quiz_machine.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 import math, os, tqdm, warnings, sys
   9
  10 import torch, torchvision
  11
  12 from torch import nn
  13 from torch.nn import functional as F
  14
  15 import mygpt
  16 from mygpt import BracketedSequence
  17
  18 import threading
  19
  20 ######################################################################
  21 # if output is log(P(X=y)) and target is Y, returns -log P(X=Y) + H(X
  22 # | X != Y)
  23
  24
  25 # output is NxCxT and target is NxT
  26 def confusion(output, target, reduction="mean"):
  27     N, C, T = output.shape
  28     output = output.permute(0, 2, 1).reshape(-1, C)
  29     target = target.flatten()
  30     all_t = torch.arange(N * T, device=output.device)
  31     output = output.log_softmax(dim=-1)
  32     result = -output[all_t, target]
  33
  34     output[all_t, target] = float("-inf")
  35     output = output.log_softmax(dim=-1)
  36     e = output.exp()
  37     output[all_t, target] = 0
  38     result = result - (output * e).sum(-1)
  39
  40     if reduction == "none":
  41         return result.reshape(N, T)
  42     elif reduction == "mean":
  43         return result.reshape(N, T).mean()
  44     elif reduction == "sum":
  45         return result.reshape(N, T).sum()
  46     else:
  47         raise ValueError(f"unknown reduction '{reduction}'.")
  48
  49
  50 ######################################################################
  51
  52 # ar_mask is a tensor with 0s and 1s, of same shape as input, with
  53 # 1s where tokens should be generated. The others are kept
  54 # unchanged.
  55
  56
  57 def one_batch_masked_inplace_autoregression(
  58     model,
  59     input,
  60     ar_mask,
  61     seq_logproba,
  62     temperature,
  63     deterministic_synthesis,
  64 ):
  65     to_generate = (ar_mask.sum(0) > 0).nonzero()
  66
  67     if to_generate.min() > 0:
  68         model(
  69             BracketedSequence(input, 0, to_generate.min())
  70         )  # Needed to initialize the model's cache
  71     for s in range(to_generate.min(), to_generate.max() + 1):
  72         output = model(BracketedSequence(input, s, 1)).x
  73
  74         logits = output[:, s]
  75
  76         logits = (logits / temperature).log_softmax(dim=-1)
  77
  78         if deterministic_synthesis:
  79             t_next = logits.argmax(-1)
  80         else:
  81             dist = torch.distributions.categorical.Categorical(logits=logits)
  82             t_next = dist.sample()
  83
  84         all_n = torch.arange(t_next.size(0))
  85
  86         seq_logproba += logits[all_n, t_next]
  87
  88         input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
  89
  90
  91 def masked_inplace_autoregression(
  92     model,
  93     batch_size,
  94     input,
  95     ar_mask,
  96     seq_logproba,
  97     temperature,
  98     deterministic_synthesis,
  99     forbidden_tokens=None,
 100     logit_biases=None,
 101     progress_bar_desc=None,
 102     device=torch.device("cpu"),
 103 ):
 104     assert input.size() == ar_mask.size()
 105
 106     batches = zip(
 107         input.split(batch_size),
 108         ar_mask.split(batch_size),
 109         seq_logproba.split(batch_size),
 110     )
 111
 112     if progress_bar_desc is not None:
 113         batches = tqdm.tqdm(
 114             batches,
 115             dynamic_ncols=True,
 116             desc=progress_bar_desc,
 117             total=(input.size(0) + batch_size - 1) // batch_size,
 118         )
 119
 120     with torch.autograd.no_grad():
 121         t = model.training
 122         model.eval()
 123
 124         for input, ar_mask, seq_logproba in batches:
 125             one_batch_masked_inplace_autoregression(
 126                 model=model,
 127                 input=input,
 128                 ar_mask=ar_mask,
 129                 seq_logproba=seq_logproba,
 130                 temperature=temperature,
 131                 deterministic_synthesis=deterministic_synthesis,
 132             )
 133
 134         model.train(t)
 135
 136
 137 ######################################################################
 138
 139
 140 class QuizMachine:
 141     def indices_forward_and_backward(self, quizzes):
 142         i_forward = quizzes[:, 0] == self.token_forward
 143         j_forward = quizzes[:, 1 + self.prompt_len] == self.token_forward
 144         i_backward = quizzes[:, 0] == self.token_backward
 145         j_backward = quizzes[:, 1 + self.answer_len] == self.token_backward
