# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Encoder definition."""
import logging
import torch
from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
from espnet.nets.pytorch_backend.transducer.vgg2l import VGG2L
from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
from espnet.nets.pytorch_backend.transformer.dynamic_conv import DynamicConvolution
from espnet.nets.pytorch_backend.transformer.dynamic_conv2d import DynamicConvolution2D
from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
from espnet.nets.pytorch_backend.transformer.encoder_layer import EncoderLayer
from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
from espnet.nets.pytorch_backend.transformer.lightconv import LightweightConvolution
from espnet.nets.pytorch_backend.transformer.lightconv2d import LightweightConvolution2D
from espnet.nets.pytorch_backend.transformer.multi_layer_conv import (
Conv1dLinear,
MultiLayeredConv1d,
)
from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (
PositionwiseFeedForward,
)
from espnet.nets.pytorch_backend.transformer.repeat import repeat
from espnet.nets.pytorch_backend.transformer.subsampling import (
Conv2dSubsampling,
Conv2dSubsampling6,
Conv2dSubsampling8,
)
def _pre_hook(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
# https://github.com/espnet/espnet/commit/21d70286c354c66c0350e65dc098d2ee236faccc#diff-bffb1396f038b317b2b64dd96e6d3563
rename_state_dict(prefix + "input_layer.", prefix + "embed.", state_dict)
# https://github.com/espnet/espnet/commit/3d422f6de8d4f03673b89e1caef698745ec749ea#diff-bffb1396f038b317b2b64dd96e6d3563
rename_state_dict(prefix + "norm.", prefix + "after_norm.", state_dict)
[docs]class Encoder(torch.nn.Module):
"""Transformer encoder module.
Args:
idim (int): Input dimension.
attention_dim (int): Dimension of attention.
attention_heads (int): The number of heads of multi head attention.
conv_wshare (int): The number of kernel of convolution. Only used in
selfattention_layer_type == "lightconv*" or "dynamiconv*".
conv_kernel_length (Union[int, str]): Kernel size str of convolution
(e.g. 71_71_71_71_71_71). Only used in selfattention_layer_type
== "lightconv*" or "dynamiconv*".
conv_usebias (bool): Whether to use bias in convolution. Only used in
selfattention_layer_type == "lightconv*" or "dynamiconv*".
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, torch.nn.Module]): Input layer type.
pos_enc_class (torch.nn.Module): Positional encoding module class.
`PositionalEncoding `or `ScaledPositionalEncoding`
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.
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,
attention_dim=256,
attention_heads=4,
conv_wshare=4,
conv_kernel_length="11",
conv_usebias=False,
linear_units=2048,
num_blocks=6,
dropout_rate=0.1,
positional_dropout_rate=0.1,
attention_dropout_rate=0.0,
input_layer="conv2d",
pos_enc_class=PositionalEncoding,
normalize_before=True,
concat_after=False,
positionwise_layer_type="linear",
positionwise_conv_kernel_size=1,
selfattention_layer_type="selfattn",
padding_idx=-1,
stochastic_depth_rate=0.0,
intermediate_layers=None,
ctc_softmax=None,
conditioning_layer_dim=None,
):
"""Construct an Encoder object."""
super(Encoder, self).__init__()
self._register_load_state_dict_pre_hook(_pre_hook)
self.conv_subsampling_factor = 1
if input_layer == "linear":
self.embed = torch.nn.Sequential(
torch.nn.Linear(idim, attention_dim),
torch.nn.LayerNorm(attention_dim),
torch.nn.Dropout(dropout_rate),
torch.nn.ReLU(),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer == "conv2d":
self.embed = Conv2dSubsampling(idim, attention_dim, dropout_rate)
self.conv_subsampling_factor = 4
elif input_layer == "conv2d-scaled-pos-enc":
self.embed = Conv2dSubsampling(
idim,
attention_dim,
dropout_rate,
pos_enc_class(attention_dim, positional_dropout_rate),
)
self.conv_subsampling_factor = 4
elif input_layer == "conv2d6":
self.embed = Conv2dSubsampling6(idim, attention_dim, dropout_rate)
self.conv_subsampling_factor = 6
elif input_layer == "conv2d8":
self.embed = Conv2dSubsampling8(idim, attention_dim, dropout_rate)
self.conv_subsampling_factor = 8
elif input_layer == "vgg2l":
self.embed = VGG2L(idim, attention_dim)
self.conv_subsampling_factor = 4
elif input_layer == "embed":
self.embed = torch.nn.Sequential(
torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif isinstance(input_layer, torch.nn.Module):
self.embed = torch.nn.Sequential(
input_layer,
pos_enc_class(attention_dim, positional_dropout_rate),
)
elif input_layer is None:
