File size: 20,866 Bytes
be639cb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 |
import torch
import torch.nn as nn
import numpy as np
# ---------------- Meta Components ----------------
class MetaNet(nn.Module):
def __init__(self, input_dim, xprime_dim):
super().__init__()
self.layer1 = nn.Linear(1, input_dim * xprime_dim)
self.layer2 = nn.Linear(input_dim * xprime_dim, input_dim * xprime_dim)
self.input_dim = input_dim
self.xprime_dim = xprime_dim
def forward(self, x_feat): # x_feat: [B, 1]
B = x_feat.size(0)
out = torch.tanh(self.layer1(x_feat)) # [B, 32]
out = torch.tanh(self.layer2(out)) # [B, input_dim * xprime_dim]
return out.view(B, self.input_dim, self.xprime_dim) # [B, input_dim, xprime_dim]
class GatingNet(nn.Module):
def __init__(self, hidden_size, num_experts=3):
super().__init__()
self.layer1 = nn.Linear(hidden_size, hidden_size)
self.layer2 = nn.Linear(hidden_size, num_experts)
def forward(self, h, epoch=None, top_k=None, warmup_epochs=0):
logits = self.layer2(torch.tanh(self.layer1(h))) # [B, num_experts]
if (epoch is None) or (top_k is None) or (epoch < warmup_epochs):
return torch.softmax(logits, dim=-1)
topk_vals, topk_idx = torch.topk(logits, k=top_k, dim=-1)
mask = torch.zeros_like(logits).scatter(1, topk_idx, 1.0)
masked_logits = logits.masked_fill(mask == 0, float('-inf'))
return torch.softmax(masked_logits, dim=-1)
class MetaTransformBlock(nn.Module):
def __init__(self, xprime_dim, num_experts=3, input_dim=1, hidden_size=64):
super().__init__()
self.meta_temp = MetaNet(input_dim, xprime_dim)
self.meta_work = MetaNet(input_dim, xprime_dim)
self.meta_season = MetaNet(input_dim, xprime_dim)
self.gating = GatingNet(hidden_size, num_experts) # Use hidden_size here
self.ln = nn.LayerNorm([input_dim, xprime_dim])
self.theta0 = nn.Parameter(torch.zeros(1, input_dim, xprime_dim))
def forward(self, h_prev_rnn, x_l, x_t, x_w, x_s, epoch=None, top_k=None, warmup_epochs=0):
w_temp = self.ln(self.meta_temp(x_t)) # [B, input_dim, xprime_dim]
w_work = self.ln(self.meta_work(x_w)) # [B, input_dim, xprime_dim]
w_seas = self.ln(self.meta_season(x_s)) # [B, input_dim, xprime_dim]
gates = self.gating(h_prev_rnn, epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs) # [B, num_experts]
W_experts = torch.stack([w_temp, w_work, w_seas], dim=1) # [B, num_experts, input_dim, xprime_dim]
gates_expanded = gates.view(gates.size(0), gates.size(1), 1, 1) # [B, num_experts, 1, 1]
theta_dynamic = (W_experts * gates_expanded).sum(dim=1) # [B, input_dim, xprime_dim]
theta = theta_dynamic + self.theta0 # [B, input_dim, xprime_dim]
x_prime = torch.bmm(x_l.unsqueeze(1), theta).squeeze(1) # [B, xprime_dim]
return x_prime, theta
# ---------------- Encoder ----------------
class Encoder_meta(nn.Module):
def __init__(self, xprime_dim, hidden_size, num_layers=1, dropout=0.1):
super().__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.rnn = nn.GRU(xprime_dim, hidden_size, num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0)
def forward(self, x_l_seq, x_t_seq, x_w_seq, x_s_seq,
transform_block, h_init=None, epoch=None, top_k=None, warmup_epochs=0):
B, T, _ = x_l_seq.shape
h_rnn = torch.zeros(self.num_layers, B, self.hidden_size, device=x_l_seq.device) if h_init is None else h_init
for t in range(T):
h_for_meta = h_rnn[-1]
x_prime, _ = transform_block(h_for_meta,
x_l_seq[:, t], x_t_seq[:, t],
x_w_seq[:, t], x_s_seq[:, t],
epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
x_prime = x_prime.unsqueeze(1)
_, h_rnn = self.rnn(x_prime, h_rnn)
return h_rnn # [num_layers, B, hidden_size]
# ---------------- Decoder ----------------
class Decoder_meta(nn.Module):
def __init__(self, xprime_dim, latent_size, output_len, output_dim=1,
num_layers=1, dropout=0.1, hidden_size=None):
super().__init__()
self.latent_size = latent_size
self.output_len = output_len
self.output_dim = output_dim
self.num_layers = num_layers
self.rnn = nn.GRU(xprime_dim, latent_size, num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0)
self.head = nn.Linear(latent_size, output_len * output_dim)
# Layer-wise projection from encoder hidden_size → decoder latent_size
assert hidden_size is not None, "You must provide hidden_size for projection."
