mygpt.py

   1 #!/usr/bin/env python
   2
   3 # Any copyright is dedicated to the Public Domain.
   4 # https://creativecommons.org/publicdomain/zero/1.0/
   5
   6 # Written by Francois Fleuret <francois@fleuret.org>
   7
   8 # This is an implementation from scratch of a "GPT", that is a model
   9 # composed of several causal self-attention blocks. It is equipped
  10 # with a caching mechanism for keys and values to avoid a O(N^3) cost
  11 # for auto-regression.
  12
  13 # This implementation is equipped with RNN layers to replace the MHA
  14
  15 import math, warnings
  16
  17 import torch, einops
  18
  19 from torch import nn
  20 from torch.nn import functional as F
  21
  22 import ffutils
  23
  24 # import memload
  25
  26 ######################################################################
  27
  28 # A BracketedSequence is a BxTx... tensor with a first and a nb time
  29 # steps to compute.
  30
  31 # Modules able to process it expect that they will have to process a
  32 # first bracket starting at t=0, followed by a succession of brackets
  33 # that move forward in time, do not overlap, and cover the axis T with
  34 # no holes.
  35 #
  36 # Although it is more general, for a classical prompt-conditioned
  37 # auto-regressive process it will be a first bracket starting at 0 and
  38 # of arbitrary length for the "prompt", followed by brackets of length
  39 # 1 for the successive tokens.
  40 #
  41 # Modules able to process brackets may implement a cache that is
  42 # resetted when init_cache is True
  43
  44
  45 class BracketedSequence:
  46     def __init__(self, x, first=None, nb=None, init_cache=None):
  47         self.x = x
  48         assert (first is None and nb is None and init_cache is None) or (
  49             first is not None and nb is not None and init_cache is not None
  50         )
  51
  52         self.first = 0 if first is None else first
  53         self.nb = x.size(1) if nb is None else nb
  54         self.init_cache = True if init_cache is None else init_cache
  55
  56     def slice(self):
  57         return self.x[:, self.first : self.first + self.nb]
  58
  59     def complete(self):
  60         return self.first == 0 and self.nb == self.x.size(1)
  61
  62
  63 ######################################################################
  64
  65
  66 class CacheWrapper(nn.Module):
  67     def __init__(self, *f):
  68         super().__init__()
  69         self.f = f[0] if len(f) == 1 else nn.Sequential(*f)
  70
  71     def forward(self, bs):
  72         if bs.init_cache:
  73             y = self.f(bs.slice())
  74             self.cache_y = y.new(*((y.size(0), bs.x.size(1)) + y.size()[2:]))
  75             self.cache_y[:, bs.first : bs.first + bs.nb] = y
  76         else:
  77             assert tuple(bs.x.size()[:2]) == tuple(self.cache_y.size()[:2])
  78             assert bs.first + bs.nb <= self.cache_y.size(1)
  79             self.cache_y[:, bs.first : bs.first + bs.nb] = self.f(bs.slice())
  80
  81         return BracketedSequence(self.cache_y, bs.first, bs.nb, bs.init_cache)
  82
  83
  84 ##############################
  85
  86
  87 class WithResidual(nn.Module):
  88     def __init__(self, *f):
  89         super().__init__()
  90         self.f = f[0] if len(f) == 1 else nn.Sequential(*f)
  91
  92     def forward(self, bs):
  93         return BracketedSequence(bs.x + self.f(bs).x, bs.first, bs.nb, bs.init_cache)
  94
  95
  96 ##############################
  97
  98
  99 class AddPositionalEncoding(nn.Module):
 100     def __init__(self, len_max):
 101         super().__init__()
 102         self.len_max = len_max
 103
 104     # [Vaswani et al 2018] PE_{t,2i} = sin(t/(L^{2i/D})), PE_{t,2i+1} = cos(t/(L^{2i/D}))
 105
 106     def forward(self, bs):
 107         if bs.init_cache:
 108             t = torch.arange(bs.x.size(1), dtype=bs.x.dtype, device=bs.x.device)[
 109                 :, None
 110             ]
 111             j = torch.arange(bs.x.size(2), dtype=bs.x.dtype, device=bs.x.device)[
 112                 None, :
 113             ]
 114             k = j % 2
 115             self.pe = torch.sin(
 116                 t / (self.len_max ** ((j - k) / bs.x.size(2))) + math.pi / 2 * k
 117             )
 118             self.cache_y = bs.x.new(bs.x.size())
 119
 120         self.cache_y[:, bs.first : bs.first + bs.nb] = (
 121             bs.slice() + self.pe[bs.first : bs.first + bs.nb]
 122         )
 123
 124         return BracketedSequence(self.cache_y, bs.first, bs.nb, bs.init_cache)
 125
 126
 127 import pscan
 128
 129 # X is /.../xTxD   A is /.../xT   Y_init is /.../xD
 130
