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         if self.prompt_len is None:
 223             self.prompt_len = prompts.size(1)
 224
 225         if self.answer_len is None:
 226             self.answer_len = answers.size(1)
 227
 228         assert prompts.size(1) == self.prompt_len and answers.size(1) == self.answer_len
 229
 230         result = []
 231
 232         for prompt, answer in zip(prompts, answers):
 233             a = [
 234                 torch.tensor([self.token_forward]),
 235                 prompt,
 236                 torch.tensor([self.token_forward]),
 237                 answer,
 238             ]
 239
 240             result.append(torch.cat(a, dim=0)[None, :])
 241
 242         return torch.cat(result, dim=0)
 243
 244     def __init__(
 245         self,
 246         problem,
 247         nb_train_samples,
 248         nb_test_samples,
 249         back_accuracy,
 250         batch_size,
 251         result_dir,
 252         logger,
 253         device=torch.device("cpu"),
 254     ):
 255         super().__init__()
 256
 257         v = problem.nb_token_values()
 258         self.token_forward = v
 259         self.token_backward = v + 1
 260         self.nb_token_values = v + 2
 261
 262         self.problem = problem
 263         self.back_accuracy = back_accuracy
 264         self.batch_size = batch_size
 265         self.device = device
 266         self.logger = logger
 267         self.prompt_len = None
 268         self.answer_len = None
 269
 270         self.LOCK_C_QUIZZES = threading.Lock()
 271         self.train_c_quizzes = []
 272         self.test_c_quizzes = []
 273
 274     def save_quiz_illustrations(
 275         self,
 276         result_dir,
 277         filename_prefix,
 278         quizzes,
 279         mistakes=None,
 280     ):
 281         quizzes = quizzes.clone().to("cpu")
 282         n_forward = quizzes[quizzes[:, 0] == self.token_forward]
 283         n_backward = quizzes[:, 0] == self.token_backward
 284         backward = quizzes[n_backward]
 285         assert n_forward.size(0) + backward.size(0) == quizzes.size(0)
 286         quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
 287
 288         predicted_prompts = n_backward.long()
 289         predicted_answers = 1 - predicted_prompts
 290         if mistakes is not None:
 291             # 0/-1/+1 ~ not-to-predict / predicted wrong / predicted correct
 292             predicted_prompts *= mistakes.to("cpu")
 293             predicted_answers *= mistakes.to("cpu")
 294         else:
 295             # 0/2 ~ not-to-predict / to predict
 296             predicted_prompts *= 2
 297             predicted_answers *= 2
 298
 299         self.problem.save_quiz_illustrations(
 300             result_dir,
 301             filename_prefix,
 302             quizzes[:, 1 : 1 + self.prompt_len],
 303             quizzes[:, 2 + self.prompt_len :],
 304             predicted_prompts,
 305             predicted_answers,
 306         )
 307
 308     def vocabulary_size(self):
 309         return self.nb_token_values
 310
 311     ######################################################################
 312
 313     def batches(self, model, split="train", desc=None):
 314         assert split in {"train", "test"}
 315
 316         with self.LOCK_C_QUIZZES:
 317             if split == "train":
 318                 w_quizzes = model.train_w_quizzes
 319                 c_quizzes = self.train_c_quizzes
 320             else:
 321                 w_quizzes = model.test_w_quizzes
 322                 c_quizzes = self.test_c_quizzes
 323
 324             if len(c_quizzes) > 0:
 325                 c_quizzes = torch.cat(c_quizzes, dim=0)
 326                 if c_quizzes.size(0) > w_quizzes.size(0) // 2:
 327                     i = torch.randperm(c_quizzes.size(0))[: w_quizzes.size(0) // 2]
 328                     c_quizzes = c_quizzes[i]
 329
 330                 i = torch.randperm(w_quizzes.size(0))[
 331                     : w_quizzes.size(0) - c_quizzes.size(0)
 332                 ]
 333                 w_quizzes = w_quizzes[i]
 334
 335                 self.nb_batch_w_quizzes = w_quizzes.size(0)
 336                 self.nb_batch_c_quizzes = c_quizzes.size(0)
 337
 338                 input = torch.cat([w_quizzes, c_quizzes], dim=0)
 339             else:
 340                 input = w_quizzes
 341                 self.nb_batch_w_quizzes = w_quizzes.size(0)
 342                 self.nb_batch_c_quizzes = 0
 343
 344         # Shuffle
 345         input = input[torch.randperm(input.size(0))]
 346
 347         if desc is None:
 348             desc = f"epoch-{split}"
 349         for batch in tqdm.tqdm(
 350             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 351         ):
 352             yield batch
 353
 354     ######################################################################
 355
 356     def produce_results(
 357         self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
