quizz_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     forbidden_tokens=None,
  33     forced_biases=None,
  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 forbidden_tokens is not None:
  49             logits = logits.masked_fill(forbidden_tokens, float("-inf"))
  50
  51         if forced_biases is not None:
  52             logits = logits + forced_biases[None, :]
  53
  54         if deterministic_synthesis:
  55             t_next = logits.argmax(-1)
  56         else:
  57             dist = torch.distributions.categorical.Categorical(logits=logits)
  58             t_next = dist.sample()
  59
  60         all_n = torch.arange(t_next.size(0))
  61         seq_logproba += logits[all_n, t_next].sum(dim=-1)
  62
  63         input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
  64
  65
  66 def masked_inplace_autoregression(
  67     model,
  68     batch_size,
  69     input,
  70     ar_mask,
  71     seq_logproba,
  72     temperature,
  73     deterministic_synthesis,
  74     forbidden_tokens=None,
  75     logit_biases=None,
  76     progress_bar_desc=None,
  77     device=torch.device("cpu"),
  78 ):
  79     assert input.size() == ar_mask.size()
  80
  81     batches = zip(
  82         input.split(batch_size),
  83         ar_mask.split(batch_size),
  84         seq_logproba.split(batch_size),
  85     )
  86
  87     if progress_bar_desc is not None:
  88         batches = tqdm.tqdm(
  89             batches,
  90             dynamic_ncols=True,
  91             desc=progress_bar_desc,
  92             total=(input.size(0) + batch_size - 1) // batch_size,
  93         )
  94
  95     with torch.autograd.no_grad():
  96         t = model.training
  97         model.eval()
  98
  99         for input, ar_mask, seq_logproba in batches:
 100             one_batch_masked_inplace_autoregression(
 101                 model=model,
 102                 input=input,
 103                 ar_mask=ar_mask,
 104                 seq_logproba=seq_logproba,
 105                 temperature=temperature,
 106                 deterministic_synthesis=deterministic_synthesis,
 107                 forbidden_tokens=forbidden_tokens,
 108                 forced_biases=logit_biases,
 109             )
 110
 111         model.train(t)
 112
 113
 114 ######################################################################
 115
 116
 117 class QuizzMachine:
 118     def make_ar_mask(self, input):
 119         b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
 120         return b.long()[None, :].expand_as(input)
 121
 122     def __init__(
 123         self,
 124         problem,
 125         nb_train_samples,
 126         nb_test_samples,
 127         batch_size,
 128         result_dir,
 129         logger,
 130         device=torch.device("cpu"),
 131     ):
 132         super().__init__()
 133
 134         self.problem = problem
 135         self.batch_size = batch_size
 136         self.device = device
 137         self.logger = logger
 138
 139         self.train_w_quizzes = self.problem.generate_token_sequences(
 140             nb_train_samples
 141         ).to(device)
 142
 143         self.test_w_quizzes = self.problem.generate_token_sequences(nb_test_samples).to(
 144             device
 145         )
 146
 147         self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
 148
 149         self.train_c_quizzes = []
 150         self.test_c_quizzes = []
 151
 152         if result_dir is not None:
 153             self.problem.save_quizzes(
 154                 self.train_w_quizzes[:72], result_dir, "culture_w_quizzes"
 155             )
 156
 157     def batches(self, split="train", desc=None):
 158         assert split in {"train", "test"}
 159         if split == "train":
 160             w_quizzes = self.train_w_quizzes
 161             c_quizzes = self.train_c_quizzes
 162         else:
 163             w_quizzes = self.test_w_quizzes
 164             c_quizzes = self.test_c_quizzes
 165
 166         if len(c_quizzes) > 0:
 167             c_quizzes = torch.cat(c_quizzes, dim=0)
 168             if c_quizzes.size(0) > w_quizzes.size(0) // 2:
 169                 i = torch.randperm(c_quizzes.size(0))[: w_quizzes.size(0) // 2]
 170                 c_quizzes = c_quizzes[i]
 171
 172             i = torch.randperm(w_quizzes.size(0))[
 173                 : w_quizzes.size(0) - c_quizzes.size(0)
 174             ]
 175             w_quizzes = w_quizzes[i]
 176
 177             self.nb_batch_w_quizzes = w_quizzes.size(0)
 178             self.nb_batch_c_quizzes = c_quizzes.size(0)
 179
 180             input = torch.cat([w_quizzes, c_quizzes], dim=0)