self.embed = torch.nn.Sequential(
pos_enc_class(attention_dim, positional_dropout_rate)
)
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
positionwise_layer, positionwise_layer_args = self.get_positionwise_layer(
positionwise_layer_type,
attention_dim,
linear_units,
dropout_rate,
positionwise_conv_kernel_size,
)
if selfattention_layer_type in [
"selfattn",
"rel_selfattn",
"legacy_rel_selfattn",
]:
logging.info("encoder self-attention layer type = self-attention")
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = [
(
attention_heads,
attention_dim,
attention_dropout_rate,
)
] * num_blocks
elif selfattention_layer_type == "lightconv":
logging.info("encoder self-attention layer type = lightweight convolution")
encoder_selfattn_layer = LightweightConvolution
encoder_selfattn_layer_args = [
(
conv_wshare,
attention_dim,
attention_dropout_rate,
int(conv_kernel_length.split("_")[lnum]),
False,
conv_usebias,
)
for lnum in range(num_blocks)
]
elif selfattention_layer_type == "lightconv2d":
logging.info(
"encoder self-attention layer "
"type = lightweight convolution 2-dimensional"
)
encoder_selfattn_layer = LightweightConvolution2D
encoder_selfattn_layer_args = [
(
conv_wshare,
attention_dim,
attention_dropout_rate,
int(conv_kernel_length.split("_")[lnum]),
False,
conv_usebias,
)
for lnum in range(num_blocks)
]
elif selfattention_layer_type == "dynamicconv":
logging.info("encoder self-attention layer type = dynamic convolution")
encoder_selfattn_layer = DynamicConvolution
encoder_selfattn_layer_args = [
(
conv_wshare,
attention_dim,
attention_dropout_rate,
int(conv_kernel_length.split("_")[lnum]),
False,
conv_usebias,
)
for lnum in range(num_blocks)
]
elif selfattention_layer_type == "dynamicconv2d":
logging.info(
"encoder self-attention layer type = dynamic convolution 2-dimensional"
)
encoder_selfattn_layer = DynamicConvolution2D
encoder_selfattn_layer_args = [
(
conv_wshare,
attention_dim,
attention_dropout_rate,
int(conv_kernel_length.split("_")[lnum]),
False,
conv_usebias,
)
for lnum in range(num_blocks)
]
else:
raise NotImplementedError(selfattention_layer_type)
self.encoders = repeat(
num_blocks,
lambda lnum: EncoderLayer(
attention_dim,
encoder_selfattn_layer(*encoder_selfattn_layer_args[lnum]),
positionwise_layer(*positionwise_layer_args),
dropout_rate,
normalize_before,
concat_after,
stochastic_depth_rate * float(1 + lnum) / num_blocks,
),
)
if self.normalize_before:
self.after_norm = LayerNorm(attention_dim)
self.intermediate_layers = intermediate_layers
self.use_conditioning = True if ctc_softmax is not None else False
if self.use_conditioning:
self.ctc_softmax = ctc_softmax
self.conditioning_layer = torch.nn.Linear(
conditioning_layer_dim, attention_dim
)
[docs] def get_positionwise_layer(
self,
positionwise_layer_type="linear",
attention_dim=256,
linear_units=2048,
dropout_rate=0.1,
positionwise_conv_kernel_size=1,
):
"""Define positionwise layer."""
if positionwise_layer_type == "linear":
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (attention_dim, linear_units, dropout_rate)
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
[docs] def forward(self, xs, masks):
"""Encode input sequence.
Args:
xs (torch.Tensor): Input tensor (#batch, time, idim).
masks (torch.Tensor): Mask tensor (#batch, 1, time).
Returns:
torch.Tensor: Output tensor (#batch, time, attention_dim).
torch.Tensor: Mask tensor (#batch, 1, time).
"""
if isinstance(
self.embed,
(Conv2dSubsampling, Conv2dSubsampling6, Conv2dSubsampling8, VGG2L),
):
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
):
encoder_output = xs
# intermediate branches also require normalization.
if self.normalize_before:
encoder_output = self.after_norm(encoder_output)
intermediate_outputs.append(encoder_output)
if self.use_conditioning:
intermediate_result = self.ctc_softmax(encoder_output)
xs = xs + self.conditioning_layer(intermediate_result)
if self.normalize_before:
xs = self.after_norm(xs)
if self.intermediate_layers is not None:
return xs, masks, intermediate_outputs
return xs, masks
[docs] def forward_one_step(self, xs, masks, *, cache=None):
"""Encode input frame.
Args:
xs (torch.Tensor): Input tensor.
masks (torch.Tensor): Mask tensor.
cache (List[torch.Tensor]): List of cache tensors.
Returns:
torch.Tensor: Output tensor.
torch.Tensor: Mask tensor.
List[torch.Tensor]: List of new cache tensors.
"""
if isinstance(self.embed, Conv2dSubsampling):
xs, masks = self.embed(xs, masks)
else:
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