self.project = nn.ModuleList([
nn.Linear(hidden_size, latent_size) for _ in range(num_layers)
])
def forward(self, x_l_seq, x_t_seq, x_w_seq, x_s_seq,
h_init, transform_block,
epoch=None, top_k=None, warmup_epochs=0):
B, L, _ = x_l_seq.shape
# Project each layer of encoder hidden state to latent size
h_rnn = torch.stack([
self.project[i](h_init[i]) for i in range(self.num_layers)
], dim=0) # [num_layers, B, latent_size]
preds = []
# Step 0
h_last = h_rnn[-1] # [B, latent_size]
pred_0 = self.head(h_last).view(B, self.output_len, self.output_dim)
preds.append(pred_0.unsqueeze(1)) # [B, 1, output_len, output_dim]
# Steps 1 to L
for t in range(L):
h_for_meta = h_rnn[-1]
x_prime, _ = transform_block(h_for_meta,
x_l_seq[:, t], x_t_seq[:, t],
x_w_seq[:, t], x_s_seq[:, t],
epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
x_prime = x_prime.unsqueeze(1)
out_t, h_rnn = self.rnn(x_prime, h_rnn)
pred_t = self.head(out_t.squeeze(1)).view(B, self.output_len, self.output_dim)
preds.append(pred_t.unsqueeze(1))
preds = torch.cat(preds, dim=1) # [B, L+1, output_len, output_dim]
return preds
# ---------------- Full Seq2Seq Model ----------------
class Seq2Seq_meta(nn.Module):
def __init__(self, xprime_dim, input_dim, hidden_size, latent_size,
output_len, output_dim=1, num_layers=1, dropout=0.1, num_experts=3):
super().__init__()
self.transform_enc = MetaTransformBlock(
xprime_dim=xprime_dim,
num_experts=num_experts,
input_dim=input_dim,
hidden_size=hidden_size # encoder hidden_size
)
self.transform_dec = MetaTransformBlock(
xprime_dim=xprime_dim,
num_experts=num_experts,
input_dim=input_dim,
hidden_size=latent_size # decoder latent_size
)
self.encoder = Encoder_meta(
xprime_dim=xprime_dim,
hidden_size=hidden_size,
num_layers=num_layers,
dropout=dropout)
self.decoder = Decoder_meta(
xprime_dim=xprime_dim,
latent_size=latent_size,
output_len=output_len,
output_dim=output_dim,
num_layers=num_layers,
dropout=dropout,
hidden_size=hidden_size # for projection from encoder hidden
)
def forward(self,
enc_l, enc_t, enc_w, enc_s,
dec_l, dec_t, dec_w, dec_s,
epoch=None, top_k=None, warmup_epochs=0):
h_enc = self.encoder(enc_l, enc_t, enc_w, enc_s,
transform_block=self.transform_enc,
epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
preds = self.decoder(dec_l, dec_t, dec_w, dec_s,
h_init=h_enc,
transform_block=self.transform_dec,
epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
return preds
# ---------------- Encoder ----------------
class VariationalEncoder_meta(nn.Module):
def __init__(self, xprime_dim, hidden_size, latent_size, num_layers=1, dropout=0.1):
super().__init__()
self.hidden_size = hidden_size
self.latent_size = latent_size
self.num_layers = num_layers
self.rnn = nn.GRU(xprime_dim, hidden_size, num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0)
self.mu_layer = nn.Linear(hidden_size, latent_size)
self.logvar_layer = nn.Linear(hidden_size, latent_size)
def forward(self, x_l_seq, x_t_seq, x_w_seq, x_s_seq,
transform_block, h_init=None, epoch=None, top_k=None, warmup_epochs=0):
B, T, _ = x_l_seq.shape
h_rnn = torch.zeros(self.num_layers, B, self.hidden_size, device=x_l_seq.device) if h_init is None else h_init
for t in range(T):
h_for_meta = h_rnn[-1]
x_prime, _ = transform_block(h_for_meta,
x_l_seq[:, t], x_t_seq[:, t],
x_w_seq[:, t], x_s_seq[:, t],
epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
x_prime = x_prime.unsqueeze(1)
_, h_rnn = self.rnn(x_prime, h_rnn)
