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
   9
  10 import torch, torchvision
  11 from torch import nn
  12 from torch.nn import functional as F
  13
  14 import ffutils
  15 import mygpt, tasks, problems
  16
  17 ######################################################################
  18
  19 if torch.cuda.is_available():
  20     device = torch.device("cuda")
  21     torch.backends.cuda.matmul.allow_tf32 = True
  22 else:
  23     device = torch.device("cpu")
  24
  25 ######################################################################
  26
  27 parser = argparse.ArgumentParser(
  28     description="An implementation of GPT with cache.",
  29     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
  30 )
  31
  32 parser.add_argument(
  33     "--task",
  34     type=str,
  35     default="twotargets",
  36     help="byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp",
  37 )
  38
  39 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
  40
  41 parser.add_argument("--result_dir", type=str, default=None)
  42
  43 parser.add_argument("--seed", type=int, default=0)
  44
  45 parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
  46
  47 ########################################
  48
  49 parser.add_argument("--nb_epochs", type=int, default=25)
  50
  51 parser.add_argument("--batch_size", type=int, default=None)
  52
  53 parser.add_argument("--nb_train_samples", type=int, default=None)
  54
  55 parser.add_argument("--nb_test_samples", type=int, default=None)
  56
  57 parser.add_argument("--optim", type=str, default="adam")
  58
  59 parser.add_argument("--learning_rate", type=float, default=1e-4)
  60
  61 parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
  62
  63 ########################################
  64
  65 parser.add_argument("--model", type=str, default=None)
  66
  67 parser.add_argument("--dim_model", type=int, default=None)
  68
  69 parser.add_argument("--dim_keys", type=int, default=None)
  70
  71 parser.add_argument("--dim_hidden", type=int, default=None)
  72
  73 parser.add_argument("--nb_heads", type=int, default=None)
  74
  75 parser.add_argument("--nb_blocks", type=int, default=None)
  76
  77 parser.add_argument("--dropout", type=float, default=0.1)
  78
  79 ########################################
  80
  81 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
  82
  83 parser.add_argument("--no_checkpoint", action="store_true", default=False)
  84
  85 parser.add_argument("--overwrite_results", action="store_true", default=False)
  86
  87 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
  88
  89 ##############################
  90 # rpl options
  91
  92 parser.add_argument("--rpl_nb_starting_values", type=int, default=3)
  93
  94 parser.add_argument("--rpl_max_input", type=int, default=9)
  95
  96 parser.add_argument("--rpl_prog_len", type=int, default=8)
  97
  98 parser.add_argument("--rpl_nb_runs", type=int, default=5)
  99
 100 parser.add_argument("--rpl_no_prog", action="store_true", default=False)
 101
 102 ##############################
 103 # grid options
 104
 105 parser.add_argument("--grid_size", type=int, default=6)
 106
 107 ##############################
 108 # picoclvr options
 109
 110 parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
 111
 112 parser.add_argument("--picoclvr_height", type=int, default=12)
 113
 114 parser.add_argument("--picoclvr_width", type=int, default=16)
 115
 116 parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
 117
 118 ##############################
 119 # Maze options
 120
 121 parser.add_argument("--maze_height", type=int, default=13)
 122
 123 parser.add_argument("--maze_width", type=int, default=21)
 124
 125 parser.add_argument("--maze_nb_walls", type=int, default=15)
 126
 127 ##############################
 128 # Snake options
 129
 130 parser.add_argument("--snake_height", type=int, default=9)
 131
 132 parser.add_argument("--snake_width", type=int, default=12)
 133
 134 parser.add_argument("--snake_nb_colors", type=int, default=5)
 135
 136 parser.add_argument("--snake_length", type=int, default=200)
 137
 138 ##############################
 139 # Stack options
 140
 141 parser.add_argument("--stack_nb_steps", type=int, default=100)
 142
 143 parser.add_argument("--stack_nb_stacks", type=int, default=3)
 144
 145 parser.add_argument("--stack_nb_digits", type=int, default=3)
 146
 147 parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.75)
 148
 149 ##############################
 150 # Expr options
 151
 152 parser.add_argument("--expr_nb_variables", type=int, default=5)
