import torch import torch.nn as nn import torch.nn.functional as F from layers.SelfAttention_Family import FullAttention, AttentionLayer from layers.Embed import DataEmbedding_inverted, PositionalEmbedding import numpy as np class FlattenHead(nn.Module): def __init__(self, n_vars, nf, target_window, head_dropout=0): super().__init__() self.n_vars = n_vars self.flatten = nn.Flatten(start_dim=-2) self.linear = nn.Linear(nf, target_window) self.dropout = nn.Dropout(head_dropout) def forward(self, x): # x: [bs x nvars x d_model x patch_num] x = self.flatten(x) x = self.linear(x) x = self.dropout(x) return x class EnEmbedding(nn.Module): def __init__(self, n_vars, d_model, patch_len, dropout): super(EnEmbedding, self).__init__() # Patching self.patch_len = patch_len self.value_embedding = nn.Linear(patch_len, d_model, bias=False) self.glb_token = nn.Parameter(torch.randn(1, n_vars, 1, d_model)) self.position_embedding = PositionalEmbedding(d_model) self.dropout = nn.Dropout(dropout) def forward(self, x): # do patching n_vars = x.shape[1] glb = self.glb_token.repeat((x.shape[0], 1, 1, 1)) x = x.unfold(dimension=-1, size=self.patch_len, step=self.patch_len) x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) # Input encoding x = self.value_embedding(x) + self.position_embedding(x) x = torch.reshape(x, (-1, n_vars, x.shape[-2], x.shape[-1])) x = torch.cat([x, glb], dim=2) x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) return self.dropout(x), n_vars class Encoder(nn.Module): def __init__(self, layers, norm_layer=None, projection=None): super(Encoder, self).__init__() self.layers = nn.ModuleList(layers) self.norm = norm_layer self.projection = projection def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None): attns = [] for layer in self.layers: x, attn = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask, tau=tau, delta=delta) attns.append(attn) if self.norm is not None: x = self.norm(x) if self.projection is not None: x = self.projection(x) return x, attns class EncoderLayer(nn.Module): def __init__(self, self_attention, cross_attention, d_model, d_ff=None, dropout=0.1, activation="relu"): super(EncoderLayer, self).__init__() d_ff = d_ff or 4 * d_model self.self_attention = self_attention self.cross_attention = cross_attention self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1) self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1) self.norm1 = nn.LayerNorm(d_model) self.norm2 = nn.LayerNorm(d_model) self.norm3 = nn.LayerNorm(d_model) self.dropout = nn.Dropout(dropout) self.activation = F.relu if activation == "relu" else F.gelu def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None): B, L, D = cross.shape x = x + self.dropout(self.self_attention( x, x, x, attn_mask=x_mask, tau=tau, delta=None )[0]) x = self.norm1(x) x_glb_ori = x[:, -1, :].unsqueeze(1) x_glb = torch.reshape(x_glb_ori, (B, -1, D)) # <<< FIX: Capture attention weights from cross_attention >>> x_glb_attn, cross_attn_weights = self.cross_attention( x_glb, cross, cross, attn_mask=cross_mask, tau=tau, delta=delta ) x_glb_attn = self.dropout(x_glb_attn) x_glb_attn = torch.reshape(x_glb_attn, (x_glb_attn.shape[0] * x_glb_attn.shape[1], x_glb_attn.shape[2])).unsqueeze(1) x_glb = x_glb_ori + x_glb_attn x_glb = self.norm2(x_glb) y = x = torch.cat([x[:, :-1, :], x_glb], dim=1) y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1)))) y = self.dropout(self.conv2(y).transpose(-1, 1)) # <<< FIX: Return captured attention weights >>> return self.norm3(x + y), cross_attn_weights class Model(nn.Module): def __init__(self, configs): super(Model, self).__init__() self.task_name = configs.task_name self.features = configs.features self.seq_len = configs.seq_len self.pred_len = configs.pred_len self.use_norm = configs.use_norm self.patch_len = configs.patch_len self.patch_num = int(configs.seq_len // configs.patch_len) self.n_vars = 1 if configs.features == 'MS' else configs.enc_in # Embedding self.en_embedding = EnEmbedding(self.n_vars, configs.d_model, self.patch_len, configs.dropout) self.ex_embedding = DataEmbedding_inverted(configs.seq_len, configs.d_model, configs.embed, configs.freq, configs.dropout) # Encoder-only architecture self.encoder = Encoder( [ EncoderLayer( AttentionLayer( # Self-Attention FullAttention(False, configs.factor, attention_dropout=configs.dropout, output_attention=False), # This remains False configs.d_model, configs.n_heads), AttentionLayer( # Cross-Attention FullAttention(False, configs.factor, attention_dropout=configs.dropout, output_attention=True), # <<< FIX: This is set to True >>> configs.d_model, configs.n_heads), configs.d_model, configs.d_ff, dropout=configs.dropout, activation=configs.activation, ) for l in range(configs.e_layers) ], norm_layer=torch.nn.LayerNorm(configs.d_model) ) self.head_nf = configs.d_model * (self.patch_num + 1) self.head = FlattenHead(configs.enc_in, self.head_nf, configs.pred_len, head_dropout=configs.dropout) def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): if self.use_norm: means = x_enc.mean(1, keepdim=True).detach() x_enc = x_enc - means stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) x_enc /= stdev _, _, N = x_enc.shape en_embed, n_vars = self.en_embedding(x_enc[:, :, -1].unsqueeze(-1).permute(0, 2, 1)) ex_embed = self.ex_embedding(x_enc[:, :, :-1], x_mark_enc) # <<< FIX: Capture attentions from encoder >>> enc_out, attns = self.encoder(en_embed, ex_embed) enc_out = torch.reshape( enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1])) enc_out = enc_out.permute(0, 1, 3, 2) dec_out = self.head(enc_out) dec_out = dec_out.permute(0, 2, 1) if self.use_norm: dec_out = dec_out * (stdev[:, 0, -1:].unsqueeze(1).repeat(1, self.pred_len, 1)) dec_out = dec_out + (means[:, 0, -1:].unsqueeze(1).repeat(1, self.pred_len, 1)) # <<< FIX: Return attentions >>> return dec_out, attns def forecast_multi(self, x_enc, x_mark_enc, x_dec, x_mark_dec): if self.use_norm: means = x_enc.mean(1, keepdim=True).detach() x_enc = x_enc - means stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) x_enc /= stdev _, _, N = x_enc.shape en_embed, n_vars = self.en_embedding(x_enc.permute(0, 2, 1)) ex_embed = self.ex_embedding(x_enc, x_mark_enc) # <<< FIX: Capture attentions from encoder >>> enc_out, attns = self.encoder(en_embed, ex_embed) enc_out = torch.reshape( enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1])) enc_out = enc_out.permute(0, 1, 3, 2) dec_out = self.head(enc_out) dec_out = dec_out.permute(0, 2, 1) if self.use_norm: dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1)) dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1)) # <<< FIX: Return attentions >>> return dec_out, attns def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None): if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': # <<< FIX: Capture attentions from forecast methods >>> if self.features == 'M': dec_out, attns = self.forecast_multi(x_enc, x_mark_enc, x_dec, x_mark_dec) else: dec_out, attns = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec) # <<< FIX: Return predictions and attentions as a tuple >>> return dec_out[:, -self.pred_len:, :], attns else: return None