h_last = h_rnn[-1] # [B, hidden_size]
mu = self.mu_layer(h_last)
logvar = self.logvar_layer(h_last)
return mu, logvar
class VariationalDecoder_meta_predvar(nn.Module):
def __init__(self, xprime_dim, latent_size, output_len, output_dim=1,
num_layers=1, dropout=0.1):
super().__init__()
self.latent_size = latent_size
self.output_len = output_len
self.output_dim = output_dim
self.num_layers = num_layers
self.rnn = nn.GRU(xprime_dim, latent_size, num_layers,
batch_first=True,
dropout=dropout if num_layers > 1 else 0)
# Separate heads for mean and log-variance
self.head_mu = nn.Linear(latent_size, output_len * output_dim)
self.head_logvar = nn.Linear(latent_size, output_len * output_dim)
def forward(self, x_l_seq, x_t_seq, x_w_seq, x_s_seq,
z_latent, transform_block,
epoch=None, top_k=None, warmup_epochs=0):
B, L, _ = x_l_seq.shape
h_rnn = z_latent.unsqueeze(0).repeat(self.num_layers, 1, 1) # [num_layers, B, latent_size]
mu_preds = []
logvar_preds = []
# Step 0
h_last = h_rnn[-1]
mu_0 = self.head_mu(h_last).view(B, self.output_len, self.output_dim)
logvar_0 = self.head_logvar(h_last).view(B, self.output_len, self.output_dim)
mu_preds.append(mu_0.unsqueeze(1)) # [B, 1, output_len, output_dim]
logvar_preds.append(logvar_0.unsqueeze(1)) # same shape
# Steps 1 to L
for t in range(L):
h_for_meta = h_rnn[-1]
x_prime, _ = transform_block(h_for_meta,
x_l_seq[:, t], x_t_seq[:, t],
x_w_seq[:, t], x_s_seq[:, t],
epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
x_prime = x_prime.unsqueeze(1)
out_t, h_rnn = self.rnn(x_prime, h_rnn)
mu_t = self.head_mu(out_t.squeeze(1)).view(B, self.output_len, self.output_dim)
logvar_t = self.head_logvar(out_t.squeeze(1)).view(B, self.output_len, self.output_dim)
mu_preds.append(mu_t.unsqueeze(1))
logvar_preds.append(logvar_t.unsqueeze(1))
# Stack across time
mu_preds = torch.cat(mu_preds, dim=1) # [B, L+1, output_len, output_dim]
logvar_preds = torch.cat(logvar_preds, dim=1) # same shape
return mu_preds, logvar_preds
# ---------------- Full Seq2Seq Model ----------------
class VariationalSeq2Seq_meta(nn.Module):
def __init__(self, xprime_dim, input_dim, hidden_size, latent_size,
output_len, output_dim=1, num_layers=1, dropout=0.1, num_experts=3):
super().__init__()
self.transform_enc = MetaTransformBlock(
xprime_dim=xprime_dim,
num_experts=num_experts,
input_dim=input_dim,
hidden_size=hidden_size # encoder hidden size
)
self.transform_dec = MetaTransformBlock(
xprime_dim=xprime_dim,
num_experts=num_experts,
input_dim=input_dim,
hidden_size=latent_size # decoder latent size
)
self.encoder = VariationalEncoder_meta(
xprime_dim=xprime_dim,
hidden_size=hidden_size,
latent_size=latent_size,
num_layers=num_layers,
dropout=dropout
)
# self.decoder = VariationalDecoder_meta_fixvar(
# xprime_dim=xprime_dim,
# latent_size=latent_size,
# output_len=output_len,
# output_dim=output_dim,
# num_layers=num_layers,
# dropout=dropout
# )
self.decoder = VariationalDecoder_meta_predvar(
xprime_dim=xprime_dim,
latent_size=latent_size,
output_len=output_len,
output_dim=output_dim,
num_layers=num_layers,
dropout=dropout
)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def forward(self,
enc_l, enc_t, enc_w, enc_s,
dec_l, dec_t, dec_w, dec_s,
epoch=None, top_k=None, warmup_epochs=0):
mu, logvar = self.encoder(enc_l, enc_t, enc_w, enc_s,
transform_block=self.transform_enc,
epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
z = self.reparameterize(mu, logvar) # [B, latent_size]