 131
 132 def pscan_dim(A, X, Y_init, dim=-2):
 133     s = X.size()
 134     a, T, b = s[:dim].numel(), s[dim], s[dim + 1 :].numel()
 135
 136     A = A.reshape(a, T, *s[dim + 1 : -1])
 137     X = X.reshape(a, T, *s[dim + 1 : -1], -1)
 138
 139     if Y_init is None:
 140         Y_init = X.new_zeros(a, *s[dim + 1 : -1], X.size(-1))
 141     else:
 142         Y_init = Y_init.reshape(a, *s[dim + 1 : -1], -1)
 143
 144     Y = pscan.pscan(A, X, Y_init).reshape(s)
 145
 146     return Y
 147
 148
 149 def pscan_rgrad(grad_Y, A, X, Y_init, dim=-2, eps=1e-2):
 150     with torch.no_grad():
 151         s_A, s_X = 0, 0
 152         for t in range(X.size(dim) - 1, 0, -1):
 153             delta = (grad_Y[t] - s_A) / A[t].grad
 154             s_A += A[t].grad * delta
 155             A[t].grad = delta
 156             delta = (grad_Y[t] - s_X) / X[t].grad
 157             s_X += X[t].grad * delta
 158             X[t].grad = delta
 159
 160
 161 def pscan_shape(A, X, Y_init):
 162     s = X.size()
 163     A = A.reshape(-1, s[-2])
 164     X = X.reshape(-1, s[-2], s[-1])
 165
 166     if Y_init is None:
 167         Y_init = X.new_zeros(X.size(0), s[-1])
 168     else:
 169         Y_init = Y_init.reshape(-1, s[-1])
 170
 171     Y = pscan.pscan(A, X, Y_init).reshape(s)
 172
 173     return Y
 174
 175
 176 def nsum_shape(X, Y_init):
 177     s = X.size()
 178     X = X.reshape(-1, s[-2], s[-1])  # ntd
 179
 180     Y = 0 if Y_init is None else Y_init.reshape(-1, s[-1])
 181     result = []
 182
 183     for k in range(X.size(1)):
 184         Y = Y + X[:, k]
 185         Y = Y / Y.norm(dim=-1, keepdim=True).clamp(min=1)
 186         result.append(Y)
 187
 188     return torch.cat(result, dim=1).reshape(s)
 189
 190
 191 ##############################
 192
 193
 194 class DumbRec(nn.Module):
 195     def __init__(
 196         self,
 197         dim_model,
 198         dim_qk,
 199         dim_v,
 200         nb_heads,
 201         nb_lines,
 202         attention_dropout=0.0,
 203         len_max=1e5,
 204         logger=print,
 205         **kwargs,
 206     ):
 207         super().__init__()
 208
 209         def randw(*d):
 210             return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1]))
 211
 212         self.nb_lines = nb_lines
 213         self.attention_dropout = attention_dropout
 214
 215         self.k_star = randw(nb_lines, dim_qk)
 216
 217         self.w_qw = randw(nb_heads, dim_qk, dim_model)
 218         self.w_qr = randw(nb_heads, dim_qk, dim_model)
 219         # self.w_k = randw(nb_heads, dim_qk, dim_model)
 220         self.w_v = randw(nb_heads, dim_v, dim_model)
 221         self.w_o = randw(dim_v * nb_heads, dim_model)
 222
 223     def reset_inner_loss(self):
 224         self.acc_attention = 0
 225         self.acc_nb = 0
 226
 227     def get_inner_loss(self):
 228         warnings.warn("l2 regularization", RuntimeWarning)
 229         return (self.acc_attention / self.acc_nb).pow(2).sum()
 230         # return torch.tensor([0], device=self.w_qw.device)
 231
 232     def forward(self, bs):
 233         x_q, t0, t1 = bs.x, bs.first, bs.first + bs.nb
 234
 235         if bs.init_cache:
 236             self.rec_v = x_q.new_zeros(
 237                 x_q.size(0), self.nb_lines, x_q.size(1), self.w_v.size(1)
 238             )
 239             # self.rec_k = x_q.new_zeros(
 240             # x_q.size(0), self.nb_lines, x_q.size(1), self.w_k.size(1)
 241             # )
 242             self.cache_y = x_q.new_zeros(x_q.size(0), x_q.size(1), self.w_o.size(1))
 243
 244         ######################################################################
 245         # Prepare the keys
 246
 247         k_star = self.k_star[:, None, :].expand(-1, t1 - t0, -1)
 248
 249         warnings.warn("rotating key barrel", RuntimeWarning)
 250         k_star = self.k_star[:, None, :].expand(-1, x_q.size(1), -1)
 251         t_barrel = torch.arange(t0, t1, device=k_star.device)
 252         t_barrel = t_barrel[None, :].expand(k_star.size(0), t1 - t0)
 253         l_barrel = (
 254             torch.arange(k_star.size(0), device=k_star.device)[:, None] + t_barrel
 255         ) % k_star.size(0)
 256         k_star = k_star[l_barrel, t_barrel]
 257
 258         ######################################################################
 259         # Compute the recurrent state
 260
 261         qw = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_qw)
 262
 263         v = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_v)
 264         # k = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_k)
 265
 266         aw = torch.einsum(
 267             "nhtd,ltd->nhlt",
