PoeticTTS / Layers /Attention.py
Florian Lux
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# Written by Shigeki Karita, 2019
# Published under Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
# Adapted by Florian Lux, 2021
"""Multi-Head Attention layer definition."""
import math
import numpy
import torch
from torch import nn
from Utility.utils import make_non_pad_mask
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, 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.
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 (#batch, n_head, time1, d_k).
torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
torch.Tensor: Transformed value tensor (#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, scores, mask):
"""
Compute attention context vector.
Args:
value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
mask (torch.Tensor): Mask (#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)
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.
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 = 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.
Details can be found in https://github.com/espnet/espnet/pull/2816.
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.
zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
"""
def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
"""Construct an RelPositionMultiHeadedAttention object."""
super().__init__(n_head, n_feat, dropout_rate)
self.zero_triu = zero_triu
# 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):
"""
Compute relative positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
time1 means the length of query vector.
Returns:
torch.Tensor: Output tensor.
"""
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)[:, :, :, : x.size(-1) // 2 + 1] # only keep the positions from 0 to time2
if self.zero_triu:
ones = torch.ones((x.size(2), x.size(3)), device=x.device)
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.
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, 2*time1-1, 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, 2*time1-1, 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, 2*time1-1)
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)
class GuidedAttentionLoss(torch.nn.Module):
"""
Guided attention loss function module.
This module calculates the guided attention loss described
in `Efficiently Trainable Text-to-Speech System Based
on Deep Convolutional Networks with Guided Attention`_,
which forces the attention to be diagonal.
.. _`Efficiently Trainable Text-to-Speech System
Based on Deep Convolutional Networks with Guided Attention`:
https://arxiv.org/abs/1710.08969
"""
def __init__(self, sigma=0.4, alpha=1.0):
"""
Initialize guided attention loss module.
Args:
sigma (float, optional): Standard deviation to control
how close attention to a diagonal.
alpha (float, optional): Scaling coefficient (lambda).
reset_always (bool, optional): Whether to always reset masks.
"""
super(GuidedAttentionLoss, self).__init__()
self.sigma = sigma
self.alpha = alpha
self.guided_attn_masks = None
self.masks = None
def _reset_masks(self):
self.guided_attn_masks = None
self.masks = None
def forward(self, att_ws, ilens, olens):
"""
Calculate forward propagation.
Args:
att_ws (Tensor): Batch of attention weights (B, T_max_out, T_max_in).
ilens (LongTensor): Batch of input lenghts (B,).
olens (LongTensor): Batch of output lenghts (B,).
Returns:
Tensor: Guided attention loss value.
"""
self._reset_masks()
self.guided_attn_masks = self._make_guided_attention_masks(ilens, olens).to(att_ws.device)
self.masks = self._make_masks(ilens, olens).to(att_ws.device)
losses = self.guided_attn_masks * att_ws
loss = torch.mean(losses.masked_select(self.masks))
self._reset_masks()
return self.alpha * loss
def _make_guided_attention_masks(self, ilens, olens):
n_batches = len(ilens)
max_ilen = max(ilens)
max_olen = max(olens)
guided_attn_masks = torch.zeros((n_batches, max_olen, max_ilen), device=ilens.device)
for idx, (ilen, olen) in enumerate(zip(ilens, olens)):
guided_attn_masks[idx, :olen, :ilen] = self._make_guided_attention_mask(ilen, olen, self.sigma)
return guided_attn_masks
@staticmethod
def _make_guided_attention_mask(ilen, olen, sigma):
"""
Make guided attention mask.
"""
grid_x, grid_y = torch.meshgrid(torch.arange(olen, device=olen.device).float(), torch.arange(ilen, device=ilen.device).float())
return 1.0 - torch.exp(-((grid_y / ilen - grid_x / olen) ** 2) / (2 * (sigma ** 2)))
@staticmethod
def _make_masks(ilens, olens):
"""
Make masks indicating non-padded part.
Args:
ilens (LongTensor or List): Batch of lengths (B,).
olens (LongTensor or List): Batch of lengths (B,).
Returns:
Tensor: Mask tensor indicating non-padded part.
dtype=torch.uint8 in PyTorch 1.2-
dtype=torch.bool in PyTorch 1.2+ (including 1.2)
"""
in_masks = make_non_pad_mask(ilens, device=ilens.device) # (B, T_in)
out_masks = make_non_pad_mask(olens, device=olens.device) # (B, T_out)
return out_masks.unsqueeze(-1) & in_masks.unsqueeze(-2) # (B, T_out, T_in)
class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss):
"""
Guided attention loss function module for multi head attention.
Args:
sigma (float, optional): Standard deviation to control
how close attention to a diagonal.
alpha (float, optional): Scaling coefficient (lambda).
reset_always (bool, optional): Whether to always reset masks.
"""
def forward(self, att_ws, ilens, olens):
"""
Calculate forward propagation.
Args:
att_ws (Tensor):
Batch of multi head attention weights (B, H, T_max_out, T_max_in).
ilens (LongTensor): Batch of input lenghts (B,).
olens (LongTensor): Batch of output lenghts (B,).
Returns:
Tensor: Guided attention loss value.
"""
if self.guided_attn_masks is None:
self.guided_attn_masks = (self._make_guided_attention_masks(ilens, olens).to(att_ws.device).unsqueeze(1))
if self.masks is None:
self.masks = self._make_masks(ilens, olens).to(att_ws.device).unsqueeze(1)
losses = self.guided_attn_masks * att_ws
loss = torch.mean(losses.masked_select(self.masks))
if self.reset_always:
self._reset_masks()
return self.alpha * loss