# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Multi-Head Attention layer definition.""" import math import logging from typing import Optional, Tuple import paddle from paddle import nn from paddle.nn import functional as F from paddle.nn import initializer as I logger = logging.getLogger(__name__) __all__ = ["MultiHeadedAttention", "RelPositionMultiHeadedAttention"] class MultiHeadedAttention(nn.Layer): """Multi-Head Attention layer.""" def __init__(self, n_head: int, n_feat: int, dropout_rate: float): """Construct an MultiHeadedAttention object. Args: n_head (int): The number of heads. n_feat (int): The number of features. dropout_rate (float): Dropout rate. """ 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: paddle.Tensor, key: paddle.Tensor, value: paddle.Tensor ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]: """Transform query, key and value. Args: query (paddle.Tensor): Query tensor (#batch, time1, size). key (paddle.Tensor): Key tensor (#batch, time2, size). value (paddle.Tensor): Value tensor (#batch, time2, size). Returns: paddle.Tensor: Transformed query tensor, size (#batch, n_head, time1, d_k). paddle.Tensor: Transformed key tensor, size (#batch, n_head, time2, d_k). paddle.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([0, 2, 1, 3]) # (batch, head, time1, d_k) k = k.transpose([0, 2, 1, 3]) # (batch, head, time2, d_k) v = v.transpose([0, 2, 1, 3]) # (batch, head, time2, d_k) return q, k, v def forward_attention(self, value: paddle.Tensor, scores: paddle.Tensor, mask: Optional[paddle.Tensor]) -> paddle.Tensor: """Compute attention context vector. Args: value (paddle.Tensor): Transformed value, size (#batch, n_head, time2, d_k). scores (paddle.Tensor): Attention score, size (#batch, n_head, time1, time2). mask (paddle.Tensor): Mask, size (#batch, 1, time2) or (#batch, time1, time2). Returns: paddle.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 = paddle.softmax( scores, axis=-1).masked_fill(mask, 0.0) # (batch, head, time1, time2) else: attn = paddle.softmax( scores, axis=-1) # (batch, head, time1, time2) p_attn = self.dropout(attn) x = paddle.matmul(p_attn, value) # (batch, head, time1, d_k) x = x.transpose([0, 2, 1, 3]).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: paddle.Tensor, key: paddle.Tensor, value: paddle.Tensor, mask: Optional[paddle.Tensor]) -> paddle.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). Returns: torch.Tensor: Output tensor (#batch, time1, d_model). """ q, k, v = self.forward_qkv(query, key, value) scores = paddle.matmul(q, k.transpose( [0, 1, 3, 2])) / math.sqrt(self.d_k) return self.forward_attention(v, scores, mask) class RelPositionMultiHeadedAttention(MultiHeadedAttention): """Multi-Head Attention layer with relative position encoding.""" def __init__(self, n_head, n_feat, dropout_rate): """Construct an RelPositionMultiHeadedAttention object. 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. """ super().__init__(n_head, n_feat, dropout_rate) # linear transformation for positional encoding self.linear_pos = nn.Linear(n_feat, n_feat, bias_attr=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, pos_emb: torch.Tensor, mask: Optional[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). pos_emb (torch.Tensor): Positional embedding tensor (#batch, time2, size). mask (torch.Tensor): Mask tensor (#batch, 1, time2) or (#batch, time1, time2). 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)