时间序列（Time-Series）AutoCorrelation.py代码解析
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
import math
from math import sqrt
import os
class AutoCorrelation(nn.Module):
"""
AutoCorrelation Mechanism with the following two phases:
(1) period-based dependencies discovery
(2) time delay aggregation
This block can replace the self-attention family mechanism seamlessly.
"""
#构造函数
def __init__(self, mask_flag=True, factor=1, scale=None, attention_dropout=0.1, output_attention=False):
super(AutoCorrelation, self).__init__()
self.factor = factor #缩放因子
self.scale = scale #尺度
self.mask_flag = mask_flag #掩码
self.output_attention = output_attention #注意力权重
self.dropout = nn.Dropout(attention_dropout) #注意力机制的dropout率
#实现自相关的训练聚合，该方法首先要找到最重要的时间延迟，然后根据延迟进行聚合
def time_delay_agg_training(self, values, corr):
"""
SpeedUp version of Autocorrelation (a batch-normalization style design)
This is for the training phase.
"""
head = values.shape[1]
channel = values.shape[2]
length = values.shape[3]
# find top k
top_k = int(self.factor * math.log(length))
mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)
index = torch.topk(torch.mean(mean_value, dim=0), top_k, dim=-1)[1]
weights = torch.stack([mean_value[:, index[i]] for i in range(top_k)], dim=-1)
# update corr
tmp_corr = torch.softmax(weights, dim=-1)
# aggregation
tmp_values = values
delays_agg = torch.zeros_like(values).float()
for i in range(top_k):
pattern = torch.roll(tmp_values, -int(index[i]), -1)
delays_agg = delays_agg + pattern * \
(tmp_corr[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length))
return delays_agg
#用于推断阶段，处理值和相关性，以计算时间延迟的聚合
def time_delay_agg_inference(self, values, corr):
"""
SpeedUp version of Autocorrelation (a batch-normalization style design)
This is for the inference phase.
"""
batch = values.shape[0]
head = values.shape[1]
channel = values.shape[2]
length = values.shape[3]
# index init
#init_index = torch.arange(length).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(batch, head, channel, 1).cuda()
init_index = torch.arange(length).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(batch, head, channel, 1)
# find top k
top_k = int(self.factor * math.log(length))
mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)
weights, delay = torch.topk(mean_value, top_k, dim=-1)
# update corr
tmp_corr = torch.softmax(weights, dim=-1)
# aggregation
tmp_values = values.repeat(1, 1, 1, 2)
delays_agg = torch.zeros_like(values).float()
for i in range(top_k):
tmp_delay = init_index + delay[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length)
pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)
delays_agg = delays_agg + pattern * \
(tmp_corr[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length))
return delays_agg
#实现标准自相关的方法，不进行任何类型的优化
def time_delay_agg_full(self, values, corr):
"""
Standard version of Autocorrelation
"""
batch = values.shape[0]
head = values.shape[1]
channel = values.shape[2]
length = values.shape[3]
# index init
init_index = torch.arange(length).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(batch, head, channel, 1).cuda()
# find top k
top_k = int(self.factor * math.log(length))
weights, delay = torch.topk(corr, top_k, dim=-1)
# update corr
tmp_corr = torch.softmax(weights, dim=-1)
# aggregation
tmp_values = values.repeat(1, 1, 1, 2)
delays_agg = torch.zeros_like(values).float()
for i in range(top_k):
tmp_delay = init_index + delay[..., i].unsqueeze(-1)
pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)
delays_agg = delays_agg + pattern * (tmp_corr[..., i].unsqueeze(-1))
return delays_agg
#这是 AutoCorrelation 类的核心，PyTorch在模型前向传播时自动调用它。它接受查询 (queries)，键 (keys)，值 (values) 以及注意力掩码 (attn_mask)，应用傅立叶变换来发现周期性依赖，然后使用聚合函数来处理时间延迟。
def forward(self, queries, keys, values, attn_mask):
B, L, H, E = queries.shape
_, S, _, D = values.shape
if L > S:
zeros = torch.zeros_like(queries[:, :(L - S), :]).float()
values = torch.cat([values, zeros], dim=1)
keys = torch.cat([keys, zeros], dim=1)
else:
values = values[:, :L, :, :]
keys = keys[:, :L, :, :]
# period-based dependencies
q_fft = torch.fft.rfft(queries.permute(0, 2, 3, 1).contiguous(), dim=-1)
k_fft = torch.fft.rfft(keys.permute(0, 2, 3, 1).contiguous(), dim=-1)
res = q_fft * torch.conj(k_fft)
corr = torch.fft.irfft(res, dim=-1)
# time delay agg
if self.training:
V = self.time_delay_agg_training(values.permute(0, 2, 3, 1).contiguous(), corr).permute(0, 3, 1, 2)
else:
V = self.time_delay_agg_inference(values.permute(0, 2, 3, 1).contiguous(), corr).permute(0, 3, 1, 2)
if self.output_attention:
return (V.contiguous(), corr.permute(0, 3, 1, 2))
else:
return (V.contiguous(), None)
#提供完整的注意力层接口
class AutoCorrelationLayer(nn.Module):
def __init__(self, correlation, d_model, n_heads, d_keys=None,
d_values=None):
super(AutoCorrelationLayer, self).__init__()
d_keys = d_keys or (d_model // n_heads)
d_values = d_values or (d_model // n_heads)
self.inner_correlation = correlation
self.query_projection = nn.Linear(d_model, d_keys * n_heads)
self.key_projection = nn.Linear(d_model, d_keys * n_heads)
self.value_projection = nn.Linear(d_model, d_values * n_heads)
self.out_projection = nn.Linear(d_values * n_heads, d_model)
self.n_heads = n_heads
def forward(self, queries, keys, values, attn_mask):
B, L, _ = queries.shape
_, S, _ = keys.shape
H = self.n_heads
queries = self.query_projection(queries).view(B, L, H, -1)
keys = self.key_projection(keys).view(B, S, H, -1)
values = self.value_projection(values).view(B, S, H, -1)
out, attn = self.inner_correlation(
queries,
keys,
values,
attn_mask
)
out = out.view(B, L, -1)
return self.out_projection(out), attn