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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
import torch
import torch.nn as nn
from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
from typing import Tuple
class RelPositionMultiHeadedAttention(MultiHeadedAttention):
"""Multi-Head Attention layer with relative position encoding.
Paper: https://arxiv.org/abs/1901.02860
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,
do_rel_shift=False,
adaptive_scale=False,
init_weights=False,
):
"""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)
# 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.do_rel_shift = do_rel_shift
self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
torch.nn.init.xavier_uniform_(self.pos_bias_u)
torch.nn.init.xavier_uniform_(self.pos_bias_v)
self.adaptive_scale = adaptive_scale
self.ada_scale = nn.Parameter(
torch.ones([1, 1, n_feat]), requires_grad=adaptive_scale
)
self.ada_bias = nn.Parameter(
torch.zeros([1, 1, n_feat]), requires_grad=adaptive_scale
)
if init_weights:
self.init_weights()
def init_weights(self):
input_max = (self.h * self.d_k) ** -0.5
torch.nn.init.uniform_(self.linear_q.weight, -input_max, input_max)
torch.nn.init.uniform_(self.linear_q.bias, -input_max, input_max)
torch.nn.init.uniform_(self.linear_k.weight, -input_max, input_max)
torch.nn.init.uniform_(self.linear_k.bias, -input_max, input_max)
torch.nn.init.uniform_(self.linear_v.weight, -input_max, input_max)
torch.nn.init.uniform_(self.linear_v.bias, -input_max, input_max)
torch.nn.init.uniform_(self.linear_pos.weight, -input_max, input_max)
torch.nn.init.uniform_(self.linear_out.weight, -input_max, input_max)
torch.nn.init.uniform_(self.linear_out.bias, -input_max, input_max)
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 forward_attention(
self,
value: torch.Tensor,
scores: torch.Tensor,
mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
) -> 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.
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"))
# (batch, head, time1, time2)
attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
# 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)
x = (
x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
) # (batch, time1, d_model)
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), (0, 0, 0) means fake mask.
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`
"""
if self.adaptive_scale:
query = self.ada_scale * query + self.ada_bias
key = self.ada_scale * key + self.ada_bias
value = self.ada_scale * value + self.ada_bias
q, k, v = self.forward_qkv(query, key, value)
q = q.transpose(1, 2) # (batch, time1, head, d_k)
# NOTE(xcsong):
# when export onnx model, for 1st chunk, we feed
# cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
# or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
# In all modes, `if cache.size(0) > 0` will alwayse be `True`
# and we will always do splitting and
# concatnation(this will simplify onnx export). Note that
# it's OK to concat & split zero-shaped tensors(see code below).
# when export jit model, for 1st chunk, we always feed
# cache(0, 0, 0, 0) since jit supports dynamic if-branch.
# >>> a = torch.ones((1, 2, 0, 4))
# >>> b = torch.ones((1, 2, 3, 4))
# >>> c = torch.cat((a, b), dim=2)
# >>> torch.equal(b, c) # True
# >>> d = torch.split(a, 2, dim=-1)
# >>> torch.equal(d[0], d[1]) # True
if cache.size(0) > 0:
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)
# NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
# non-trivial to calculate `next_cache_start` here.
new_cache = torch.cat((k, v), dim=-1)
n_batch_pos = pos_emb.size(0)
p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
p = p.transpose(1, 2) # (batch, head, time1, d_k)
# (batch, head, time1, d_k)
q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
# (batch, head, time1, d_k)
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.
if self.do_rel_shift:
matrix_bd = self.rel_shift(matrix_bd)
scores = (matrix_ac + matrix_bd) / math.sqrt(
self.d_k
) # (batch, head, time1, time2)
return self.forward_attention(v, scores, mask), new_cache
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