 181         else:
 182             input = w_quizzes
 183             self.nb_batch_w_quizzes = w_quizzes.size(0)
 184             self.nb_batch_c_quizzes = 0
 185
 186         # Shuffle
 187         input = input[torch.randperm(input.size(0))]
 188
 189         if desc is None:
 190             desc = f"epoch-{split}"
 191         for batch in tqdm.tqdm(
 192             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 193         ):
 194             yield batch
 195
 196     def vocabulary_size(self):
 197         return self.nb_codes
 198
 199     def produce_results(
 200         self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
 201     ):
 202         def compute_accuracy(input):
 203             input = input[:nmax]
 204             ar_mask = self.make_ar_mask(input)
 205             result = input.clone() * (1 - ar_mask)
 206             seq_logproba = torch.empty(input.size(0), device=self.device)
 207
 208             masked_inplace_autoregression(
 209                 model=model,
 210                 batch_size=self.batch_size,
 211                 input=result,
 212                 ar_mask=ar_mask,
 213                 seq_logproba=seq_logproba,
 214                 temperature=1.0,
 215                 deterministic_synthesis=deterministic_synthesis,
 216                 progress_bar_desc=None,
 217                 device=self.device,
 218             )
 219
 220             nb_total, nb_correct = (
 221                 input.size(0),
 222                 (input == result).long().min(dim=1).values.sum(),
 223             )
 224
 225             return nb_total, nb_correct
 226
 227         train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes)
 228
 229         self.logger(
 230             f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 231         )
 232
 233         test_nb_total, test_nb_correct = compute_accuracy(self.test_w_quizzes)
 234
 235         self.logger(
 236             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 237         )
 238
 239         main_test_accuracy = test_nb_correct / test_nb_total
 240         self.logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
 241
 242         ##############################
 243
 244         input = self.test_w_quizzes[:96]
 245         ar_mask = self.make_ar_mask(input)
 246         result = input.clone() * (1 - ar_mask)
 247         seq_logproba = torch.empty(input.size(0), device=self.device)
 248
 249         masked_inplace_autoregression(
 250             model=model,
 251             batch_size=self.batch_size,
 252             input=result,
 253             ar_mask=ar_mask,
 254             seq_logproba=seq_logproba,
 255             temperature=1.0,
 256             deterministic_synthesis=deterministic_synthesis,
 257             progress_bar_desc=None,
 258             device=self.device,
 259         )
 260
 261         self.problem.save_quizzes(
 262             result[:72], result_dir, f"culture_prediction_{n_epoch:04d}_{model.id:02d}"
 263         )
 264
 265         return main_test_accuracy
 266
 267     def renew_w_quizzes(self, nb, for_train=True):
 268         input = self.train_w_quizzes if for_train else self.test_w_quizzes
 269         nb = min(nb, input.size(0))
 270         input[:-nb] = input[nb:].clone()
 271         input[-nb:] = self.problem.generate_token_sequences(nb).to(self.device)
 272
 273     def store_c_quizzes(self, new_c_quizzes, for_train=True):
 274         if for_train:
 275             self.train_c_quizzes.append(new_c_quizzes)
 276         else:
 277             self.test_c_quizzes.append(new_c_quizzes)
 278
 279     def reverse_time(self, c_quizzes):
 280         token_forward, token_backward = self.problem.direction_tokens()
 281
 282         l = (c_quizzes.size(1) - 1) // 2
 283         direction = c_quizzes[:, l : l + 1]
 284         direction = self.problem.token_forward * (
 285             direction == self.problem.token_backward
 286         ) + self.problem.token_backward * (direction == self.problem.token_forward)
 287
 288         return torch.cat([c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1)
 289
 290     def compute_correctness(
 291         self, c_quizzes, models_for_validation, both_directions=True
 292     ):
 293         reversed_c_quizzes = self.reverse_time(c_quizzes)
 294
 295         ar_mask = self.make_ar_mask(c_quizzes)
 296         seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
 297
 298         # Check how many of models can solve the quizzes in both directions
 299
 300         nb_correct = 0
 301
