main.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, sys, argparse, time, tqdm, os, datetime, warnings
   9
  10 import torch, torchvision
  11 from torch import nn
  12 from torch.nn import functional as F
  13
  14 import ffutils
  15
  16 import mygpt
  17 import sky, grids, quiz_machine
  18
  19 import threading
  20
  21 import torch.multiprocessing as mp
  22
  23 ######################################################################
  24
  25 parser = argparse.ArgumentParser(
  26     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
  27 )
  28
  29 parser.add_argument("--log_filename", type=str, default="train.log")
  30
  31 parser.add_argument("--result_dir", type=str, default=None)
  32
  33 parser.add_argument("--seed", type=int, default=0)
  34
  35 parser.add_argument("--resume", action="store_true", default=False)
  36
  37 parser.add_argument("--max_percents_of_test_in_train", type=int, default=-1)
  38
  39 ########################################
  40
  41 parser.add_argument("--nb_epochs", type=int, default=10000)
  42
  43 parser.add_argument("--batch_size", type=int, default=None)
  44
  45 parser.add_argument("--physical_batch_size", type=int, default=None)
  46
  47 parser.add_argument("--nb_train_samples", type=int, default=None)
  48
  49 parser.add_argument("--nb_test_samples", type=int, default=None)
  50
  51 parser.add_argument("--learning_rate", type=float, default=5e-4)
  52
  53 ########################################
  54
  55 parser.add_argument("--model", type=str, default=None)
  56
  57 parser.add_argument("--dim_model", type=int, default=None)
  58
  59 parser.add_argument("--dim_keys", type=int, default=None)
  60
  61 parser.add_argument("--dim_hidden", type=int, default=None)
  62
  63 parser.add_argument("--nb_heads", type=int, default=None)
  64
  65 parser.add_argument("--nb_blocks", type=int, default=None)
  66
  67 parser.add_argument("--dropout", type=float, default=0.1)
  68
  69 ########################################
  70
  71 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
  72
  73 parser.add_argument("--problem", type=str, default="grids")
  74
  75 parser.add_argument("--nb_threads", type=int, default=1)
  76
  77 parser.add_argument("--gpus", type=str, default="all")
  78
  79 parser.add_argument("--nb_gpts", type=int, default=5)
  80
  81 parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
  82
  83 parser.add_argument("--proba_understands", type=float, default=0.99)
  84
  85 parser.add_argument("--proba_not_understands", type=float, default=0.5)
  86
  87 parser.add_argument("--generation_temperature", type=float, default=2.0)
  88
  89 parser.add_argument("--dirty_debug", action="store_true", default=False)
  90
  91 ######################################################################
  92
  93 grids_tasks = ", ".join(
  94     [x.__name__.removeprefix("task_") for x in grids.Grids().all_tasks]
  95 )
  96
  97 parser.add_argument(
  98     "--grids_tasks",
  99     type=str,
 100     default=None,
 101     help="A comma-separated subset of: " + grids_tasks + ", or None for all.",
 102 )
 103
 104 ######################################################################
 105
 106 parser.add_argument("--sky_height", type=int, default=6)
 107
 108 parser.add_argument("--sky_width", type=int, default=8)
 109
 110 parser.add_argument("--sky_nb_birds", type=int, default=3)
 111
 112 parser.add_argument("--sky_nb_iterations", type=int, default=2)
 113
 114 parser.add_argument("--sky_speed", type=int, default=3)
 115
 116 ######################################################################
 117
 118 args = parser.parse_args()
 119
 120 if args.result_dir is None:
 121     args.result_dir = f"results_culture"
 122
 123 ######################################################################
 124
 125 default_args = {
 126     "model": "37M",
 127     "batch_size": 25,
 128     "nb_train_samples": 100000,
 129     "nb_test_samples": 10000,
 130 }
 131
 132 for k, v in default_args.items():
 133     if getattr(args, k) is None:
 134         setattr(args, k, v)
 135
 136 ######################################################################
 137
 138 default_model_args = {
 139     "17K": {