 146         assert torch.logical_or(
 147             torch.logical_and(i_forward, j_forward),
 148             torch.logical_and(i_backward, j_backward),
 149         ).all()
 150         return i_forward, i_backward
 151
 152     def non_trivial(self, quizzes):
 153         quizzes = quizzes.clone()
 154         n_forward = quizzes[quizzes[:, 0] == self.token_forward]
 155         n_backward = quizzes[:, 0] == self.token_backward
 156         backward = quizzes[n_backward]
 157         quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
 158         return torch.logical_not(
 159             self.problem.trivial_prompts_and_answers(
 160                 quizzes[:, 1 : 1 + self.prompt_len],
 161                 quizzes[:, 2 + self.prompt_len :],
 162             )
 163         )
 164
 165     def reverse_time(self, quizzes):
 166         i_forward, i_backward = self.indices_forward_and_backward(quizzes)
 167
 168         forward_to_backward = torch.cat(
 169             [
 170                 quizzes[:, 0:1],
 171                 quizzes[:, 2 + self.prompt_len : 2 + self.prompt_len + self.answer_len],
 172                 quizzes[:, 1 + self.prompt_len : 1 + self.prompt_len + 1],
 173                 quizzes[:, 1 : 1 + self.prompt_len],
 174             ],
 175             dim=1,
 176         )
 177
 178         forward_to_backward[:, 0] = self.token_backward
 179         forward_to_backward[:, 1 + self.answer_len] = self.token_backward
 180
 181         backward_to_forward = torch.cat(
 182             [
 183                 quizzes[:, 0:1],
 184                 quizzes[:, 2 + self.answer_len :],
 185                 quizzes[:, 1 + self.answer_len : 2 + self.answer_len],
 186                 quizzes[:, 1 : 1 + self.answer_len],
 187             ],
 188             dim=1,
 189         )
 190
 191         backward_to_forward[:, 0] = self.token_forward
 192         backward_to_forward[:, 1 + self.prompt_len] = self.token_forward
 193
 194         m = i_forward.long()[:, None]
 195
 196         return m * forward_to_backward + (1 - m) * backward_to_forward
 197
 198     def reverse_random_half_in_place(self, quizzes):
 199         i = torch.rand(quizzes.size(0)) < 0.5
 200         if i.any():
 201             quizzes[i] = self.reverse_time(quizzes[i])
 202
 203     def make_ar_mask(self, quizzes, first=False):
 204         i_forward, i_backward = self.indices_forward_and_backward(quizzes)
 205
 206         t = torch.arange(quizzes.size(1), device=quizzes.device)
 207
 208         if first:
 209             m_forward = (t >= 1).long() * (t < 1 + self.prompt_len).long()
 210             m_backward = (t >= 1).long() * (t < 1 + self.answer_len).long()
 211         else:
 212             m_forward = (t >= 2 + self.prompt_len).long()
 213             m_backward = (t >= 2 + self.answer_len).long()
 214
 215         m = i_forward.long()[:, None]
 216
 217         return m * m_forward + (1 - m) * m_backward
 218
 219     def generate_token_sequences(self, nb):
 220         prompts, answers = self.problem.generate_prompts_and_answers(nb)
 221
 222         print(f"DEBUG {prompts.size()=} {answers.size()=}")
 223         sys.stdout.flush()
 224
 225         if self.prompt_len is None:
 226             self.prompt_len = prompts.size(1)
 227
 228         if self.answer_len is None:
 229             self.answer_len = answers.size(1)
 230
 231         assert prompts.size(1) == self.prompt_len and answers.size(1) == self.answer_len
 232
 233         result = []
 234
 235         for prompt, answer in zip(prompts, answers):
 236             a = [
 237                 torch.tensor([self.token_forward]),
 238                 prompt,
 239                 torch.tensor([self.token_forward]),
 240                 answer,
 241             ]
 242
 243             result.append(torch.cat(a, dim=0)[None, :])
 244
 245         return torch.cat(result, dim=0)
 246
 247     def __init__(
 248         self,
 249         problem,
 250         nb_train_samples,
 251         nb_test_samples,
 252         back_accuracy,
 253         batch_size,
 254         result_dir,
 255         logger,
 256         device=torch.device("cpu"),
 257     ):
 258         super().__init__()
 259
 260         v = problem.nb_token_values()
 261         self.token_forward = v
 262         self.token_backward = v + 1
 263         self.nb_token_values = v + 2
 264
 265         self.problem = problem
 266         self.back_accuracy = back_accuracy
 267         self.batch_size = batch_size
