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# -*- 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
from espnet2.asr.encoder.Spike_driven.Q_trick import MultiSpike
class Q_MultiHeadedAttention_HierDecay(nn.Module):
"""Implementation of HD-RepSSA_S
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
layer_id (int): Layer ID for decay calculation.
"""
def __init__(self, n_head, n_feat, dropout_rate, layer_id):
"""Construct an MultiHeadedAttention object."""
super(Q_MultiHeadedAttention_HierDecay_v2, 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, bias=False)
self.linear_k = nn.Linear(n_feat, n_feat, bias=False)
self.linear_v = nn.Linear(n_feat, n_feat, bias=False)
self.v_sn = MultiSpike(n_feat)
self.output_sn = MultiSpike(n_feat)
self.linear_out = nn.Linear(n_feat, n_feat)
self.attn = None
self.dropout = nn.Dropout(p=dropout_rate)
# Hierarchical decay calculation
layer_decay = 1 - 2 ** (-5 - layer_id)
decay = torch.log(torch.tensor(layer_decay).repeat(n_head))
self.register_buffer("decay", decay)
def forward_qkv(self, query, key, value, iiter):
"""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.v_sn(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, inner_mask, iiter):
"""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).
inner_mask (torch.Tensor): Inner mask for hierarchical decay.
Returns:
torch.Tensor: Transformed value (#batch, time1, d_model)
weighted by the attention score (#batch, time1, time2).
"""
n_batch = value.size(0)
scores += inner_mask
if mask is not None:
mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
min_value = torch.finfo(scores.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)
x = self.output_sn(x)
return self.linear_out(x) # (batch, time1, d_model)
def forward(self, query, key, value, mask, iiter):
"""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).
"""
slen = query.shape[1] if query.shape[1]==key.shape[1] else mask.shape[1]
index = torch.arange(slen).to(self.decay)
inner_mask = torch.abs(index.view(slen,1) - index.view(1, slen))
inner_mask = inner_mask * self.decay[:, None, None]
q, k, v = self.forward_qkv(query, key, value, iiter)
scores = torch.matmul(q, k.transpose(-2, -1))/ math.sqrt(self.d_k)
return self.forward_attention(v, scores, mask, inner_mask, iiter)
class Q_MultiHeadedAttention_HierDecay_woSoftMax(Q_MultiHeadedAttention):
"""Implementation of HD-RepSSA_S
Args:
n_head (int): The number of heads.
n_feat (int): The number of features.
dropout_rate (float): Dropout rate.
layer_id (int): Layer ID for decay calculation.
"""
def __init__(self, n_head, n_feat, dropout_rate, layer_id):
"""Construct an MultiHeadedAttention object."""
super().__init__(n_head, n_feat, dropout_rate)
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, bias=False)
self.linear_k = nn.Linear(n_feat, n_feat, bias=False)
self.linear_v = nn.Linear(n_feat, n_feat, bias=False)
self.q_sn = MultiSpike(n_feat)
self.k_sn = MultiSpike(n_feat)
self.v_sn = MultiSpike(n_feat)
self.output_sn = MultiSpike(n_feat)
self.linear_out = nn.Linear(n_feat, n_feat)
self.attn = None
self.dropout = nn.Dropout(p=dropout_rate)
layer_decay = 1 - 2 ** (-5 - layer_id)
decay = torch.log(torch.tensor(layer_decay).repeat(n_head))
self.register_buffer("decay", decay)
self.ln = torch.nn.LayerNorm(self.d_k)
def forward_qkv(self, query, key, value, iiter):
"""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.q_sn(self.linear_q(query)).view(n_batch, -1, self.h, self.d_k)
k = self.k_sn(self.linear_k(key)).view(n_batch, -1, self.h, self.d_k)
v = self.v_sn(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, inner_mask, iiter):
"""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)
scores = scores / scores.detach().abs().sum(dim=-1, keepdim=True).clamp(min=1, max=5e4)
if mask is not None:
mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
min_value = torch.finfo(scores.dtype).min
scores = scores.masked_fill(mask, min_value)
self.attn = scores.masked_fill(
mask, 0.0
) # (batch, head, time1, time2)
else:
self.attn = scores # (batch, head, time1, time2)
self.attn = inner_mask * self.attn
p_attn = self.dropout(self.attn)
x = self.ln(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)
x = self.output_sn(x)
return self.linear_out(x) # (batch, time1, d_model)
def forward(self, query, key, value, mask, iiter):
"""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).
"""
slen = query.shape[1] if query.shape[1]==key.shape[1] else mask.shape[1]
index = torch.arange(slen).to(self.decay)
inner_mask = torch.abs(index.view(slen,1) - index.view(1, slen))
inner_mask = torch.exp(inner_mask * self.decay[:, None, None])
q, k, v = self.forward_qkv(query, key, value, iiter)
scores = torch.matmul(q, k.transpose(-2, -1))
return self.forward_attention(v, scores, mask, inner_mask, iiter)
class Q_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(Q_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, bias=False)
self.linear_k = nn.Linear(n_feat, n_feat, bias=False)
self.linear_v = nn.Linear(n_feat, n_feat, bias=False)
self.v_sn = MultiSpike(n_feat)
self.output_sn = MultiSpike(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, iiter):
"""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.v_sn(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, iiter):
"""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 = torch.finfo(scores.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)
x = self.output_sn(x)
return self.linear_out(x) # (batch, time1, d_model)
def forward(self, query, key, value, mask, iiter):
"""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, iiter)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
return self.forward_attention(v, scores, mask, iiter)
class Q_MultiHeadedAttention_woSoftMax(Q_MultiHeadedAttention):
"""Multi-Head Attention layer without SoftMax.
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().__init__(n_head, n_feat, dropout_rate)
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, bias=False)
self.linear_k = nn.Linear(n_feat, n_feat, bias=False)
self.linear_v = nn.Linear(n_feat, n_feat, bias=False)
self.q_sn = MultiSpike(n_feat)
self.k_sn = MultiSpike(n_feat)
self.v_sn = MultiSpike(n_feat)
self.output_sn = MultiSpike(n_feat)
self.linear_out = nn.Linear(n_feat, n_feat)
self.attn = None
# self.dropout = nn.Dropout(p=dropout_rate)
self.ln = torch.nn.LayerNorm(self.d_k)
# self.scale = self.d_k ** -0.5
def forward_qkv(self, query, key, value, iiter):
"""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)
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 = self.q_sn(self.linear_q(query)).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, iiter):
"""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)
scores = scores / scores.detach().abs().sum(dim=-1, keepdim=True).clamp(min=1, max=5e4)
if mask is not None:
mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
min_value = torch.finfo(scores.dtype).min
scores = scores.masked_fill(mask, min_value)
self.attn = scores.masked_fill(
mask, 0.0
) # (batch, head, time1, time2)
else:
self.attn = scores # (batch, head, time1, time2)
p_attn = self.dropout(self.attn)
x = self.ln(torch.matmul(p_attn, value))# (batch, head, time1, d_k)
# x = torch.matmul(p_attn, value) * self.scale# (batch, head, time1, d_k)
x = (
x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
) # (batch, time1, d_model)
x = self.output_sn(x)
return self.linear_out(x) # (batch, time1, d_model)
def forward(self, query, key, value, mask, iiter):
"""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, iiter)
scores = torch.matmul(q, k.transpose(-2, -1))
return self.forward_attention(v, scores, mask, iiter)
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