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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}
# }
#
"""Multi-Head Attention layer definition."""
import math
from typing import Tuple, Optional
import torch
from torch import nn
import torch.nn.functional as F
from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
class GroupedRelPositionMultiHeadedAttention(MultiHeadedAttention):
"""Multi-Head Attention layer with relative position encoding.
Paper:
https://arxiv.org/abs/1901.02860
https://arxiv.org/abs/2109.01163
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
"""
def __init__(self, n_head, n_feat, dropout_rate, group_size=3):
"""Construct an RelPositionMultiHeadedAttention object."""
super().__init__(n_head, n_feat, dropout_rate)
# linear transformation for positional encoding
self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
self.group_size = group_size
self.d_k = n_feat // n_head # for GroupedAttention
self.n_feat = n_feat
# these two learnable bias are used in matrix c and matrix d
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k * self.group_size))
self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k * self.group_size))
torch.nn.init.xavier_uniform_(self.pos_bias_u)
torch.nn.init.xavier_uniform_(self.pos_bias_v)
def rel_shift(self, x, zero_triu: bool = False):
"""Compute relative positinal encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, size).
zero_triu (bool): If true, return the lower triangular part of
the matrix.
Returns:
torch.Tensor: Output tensor.
"""
zero_pad = torch.zeros(
(x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype
)
x_padded = torch.cat([zero_pad, x], dim=-1)
x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2))
x = x_padded[:, :, 1:].view_as(x)
if zero_triu:
ones = torch.ones((x.size(2), x.size(3)))
x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
return x
def pad4group(self, Q, K, V, P, mask, group_size: int = 3):
"""
q: (#batch, time1, size) -> (#batch, head, time1, size/head)
k,v: (#batch, time2, size) -> (#batch, head, time2, size/head)
p: (#batch, time2, size)
"""
# Compute Overflows
overflow_Q = Q.size(2) % group_size
overflow_KV = K.size(2) % group_size
# if-else for ONNX export
# 0 // 0.00000000000000001 = 0
# 1 // 1.00000000000000001 = 1
padding_Q = (group_size - overflow_Q) * int(
overflow_Q // (overflow_Q + 0.00000000000000001)
)
padding_KV = (group_size - overflow_KV) * int(
overflow_KV // (overflow_KV + 0.00000000000000001)
)
batch_size, _, seq_len_KV, _ = K.size()
# Input Padding (B, T, D) -> (B, T + P, D)
Q = F.pad(Q, (0, 0, 0, padding_Q), value=0.0)
K = F.pad(K, (0, 0, 0, padding_KV), value=0.0)
V = F.pad(V, (0, 0, 0, padding_KV), value=0.0)
if mask is not None and mask.size(2) > 0: # time2 > 0:
mask = mask[:, ::group_size, ::group_size]
Q = (
Q.transpose(1, 2)
.contiguous()
.view(batch_size, -1, self.h, self.d_k * group_size)
.transpose(1, 2)
)
K = (
K.transpose(1, 2)
.contiguous()
.view(batch_size, -1, self.h, self.d_k * group_size)
.transpose(1, 2)
)
V = (
V.transpose(1, 2)
.contiguous()
.view(batch_size, -1, self.h, self.d_k * group_size)
.transpose(1, 2)
)
# process pos_emb
P_batch_size = P.size(0)
overflow_P = P.size(1) % group_size
padding_P = group_size - overflow_P if overflow_P else 0
P = F.pad(P, (0, 0, 0, padding_P), value=0.0)
P = P.view(P_batch_size, -1, self.h, self.d_k * group_size).transpose(1, 2)
return Q, K, V, P, mask, padding_Q
def forward_attention(
self,
value: torch.Tensor,
scores: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
padding_q: Optional[int] = None,
) -> torch.Tensor:
"""Compute attention context vector.