 140         "dim_model": 32,
 141         "dim_keys": 32,
 142         "dim_hidden": 32,
 143         "nb_heads": 2,
 144         "nb_blocks": 2,
 145     },
 146     "4M": {
 147         "dim_model": 256,
 148         "dim_keys": 32,
 149         "dim_hidden": 1024,
 150         "nb_heads": 4,
 151         "nb_blocks": 6,
 152     },
 153     "37M": {
 154         "dim_model": 512,
 155         "dim_keys": 64,
 156         "dim_hidden": 2048,
 157         "nb_heads": 8,
 158         "nb_blocks": 12,
 159     },
 160     "122M": {
 161         "dim_model": 768,
 162         "dim_keys": 64,
 163         "dim_hidden": 2048,
 164         "nb_heads": 8,
 165         "nb_blocks": 24,
 166     },
 167     "352M": {
 168         "dim_model": 1024,
 169         "dim_keys": 64,
 170         "dim_hidden": 2048,
 171         "nb_heads": 8,
 172         "nb_blocks": 48,
 173     },
 174 }
 175
 176 if args.model in default_model_args:
 177     for k, v in default_model_args[args.model].items():
 178         if getattr(args, k) is None:
 179             setattr(args, k, v)
 180 else:
 181     raise ValueError(f"Unknown model {args.model}")
 182
 183 ######################################################################
 184
 185 if args.resume:
 186     assert os.path.isdir(args.result_dir)
 187
 188 else:
 189     try:
 190         os.mkdir(args.result_dir)
 191     except FileExistsError:
 192         print(f"result directory {args.result_dir} already exists")
 193         exit(1)
 194
 195 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 196
 197 if args.seed >= 0:
 198     # torch.backends.cudnn.deterministic = True
 199     # torch.backends.cudnn.benchmark = False
 200     # torch.use_deterministic_algorithms(True)
 201     torch.manual_seed(args.seed)
 202     if torch.cuda.is_available():
 203         torch.cuda.manual_seed_all(args.seed)
 204
 205 ######################################################################
 206
 207
 208 def log_string(s):
 209     t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
 210
 211     if log_file is not None:
 212         log_file.write(t + s + "\n")
 213         log_file.flush()
 214
 215     print(t + s)
 216     sys.stdout.flush()
 217
 218
 219 log_string(f"argv {' '.join(sys.argv)}")
 220
 221 for n in vars(args):
 222     log_string(f"args.{n} {getattr(args, n)}")
 223
 224
 225 ######################################################################
 226
 227 if args.gpus == "all":
 228     gpus_idx = range(torch.cuda.device_count())
 229 else:
 230     gpus_idx = [int(k) for k in args.gpus.split(",")]
 231
 232 gpus = [torch.device(f"cuda:{n}") for n in gpus_idx]
 233
 234 if torch.cuda.is_available():
 235     main_device = gpus[0]
 236 else:
 237     assert len(gpus) == 0
 238     main_device = torch.device("cpu")
 239
 240 if args.dirty_debug:
 241     args.nb_train_samples = 2500
 242     args.nb_test_samples = 100
 243
 244 if args.physical_batch_size is None:
 245     args.physical_batch_size = args.batch_size
 246 else:
 247     assert args.batch_size % args.physical_batch_size == 0
 248
 249 assert args.nb_train_samples % args.batch_size == 0
 250 assert args.nb_test_samples % args.batch_size == 0
 251
 252 if args.problem == "sky":
 253     problem = sky.Sky(
 254         height=args.sky_height,
 255         width=args.sky_width,
 256         nb_birds=args.sky_nb_birds,
 257         nb_iterations=args.sky_nb_iterations,
 258         speed=args.sky_speed,
 259         max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
 260         chunk_size=100,
 261         nb_threads=args.nb_threads,
 262     )
 263     back_accuracy = False
 264 elif args.problem == "grids":
 265     problem = grids.Grids(
 266         max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
 267         chunk_size=100,
 268         nb_threads=args.nb_threads,
 269         tasks=args.grids_tasks,
 270     )
 271     back_accuracy = True
 272 else:
 273     raise ValueError
 274
 275 problem.save_some_examples(args.result_dir)
 276
 277 quiz_machine = quiz_machine.QuizMachine(
 278     problem=problem,
 279     nb_train_samples=args.nb_train_samples,
 280     nb_test_samples=args.nb_test_samples,
 281     back_accuracy=back_accuracy,