mu_preds, logvar_preds = self.decoder(dec_l, dec_t, dec_w, dec_s,
z_latent=z,
transform_block=self.transform_dec,
epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
return mu_preds, logvar_preds, mu, logvar
# # ---------------- Decoder v1: fixed variance ----------------
# class VariationalDecoder_meta_fixvar(nn.Module):
# def __init__(self, xprime_dim, latent_size, output_len, output_dim=1,
# num_layers=1, dropout=0.1, fixed_var_value=0.01):
# super().__init__()
# self.latent_size = latent_size
# self.output_len = output_len
# self.output_dim = output_dim
# self.num_layers = num_layers
#
# self.rnn = nn.GRU(xprime_dim, latent_size, num_layers,
# batch_first=True,
# dropout=dropout if num_layers > 1 else 0)
#
# self.head = nn.Linear(latent_size, output_len * output_dim)
#
# # Fixed log-variance (scalar)
# self.fixed_logvar = torch.tensor(np.log(fixed_var_value), dtype=torch.float32)
#
# def forward(self, x_l_seq, x_t_seq, x_w_seq, x_s_seq,
# z_latent, transform_block,
# epoch=None, top_k=None, warmup_epochs=0):
# B, L, _ = x_l_seq.shape
#
# h_rnn = z_latent.unsqueeze(0).repeat(self.num_layers, 1, 1) # [num_layers, B, latent_size]
#
# mu_preds = []
#
# # Step 0
# h_last = h_rnn[-1]
# mu_0 = self.head(h_last).view(B, self.output_len, self.output_dim)
# mu_preds.append(mu_0.unsqueeze(1)) # [B, 1, output_len, output_dim]
#
# # Steps 1 to L
# for t in range(L):
# h_for_meta = h_rnn[-1]
# x_prime, _ = transform_block(h_for_meta,
# x_l_seq[:, t], x_t_seq[:, t],
# x_w_seq[:, t], x_s_seq[:, t],
# epoch=epoch, top_k=top_k, warmup_epochs=warmup_epochs)
# x_prime = x_prime.unsqueeze(1)
# out_t, h_rnn = self.rnn(x_prime, h_rnn)
#
# mu_t = self.head(out_t.squeeze(1)).view(B, self.output_len, self.output_dim)
# mu_preds.append(mu_t.unsqueeze(1))
#
# mu_preds = torch.cat(mu_preds, dim=1) # [B, L+1, output_len, output_dim]
#
# # Now create logvar_preds: same shape, filled with fixed_logvar
# logvar_preds = self.fixed_logvar.expand_as(mu_preds).to(mu_preds.device)
#
# return mu_preds, logvar_preds
#
# ---------------- Decoder v2: predicted variance ----------------
#
# ## LSTM
# import torch, torch.nn as nn
# import torch.nn.functional as F
#
# class LSTM_Baseline(nn.Module):
# """
# Simple encoder‑decoder LSTM baseline.
# • All four modal inputs (load, temp, workday, season) are concatenated along feature dim
# so the external information is still available, but the model is otherwise “plain”.