 268             qw,
 269             k_star,
 270         ) / math.sqrt(self.w_qw.size(1))
 271
 272         aw = aw.softmax(dim=2)  # nhlt
 273
 274         if self.train:
 275             self.acc_attention += aw.sum(dim=(0, 1, 3))
 276             self.acc_nb += aw.size(0) * aw.size(1) * aw.size(3)
 277
 278         aw = F.dropout(aw, self.attention_dropout, self.training)
 279
 280         A = 1 - aw.sum(dim=1)  # nlt
 281
 282         V = torch.einsum("nhlt,nhtd->nltd", aw, v).contiguous()
 283         # K = torch.einsum("nhlt,nhtd->nltd", aw, k).contiguous()
 284
 285         if t0 == 0:
 286             V0 = None
 287             # K0 = None
 288         else:
 289             V0 = self.rec_v[:, :, t0 - 1]
 290             # K0 = self.rec_k[:, :, t0 - 1]
 291
 292         self.rec_v[:, :, t0:t1] = pscan_shape(A, V, V0)
 293         # self.rec_k[:, :, t0:t1] = pscan_shape(A, K, K0)
 294
 295         ######################################################################
 296         # compute the readout
 297
 298         qr = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_qr)
 299
 300         ar = torch.einsum(
 301             "nhtd,ld->nhlt",
 302             qr,
 303             # self.rec_k[:, :, t0:t1],
 304             self.k_star,
 305         ) / math.sqrt(self.w_qr.size(1))
 306
 307         ar = ar.softmax(dim=2)  # nhlt
 308
 309         ar = F.dropout(ar, self.attention_dropout, self.training)
 310
 311         y = torch.einsum(
 312             "nhlt,nltd->nthd",
 313             ar,
 314             self.rec_v[:, :, t0:t1],
 315         ).flatten(2)
 316
 317         self.cache_y[:, t0:t1] = y @ self.w_o
 318
 319         return BracketedSequence(self.cache_y, t0, t1 - t0, bs.init_cache)
 320
 321
 322 ##############################
 323
 324
 325 class KVRec(nn.Module):
 326     def __init__(
 327         self,
 328         dim_model,
 329         dim_qk,
 330         dim_v,
 331         nb_heads,
 332         nb_lines,
 333         attention_dropout=0.0,
 334         len_max=1e5,
 335         logger=print,
 336         **kwargs,
 337     ):
 338         super().__init__()
 339
 340         def randw(*d):
 341             return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1]))
 342
 343         self.nb_lines = nb_lines
 344         self.attention_dropout = attention_dropout
 345
 346         self.k_star = randw(nb_lines, dim_qk)
 347
 348         self.w_qw = randw(nb_heads, dim_qk, dim_model)
 349         self.w_qr = randw(nb_heads, dim_qk, dim_model)
 350         self.w_k = randw(nb_heads, dim_qk, dim_model)
 351         self.w_v = randw(nb_heads, dim_v, dim_model)
 352         self.w_o = randw(dim_v * nb_heads, dim_model)
 353
 354     def reset_inner_loss(self):
 355         self.acc_attention = 0
 356         self.acc_nb = 0
 357
 358     def get_inner_loss(self):
 359         warnings.warn("l2 regularization", RuntimeWarning)
 360         return (self.acc_attention / self.acc_nb).pow(2).sum()
 361         # return torch.tensor([0], device=self.w_qw.device)
 362         # warnings.warn("side regularization", RuntimeWarning)
 363         # return (
 364         # (0.5 / self.nb_lines - self.acc_attention / self.acc_nb).clamp(min=0).sum()
 365         # )
 366         # return torch.tensor([0], device=self.w_qw.device)
 367
 368     def forward(self, bs):
 369         x_q, t0, t1 = bs.x, bs.first, bs.first + bs.nb
 370
 371         if bs.init_cache:
 372             self.rec_v = x_q.new_zeros(
 373                 x_q.size(0), self.nb_lines, x_q.size(1), self.w_v.size(1)
 374             )
 375             self.rec_k = x_q.new_zeros(
 376                 x_q.size(0), self.nb_lines, x_q.size(1), self.w_k.size(1)
 377             )
 378             self.cache_y = x_q.new_zeros(x_q.size(0), x_q.size(1), self.w_o.size(1))
 379
 380         ######################################################################
 381         # Prepare the keys
 382
 383         k_star = self.k_star[:, None, :].expand(-1, t1 - t0, -1)
 384
 385         warnings.warn("rotating key barrel", RuntimeWarning)
 386         k_star = self.k_star[:, None, :].expand(-1, x_q.size(1), -1)
 387         t_barrel = torch.arange(t0, t1, device=k_star.device)
 388         t_barrel = t_barrel[None, :].expand(k_star.size(0), t1 - t0)
 389         l_barrel = (
 390             torch.arange(k_star.size(0), device=k_star.device)[:, None] + t_barrel
 391         ) % k_star.size(0)
 392         k_star = k_star[l_barrel, t_barrel]
 393
 394         ######################################################################
 395         # Compute the recurrent state
 396
