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