# • The forward signature (extra **kwargs) lets the old training loop pass epoch/top_k/warmup
# without breaking anything.
# """
# def __init__(
# self,
# input_dim: int, # 1 → only the scalar value of each channel
# hidden_size: int, # e.g. 64
# output_len: int, # prediction horizon (3)
# output_dim: int = 1, # scalar prediction
# num_layers: int = 2,
# dropout: float = 0.1,
# ):
# super().__init__()
# self.hidden_size = hidden_size
# self.output_len = output_len
# self.output_dim = output_dim
# self.num_layers = num_layers
#
# # encoder & decoder
# self.encoder = nn.LSTM(
# input_size = input_dim * 4, # four channels concatenated
# hidden_size = hidden_size,
# num_layers = num_layers,
# batch_first = True,
# dropout = dropout if num_layers > 1 else 0.0,
# )
# self.decoder = nn.LSTM(
# input_size = input_dim * 4,
# hidden_size = hidden_size,
# num_layers = num_layers,
# batch_first = True,
# dropout = dropout if num_layers > 1 else 0.0,
# )
#
# self.out_layer = nn.Linear(hidden_size, output_dim)
#
# def forward(
# self,
# enc_l, enc_t, enc_w, enc_s,
# dec_l, dec_t, dec_w, dec_s,
# *unused, **unused_kw,
# ):
# """
# enc_* : [B, Lenc, 1] (load / temp / workday / season)
# dec_* : [B, Ldec, 1]
# return: [B, Lenc+1, output_len, 1] (to keep your downstream code intact)
# """
# B, Lenc, _ = enc_l.shape
#
# # 1) ---------- Encode ----------
# enc_in = torch.cat([enc_l, enc_t, enc_w, enc_s], dim=-1) # [B, Lenc, 4]
# _, (h_n, c_n) = self.encoder(enc_in) # carry hidden to decoder
#
# # 2) ---------- Decode ----------
# Ldec = dec_l.size(1) # usually 1 step (the teacher‑force token)
# dec_in = torch.cat([dec_l, dec_t, dec_w, dec_s], dim=-1) # [B, Ldec, 4]
# dec_out, _ = self.decoder(dec_in, (h_n, c_n)) # [B, Ldec, H]
# y0 = self.out_layer(dec_out[:, -1]) # last step → [B, output_dim]
#
# # 3) ---------- Autoregressive forecast ----------
# preds = []
# ht, ct = h_n, c_n
# xt = dec_in[:, -1] # start token
# for _ in range(self.output_len):
# xt = xt.unsqueeze(1) # [B,1,4]
# out, (ht, ct) = self.decoder(xt, (ht, ct)) # [B,1,H]
# yt = self.out_layer(out.squeeze(1)) # [B, output_dim]
# preds.append(yt)
# # next decoder input = last prediction repeated over 4 channels
# xt = torch.cat([yt]*4, dim=-1)
#
# # 3) ---------- Autoregressive forecast ----------
# preds = torch.stack(preds, dim=1) # [B, H, 1]
#
# # 4) ---------- match original return shape ----------
# seq_len_y = enc_l.size(1) - self.output_len + 1 # <-- NEW: 168‑>166
# preds = preds.unsqueeze(1).repeat(1, seq_len_y, 1, 1)
# return preds # [B, 166, 3, 1]
#
|