 397         qw = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_qw)
 398
 399         v = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_v)
 400         k = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_k)
 401
 402         aw = torch.einsum(
 403             "nhtd,ltd->nhlt",
 404             qw,
 405             k_star,
 406         ) / math.sqrt(self.w_qw.size(1))
 407
 408         aw = aw.softmax(dim=2)  # nhlt
 409
 410         if self.train:
 411             # We want all the memory lines to be used similarly
 412             self.acc_attention += aw.sum(dim=(0, 1, 3))  # Sum accross NxHx_xT
 413             self.acc_nb += aw.size(0) * aw.size(1) * aw.size(3)
 414
 415         aw = F.dropout(aw, self.attention_dropout, self.training)
 416
 417         A = 1 - aw.sum(dim=1)  # nlt
 418
 419         V = torch.einsum("nhlt,nhtd->nltd", aw, v).contiguous()
 420         K = torch.einsum("nhlt,nhtd->nltd", aw, k).contiguous()
 421
 422         if t0 == 0:
 423             V0 = None
 424             K0 = None
 425         else:
 426             V0 = self.rec_v[:, :, t0 - 1]
 427             K0 = self.rec_k[:, :, t0 - 1]
 428
 429         self.rec_v[:, :, t0:t1] = pscan_shape(A, V, V0)
 430         self.rec_k[:, :, t0:t1] = pscan_shape(A, K, K0)
 431
 432         ######################################################################
 433         # compute the readout
 434
 435         qr = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_qr)
 436
 437         ar = torch.einsum(
 438             "nhtd,nltd->nhlt",
 439             qr,
 440             self.rec_k[:, :, t0:t1],
 441         ) / math.sqrt(self.w_qr.size(1))
 442
 443         ar = ar.softmax(dim=2)  # nhlt
 444
 445         ar = F.dropout(ar, self.attention_dropout, self.training)
 446
 447         y = torch.einsum(
 448             "nhlt,nltd->nthd",
 449             ar,
 450             self.rec_v[:, :, t0:t1],
 451         ).flatten(2)
 452
 453         self.cache_y[:, t0:t1] = y @ self.w_o
 454
 455         return BracketedSequence(self.cache_y, t0, t1 - t0, bs.init_cache)
 456
 457
 458 ##############################
 459
 460
 461 # Returns a tensor with an additional index at rank win_dim, that move
 462 # along the same dimension as dim, on a domain {0...win_size-1}, and
 463 # dim is restricted on a domain reduced by win_size-1 values.
 464
 465
 466 def moving_window(x, dim, win_dim, win_size):
 467     size, stride = x.size(), x.stride()
 468     size = size[:dim] + (size[dim] - win_size + 1,) + size[dim + 1 :]
 469     size = size[:win_dim] + (win_size,) + size[win_dim:]
 470     stride = stride[:win_dim] + (stride[dim],) + stride[win_dim:]
 471
 472     return x.as_strided(size=size, stride=stride)
 473
 474
 475 ##############################
 476
 477
 478 class Caterpillar(nn.Module):
 479     def __init__(
 480         self,
 481         dim_model,
 482         dim_qk,
 483         dim_v,
 484         nb_heads,
 485         caterpillar_length,
 486         caterpillar_height,
 487         attention_dropout=0.0,
 488         len_max=1e5,
 489         logger=print,
 490         **kwargs,
 491     ):
 492         super().__init__()
 493
 494         warnings.warn("Caterpillar", RuntimeWarning)
 495
 496         def randw(*d, amplitude=None):
 497             if amplitude is None:
 498                 amplitude = 1 / math.sqrt(d[-1])
 499             return nn.Parameter(amplitude * torch.randn(*d))
 500
 501         self.caterpillar_length = caterpillar_length
 502         self.caterpillar_height = caterpillar_height
 503         self.attention_dropout = attention_dropout
 504
 505         ######################################################################
 506         # sup_args
 507
 508         x = kwargs.get("gate_dropout")
 509         if x is None:
 510             self.proba_gate_dropout = 0.0
 511         else:
 512             self.proba_gate_dropout = float(x)
 513
 514         logger(f"self.proba_gate_dropout {self.proba_gate_dropout}")
 515
 516         x = kwargs.get("default_bg")
 517         if x is None:
 518             default_bg = -math.log(caterpillar_height - 1)
 519         else:
 520             default_bg = float(x)
 521
 522         logger(f"default_bg {default_bg}")
 523
 524         ######################################################################
 525
 526         self.w_G = randw(nb_heads, caterpillar_height, dim_model)
 527         self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), default_bg))
 528
 529         self.w_K = randw(nb_heads, dim_qk, dim_model)
 530         self.w_V = randw(nb_heads, dim_v, dim_model)
