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184 lines
6.9 KiB
Python
184 lines
6.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Multi-Head Attention layer definition."""
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import math
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import numpy
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import torch
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from torch import nn
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class MultiHeadedAttention(nn.Module):
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"""Multi-Head Attention layer.
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:param int n_head: the number of head s
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:param int n_feat: the number of features
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:param float dropout_rate: 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 MultiHeadedAttention object."""
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super(MultiHeadedAttention, 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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:param torch.Tensor query: (batch, time1, size)
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:param torch.Tensor key: (batch, time2, size)
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:param torch.Tensor value: (batch, time2, size)
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:return torch.Tensor transformed query, key and value
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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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:param torch.Tensor value: (batch, head, time2, size)
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:param torch.Tensor scores: (batch, head, time1, time2)
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:param torch.Tensor mask: (batch, 1, time2) or (batch, time1, time2)
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:return 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 = float(
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numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
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)
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scores = scores.masked_fill(mask, min_value)
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self.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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self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
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p_attn = self.dropout(self.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) # (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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:param torch.Tensor query: (batch, time1, size)
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:param torch.Tensor key: (batch, time2, size)
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:param torch.Tensor value: (batch, time2, size)
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:param torch.Tensor mask: (batch, 1, time2) or (batch, time1, time2)
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:param torch.nn.Dropout dropout:
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:return torch.Tensor: attention output (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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class RelPositionMultiHeadedAttention(MultiHeadedAttention):
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"""Multi-Head Attention layer with relative position encoding.
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Paper: https://arxiv.org/abs/1901.02860
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:param int n_head: the number of head s
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:param int n_feat: the number of features
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:param float dropout_rate: 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 RelPositionMultiHeadedAttention object."""
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super().__init__(n_head, n_feat, dropout_rate)
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# linear transformation for positional ecoding
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self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
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# these two learnable bias are used in matrix c and matrix d
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
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self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
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torch.nn.init.xavier_uniform_(self.pos_bias_u)
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torch.nn.init.xavier_uniform_(self.pos_bias_v)
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def rel_shift(self, x, zero_triu=False):
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"""Compute relative positinal encoding.
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:param torch.Tensor x: (batch, time, size)
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:param bool zero_triu: return the lower triangular part of the matrix
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"""
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zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
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x_padded = torch.cat([zero_pad, x], dim=-1)
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x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
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x = x_padded[:, :, 1:].view_as(x)
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if zero_triu:
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ones = torch.ones((x.size(2), x.size(3)))
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x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
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return x
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def forward(self, query, key, value, pos_emb, mask):
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"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
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:param torch.Tensor query: (batch, time1, size)
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:param torch.Tensor key: (batch, time2, size)
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:param torch.Tensor value: (batch, time2, size)
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:param torch.Tensor pos_emb: (batch, time1, size)
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:param torch.Tensor mask: (batch, time1, time2)
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:param torch.nn.Dropout dropout:
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:return torch.Tensor: attention output (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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q = q.transpose(1, 2) # (batch, time1, head, d_k)
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n_batch_pos = pos_emb.size(0)
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p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
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p = p.transpose(1, 2) # (batch, head, time1, d_k)
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# (batch, head, time1, d_k)
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q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
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# (batch, head, time1, d_k)
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q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
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# compute attention score
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# first compute matrix a and matrix c
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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# (batch, head, time1, time2)
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matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
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# compute matrix b and matrix d
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# (batch, head, time1, time2)
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matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
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matrix_bd = self.rel_shift(matrix_bd)
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scores = (matrix_ac + matrix_bd) / math.sqrt(
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self.d_k
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) # (batch, head, time1, time2)
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return self.forward_attention(v, scores, mask)
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