#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2024 yufan
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Multi-Head Attention Return Weight layer definition."""
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import math
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import torch
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from torch import nn
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class MultiHeadedAttentionReturnWeight(nn.Module):
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"""Multi-Head Attention layer.
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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"""
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def __init__(self, n_head, n_feat, dropout_rate):
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"""Construct an MultiHeadedAttentionReturnWeight object."""
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super(MultiHeadedAttentionReturnWeight, self).__init__()
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assert n_feat % n_head == 0
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# We assume d_v always equals d_k
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self.d_k = n_feat // n_head
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self.h = n_head
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self.linear_q = nn.Linear(n_feat, n_feat)
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self.linear_k = nn.Linear(n_feat, n_feat)
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self.linear_v = nn.Linear(n_feat, n_feat)
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self.linear_out = nn.Linear(n_feat, n_feat)
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self.attn = None
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self.dropout = nn.Dropout(p=dropout_rate)
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def forward_qkv(self, query, key, value):
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"""Transform query, key and value.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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Returns:
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torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
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torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
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torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
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"""
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n_batch = query.size(0)
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q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
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k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
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v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
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q = q.transpose(1, 2) # (batch, head, time1, d_k)
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k = k.transpose(1, 2) # (batch, head, time2, d_k)
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v = v.transpose(1, 2) # (batch, head, time2, d_k)
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return q, k, v
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def forward_attention(self, value, scores, mask):
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"""Compute attention context vector.
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Args:
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value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
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scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
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mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
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Returns:
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torch.Tensor: Transformed value (#batch, time1, d_model)
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weighted by the attention score (#batch, time1, time2).
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"""
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n_batch = value.size(0)
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if mask is not None:
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mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
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min_value = torch.finfo(scores.dtype).min
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scores = scores.masked_fill(mask, min_value)
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attn = torch.softmax(scores, dim=-1).masked_fill(
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mask, 0.0
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) # (batch, head, time1, time2)
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else:
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attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
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p_attn = self.dropout(attn)
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x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
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x = (
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x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
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) # (batch, time1, d_model)
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return self.linear_out(x), attn # (batch, time1, d_model)
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def forward(self, query, key, value, mask):
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"""Compute scaled dot product attention.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
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(#batch, time1, time2).
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Returns:
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torch.Tensor: Output tensor (#batch, time1, d_model).
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"""
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q, k, v = self.forward_qkv(query, key, value)
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scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
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return self.forward_attention(v, scores, mask)
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