 531         self.w_Q = randw(nb_heads, dim_qk, dim_model)
 532         self.w_O = randw(dim_v * nb_heads, dim_model)
 533
 534         self.init_K_rec = randw(
 535             caterpillar_height,
 536             caterpillar_length,
 537             dim_qk,
 538         )
 539         self.init_V_rec = randw(
 540             caterpillar_height,
 541             caterpillar_length,
 542             dim_v,
 543         )
 544
 545     def reset_inner_loss(self):
 546         self.acc_attention = 0
 547         self.acc_nb = 0
 548
 549     def get_inner_loss(self):
 550         # warnings.warn("l2 regularization", RuntimeWarning)
 551         # return (self.acc_attention / self.acc_nb).pow(2).sum()
 552         return torch.tensor([0], device=self.w_Q.device)
 553
 554     def forward(self, bs):
 555         # Dimensions to make the source a bit clearer, that's needed
 556
 557         X, t0, t1 = bs.slice(), bs.first, bs.first + bs.nb
 558
 559         N = bs.x.size(0)
 560         T = bs.x.size(1)
 561         H = self.w_V.size(0)
 562         DV = self.w_V.size(1)
 563         DK = self.w_K.size(1)
 564         DM = self.w_O.size(1)
 565         R = self.caterpillar_height
 566         L = self.caterpillar_length
 567
 568         assert (
 569             t0 >= L and (t1 - t0) % L == 0
 570         ), f"bs.first should be greater than caterpillar_length, and bs.nb should be a multiple of caterpillar_length"
 571
 572         # We cache values to deal efficiently with auto-regression
 573
 574         if bs.init_cache:
 575             self.rec_V = X.new_zeros(N, R, T, DV)
 576             self.rec_K = X.new_zeros(N, R, T, DK)
 577             # We start the recurrent sequences with optimizable
 578             # initial values. No idea if it helps.
 579             self.rec_V[:, :, t0 - L : t0, :] = self.init_V_rec[None, :, :, :]
 580             self.rec_K[:, :, t0 - L : t0, :] = self.init_K_rec[None, :, :, :]
 581
 582             self.cache_Y = X.new_zeros(N, T, DM)
 583
 584         V = torch.einsum("ntc,hdc->nhtd", X, self.w_V)
 585         K = torch.einsum("ntc,hdc->nhtd", X, self.w_K)
 586
 587         ######################################################################
 588         # Compute the recurrent state
 589
 590         # This is the Gating sequence that modulates the storing of
 591         # the new key and value in the R pairs of the current
 592         # stack. There are R independent gating values, which means
 593         # that the current K/V may be stored in multiple pairs of the
 594         # recurrent state, or not at all.
 595
 596         G = (
 597             torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None]
 598         ).sigmoid()
 599
 600         # Clip the gating to avoid values greater than 1 when several
 601         # heads hit the same row
 602
 603         G = G / G.sum(1, keepdim=True).clamp(min=1)
 604
 605         ######################################################################
 606
 607         def recurrence(G, V, K):
 608             # We prepare the arguments for the parallel scan
 609
 610             A = 1 - G.sum(1)
 611
 612             gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
 613             gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
 614
 615             # We start from cached values, which matters in inference
 616
 617             init_rec_V = self.rec_V[:, :, t0 - L : t0]
 618             init_rec_K = self.rec_K[:, :, t0 - L : t0]
 619
 620             # Associative scan
 621
 622             # Here there is a trick: Since the stack at position t is
 623             # computed by updating that at position t-L, the parallel
 624             # scan operates with a period of L. To do so we split the
 625             # sequence indexing in two axes, the second of size L, and
 626             # run the parallel scan using the first as the sequence index.
 627
 628             A = A.unflatten(2, (-1, L))
 629             gated_V = gated_V.unflatten(2, (-1, L))
 630             gated_K = gated_K.unflatten(2, (-1, L))
 631
 632             next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
 633             next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
 634
 635             next_V = next_V.flatten(2, 3)
 636             next_K = next_K.flatten(2, 3)
 637
 638             return next_V, next_K
 639
 640         #################################################################
 641
 642         next_V, next_K = recurrence(G, V, K)
 643
 644         if self.training and self.proba_gate_dropout > 0.0:
 645             # G is NxHxRxT where r is the caterpillar's row.
