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