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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 | |