 358     ):
 359         def compute_accuracy(input, log_prefix=None):
 360             input = input.to(self.device)
 361             ar_mask = self.make_ar_mask(input)
 362             result = input.clone() * (1 - ar_mask)
 363             seq_logproba = torch.empty(input.size(0), device=self.device)
 364
 365             masked_inplace_autoregression(
 366                 model=model,
 367                 batch_size=self.batch_size,
 368                 input=result,
 369                 ar_mask=ar_mask,
 370                 seq_logproba=seq_logproba,
 371                 temperature=1.0,
 372                 deterministic_synthesis=deterministic_synthesis,
 373                 progress_bar_desc=None,
 374                 device=self.device,
 375             )
 376
 377             correct = torch.empty(input.size(0), dtype=torch.int64, device=input.device)
 378
 379             n_forward = input[:, 0] == self.token_forward
 380             n_backward = input[:, 0] == self.token_backward
 381
 382             correct[n_forward] = (
 383                 (input[n_forward] == result[n_forward]).long().min(dim=1).values
 384             )
 385
 386             if self.back_accuracy and n_backward.any():
 387                 # accuracy of B->A*->B*=B instead of B->A*=A
 388                 back_input = self.reverse_time(result[n_backward])
 389                 back_input[:, 2 + self.prompt_len :] = input[
 390                     n_backward, 1 : 1 + self.answer_len
 391                 ]
 392                 _, correct[n_backward] = compute_accuracy(back_input)
 393
 394             if log_prefix is not None:
 395                 forward_nb_correct = correct[n_forward].sum()
 396                 forward_nb_total = correct[n_forward].size(0)
 397                 backward_nb_correct = correct[n_backward].sum()
 398                 backward_nb_total = correct[n_backward].size(0)
 399
 400                 self.logger(
 401                     f"{log_prefix}_accuracy {n_epoch} model {model.id} forward {forward_nb_correct} / {forward_nb_total} backward {backward_nb_correct} / {backward_nb_total}"
 402                 )
 403
 404             return result, correct
 405
 406         # compute_accuracy(model.train_w_quizzes[:nmax], log_prefix="train")
 407
 408         test_result, test_correct = compute_accuracy(
 409             model.test_w_quizzes[:nmax], log_prefix="test"
 410         )
 411
 412         main_test_accuracy = test_correct.sum() / test_correct.size(0)
 413         self.logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
 414
 415         ##############################
 416
 417         self.save_quiz_illustrations(
 418             result_dir,
 419             f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
 420             quizzes=test_result[:72],
 421             mistakes=test_correct[:72] * 2 - 1,
 422         )
 423
 424         return main_test_accuracy
 425
 426     ######################################################################
 427
 428     def renew_w_quizzes(self, model, nb, for_train=True):
 429         input = model.train_w_quizzes if for_train else model.test_w_quizzes
 430         nb = min(nb, input.size(0))
 431         input[:-nb] = input[nb:].clone()
 432         fresh_w_quizzes = self.generate_token_sequences(nb)
 433         self.reverse_random_half_in_place(fresh_w_quizzes)
 434         input[-nb:] = fresh_w_quizzes.to("cpu")
 435
 436     ######################################################################
 437
 438     def store_c_quizzes(self, new_c_quizzes, for_train=True):
 439         with self.LOCK_C_QUIZZES:
 440             if for_train:
 441                 self.train_c_quizzes.append(new_c_quizzes.to("cpu"))
 442             else:
 443                 self.test_c_quizzes.append(new_c_quizzes.to("cpu"))
 444
 445     def save_c_quizzes(self, filename):
 446         torch.save((self.train_c_quizzes, self.test_c_quizzes), filename)
 447
 448     def load_c_quizzes(self, filename):
 449         self.train_c_quizzes, self.test_c_quizzes = torch.load(filename)
 450
 451     ######################################################################
 452
 453     def logproba_of_solutions(self, models, c_quizzes):
 454         logproba = c_quizzes.new_zeros(
 455             c_quizzes.size(0), len(models), device=self.device, dtype=torch.float32
 456         )
 457
 458         for model in models:
 459             with torch.autograd.no_grad():
 460                 t = model.training
 461                 model.eval()
 462
 463                 for input, l in zip(
 464                     c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
 465                 ):
 466                     input = input.to(self.device)
 467                     ar_mask = self.make_ar_mask(input)
 468                     output = model(mygpt.BracketedSequence(input)).x
 469                     ce = (