 282     batch_size=args.physical_batch_size,
 283     result_dir=args.result_dir,
 284     logger=log_string,
 285     device=main_device,
 286 )
 287
 288 ######################################################################
 289
 290 log_string(f"main_device {main_device} gpus {[ str(g) for g in gpus]}")
 291
 292 vocabulary_size = quiz_machine.vocabulary_size()
 293
 294 log_string(f"vocabulary_size {vocabulary_size}")
 295
 296 ######################################################################
 297
 298
 299 def run_tests(model, quiz_machine, deterministic_synthesis, local_device=main_device):
 300     with torch.autograd.no_grad():
 301         model.eval().to(local_device)
 302
 303         nb_test_samples, acc_test_loss = 0, 0.0
 304         nb_samples_accumulated = 0
 305
 306         for input in quiz_machine.batches(model, split="test"):
 307             input = input.to(local_device)
 308
 309             bs = model(mygpt.BracketedSequence(input))
 310             output = bs.x
 311
 312             loss = F.cross_entropy(output.transpose(1, 2), input)
 313
 314             acc_test_loss += loss.item() * input.size(0)
 315
 316             nb_test_samples += input.size(0)
 317
 318         test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
 319
 320         log_string(f"test_perplexity {n_epoch} model {model.id} {test_perplexity}")
 321
 322         model.main_test_accuracy = quiz_machine.produce_results(
 323             n_epoch=n_epoch,
 324             model=model,
 325             result_dir=args.result_dir,
 326             deterministic_synthesis=deterministic_synthesis,
 327         )
 328
 329
 330 def one_epoch(model, quiz_machine, local_device=main_device):
 331     model.to(local_device).train()
 332
 333     optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
 334
 335     nb_train_samples, acc_train_loss = 0, 0.0
 336
 337     for input in quiz_machine.batches(model, split="train"):
 338         input = input.to(local_device)
 339
 340         if nb_train_samples % args.batch_size == 0:
 341             optimizer.zero_grad()
 342
 343         output = model(mygpt.BracketedSequence(input)).x
 344         loss = F.cross_entropy(output.transpose(1, 2), input)
 345         acc_train_loss += loss.item() * input.size(0)
 346
 347         nb_train_samples += input.size(0)
 348
 349         loss.backward()
 350
 351         if nb_train_samples % args.batch_size == 0:
 352             optimizer.step()
 353
 354     train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 355
 356     log_string(f"train_perplexity {n_epoch} model {model.id} {train_perplexity}")
 357
 358     run_tests(model, quiz_machine, deterministic_synthesis=False)
 359
 360     model.to(main_device)
 361
 362
 363 ######################################################################
 364
 365
 366 def standard_validity(logproba):
 367     l = logproba.sort(dim=-1).values
 368     return (l[:, 0] < math.log(args.proba_not_understands)) & (
 369         l[:, 1] > math.log(args.proba_understands)
 370     )
 371
 372
 373 def valid_quizzes_and_logprobas(recorded, criteria):
 374     validated_quizzes, validated_logprobas = [], []
 375     for q, lp in recorded:
 376         validated_indices = criteria(lp)
 377         validated_quizzes.append(q[validated_indices])
 378         validated_logprobas.append(lp[validated_indices])
 379
 380     if len(validated_quizzes) > 0:
 381         return torch.cat(validated_quizzes, dim=0), torch.cat(
 382             validated_logprobas, dim=0
 383         )
 384     else:
 385         return None, None
 386
 387
 388 ######################################################################
 389
 390
 391 def create_c_quizzes(models, quiz_machine, nb_for_train=1000, nb_for_test=100):
 392     nb_to_create = nb_for_train + nb_for_test
 393
 394     recorded_quizzes_logprobas = []
 395
 396     nb_validated = 0
 397
 398     while nb_validated < nb_to_create:
 399         model_for_generation = models[torch.randint(len(models), (1,))]
 400
 401         c_quizzes = quiz_machine.generate_quizzes(
 402             nb_to_create,
 403             model_for_generation=model_for_generation,
 404             temperature=args.generation_temperature,
