#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright 2019 Shigeki Karita # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) """Multi-Head Attention layer definition.""" import math from typing import Optional, Tuple import torch from torch import nn class MultiHeadedAttention(nn.Module): """Multi-Head Attention layer. 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: int, n_feat: int, dropout_rate: float): """Construct an MultiHeadedAttention object.""" super().__init__() assert n_feat % n_head == 0 # We assume d_v always equals d_k self.d_k = n_feat // n_head self.h = n_head self.linear_q = nn.Linear(n_feat, n_feat) self.linear_k = nn.Linear(n_feat, n_feat) self.linear_v = nn.Linear(n_feat, n_feat) self.linear_out = nn.Linear(n_feat, n_feat) self.dropout = nn.Dropout(p=dropout_rate) def forward_qkv( self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Transform query, key and value. 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). Returns: torch.Tensor: Transformed query tensor, size (#batch, n_head, time1, d_k). torch.Tensor: Transformed key tensor, size (#batch, n_head, time2, d_k). torch.Tensor: Transformed value tensor, size (#batch, n_head, time2, d_k). """ n_batch = query.size(0) q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) q = q.transpose(1, 2) # (batch, head, time1, d_k) k = k.transpose(1, 2) # (batch, head, time2, d_k) v = v.transpose(1, 2) # (batch, head, time2, d_k) return q, k, v def forward_attention(self, value: torch.Tensor, scores: torch.Tensor, mask: Optional[torch.Tensor]) -> 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). Returns: torch.Tensor: Transformed value (#batch, time1, d_model) weighted by the attention score (#batch, time1, time2). """ n_batch = value.size(0) if mask is not None: mask = mask.unsqueeze(1).eq(0) # (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) 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: Optional[torch.Tensor], pos_emb: torch.Tensor = torch.empty(0),) -> torch.Tensor: """Compute scaled dot product attention. 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). 1.When applying cross attention between decoder and encoder, the batch padding mask for input is in (#batch, 1, T) shape. 2.When applying self attention of encoder, the mask is in (#batch, T, T) shape. 3.When applying self attention of decoder, the mask is in (#batch, L, L) shape. 4.If the different position in decoder see different block of the encoder, such as Mocha, the passed in mask could be in (#batch, L, T) shape. But there is no such case in current Wenet. Returns: torch.Tensor: Output tensor (#batch, time1, d_model). """ q, k, v = self.forward_qkv(query, key, value) scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) return self.forward_attention(v, scores, mask) 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): """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.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) 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(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, mask: Optional[torch.Tensor], pos_emb: 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). Returns: torch.Tensor: Output tensor (#batch, time1, d_model). """ q, k, v = self.forward_qkv(query, key, value) q = q.transpose(1, 2) # (batch, time1, head, d_k) 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. # 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)