 268         self.device = device
 269         self.logger = logger
 270         self.prompt_len = None
 271         self.answer_len = None
 272
 273         self.LOCK_C_QUIZZES = threading.Lock()
 274         self.train_c_quizzes = []
 275         self.test_c_quizzes = []
 276
 277     def save_quiz_illustrations(
 278         self,
 279         result_dir,
 280         filename_prefix,
 281         quizzes,
 282         mistakes=None,
 283     ):
 284         quizzes = quizzes.clone().to("cpu")
 285         n_forward = quizzes[quizzes[:, 0] == self.token_forward]
 286         n_backward = quizzes[:, 0] == self.token_backward
 287         backward = quizzes[n_backward]
 288         assert n_forward.size(0) + backward.size(0) == quizzes.size(0)
 289         quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
 290
 291         predicted_prompts = n_backward.long()
 292         predicted_answers = 1 - predicted_prompts
 293         if mistakes is not None:
 294             # 0/-1/+1 ~ not-to-predict / predicted wrong / predicted correct
 295             predicted_prompts *= mistakes.to("cpu")
 296             predicted_answers *= mistakes.to("cpu")
 297         else:
 298             # 0/2 ~ not-to-predict / to predict
 299             predicted_prompts *= 2
 300             predicted_answers *= 2
 301
 302         self.problem.save_quiz_illustrations(
 303             result_dir,
 304             filename_prefix,
 305             quizzes[:, 1 : 1 + self.prompt_len],
 306             quizzes[:, 2 + self.prompt_len :],
 307             predicted_prompts,
 308             predicted_answers,
 309         )
 310
 311     def vocabulary_size(self):
 312         return self.nb_token_values
 313
 314     ######################################################################
 315
 316     def batches(self, model, split="train", desc=None):
 317         assert split in {"train", "test"}
 318
 319         with self.LOCK_C_QUIZZES:
 320             if split == "train":
 321                 w_quizzes = model.train_w_quizzes
 322                 c_quizzes = self.train_c_quizzes
 323             else:
 324                 w_quizzes = model.test_w_quizzes
 325                 c_quizzes = self.test_c_quizzes
 326
 327             if len(c_quizzes) > 0:
 328                 c_quizzes = torch.cat(c_quizzes, dim=0)
 329                 if c_quizzes.size(0) > w_quizzes.size(0) // 2:
 330                     i = torch.randperm(c_quizzes.size(0))[: w_quizzes.size(0) // 2]
 331                     c_quizzes = c_quizzes[i]
 332
 333                 i = torch.randperm(w_quizzes.size(0))[
 334                     : w_quizzes.size(0) - c_quizzes.size(0)
 335                 ]
 336                 w_quizzes = w_quizzes[i]
 337
 338                 self.nb_batch_w_quizzes = w_quizzes.size(0)
 339                 self.nb_batch_c_quizzes = c_quizzes.size(0)
 340
 341                 input = torch.cat([w_quizzes, c_quizzes], dim=0)
 342             else:
 343                 input = w_quizzes
 344                 self.nb_batch_w_quizzes = w_quizzes.size(0)
 345                 self.nb_batch_c_quizzes = 0
 346
 347         # Shuffle
 348         input = input[torch.randperm(input.size(0))]
 349
 350         if desc is None:
 351             desc = f"epoch-{split}"
 352         for batch in tqdm.tqdm(
 353             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 354         ):
 355             yield batch
 356
 357     ######################################################################
 358
 359     def produce_results(
 360         self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
 361     ):
 362         def compute_accuracy(input, log_prefix=None):
 363             input = input.to(self.device)
 364             ar_mask = self.make_ar_mask(input)
 365             result = input.clone() * (1 - ar_mask)
 366             seq_logproba = torch.empty(input.size(0), device=self.device)
 367
 368             masked_inplace_autoregression(
 369                 model=model,
 370                 batch_size=self.batch_size,
 371                 input=result,
 372                 ar_mask=ar_mask,
 373                 seq_logproba=seq_logproba,
 374                 temperature=1.0,
 375                 deterministic_synthesis=deterministic_synthesis,
 376                 progress_bar_desc=None,
 377                 device=self.device,
 378             )
 379
 380             correct = torch.empty(input.size(0), dtype=torch.int64, device=input.device)
