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# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
"""Encoder self-attention layer definition."""
from typing import Optional, Tuple
import torch
from torch import nn
class TransformerEncoderLayer(nn.Module):
"""Encoder layer module.
Args:
size (int): Input dimension.
self_attn (torch.nn.Module): Self-attention module instance.
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
instance can be used as the argument.
feed_forward (torch.nn.Module): Feed-forward module instance.
`PositionwiseFeedForward`, instance can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool):
True: use layer_norm before each sub-block.
False: to use layer_norm after each sub-block.
"""
def __init__(
self,
size: int,
self_attn: torch.nn.Module,
feed_forward: torch.nn.Module,
dropout_rate: float,
normalize_before: bool = True,
):
"""Construct an EncoderLayer object."""
super().__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.norm1 = nn.LayerNorm(size, eps=1e-5)
self.norm2 = nn.LayerNorm(size, eps=1e-5)
self.dropout = nn.Dropout(dropout_rate)
self.size = size
self.normalize_before = normalize_before
def forward(
self,
x: torch.Tensor,
mask: torch.Tensor,
pos_emb: torch.Tensor,
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Compute encoded features.
Args:
x (torch.Tensor): (#batch, time, size)
mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
(0, 0, 0) means fake mask.
pos_emb (torch.Tensor): just for interface compatibility
to ConformerEncoderLayer
mask_pad (torch.Tensor): does not used in transformer layer,
just for unified api with conformer.
att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
cnn_cache (torch.Tensor): Convolution cache in conformer layer
(#batch=1, size, cache_t2), not used here, it's for interface
compatibility to ConformerEncoderLayer.
Returns:
torch.Tensor: Output tensor (#batch, time, size).
torch.Tensor: Mask tensor (#batch, time, time).
torch.Tensor: att_cache tensor,
(#batch=1, head, cache_t1 + time, d_k * 2).
torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2).
"""
residual = x
if self.normalize_before:
x = self.norm1(x)
x_att, new_att_cache = self.self_attn(x, x, x, mask, cache=att_cache)
x = residual + self.dropout(x_att)
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm2(x)
fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
return x, mask, new_att_cache, fake_cnn_cache
class ConformerEncoderLayer(nn.Module):
"""Encoder layer module.
Args:
size (int): Input dimension.
self_attn (torch.nn.Module): Self-attention module instance.
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
instance can be used as the argument.
feed_forward (torch.nn.Module): Feed-forward module instance.
`PositionwiseFeedForward` instance can be used as the argument.
feed_forward_macaron (torch.nn.Module): Additional feed-forward module
instance.
`PositionwiseFeedForward` instance can be used as the argument.
conv_module (torch.nn.Module): Convolution module instance.
`ConvlutionModule` instance can be used as the argument.
dropout_rate (float): Dropout rate.
normalize_before (bool):
True: use layer_norm before each sub-block.
False: use layer_norm after each sub-block.
"""
def __init__(
self,
size: int,
self_attn: torch.nn.Module,
feed_forward: Optional[nn.Module] = None,
feed_forward_macaron: Optional[nn.Module] = None,
conv_module: Optional[nn.Module] = None,
dropout_rate: float = 0.1,
normalize_before: bool = True,
):
"""Construct an EncoderLayer object."""
super().__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.feed_forward_macaron = feed_forward_macaron
self.conv_module = conv_module
self.norm_ff = nn.LayerNorm(size, eps=1e-5) # for the FNN module
self.norm_mha = nn.LayerNorm(size, eps=1e-5) # for the MHA module
if feed_forward_macaron is not None:
self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5)
self.ff_scale = 0.5
else:
self.ff_scale = 1.0
if self.conv_module is not None:
self.norm_conv = nn.LayerNorm(size, eps=1e-5) # for the CNN module
self.norm_final = nn.LayerNorm(
size, eps=1e-5
) # for the final output of the block
self.dropout = nn.Dropout(dropout_rate)
self.size = size
self.normalize_before = normalize_before
def forward(
self,
x: torch.Tensor,
mask: torch.Tensor,
pos_emb: torch.Tensor,
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Compute encoded features.
Args:
x (torch.Tensor): (#batch, time, size)
mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
(0, 0, 0) means fake mask.
pos_emb (torch.Tensor): positional encoding, must not be None
for ConformerEncoderLayer.
mask_pad (torch.Tensor): batch padding mask used for conv module.
(#batch, 1，time), (0, 0, 0) means fake mask.
att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
cnn_cache (torch.Tensor): Convolution cache in conformer layer
(#batch=1, size, cache_t2)
Returns:
torch.Tensor: Output tensor (#batch, time, size).
torch.Tensor: Mask tensor (#batch, time, time).
torch.Tensor: att_cache tensor,
(#batch=1, head, cache_t1 + time, d_k * 2).
torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
"""
# whether to use macaron style
if self.feed_forward_macaron is not None:
residual = x
if self.normalize_before:
x = self.norm_ff_macaron(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
if not self.normalize_before:
x = self.norm_ff_macaron(x)
# multi-headed self-attention module
residual = x
if self.normalize_before:
x = self.norm_mha(x)
x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache)
x = residual + self.dropout(x_att)
if not self.normalize_before:
x = self.norm_mha(x)
# convolution module
# Fake new cnn cache here, and then change it in conv_module
new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
if self.conv_module is not None:
residual = x
if self.normalize_before:
x = self.norm_conv(x)
x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
x = residual + self.dropout(x)
if not self.normalize_before:
x = self.norm_conv(x)
# feed forward module
residual = x
if self.normalize_before:
x = self.norm_ff(x)
x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm_ff(x)
if self.conv_module is not None:
x = self.norm_final(x)
return x, mask, new_att_cache, new_cnn_cache
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