 646
 647             warnings.warn("gate dropout", RuntimeWarning)
 648
 649             kill = (
 650                 torch.rand(G.size(), device=G.device) <= self.proba_gate_dropout
 651             ).float()
 652
 653             mask = 1 - kill
 654
 655             masked_next_V, masked_next_K = recurrence(G * mask, V, K)
 656
 657             next_V = next_V.detach() + (masked_next_V - masked_next_V.detach()) / (
 658                 1 - self.proba_gate_dropout
 659             )
 660             next_K = next_K.detach() + (masked_next_K - masked_next_K.detach()) / (
 661                 1 - self.proba_gate_dropout
 662             )
 663
 664         self.rec_V[:, :, t0:t1] = next_V
 665         self.rec_K[:, :, t0:t1] = next_K
 666
 667         ######################################################################
 668         # compute the readout
 669
 670         Q = torch.einsum("ntc,hdc->nhtd", X, self.w_Q)
 671
 672         # We build tensors NxHxTxFxL where N is the sample index, H
 673         # the head, T the time, F the row in the caterpillar, and L
 674         # the column in the caterpillar
 675
 676         windowed_V = moving_window(
 677             self.rec_V[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L
 678         )
 679
 680         windowed_K = moving_window(
 681             self.rec_K[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L
 682         )
 683
 684         # We have an attention score for each of the RxL values
 685
 686         ar = torch.einsum(
 687             "nhtd,nftld->nhtfl",
 688             Q,
 689             windowed_K,
 690         ) / math.sqrt(DK)
 691
 692         # softmax can operate only on one dimension, hence the
 693         # flattening
 694
 695         ar = ar.flatten(3).softmax(dim=3).view(ar.size())
 696
 697         ar = F.dropout(ar, self.attention_dropout, self.training)
 698
 699         # Compute the output for each head, flatten to concatenate
 700
 701         Y = torch.einsum(
 702             "nhtfl,nftld->nthd",
 703             ar,
 704             windowed_V,
 705         ).flatten(2)
 706
 707         # Compute the final output
 708
 709         self.cache_Y[:, t0:t1] = Y @ self.w_O
 710
 711         return BracketedSequence(self.cache_Y, t0, t1 - t0, bs.init_cache)
 712
 713
 714 ##############################
 715
 716
 717 class QKVAttention(nn.Module):
 718     def __init__(
 719         self,
 720         dim_model,
 721         dim_qk,
 722         dim_v,
 723         nb_heads=1,
 724         causal=False,
 725         attention_dropout=0.0,
 726         logger=print,
 727         **kwargs,
 728     ):
 729         super().__init__()
 730
 731         def randw(*d):
 732             return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1]))
 733
 734         self.causal = causal
 735         self.attention_dropout = attention_dropout
 736         self.record_attention = False
 737
 738         self.w_q = randw(nb_heads, dim_qk, dim_model)
 739         self.w_k = randw(nb_heads, dim_qk, dim_model)
 740         self.w_v = randw(nb_heads, dim_v, dim_model)
 741         self.w_o = randw(dim_v * nb_heads, dim_model)
 742
 743     def forward(self, bs):
 744         x_q = bs.x
 745
 746         assert (
 747             self.causal or bs.complete()
 748         ), "Partial evaluation is only possible for causal models"
 749
 750         if bs.init_cache:
 751             self.cache_k = x_q.new_zeros(
 752                 x_q.size(0), self.w_k.size(0), x_q.size(1), self.w_k.size(1)
 753             )
 754             self.cache_v = x_q.new_zeros(
 755                 x_q.size(0), self.w_v.size(0), x_q.size(1), self.w_v.size(1)
 756             )
 757             self.cache_y = x_q.new_zeros(x_q.size(0), x_q.size(1), self.w_o.size(1))
 758
 759         q = torch.einsum("ntc,hdc->nhtd", x_q[:, bs.first : bs.first + bs.nb], self.w_q)
 760
 761         self.cache_k[:, :, bs.first : bs.first + bs.nb] = torch.einsum(
 762             "ntc,hdc->nhtd", x_q[:, bs.first : bs.first + bs.nb], self.w_k
 763         )
 764         self.cache_v[:, :, bs.first : bs.first + bs.nb] = torch.einsum(
 765             "ntc,hdc->nhtd", x_q[:, bs.first : bs.first + bs.nb], self.w_v
 766         )
 767
 768         a = torch.einsum(
 769             "nhtd,nhsd->nhts", q, self.cache_k[:, :, : bs.first + bs.nb]
 770         ) / math.sqrt(self.w_q.size(1))
 771
 772         if self.causal:
 773             if bs.init_cache:
 774                 self.cache_attzero = (
 775                     torch.arange(x_q.size(1), device=q.device)[None, None, :, None]
 776                     < torch.arange(x_q.size(1), device=q.device)[None, None, None, :]
 777                 )
 778             a = a.masked_fill(
 779                 self.cache_attzero[
 780                     :, :, bs.first : bs.first + bs.nb, : bs.first + bs.nb
 781                 ],
 782                 float("-inf"),
 783             )
 784
 785         a = a.softmax(dim=3)
 786
 787         if self.record_attention:
 788             self.a = a
 789
 790         a = F.dropout(a, self.attention_dropout, self.training)
 791
 792         y = torch.einsum(
 793             "nhts,nhsd->nthd", a, self.cache_v[:, :, : bs.first + bs.nb]
 794         ).flatten(2)
 795
 796         self.cache_y[:, bs.first : bs.first + bs.nb] = y @ self.w_o
 797
 798         return BracketedSequence(self.cache_y, bs.first, bs.nb, bs.init_cache)
 799
 800
 801 ##############################
 802
 803
 804 class MyGPT(nn.Module):
 805     def __init__(
 806         self,
 807         vocabulary_size,
 808         dim_model,
 809         dim_keys,
 810         dim_hidden,
 811         nb_heads,
 812         nb_blocks,
 813         nb_lines=None,
 814         caterpillar_height=None,
 815         causal=False,
 816         dropout=0.0,
 817         len_max=1e5,
 818         attention_layer="kvrec",
 819         logger=print,
 820         **kwargs,
 821     ):
 822         super().__init__()
 823
 824         assert attention_layer in {
 825             "mha",
 826             "dumbrec",
 827             "kvrec",
 828             "caterpillar",
 829         }, f"Unknown attention operator {attention_layer}."