 381
 382             n_forward = input[:, 0] == self.token_forward
 383             n_backward = input[:, 0] == self.token_backward
 384
 385             correct[n_forward] = (
 386                 (input[n_forward] == result[n_forward]).long().min(dim=1).values
 387             )
 388
 389             if self.back_accuracy and n_backward.any():
 390                 # accuracy of B->A*->B*=B instead of B->A*=A
 391                 back_input = self.reverse_time(result[n_backward])
 392                 back_input[:, 2 + self.prompt_len :] = input[
 393                     n_backward, 1 : 1 + self.answer_len
 394                 ]
 395                 _, correct[n_backward] = compute_accuracy(back_input)
 396
 397             if log_prefix is not None:
 398                 forward_nb_correct = correct[n_forward].sum()
 399                 forward_nb_total = correct[n_forward].size(0)
 400                 backward_nb_correct = correct[n_backward].sum()
 401                 backward_nb_total = correct[n_backward].size(0)
 402
 403                 self.logger(
 404                     f"{log_prefix}_accuracy {n_epoch} model {model.id} forward {forward_nb_correct} / {forward_nb_total} backward {backward_nb_correct} / {backward_nb_total}"
 405                 )
 406
 407             return result, correct
 408
 409         # compute_accuracy(model.train_w_quizzes[:nmax], log_prefix="train")
 410
 411         test_result, test_correct = compute_accuracy(
 412             model.test_w_quizzes[:nmax], log_prefix="test"
 413         )
 414
 415         main_test_accuracy = test_correct.sum() / test_correct.size(0)
 416         self.logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
 417
 418         ##############################
 419
 420         self.save_quiz_illustrations(
 421             result_dir,
 422             f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
 423             quizzes=test_result[:72],
 424             mistakes=test_correct[:72] * 2 - 1,
 425         )
 426
 427         return main_test_accuracy
 428
 429     ######################################################################
 430
 431     def renew_w_quizzes(self, model, nb, for_train=True):
 432         input = model.train_w_quizzes if for_train else model.test_w_quizzes
 433         nb = min(nb, input.size(0))
 434         input[:-nb] = input[nb:].clone()
 435         fresh_w_quizzes = self.generate_token_sequences(nb)
 436         self.reverse_random_half_in_place(fresh_w_quizzes)
 437         input[-nb:] = fresh_w_quizzes.to("cpu")
 438
 439     ######################################################################
 440
 441     def store_c_quizzes(self, new_c_quizzes, for_train=True):
 442         with self.LOCK_C_QUIZZES:
 443             if for_train:
 444                 self.train_c_quizzes.append(new_c_quizzes.to("cpu"))
 445             else:
 446                 self.test_c_quizzes.append(new_c_quizzes.to("cpu"))
 447
 448     def save_c_quizzes(self, filename):
 449         torch.save((self.train_c_quizzes, self.test_c_quizzes), filename)
 450
 451     def load_c_quizzes(self, filename):
 452         self.train_c_quizzes, self.test_c_quizzes = torch.load(filename)
 453
 454     ######################################################################
 455
 456     def logproba_of_solutions(self, models, c_quizzes):
 457         logproba = c_quizzes.new_zeros(
 458             c_quizzes.size(0), len(models), device=self.device, dtype=torch.float32
 459         )
 460
 461         for model in models:
 462             with torch.autograd.no_grad():
 463                 t = model.training
 464                 model.eval()
 465
 466                 for input, l in zip(
 467                     c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
 468                 ):
 469                     input = input.to(self.device)
 470                     ar_mask = self.make_ar_mask(input)
 471                     output = model(mygpt.BracketedSequence(input)).x
 472                     ce = (
 473                         F.cross_entropy(output.transpose(1, 2), input, reduction="none")
 474                         * ar_mask
 475                     )
 476                     l[:, model.id] = -ce.sum(dim=-1)
 477
 478                 model.train(t)
 479
 480         return logproba.to("cpu")
 481
 482     ###############################################################
 483
 484     def compute_correctness(
 485         self,
 486         c_quizzes,
