding_idx (int): Padding idx for input_layer=embed.
stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
indices start from 1.
if not None, intermediate outputs are returned (which changes return type
signature.)
encoder_type (str): "transformer", or "conformer".
"""
def __init__(self,
idim: int,
attention_dim: int=256,
attention_heads: int=4,
linear_units: int=2048,
num_blocks: int=6,
dropout_rate: float=0.1,
positional_dropout_rate: float=0.1,
attention_dropout_rate: float=0.0,
input_layer: str="conv2d",
normalize_before: bool=True,
concat_after: bool=False,
positionwise_layer_type: str="linear",
positionwise_conv_kernel_size: int=1,
macaron_style: bool=False,
pos_enc_layer_type: str="abs_pos",
selfattention_layer_type: str="selfattn",
activation_type: str="swish",
use_cnn_module: bool=False,
zero_triu: bool=False,
cnn_module_kernel: int=31,
padding_idx: int=-1,
stochastic_depth_rate: float=0.0,
intermediate_layers: Union[List[int], None]=None,
encoder_type: str="transformer"):
"""Construct an Base Encoder object."""
super().__init__()
activation = get_activation(activation_type)
pos_enc_class = self.get_pos_enc_class(pos_enc_layer_type,
selfattention_layer_type)
self.encoder_type = encoder_type
self.conv_subsampling_factor = 1
self.embed = self.get_embed(
idim=idim,
input_layer=input_layer,
attention_dim=attention_dim,
pos_enc_class=pos_enc_class,
dropout_rate=dropout_rate,
positional_dropout_rate=positional_dropout_rate,
padding_idx=padding_idx)
self.normalize_before = normalize_before
# self-attention module definition
encoder_selfattn_layer, encoder_selfattn_layer_args = self.get_encoder_selfattn_layer(
selfattention_layer_type=selfattention_layer_type,
attention_heads=attention_heads,
attention_dim=attention_dim,
attention_dropout_rate=attention_dropout_rate,
zero_triu=zero_triu,
pos_enc_layer_type=pos_enc_layer_type)
# feed-forward module definition
positionwise_layer, positionwise_layer_args = self.get_positionwise_layer(
positionwise_layer_type, attention_dim, linear_units, dropout_rate,
positionwise_conv_kernel_size, activation)
# convolution module definition
convolution_layer = ConvolutionModule
convolution_layer_args = (attention_dim, cnn_module_kernel, activation)
if self.encoder_type == "transformer":
self.encoders = repeat(
num_blocks,
lambda lnum: EncoderLayer(
attention_dim,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
positionwise_layer(*positionwise_layer_args),
dropout_rate,
normalize_before,
concat_after, ), )
elif self.encoder_type == "conformer":
self.encoders = repeat(
num_blocks,
lambda lnum: ConformerEncoderLayer(
attention_dim,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
positionwise_layer(*positionwise_layer_args),
positionwise_layer(*positionwise_layer_args) if macaron_style else None,
convolution_layer(*convolution_layer_args) if use_cnn_module else None,
dropout_rate,
normalize_before,
concat_after,
stochastic_depth_rate * float(1 + lnum) / num_blocks, ), )
self.intermediate_layers = intermediate_layers
else:
raise NotImplementedError("Support only linear or conv1d.")
if self.normalize_before:
self.after_norm = LayerNorm(attention_dim)
def get_positionwise_layer(self,
positionwise_layer_type: str="linear",
attention_dim: int=256,
linear_units: int=2048,
dropout_rate: float=0.1,
positionwise_conv_kernel_size: int=1,
activation: nn.Layer=nn.ReLU()):
"""Define positionwise layer."""
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (attention_dim, linear_units,
dropout_rate, activation)
elif positionwise_layer_type == "conv1d":
positionwise_layer = MultiLayeredConv1d
positionwise_layer_args = (attention_dim, linear_units,
positionwise_conv_kernel_size,
dropout_rate, )
elif positionwise_layer_type == "conv1d-linear":
positionwise_layer = Conv1dLinear
positionwise_layer_args = (attention_dim, linear_units,
positionwise_conv_kernel_size,
dropout_rate, )
else:
raise NotImplementedError("Support only linear or conv1d.")
