118 lines
6.8 KiB
Python
118 lines
6.8 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange, repeat
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import numpy as np
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from math import sqrt
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class FullAttention(nn.Module):
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'''
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The Attention operation
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'''
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def __init__(self, scale=None, attention_dropout=0.1):
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super(FullAttention, self).__init__()
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self.scale = scale # attention计算时，缩放，公式中的根号下dk
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self.dropout = nn.Dropout(attention_dropout)
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def forward(self, queries, keys, values):
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B, L, H, E = queries.shape
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_, S, _, D = values.shape
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scale = self.scale or 1./sqrt(E)
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# qkv的计算
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scores = torch.einsum("blhe,bshe->bhls", queries, keys) # 查询序列长度L， 键序列S
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A = self.dropout(torch.softmax(scale * scores, dim=-1)) # 这里训练的时候就drop呀
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V = torch.einsum("bhls,bshd->blhd", A, values)
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return V.contiguous() # 将v放在连续内存中
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class AttentionLayer(nn.Module):
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'''
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The Multi-head Self-Attention (MSA) Layer
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'''
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def __init__(self, d_model, n_heads, d_keys=None, d_values=None, dropout = 0.1):
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super(AttentionLayer, self).__init__()
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d_keys = d_keys or (d_model//n_heads)
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d_values = d_values or (d_model//n_heads)
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self.inner_attention = FullAttention(scale=None, attention_dropout = dropout) # 全链接层
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self.query_projection = nn.Linear(d_model, d_keys * n_heads) # q
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self.key_projection = nn.Linear(d_model, d_keys * n_heads) # k
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self.value_projection = nn.Linear(d_model, d_values * n_heads) # v
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self.out_projection = nn.Linear(d_values * n_heads, d_model) # 输出层
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self.n_heads = n_heads
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def forward(self, queries, keys, values): # 原始输入作为qkv
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B, L, _ = queries.shape
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_, S, _ = keys.shape
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H = self.n_heads
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# 获取qkv矩阵
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queries = self.query_projection(queries).view(B, L, H, -1) # queries（224,28,256） -> (224,28,256) -> (224,28,4,64)
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keys = self.key_projection(keys).view(B, S, H, -1)
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values = self.value_projection(values).view(B, S, H, -1)
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out = self.inner_attention(
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queries,
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keys,
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values,
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)
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out = out.view(B, L, -1) # （224,28,256）
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return self.out_projection(out)
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class TwoStageAttentionLayer(nn.Module):
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'''
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The Two Stage Attention (TSA) Layer
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input/output shape: [batch_size, Data_dim(D), Seg_num(L), d_model]
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'''
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def __init__(self, seg_num, factor, d_model, n_heads, d_ff = None, dropout=0.1):
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super(TwoStageAttentionLayer, self).__init__()
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d_ff = d_ff or 4*d_model # 如果变量 d_ff 的值为 None 或等价于布尔值 False（例如未显式指定），则将 d_ff 设置为 4 * d_model。 如果 d_ff 已经有定义（即非 None 且布尔值为 True），那么 d_ff 保持原值
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self.time_attention = AttentionLayer(d_model, n_heads, dropout = dropout) # 用于时间维度上的注意力计算，捕捉时间序列数据中的时间依赖性
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self.dim_sender = AttentionLayer(d_model, n_heads, dropout = dropout) # 在注意力机制中，dim_sender 是发送方特征的维度，用于生成查询（Query）或键（Key）
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self.dim_receiver = AttentionLayer(d_model, n_heads, dropout = dropout) # 在注意力机制中，dim_receiver 是接收方特征的维度，用于生成值（Value）或接收注意力权重。
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self.router = nn.Parameter(torch.randn(seg_num, factor, d_model)) # (28,10,256)在复杂注意力机制中，router 帮助模型动态分配注意力资源，提升对不同特征的关注能力。
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# nn.Parameter 是一个特殊的张量类型，用于定义可以被优化器（如 SGD、Adam 等）更新的模型参数
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self.dropout = nn.Dropout(dropout)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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self.norm3 = nn.LayerNorm(d_model)
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self.norm4 = nn.LayerNorm(d_model)
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self.MLP1 = nn.Sequential(nn.Linear(d_model, d_ff), #FFN模块；
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nn.GELU(),
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nn.Linear(d_ff, d_model))
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self.MLP2 = nn.Sequential(nn.Linear(d_model, d_ff),
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nn.GELU(),
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nn.Linear(d_ff, d_model))
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def forward(self, x):
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#Cross Time Stage: Directly apply MSA to each dimension
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batch = x.shape[0]
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time_in = rearrange(x, 'b ts_d seg_num d_model -> (b ts_d) seg_num d_model') # 224,28,256 , 其中 224 = 32* 7
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time_enc = self.time_attention(
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time_in, time_in, time_in
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)
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dim_in = time_in + self.dropout(time_enc) # 残差
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dim_in = self.norm1(dim_in) # 层归一化
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dim_in = dim_in + self.dropout(self.MLP1(dim_in)) # FNN + 残差
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dim_in = self.norm2(dim_in) # 层归一化
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#Cross Dimension Stage: use a small set of learnable vectors to aggregate and distribute messages to build the D-to-D connection
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dim_send = rearrange(dim_in, '(b ts_d) seg_num d_model -> (b seg_num) ts_d d_model', b = batch) # (224,28,256) -> (896,7,256) 这样的重排使得每个批次的每个分段（segment）可以独立处理。通过将批量和分段数量结合在一起，后续的操作可以更方便地进行并行计算和消息传递。
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batch_router = repeat(self.router, 'seg_num factor d_model -> (repeat seg_num) factor d_model', repeat = batch) # (28,10,256) => (896,10,256) 28*32 = 896 , 通过重复路由器参数，确保每个批次都使用相同的路由器设置。这种方式可以保持模型的一致性，并减少模型参数的数量，同时允许每个批次的数据使用相同的学习策略。
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dim_buffer = self.dim_sender(batch_router, dim_send, dim_send) # 通过路由器将输入数据（dim_send）进行处理，生成一个中间的缓冲区（dim_buffer）。
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dim_receive = self.dim_receiver(dim_send, dim_buffer, dim_buffer) # 接收来自 dim_sender 的消息，并结合原始输入（dim_send）和缓冲区（dim_buffer）来生成接收的输出（dim_receive）
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dim_enc = dim_send + self.dropout(dim_receive) # 使用残差连接（dim_send + self.dropout(dim_receive)）来结合原始输入和接收的消息。这种方法有助于缓解深度网络中的梯度消失问题，并保持信息流动
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dim_enc = self.norm3(dim_enc) # 来标准化输出，进一步稳定训练过程
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dim_enc = dim_enc + self.dropout(self.MLP2(dim_enc)) # 通过多层感知机（MLP）对编码后的信息进行进一步的变换和学习。再次使用残差连接将 MLP 的输出与 dim_enc 结合
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dim_enc = self.norm4(dim_enc)
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final_out = rearrange(dim_enc, '(b seg_num) ts_d d_model -> b ts_d seg_num d_model', b = batch)
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return final_out
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