 302         for model in models_for_validation:
 303             result = c_quizzes.clone()
 304
 305             masked_inplace_autoregression(
 306                 model=model,
 307                 batch_size=self.batch_size,
 308                 input=result,
 309                 ar_mask=ar_mask,
 310                 seq_logproba=seq_logproba,
 311                 temperature=1.0,
 312                 deterministic_synthesis=True,
 313                 # progress_bar_desc="solving c_quizzes",
 314                 device=self.device,
 315             )
 316
 317             correct = (c_quizzes == result).long().min(dim=-1).values
 318
 319             if both_directions:
 320                 reversed_result = reversed_c_quizzes.clone()
 321
 322                 masked_inplace_autoregression(
 323                     model=model,
 324                     batch_size=self.batch_size,
 325                     input=reversed_result,
 326                     ar_mask=ar_mask,
 327                     seq_logproba=seq_logproba,
 328                     temperature=1.0,
 329                     deterministic_synthesis=True,
 330                     # progress_bar_desc="solving reversed c_quizzes",
 331                     device=self.device,
 332                 )
 333
 334                 reversed_correct = (
 335                     (reversed_c_quizzes == reversed_result).long().min(dim=-1).values
 336                 )
 337
 338                 correct *= reversed_correct
 339
 340             # endif
 341
 342             nb_correct += correct
 343
 344         return nb_correct
 345
 346     ###############################################################
 347
 348     def generate_quizzes(self, nb, model_for_generation, reverse_cleanup=False):
 349         c_quizzes = torch.empty(
 350             nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
 351         )
 352
 353         ar_mask_prompt = torch.zeros(c_quizzes.size(), device=self.device)
 354         ar_mask_prompt[:, : ar_mask_prompt.size(1) // 2 + 1] = 1
 355         ar_mask_solve = 1 - ar_mask_prompt
 356         seq_logproba = torch.empty(ar_mask_prompt.size(0), device=self.device)
 357
 358         if reverse_cleanup:
 359             warnings.warn("very high temperature with reversed cleanup", RuntimeWarning)
 360             temperature = 10.0
 361         else:
 362             temperature = 1.0
 363
 364         # warnings.warn("noise injection", RuntimeWarning)
 365         # noise_std = torch.rand(1).item()
 366         # self.logger(f"{noise_std=}")
 367
 368         # mygpt.set_noise_injection(model_for_generation, noise_std)
 369
 370         masked_inplace_autoregression(
 371             model=model_for_generation,
 372             batch_size=self.batch_size,
 373             input=c_quizzes,
 374             ar_mask=ar_mask_prompt,
 375             seq_logproba=seq_logproba,
 376             temperature=temperature,
 377             deterministic_synthesis=False,
 378             device=self.device,
 379         )
 380
 381         # mygpt.set_noise_injection(model_for_generation, 0.0)
 382
 383         ave_seq_logproba = seq_logproba.mean()
 384
 385         masked_inplace_autoregression(
 386             model=model_for_generation,
 387             batch_size=self.batch_size,
 388             input=c_quizzes,
 389             ar_mask=ar_mask_solve,
 390             seq_logproba=seq_logproba,
 391             temperature=temperature,
 392             deterministic_synthesis=True,
 393             device=self.device,
 394         )
 395
 396         if reverse_cleanup:
 397             c_quizzes = self.reverse_time(c_quizzes)
 398             masked_inplace_autoregression(
 399                 model=model_for_generation,
 400                 batch_size=self.batch_size,
 401                 input=c_quizzes,
 402                 ar_mask=ar_mask_solve,
 403                 seq_logproba=seq_logproba,
 404                 temperature=temperature,
 405                 deterministic_synthesis=True,
 406                 device=self.device,
 407             )
 408
 409             c_quizzes = self.reverse_time(c_quizzes)
 410             masked_inplace_autoregression(
 411                 model=model_for_generation,
 412                 batch_size=self.batch_size,
 413                 input=c_quizzes,
 414                 ar_mask=ar_mask_solve,
 415                 seq_logproba=seq_logproba,
 416                 temperature=temperature,
 417                 deterministic_synthesis=True,
 418                 device=self.device,
 419             )
 420
 421         return c_quizzes, seq_logproba.mean()