return positionwise_layer, positionwise_layer_args
def get_encoder_selfattn_layer(self,
selfattention_layer_type: str="selfattn",
attention_heads: int=4,
attention_dim: int=256,
attention_dropout_rate: float=0.0,
zero_triu: bool=False,
pos_enc_layer_type: str="abs_pos"):
if selfattention_layer_type == "selfattn":
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, attention_dim,
attention_dropout_rate, )
elif selfattention_layer_type == "rel_selfattn":
assert pos_enc_layer_type == "rel_pos"
encoder_selfattn_layer = RelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, attention_dim,
attention_dropout_rate, zero_triu, )
else:
raise ValueError("unknown encoder_attn_layer: " +
selfattention_layer_type)
return encoder_selfattn_layer, encoder_selfattn_layer_args
def get_pos_enc_class(self,
pos_enc_layer_type: str="abs_pos",
selfattention_layer_type: str="selfattn"):
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "scaled_abs_pos":
pos_enc_class = ScaledPositionalEncoding
elif pos_enc_layer_type == "rel_pos":
assert selfattention_layer_type == "rel_selfattn"
pos_enc_class = RelPositionalEncoding
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
return pos_enc_class
def get_embed(self,
idim,
input_layer="conv2d",
attention_dim: int=256,
pos_enc_class=PositionalEncoding,
dropout_rate: int=0.1,
positional_dropout_rate: int=0.1,
padding_idx: int=-1):
if input_layer == "linear":
embed = nn.Sequential(
nn.Linear(idim, attention_dim),
nn.LayerNorm(attention_dim),
nn.Dropout(dropout_rate),
nn.ReLU(),
pos_enc_class(attention_dim, positional_dropout_rate), )
elif input_layer == "conv2d":
embed = Conv2dSubsampling(
idim,
attention_dim,
dropout_rate,
pos_enc_class(attention_dim, positional_dropout_rate), )
self.conv_subsampling_factor = 4
elif input_layer == "embed":
embed = nn.Sequential(
nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
pos_enc_class(attention_dim, positional_dropout_rate), )
elif isinstance(input_layer, nn.Layer):
embed = nn.Sequential(
input_layer,
pos_enc_class(attention_dim, positional_dropout_rate), )
elif input_layer is None:
embed = nn.Sequential(
pos_enc_class(attention_dim, positional_dropout_rate))
else:
raise ValueError("unknown input_layer: " + input_layer)
return embed
def forward(self, xs, masks):
"""Encode input sequence.
Args:
xs (Tensor): Input tensor (#batch, time, idim).
masks (Tensor): Mask tensor (#batch, 1, time).
Returns:
Tensor: Output tensor (#batch, time, attention_dim).
Tensor: Mask tensor (#batch, 1, time).
"""
xs = self.embed(xs)
xs, masks = self.encoders(xs, masks)
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks
class TransformerEncoder(BaseEncoder):
"""Transformer encoder module.
Args:
idim (int): Input dimension.
attention_dim (int): Dimention of attention.
attention_heads (int): The number of heads of multi head attention.
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of decoder blocks.
dropout_rate (float): Dropout rate.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
attention_dropout_rate (float): Dropout rate in attention.
input_layer (Union[str, paddle.nn.Layer]): Input layer type.
pos_enc_layer_type (str): Encoder positional encoding layer type.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool): Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
selfattention_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
padding_idx (int): Padding idx for input_layer=embed.
"""
def __init__(
self,
idim,
attention_dim: int=256,
attention_heads: int=4,
linear_units: int=2048,
num_blocks: int=6,
dropout_rate: float=0.1,
positional_dropout_rate: float=0.1,
attention_dropout_rate: float=0.0,
input_layer: str="conv2d",
pos_enc_layer_type: str="abs_pos",
normalize_before: bool=True,
concat_after: bool=False,
positionwise_layer_type: str="linear",
positionwise_conv_kernel_size: int=1,
selfattention_layer_type: str="selfattn",
activation_type: str="relu",
padding_idx: int=-1, ):
"""Construct an Transformer Encoder object."""
super().__init__(
idim,
attention_dim=attention_dim,
attention_heads=attention_heads,
linear_units=linear_units,
num_blocks=num_blocks,
dropout_rate=dropout_rate,
positional_dropout_rate=positional_dropout_rate,
attention_dropout_rate=attention_dropout_rate,
input_layer=input_layer,
pos_enc_layer_type=pos_enc_layer_type,
normalize_before=normalize_before,
concat_after=concat_after,
positionwise_layer_type=positionwise_layer_type,
positionwise_conv_kernel_size=positionwise_conv_kernel_size,
selfattention_layer_type=selfattention_layer_type,
activation_type=activation_type,
padding_idx=padding_idx,
encoder_type="transformer")
def forward(self, xs, masks):
"""Encode input sequence.
Args:
xs(Tensor): Input tensor (#batch, time, idim).
masks(Tensor): Mask tensor (#batch, 1, time).