 405         )
 406
 407         c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
 408
 409         if c_quizzes.size(0) > 0:
 410             logproba = quiz_machine.logproba_of_solutions(models, c_quizzes)
 411             recorded_quizzes_logprobas.append((c_quizzes, logproba))
 412
 413             validated_quizzes, validated_logprobas = valid_quizzes_and_logprobas(
 414                 recorded_quizzes_logprobas, standard_validity
 415             )
 416
 417             if validated_quizzes is not None:
 418                 nb_validated = validated_quizzes.size(0)
 419
 420         log_string(
 421             f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
 422         )
 423
 424     # store the new c_quizzes which have been validated
 425
 426     quiz_machine.reverse_random_half_in_place(validated_quizzes)
 427     quiz_machine.store_c_quizzes(validated_quizzes[:nb_for_train], for_train=True)
 428     quiz_machine.store_c_quizzes(
 429         validated_quizzes[nb_for_train:nb_to_create], for_train=False
 430     )
 431
 432     ######################################################################
 433     # save the log probas
 434
 435     file_name = os.path.join(
 436         args.result_dir, f"culture_c_quiz_all_{n_epoch:04d}_logp.dat"
 437     )
 438
 439     with open(file_name, "w") as logp_file:
 440         for _, ll in recorded_quizzes_logprobas:
 441             for l in ll:
 442                 s = " ".join([str(x.item()) for x in l])
 443                 logp_file.write(s + "\n")
 444
 445     ######################################################################
 446     # save images with their logprobas
 447
 448     vq = validated_quizzes[:72]
 449     vl = validated_logprobas[:72]
 450
 451     if vq.size(0) > 0:
 452         prefix = f"culture_c_quiz_{n_epoch:04d}"
 453
 454         file_name = os.path.join(args.result_dir, prefix + "_logp.dat")
 455         with open(file_name, "w") as logp_file:
 456             for l in vl:
 457                 s = " ".join([str(x.item()) for x in l])
 458                 logp_file.write(s + "\n")
 459
 460         quiz_machine.save_quiz_illustrations(args.result_dir, prefix, vq)
 461
 462
 463 ######################################################################
 464
 465 models = []
 466
 467 for k in range(args.nb_gpts):
 468     log_string(f"creating model {k} and its w_quizzes")
 469     model = mygpt.MyGPT(
 470         vocabulary_size=vocabulary_size,
 471         dim_model=args.dim_model,
 472         dim_keys=args.dim_keys,
 473         dim_hidden=args.dim_hidden,
 474         nb_heads=args.nb_heads,
 475         nb_blocks=args.nb_blocks,
 476         causal=True,
 477         dropout=args.dropout,
 478     ).to(main_device)
 479
 480     model.main_test_accuracy = 0.0
 481     model.id = k
 482
 483     model.train_w_quizzes = quiz_machine.generate_token_sequences(args.nb_train_samples)
 484     quiz_machine.reverse_random_half_in_place(model.train_w_quizzes)
 485     model.test_w_quizzes = quiz_machine.generate_token_sequences(args.nb_test_samples)
 486     quiz_machine.reverse_random_half_in_place(model.test_w_quizzes)
 487
 488     models.append(model)
 489
 490 ######################################################################
 491
 492 if args.resume:
 493     try:
 494         for model in models:
 495             filename = f"gpt_{model.id:03d}.pth"
 496
 497             try:
 498                 d = torch.load(os.path.join(args.result_dir, filename))
 499                 model.load_state_dict(d[0])
 500                 model.main_test_accuracy = d[1]
 501                 log_string(f"successfully loaded {filename}")
 502             except FileNotFoundError:
 503                 log_string(f"cannot find {filename}")
 504                 pass
 505
 506         try:
 507             filename = "c_quizzes.pth"
 508             quiz_machine.load_c_quizzes(os.path.join(args.result_dir, filename))
 509             log_string(f"successfully loaded {filename}")
 510         except FileNotFoundError:
 511             log_string(f"cannot find {filename}")
 512             pass
 513
 514     except:
 515         log_string(f"error when loading {filename}.")
