#!/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 import numpy import torch from torch import nn class MultiHeadedAttention(nn.Module): """Multi-Head Attention layer. :param int n_head: the number of head s :param int n_feat: the number of features :param float dropout_rate: dropout rate """ def __init__(self, n_head, n_feat, dropout_rate): """Construct an MultiHeadedAttention object.""" super(MultiHeadedAttention, self).__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.attn = None self.dropout = nn.Dropout(p=dropout_rate) def forward_qkv(self, query, key, value): """Transform query, key and value. :param torch.Tensor query: (batch, time1, size) :param torch.Tensor key: (batch, time2, size) :param torch.Tensor value: (batch, time2, size) :return torch.Tensor transformed query, key and value """ 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, scores, mask): """Compute attention context vector. :param torch.Tensor value: (batch, head, time2, size) :param torch.Tensor scores: (batch, head, time1, time2) :param torch.Tensor mask: (batch, 1, time2) or (batch, time1, time2) :return 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) min_value = float( numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min ) scores = scores.masked_fill(mask, min_value) self.attn = torch.softmax(scores, dim=-1).masked_fill( mask, 0.0 ) # (batch, head, time1, time2) else: self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) p_attn = self.dropout(self.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, key, value, mask): """Compute 'Scaled Dot Product Attention'. :param torch.Tensor query: (batch, time1, size) :param torch.Tensor key: (batch, time2, size) :param torch.Tensor value: (batch, time2, size) :param torch.Tensor mask: (batch, 1, time2) or (batch, time1, time2) :param torch.nn.Dropout dropout: :return torch.Tensor: attention output (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 :param int n_head: the number of head s :param int n_feat: the number of features :param float dropout_rate: 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 ecoding 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=False): """Compute relative positinal encoding. :param torch.Tensor x: (batch, time, size) :param bool zero_triu: return the lower triangular part of the matrix """ zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype) x_padded = torch.cat([zero_pad, x], dim=-1) x_padded = x_padded.view(*x.size()[:2], 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, key, value, pos_emb, mask): """Compute 'Scaled Dot Product Attention' with rel. positional encoding. :param torch.Tensor query: (batch, time1, size) :param torch.Tensor key: (batch, time2, size) :param torch.Tensor value: (batch, time2, size) :param torch.Tensor pos_emb: (batch, time1, size) :param torch.Tensor mask: (batch, time1, time2) :param torch.nn.Dropout dropout: :return torch.Tensor: attention output (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)) 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)