Returns:
Tensor: Output tensor (#batch, time, attention_dim).
Tensor:Mask tensor (#batch, 1, time).
"""
xs = self.embed(xs)
xs, masks = self.encoders(xs, masks)
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks
def forward_one_step(self, xs, masks, cache=None):
"""Encode input frame.
Args:
xs (Tensor): Input tensor.
masks (Tensor): Mask tensor.
cache (List[Tensor]): List of cache tensors.
Returns:
Tensor: Output tensor.
Tensor: Mask tensor.
List[Tensor]: List of new cache tensors.
"""
xs = self.embed(xs)
if cache is None:
cache = [None for _ in range(len(self.encoders))]
new_cache = []
for c, e in zip(cache, self.encoders):
xs, masks = e(xs, masks, cache=c)
new_cache.append(xs)
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks, new_cache
class ConformerEncoder(BaseEncoder):
"""Conformer encoder module.
Args:
idim (int): Input dimension.
attention_dim (int): Dimention of attention.
attention_heads (int): The number of heads of multi head attention.
linear_units (int): The number of units of position-wise feed forward.
num_blocks (int): The number of decoder blocks.
dropout_rate (float): Dropout rate.
positional_dropout_rate (float): Dropout rate after adding positional encoding.
attention_dropout_rate (float): Dropout rate in attention.
input_layer (Union[str, nn.Layer]): Input layer type.
normalize_before (bool): Whether to use layer_norm before the first block.
concat_after (bool):Whether to concat attention layer's input and output.
if True, additional linear will be applied.
i.e. x -> x + linear(concat(x, att(x)))
if False, no additional linear will be applied. i.e. x -> x + att(x)
positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
macaron_style (bool): Whether to use macaron style for positionwise layer.
pos_enc_layer_type (str): Encoder positional encoding layer type.
selfattention_layer_type (str): Encoder attention layer type.
activation_type (str): Encoder activation function type.
use_cnn_module (bool): Whether to use convolution module.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
cnn_module_kernel (int): Kernerl size of convolution module.
padding_idx (int): Padding idx for input_layer=embed.
stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
intermediate_layers (Union[List[int], None]):indices of intermediate CTC layer. indices start from 1.
if not None, intermediate outputs are returned (which changes return type signature.)
"""
def __init__(
self,
idim: int,
attention_dim: int=256,
attention_heads: int=4,
linear_units: int=2048,
num_blocks: int=6,
dropout_rate: float=0.1,
positional_dropout_rate: float=0.1,
attention_dropout_rate: float=0.0,
input_layer: str="conv2d",
normalize_before: bool=True,
concat_after: bool=False,
positionwise_layer_type: str="linear",
positionwise_conv_kernel_size: int=1,
macaron_style: bool=False,
pos_enc_layer_type: str="rel_pos",
selfattention_layer_type: str="rel_selfattn",
activation_type: str="swish",
use_cnn_module: bool=False,
zero_triu: bool=False,
cnn_module_kernel: int=31,
padding_idx: int=-1,
stochastic_depth_rate: float=0.0,
intermediate_layers: Union[List[int], None]=None, ):
"""Construct an Conformer Encoder object."""
super().__init__(
idim=idim,
attention_dim=attention_dim,
attention_heads=attention_heads,
linear_units=linear_units,
num_blocks=num_blocks,
dropout_rate=dropout_rate,
positional_dropout_rate=positional_dropout_rate,
attention_dropout_rate=attention_dropout_rate,
input_layer=input_layer,
normalize_before=normalize_before,
concat_after=concat_after,
positionwise_layer_type=positionwise_layer_type,
positionwise_conv_kernel_size=positionwise_conv_kernel_size,
macaron_style=macaron_style,
pos_enc_layer_type=pos_enc_layer_type,
selfattention_layer_type=selfattention_layer_type,
activation_type=activation_type,
use_cnn_module=use_cnn_module,
zero_triu=zero_triu,
cnn_module_kernel=cnn_module_kernel,
padding_idx=padding_idx,
stochastic_depth_rate=stochastic_depth_rate,
intermediate_layers=intermediate_layers,
encoder_type="conformer")
def forward(self, xs, masks):
"""Encode input sequence.
Args:
xs (Tensor): Input tensor (#batch, time, idim).
masks (Tensor): Mask tensor (#batch, 1, time).
Returns:
Tensor: Output tensor (#batch, time, attention_dim).
Tensor: Mask tensor (#batch, 1, time).