Args:
value (torch.Tensor): Transformed value, size
(#batch, n_head, time2, d_k).
scores (torch.Tensor): Attention score, size
(#batch, n_head, time1, time2).
mask (torch.Tensor): Mask, size (#batch, 1, time2) or
(#batch, time1, time2), (0, 0, 0) means fake mask.
padding_q : for GroupedAttention in efficent conformer
Returns:
torch.Tensor: Transformed value (#batch, time1, d_model)
weighted by the attention score (#batch, time1, time2).
"""
n_batch = value.size(0)
# NOTE(xcsong): When will `if mask.size(2) > 0` be True?
# 1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
# 1st chunk to ease the onnx export.]
# 2. pytorch training
if mask.size(2) > 0: # time2 > 0
mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
# For last chunk, time2 might be larger than scores.size(-1)
mask = mask[:, :, :, : scores.size(-1)] # (batch, 1, *, time2)
scores = scores.masked_fill(mask, -float("inf"))
attn = torch.softmax(scores, dim=-1).masked_fill(
mask, 0.0
) # (batch, head, time1, time2)
# NOTE(xcsong): When will `if mask.size(2) > 0` be False?
# 1. onnx(16/-1, -1/-1, 16/0)
# 2. jit (16/-1, -1/-1, 16/0, 16/4)
else:
attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
p_attn = self.dropout(attn)
x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
# n_feat!=h*d_k may be happened in GroupAttention
x = (
x.transpose(1, 2).contiguous().view(n_batch, -1, self.n_feat)
) # (batch, time1, d_model)
if padding_q is not None:
# for GroupedAttention in efficent conformer
x = x[:, : x.size(1) - padding_q]
return self.linear_out(x) # (batch, time1, d_model)
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
pos_emb: torch.Tensor = torch.empty(0),
cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
(#batch, time1, time2).
pos_emb (torch.Tensor): Positional embedding tensor
(#batch, time2, size).
cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
where `cache_t == chunk_size * num_decoding_left_chunks`
and `head * d_k == size`
Returns:
torch.Tensor: Output tensor (#batch, time1, d_model).
torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
where `cache_t == chunk_size * num_decoding_left_chunks`
and `head * d_k == size`
"""
q = self.linear_q(query)
k = self.linear_k(key) # (#batch, time2, size)
v = self.linear_v(value)
p = self.linear_pos(pos_emb) # (#batch, time2, size)
batch_size, seq_len_KV, _ = k.size() # seq_len_KV = time2
# (#batch, time2, size) -> (#batch, head, time2, size/head)
q = q.view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
k = k.view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
v = v.view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
if cache.size(0) > 0:
# use attention cache
key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1)
k = torch.cat([key_cache, k], dim=2)
v = torch.cat([value_cache, v], dim=2)
new_cache = torch.cat((k, v), dim=-1)
# May be k and p does not match. eg. time2=18+18/2=27 > mask=36/2=18
if mask is not None and mask.size(2) > 0:
time2 = mask.size(2)
k = k[:, :, -time2:, :]
v = v[:, :, -time2:, :]
# q k v p: (batch, head, time1, d_k)
q, k, v, p, mask, padding_q = self.pad4group(q, k, v, p, mask, self.group_size)
# q_with_bias_u & q_with_bias_v = (batch, head, time1, d_k)
q = q.transpose(1, 2) # (batch, time1, head, d_k)
q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
# compute attention score
# first compute matrix a and matrix c
# as described in https://arxiv.org/abs/1901.02860 Section 3.3
# (batch, head, time1, time2)
matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
# compute matrix b and matrix d
# (batch, head, time1, time2)
matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
# Remove rel_shift since it is useless in speech recognition,
# and it requires special attention for streaming.
# matrix_bd = self.rel_shift(matrix_bd)
scores = (matrix_ac + matrix_bd) / math.sqrt(
self.d_k * self.group_size
) # (batch, head, time1, time2)
return self.forward_attention(v, scores, mask, padding_q), new_cache
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