 830
 831         if attention_layer == "caterpillar":
 832             assert nb_lines % caterpillar_height == 0
 833             self.caterpillar_length = nb_lines // caterpillar_height
 834             self.caterpillar_height = caterpillar_height
 835         else:
 836             self.caterpillar_length = -1
 837             self.caterpillar_height = -1
 838
 839         assert dim_model % nb_heads == 0
 840
 841         self.embedding = nn.Sequential(
 842             CacheWrapper(nn.Embedding(vocabulary_size, dim_model), nn.Dropout(dropout)),
 843             AddPositionalEncoding(len_max),
 844         )
 845
 846         trunk_blocks = []
 847
 848         def attlayer():
 849             if attention_layer == "mha":
 850                 return QKVAttention(
 851                     dim_model=dim_model,
 852                     dim_qk=dim_keys,
 853                     dim_v=dim_model // nb_heads,
 854                     nb_heads=nb_heads,
 855                     causal=causal,
 856                     attention_dropout=dropout,
 857                     logger=logger,
 858                     **kwargs,
 859                 )
 860             elif attention_layer == "dumbrec":
 861                 return DumbRec(
 862                     dim_model=dim_model,
 863                     dim_qk=dim_keys,
 864                     dim_v=dim_model // nb_heads,
 865                     nb_heads=nb_heads,
 866                     nb_lines=nb_lines,
 867                     attention_dropout=dropout,
 868                     logger=logger,
 869                     **kwargs,
 870                 )
 871             elif attention_layer == "kvrec":
 872                 return KVRec(
 873                     dim_model=dim_model,
 874                     dim_qk=dim_keys,
 875                     dim_v=dim_model // nb_heads,
 876                     nb_heads=nb_heads,
 877                     nb_lines=nb_lines,
 878                     attention_dropout=dropout,
 879                     logger=logger,
 880                     **kwargs,
 881                 )
 882             elif attention_layer == "caterpillar":
 883                 return Caterpillar(
 884                     dim_model=dim_model,
 885                     dim_qk=dim_keys,
 886                     dim_v=dim_model // nb_heads,
 887                     nb_heads=nb_heads,
 888                     caterpillar_length=self.caterpillar_length,
 889                     caterpillar_height=self.caterpillar_height,
 890                     attention_dropout=dropout,
 891                     logger=logger,
 892                     **kwargs,
 893                 )
 894             else:
 895                 raise ValueError(f"Unknown attention type {attention_layer}.")
 896
 897         for b in range(nb_blocks):
 898             trunk_blocks += [
 899                 WithResidual(
 900                     CacheWrapper(nn.LayerNorm((dim_model,))),
 901                     attlayer(),
 902                 ),
 903                 WithResidual(
 904                     CacheWrapper(
 905                         nn.LayerNorm((dim_model,)),
 906                         nn.Linear(in_features=dim_model, out_features=dim_hidden),
 907                         nn.ReLU(),
 908                         nn.Linear(in_features=dim_hidden, out_features=dim_model),
 909                         nn.Dropout(dropout),
 910                     ),
 911                 ),
 912             ]
 913
 914         self.trunk = nn.Sequential(*trunk_blocks)
 915
 916         self.readout = CacheWrapper(
 917             nn.Linear(in_features=dim_model, out_features=vocabulary_size)
 918         )
 919
 920         with torch.no_grad():
 921             for m in self.modules():
 922                 if isinstance(m, nn.Embedding):
 923                     m.weight.normal_(mean=0, std=2e-2)
 924                 elif isinstance(m, nn.LayerNorm):
 925                     m.bias.zero_()
 926                     m.weight.fill_(1.0)
 927
 928         self.reset_inner_loss()
 929
 930     def forward(self, bs):
 931         bs = BracketedSequence(F.pad(bs.x, (1, -1)), bs.first, bs.nb, bs.init_cache)
 932
 933         # To make the code simpler in the Caterpillar layer, we pad
 934         # here. It's unclear if/how much it hurts computationaly by
 935         # increasing the sequence length for the other layers
 936
 937         if self.caterpillar_length > 0:
 938             original_nb = bs.nb
 939             if bs.nb % self.caterpillar_length > 0:
 940                 bs.nb += self.caterpillar_length - bs.nb % self.caterpillar_length
 941
 942             bs = BracketedSequence(
 943                 F.pad(bs.x, (self.caterpillar_length, self.caterpillar_length)),
 944                 bs.first + self.caterpillar_length,
 945                 bs.nb,
 946                 bs.init_cache,
 947             )
 948
 949         bs = self.embedding(bs)
 950         bs = self.trunk(bs)
 951         bs = self.readout(bs)
 952
 953         if self.caterpillar_length > 0:
 954             bs = BracketedSequence(
 955                 F.pad(bs.x, (0, 0, -self.caterpillar_length, -self.caterpillar_length)),
 956                 bs.first - self.caterpillar_length,
 957                 original_nb,
 958                 bs.init_cache,
 959             )
 960
 961         return bs
 962
 963     # ar_mask is a tensor with 0s and 1s, of same shape as input, with
 964     # 1s where tokens should be generated. The others are kept
 965     # unchanged.