"""
if isinstance(self.embed, (Conv2dSubsampling)):
xs, masks = self.embed(xs, masks)
else:
xs = self.embed(xs)
if self.intermediate_layers is None:
xs, masks = self.encoders(xs, masks)
else:
intermediate_outputs = []
for layer_idx, encoder_layer in enumerate(self.encoders):
xs, masks = encoder_layer(xs, masks)
if (self.intermediate_layers is not None and
layer_idx + 1 in self.intermediate_layers):
# intermediate branches also require normalization.
encoder_output = xs
if isinstance(encoder_output, tuple):
encoder_output = encoder_output[0]
if self.normalize_before:
encoder_output = self.after_norm(encoder_output)
intermediate_outputs.append(encoder_output)
if isinstance(xs, tuple):
xs = xs[0]
if self.normalize_before:
xs = self.after_norm(xs)
if self.intermediate_layers is not None:
return xs, masks, intermediate_outputs
return xs, masks
class Conv1dResidualBlock(nn.Layer):
"""
Special module for simplified version of Encoder class.
"""
def __init__(self,
idim: int=256,
odim: int=256,
kernel_size: int=5,
dropout_rate: float=0.2):
super().__init__()
self.main_block = nn.Sequential(
nn.Conv1D(
idim, odim, kernel_size=kernel_size, padding=kernel_size // 2),
nn.ReLU(),
nn.BatchNorm1D(odim),
nn.Dropout(p=dropout_rate))
self.conv1d_residual = nn.Conv1D(idim, odim, kernel_size=1)
def forward(self, xs):
"""Encode input sequence.
Args:
xs (Tensor): Input tensor (#batch, idim, T).
Returns:
Tensor: Output tensor (#batch, odim, T).
"""
outputs = self.main_block(xs)
outputs = self.conv1d_residual(xs) + outputs
return outputs
class CNNDecoder(nn.Layer):
"""
Much simplified decoder than the original one with Prenet.
"""
def __init__(
self,
emb_dim: int=256,
odim: int=80,
kernel_size: int=5,
dropout_rate: float=0.2,
resblock_kernel_sizes: List[int]=[256, 256], ):
super().__init__()
input_shape = emb_dim
out_sizes = resblock_kernel_sizes
out_sizes.append(out_sizes[-1])
in_sizes = [input_shape] + out_sizes[:-1]
self.residual_blocks = nn.LayerList([
Conv1dResidualBlock(
idim=in_channels,
odim=out_channels,
kernel_size=kernel_size,
dropout_rate=dropout_rate, )
for in_channels, out_channels in zip(in_sizes, out_sizes)
])
self.conv1d = nn.Conv1D(
in_channels=out_sizes[-1], out_channels=odim, kernel_size=1)
def forward(self, xs, masks=None):
"""Encode input sequence.
Args:
xs (Tensor): Input tensor (#batch, time, idim).
masks (Tensor): Mask tensor (#batch, 1, time).
Returns:
Tensor: Output tensor (#batch, time, odim).
"""
# exchange the temporal dimension and the feature dimension
xs = xs.transpose([0, 2, 1])
if masks is not None:
xs = xs * masks
for layer in self.residual_blocks:
outputs = layer(xs)
if masks is not None:
# input_mask B * 1 * T
outputs = outputs * masks
xs = outputs
outputs = self.conv1d(outputs)
if masks is not None:
outputs = outputs * masks
outputs = outputs.transpose([0, 2, 1])
return outputs, masks
class CNNPostnet(nn.Layer):
def __init__(
self,
odim: int=80,
kernel_size: int=5,
dropout_rate: float=0.2,
resblock_kernel_sizes: List[int]=[256, 256], ):
super().__init__()
out_sizes = resblock_kernel_sizes
in_sizes = [odim] + out_sizes[:-1]
self.residual_blocks = nn.LayerList([
Conv1dResidualBlock(
idim=in_channels,
odim=out_channels,
kernel_size=kernel_size,
dropout_rate=dropout_rate)
for in_channels, out_channels in zip(in_sizes, out_sizes)
])
self.conv1d = nn.Conv1D(
in_channels=out_sizes[-1], out_channels=odim, kernel_size=1)
def forward(self, xs, masks=None):
"""Encode input sequence.
Args:
xs (Tensor): Input tensor (#batch, odim, time).
masks (Tensor): Mask tensor (#batch, 1, time).
Returns:
Tensor: Output tensor (#batch, odim, time).
"""
for layer in self.residual_blocks:
outputs = layer(xs)
if masks is not None:
# input_mask B * 1 * T
outputs = outputs * masks
xs = outputs
outputs = self.conv1d(outputs)
if masks is not None:
outputs = outputs * masks
return outputs