 470                         F.cross_entropy(output.transpose(1, 2), input, reduction="none")
 471                         * ar_mask
 472                     )
 473                     l[:, model.id] = -ce.sum(dim=-1)
 474
 475                 model.train(t)
 476
 477         return logproba.to("cpu")
 478
 479     ###############################################################
 480
 481     def compute_correctness(
 482         self,
 483         c_quizzes,
 484         models_for_validation,
 485         bidirectional_validation=False,
 486         deterministic_validation=True,
 487     ):
 488         if bidirectional_validation:
 489             backward_c_quizzes = self.forward_to_backward(c_quizzes)
 490
 491         seq_logproba = torch.zeros(
 492             c_quizzes.size(0),
 493             max([m.id for m in models_for_validation]) + 1,
 494             device=self.device,
 495         )
 496
 497         nb_correct = 0
 498
 499         seq_logproba[...] = 0.0
 500
 501         for model in models_for_validation:
 502             result = c_quizzes.clone()
 503
 504             ar_mask = self.make_ar_mask(result)
 505
 506             masked_inplace_autoregression(
 507                 model=model,
 508                 batch_size=self.batch_size,
 509                 input=result,
 510                 ar_mask=ar_mask,
 511                 seq_logproba=seq_logproba[:, model.id],
 512                 temperature=1.0,
 513                 deterministic_synthesis=deterministic_validation,
 514                 # progress_bar_desc="solving c_quizzes",
 515                 device=self.device,
 516             )
 517
 518             correct = (c_quizzes == result).long().min(dim=-1).values
 519
 520             if bidirectional_validation:
 521                 backward_result = backward_c_quizzes.clone()
 522
 523                 ar_mask = self.make_ar_mask(backward_result)
 524
 525                 masked_inplace_autoregression(
 526                     model=model,
 527                     batch_size=self.batch_size,
 528                     input=backward_result,
 529                     ar_mask=ar_mask,
 530                     seq_logproba=seq_logproba[:, model.id],
 531                     temperature=1.0,
 532                     deterministic_synthesis=deterministic_validation,
 533                     # progress_bar_desc="solving backward c_quizzes",
 534                     device=self.device,
 535                 )
 536
 537                 backward_correct = (
 538                     (backward_c_quizzes == backward_result).long().min(dim=-1).values
 539                 )
 540
 541                 correct *= backward_correct
 542
 543             # endif
 544
 545             nb_correct += correct
 546
 547         return nb_correct, seq_logproba
 548
 549     ###############################################################
 550
 551     def generate_quizzes(self, nb, model_for_generation, temperature=1.0):
 552         c_quizzes = torch.empty(
 553             nb,
 554             self.prompt_len + self.answer_len + 2,
 555             device=self.device,
 556             dtype=torch.int64,
 557         )
 558
 559         seq_logproba = torch.zeros(nb, device=self.device)
 560
 561         # First, we generate the answer at high temperature
 562
 563         c_quizzes[:, 0] = self.token_backward
 564         c_quizzes[:, 1 + self.answer_len] = self.token_backward
 565
 566         masked_inplace_autoregression(
 567             model=model_for_generation,
 568             batch_size=self.batch_size,
 569             input=c_quizzes,
 570             ar_mask=self.make_ar_mask(c_quizzes, first=True),
 571             seq_logproba=seq_logproba,
 572             temperature=temperature,
 573             deterministic_synthesis=False,
 574             device=self.device,
 575         )
 576
 577         # Then, we generate the prompt at low temperature
 578
 579         masked_inplace_autoregression(
 580             model=model_for_generation,
 581             batch_size=self.batch_size,
 582             input=c_quizzes,
 583             ar_mask=self.make_ar_mask(c_quizzes),
 584             seq_logproba=seq_logproba,
 585             temperature=1 / temperature,
 586             deterministic_synthesis=False,
 587             device=self.device,
 588         )
 589
 590         # Then we return the quizz, and re-generate the response, now
 591         # at low temperature
 592
 593         c_quizzes = self.reverse_time(c_quizzes)
 594
 595         masked_inplace_autoregression(
 596             model=model_for_generation,
 597             batch_size=self.batch_size,
 598             input=c_quizzes,
 599             ar_mask=self.make_ar_mask(c_quizzes),
 600             seq_logproba=seq_logproba,
 601             temperature=1 / temperature,
 602             deterministic_synthesis=False,
 603             device=self.device,
 604         )
 605
 606         return c_quizzes.to("cpu")