 966
 967     def masked_inplace_autoregression(
 968         self,
 969         input_src,
 970         ar_mask_src,
 971         forbidden_tokens=None,
 972         deterministic_synthesis=False,
 973     ):
 974         input = input_src.to(self.readout.f.weight.device)
 975         ar_mask = ar_mask_src.to(self.readout.f.weight.device)
 976         to_generate = (ar_mask.sum(0) > 0).nonzero()
 977         if to_generate.min() > 0:
 978             self(
 979                 BracketedSequence(input, 0, to_generate.min(), True)
 980             )  # Needed to initialize the model's cache
 981         for s in range(to_generate.min(), to_generate.max() + 1):
 982             output = self(BracketedSequence(input, s, 1, s == 0)).x
 983             logits = output[:, s]
 984             if forbidden_tokens is not None:
 985                 logits = logits.masked_fill(forbidden_tokens, float("-inf"))
 986             if deterministic_synthesis:
 987                 t_next = logits.argmax(1)
 988             else:
 989                 dist = torch.distributions.categorical.Categorical(logits=logits)
 990                 t_next = dist.sample()
 991             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 992
 993         input_src.copy_(input)
 994
 995     def reset_inner_loss(self):
 996         for m in self.modules():
 997             if m is not self and hasattr(m, "reset_inner_loss"):
 998                 m.reset_inner_loss()
 999
1000     def get_inner_loss(self):
1001         l = torch.tensor([0.0], device=self.readout.f.weight.device)
1002         for m in self.modules():
1003             if m is not self and hasattr(m, "get_inner_loss"):
1004                 l += m.get_inner_loss()
1005         return l
1006
1007     def record_attention(self, v=True):
1008         for m in self.modules():
1009             if isinstance(m, QKVAttention):
1010                 m.record_attention = v
1011
1012     def retrieve_attention(self):
1013         a = []
1014         for m in self.modules():
1015             if isinstance(m, QKVAttention):
1016                 a.append(m.a)
1017         return a
1018
1019
1020 ######################################################################
1021
1022 if __name__ == "__main__":
1023     print("Basic check.")
1024
1025     m = Caterpillar(
1026         dim_model=4,
1027         dim_qk=3,
1028         dim_v=7,
1029         nb_heads=1,
1030         caterpillar_length=7,
1031         caterpillar_height=3,
1032         attention_dropout=0.0,
1033     )
1034
1035     m.reset_inner_loss()
1036     x = torch.randn(1, 21 + 2 * 7, 4)
1037     y1 = m(BracketedSequence(x, first=7, nb=21, init_cache=True)).x[:, 7:28]
1038     y2 = m(BracketedSequence(x, first=7, nb=21, init_cache=True)).x[:, 7:28]
1039     y3a = m(BracketedSequence(x, first=7, nb=14, init_cache=True)).x[:, 7:21]
1040     y3b = m(BracketedSequence(x, first=21, nb=7, init_cache=False)).x[:, 21:28]
1041     print((y1 - y2).abs().max())
1042     print((y1 - torch.cat([y3a, y3b], dim=1)).abs().max())
1043     exit(0)
1044
1045     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
1046
1047     vocabulary_size = 128
1048     x = torch.randint(vocabulary_size, (6, 1024))
1049
1050     model = MyGPT(
1051         vocabulary_size=vocabulary_size,
1052         dim_model=512,
1053         dim_keys=64,
1054         dim_hidden=2048,
1055         nb_heads=8,
1056         nb_lines=128,
1057         nb_blocks=12,
1058         dropout=0.1,
1059         causal=True,
1060     )
1061
1062     x = x.to(device)
1063     model.to(device)
1064
1065     import time, sys
1066
1067     # import torchvision.models as models
1068     # from torch.profiler import profile, record_function, ProfilerActivity
1069
1070     # with profile(activities=[ProfilerActivity.CPU,  ProfilerActivity.CUDA], profile_memory=True, record_shapes=True) as prof:
1071     # with record_function("model_inference"):
1072
1073     model.eval()
1074     for i in range(3):
1075         start_time = time.perf_counter()
1076         for k in range(10):
1077             model(BracketedSequence(x))
1078         duration = time.perf_counter() - start_time
1079         print(duration)
1080         sys.stdout.flush()
1081
1082     # print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10))
1083     # print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
1084
1085     # print("##############################################################")
1086     # y2 = torch.randn_like(y1)
1087     # for s in range(x.size(1)):
1088     # z = model(BracketedSequence(x, s, 1))
1089     # y2[:, s : s + 1] = z.slice()
1090
1091     # print(f"error={((y1 - y2).norm() / (y1.norm() + y2.norm())).item()}")
1092
1093 ######################################################################