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