 487         models_for_validation,
 488         bidirectional_validation=False,
 489         deterministic_validation=True,
 490     ):
 491         if bidirectional_validation:
 492             backward_c_quizzes = self.forward_to_backward(c_quizzes)
 493
 494         seq_logproba = torch.zeros(
 495             c_quizzes.size(0),
 496             max([m.id for m in models_for_validation]) + 1,
 497             device=self.device,
 498         )
 499
 500         nb_correct = 0
 501
 502         seq_logproba[...] = 0.0
 503
 504         for model in models_for_validation:
 505             result = c_quizzes.clone()
 506
 507             ar_mask = self.make_ar_mask(result)
 508
 509             masked_inplace_autoregression(
 510                 model=model,
 511                 batch_size=self.batch_size,
 512                 input=result,
 513                 ar_mask=ar_mask,
 514                 seq_logproba=seq_logproba[:, model.id],
 515                 temperature=1.0,
 516                 deterministic_synthesis=deterministic_validation,
 517                 # progress_bar_desc="solving c_quizzes",
 518                 device=self.device,
 519             )
 520
 521             correct = (c_quizzes == result).long().min(dim=-1).values
 522
 523             if bidirectional_validation:
 524                 backward_result = backward_c_quizzes.clone()
 525
 526                 ar_mask = self.make_ar_mask(backward_result)
 527
 528                 masked_inplace_autoregression(
 529                     model=model,
 530                     batch_size=self.batch_size,
 531                     input=backward_result,
 532                     ar_mask=ar_mask,
 533                     seq_logproba=seq_logproba[:, model.id],
 534                     temperature=1.0,
 535                     deterministic_synthesis=deterministic_validation,
 536                     # progress_bar_desc="solving backward c_quizzes",
 537                     device=self.device,
 538                 )
 539
 540                 backward_correct = (
 541                     (backward_c_quizzes == backward_result).long().min(dim=-1).values
 542                 )
 543
 544                 correct *= backward_correct
 545
 546             # endif
 547
 548             nb_correct += correct
 549
 550         return nb_correct, seq_logproba
 551
 552     ###############################################################
 553
 554     def generate_quizzes(self, nb, model_for_generation, temperature=1.0):
 555         c_quizzes = torch.empty(
 556             nb,
 557             self.prompt_len + self.answer_len + 2,
 558             device=self.device,
 559             dtype=torch.int64,
 560         )
 561
 562         seq_logproba = torch.zeros(nb, device=self.device)
 563
 564         # First, we generate the answer at high temperature
 565
 566         c_quizzes[:, 0] = self.token_backward
 567         c_quizzes[:, 1 + self.answer_len] = self.token_backward
 568
 569         masked_inplace_autoregression(
 570             model=model_for_generation,
 571             batch_size=self.batch_size,
 572             input=c_quizzes,
 573             ar_mask=self.make_ar_mask(c_quizzes, first=True),
 574             seq_logproba=seq_logproba,
 575             temperature=temperature,
 576             deterministic_synthesis=False,
 577             device=self.device,
 578         )
 579
 580         # Then, we generate the prompt at low temperature
 581
 582         masked_inplace_autoregression(
 583             model=model_for_generation,
 584             batch_size=self.batch_size,
 585             input=c_quizzes,
 586             ar_mask=self.make_ar_mask(c_quizzes),
 587             seq_logproba=seq_logproba,
 588             temperature=1 / temperature,
 589             deterministic_synthesis=False,
 590             device=self.device,
 591         )
 592
 593         # Then we return the quizz, and re-generate the response, now
 594         # at low temperature
 595
 596         c_quizzes = self.reverse_time(c_quizzes)
 597
 598         masked_inplace_autoregression(
 599             model=model_for_generation,
 600             batch_size=self.batch_size,
 601             input=c_quizzes,
 602             ar_mask=self.make_ar_mask(c_quizzes),
 603             seq_logproba=seq_logproba,
 604             temperature=1 / temperature,
 605             deterministic_synthesis=False,
 606             device=self.device,
 607         )
 608
 609         return c_quizzes.to("cpu")