 516         exit(1)
 517
 518 ######################################################################
 519
 520 nb_parameters = sum(p.numel() for p in models[0].parameters())
 521 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 522
 523 ######################################################################
 524
 525 # Compute the entropy of the training tokens
 526
 527 token_count = 0
 528 for input in quiz_machine.batches(models[0], split="train", desc="train-entropy"):
 529     token_count += F.one_hot(input, num_classes=quiz_machine.vocabulary_size()).sum(
 530         (0, 1)
 531     )
 532 token_probas = token_count / token_count.sum()
 533 entropy = -torch.xlogy(token_probas, token_probas).sum()
 534 train_set_perplexity = math.exp(entropy)
 535
 536 ######################################################################
 537 # A bit of paranoia never hurts
 538
 539 if args.max_percents_of_test_in_train >= 0:
 540
 541     def subsets_as_tuples(batches, cs):
 542         s = set()
 543         for batch in batches:
 544             for x in batch:
 545                 s.add(tuple([v.item() for v in x]))
 546                 if len(s) == cs:
 547                     yield s
 548                     s = set()
 549         yield s
 550
 551     nb_test, nb_in_train = 0, 0
 552     for test_subset in subsets_as_tuples(
 553         quiz_machine.batches(models[0], split="test", desc="test-check"), 25000
 554     ):
 555         in_train = set()
 556         for train_subset in subsets_as_tuples(
 557             quiz_machine.batches(models[0], split="train", desc="train-check"), 25000
 558         ):
 559             in_train.update(test_subset.intersection(train_subset))
 560         nb_in_train += len(in_train)
 561         nb_test += len(test_subset)
 562
 563     log_string(
 564         f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
 565     )
 566
 567     assert (
 568         nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
 569     ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
 570
 571 ######################################################################
 572
 573 nb_new_c_quizzes_for_train = args.nb_train_samples // 50
 574 nb_new_c_quizzes_for_test = args.nb_test_samples // 50
 575
 576 log_string(
 577     f"nb_new_c_quizzes_for_train {nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {nb_new_c_quizzes_for_test}"
 578 )
 579
 580 ######################################################################
 581
 582 if args.dirty_debug:
 583     args.accuracy_to_make_c_quizzes = 0.0
 584     args.nb_gpts = 2
 585     nb_new_c_quizzes_for_train = 100
 586     nb_new_c_quizzes_for_test = 10
 587
 588     def standard_validity(logproba):
 589         l = logproba.sort(dim=-1).values
 590         return l[:, 0] < math.log(0.5)
 591
 592
 593 ######################################################################
 594
 595 for n_epoch in range(args.nb_epochs):
 596     log_string(f"--- epoch {n_epoch} ----------------------------------------")
 597
 598     cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
 599     log_string(f"current_test_accuracies {cta}")
 600
 601     ##################################################
 602     # Select, improve, and eval the worst model
 603
 604     ranked_models = sorted(models, key=lambda m: float(m.main_test_accuracy))
 605
 606     weakest_models = ranked_models[: len(gpus)]
 607
 608     threads = []
 609
 610     for gpu, model in zip(gpus, weakest_models):
 611         log_string(f"training model {model.id}")
 612
 613         t = threading.Thread(
 614             target=one_epoch, daemon=True, args=(model, quiz_machine, gpu)
 615         )
 616
 617         threads.append(t)
 618
 619         t.start()
 620
 621     for t in threads:
 622         t.join()
 623
 624     # Save the models to disk
 625
 626     for model in weakest_models:
 627         filename = f"gpt_{model.id:03d}.pth"
 628         torch.save(
 629             (model.state_dict(), model.main_test_accuracy),
 630             os.path.join(args.result_dir, filename),
 631         )
 632         log_string(f"wrote {filename}")
 633
 634     # Renew the training samples
 635
 636     for model in weakest_models:
 637         quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
 638
 639     ##################################################
 640     # If all the models are good enough, generate new quizzes and
 641     # re-compute the test errors
 642
 643     if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
 644         create_c_quizzes(
 645             models,
 646             quiz_machine,
 647             nb_for_train=nb_new_c_quizzes_for_train,
 648             nb_for_test=nb_new_c_quizzes_for_test,
 649         )
 650
 651         filename = "c_quizzes.pth"
 652         quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
 653         log_string(f"wrote {filename}")
 654
 655 ######################################################################