 153
 154 parser.add_argument("--expr_sequence_length", type=int, default=40)
 155
 156 parser.add_argument("--expr_operand_max", type=int, default=9)
 157
 158 parser.add_argument("--expr_result_max", type=int, default=99)
 159
 160 parser.add_argument("--expr_input_file", type=str, default=None)
 161
 162 ##############################
 163 # Mixing
 164
 165 parser.add_argument("--mixing_hard", action="store_true", default=False)
 166
 167 parser.add_argument("--mixing_deterministic_start", action="store_true", default=False)
 168
 169 ######################################################################
 170
 171 args = parser.parse_args()
 172
 173 assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
 174
 175 if args.result_dir is None:
 176     args.result_dir = f"results_{args.task}"
 177
 178 ######################################################################
 179
 180 default_task_args = {
 181     "addition": {
 182         "model": "352M",
 183         "batch_size": 25,
 184         "nb_train_samples": 250000,
 185         "nb_test_samples": 10000,
 186     },
 187     "byheart": {
 188         "model": "37M",
 189         "batch_size": 25,
 190         "nb_train_samples": 50000,
 191         "nb_test_samples": 10000,
 192     },
 193     "expr": {
 194         "model": "352M",
 195         "batch_size": 25,
 196         "nb_train_samples": 2500000,
 197         "nb_test_samples": 10000,
 198     },
 199     "grid": {
 200         "model": "37M",
 201         "batch_size": 25,
 202         "nb_train_samples": 250000,
 203         "nb_test_samples": 10000,
 204     },
 205     "qmlp": {
 206         "model": "37M",
 207         "batch_size": 10,
 208         "nb_train_samples": 100000,
 209         "nb_test_samples": 1000,
 210     },
 211     "guessop": {
 212         "model": "352M",
 213         "batch_size": 25,
 214         "nb_train_samples": 1000000,
 215         "nb_test_samples": 10000,
 216     },
 217     "learnop": {
 218         "model": "37M",
 219         "batch_size": 25,
 220         "nb_train_samples": 50000,
 221         "nb_test_samples": 10000,
 222     },
 223     "maze": {
 224         "model": "37M",
 225         "batch_size": 5,
 226         "nb_train_samples": 100000,
 227         "nb_test_samples": 10000,
 228     },
 229     "picoclvr": {
 230         "model": "37M",
 231         "batch_size": 25,
 232         "nb_train_samples": 250000,
 233         "nb_test_samples": 10000,
 234     },
 235     "rpl": {
 236         "model": "352M",
 237         "batch_size": 5,
 238         "nb_train_samples": 2500000,
 239         "nb_test_samples": 10000,
 240     },
 241     "snake": {
 242         "model": "37M",
 243         "batch_size": 25,
 244         "nb_train_samples": 250000,
 245         "nb_test_samples": 10000,
 246     },
 247     "stack": {
 248         "model": "37M",
 249         "batch_size": 25,
 250         "nb_train_samples": 100000,
 251         "nb_test_samples": 1000,
 252     },
 253     "twotargets": {
 254         "model": "37M",
 255         "batch_size": 25,
 256         "nb_train_samples": 50000,
 257         "nb_test_samples": 10000,
 258     },
 259     "memory": {
 260         "model": "4M",
 261         "batch_size": 100,
 262         "nb_train_samples": 5000,
 263         "nb_test_samples": 1000,
 264     },
 265     "mixing": {
 266         "model": "37M",
 267         "batch_size": 25,
 268         "nb_train_samples": 250000,
 269         "nb_test_samples": 10000,
 270     },
 271     "mnist": {
 272         "model": "37M",
 273         "batch_size": 10,
 274         "nb_train_samples": 60000,
 275         "nb_test_samples": 10000,
 276     },
 277 }
 278
 279 if args.task in default_task_args:
 280     for k, v in default_task_args[args.task].items():
 281         if getattr(args, k) is None:
 282             setattr(args, k, v)
 283
 284 ######################################################################
 285
 286 default_model_args = {
 287     "17K": {
 288         "dim_model": 32,
 289         "dim_keys": 32,
 290         "dim_hidden": 32,
 291         "nb_heads": 2,
 292         "nb_blocks": 2,
 293     },
 294     "4M": {
 295         "dim_model": 256,
 296         "dim_keys": 32,
 297         "dim_hidden": 1024,
 298         "nb_heads": 4,
 299         "nb_blocks": 6,
 300     },
 301     "37M": {
 302         "dim_model": 512,
 303         "dim_keys": 64,
 304         "dim_hidden": 2048,
 305         "nb_heads": 8,
 306         "nb_blocks": 12,
 307     },
 308     "122M": {
 309         "dim_model": 768,
 310         "dim_keys": 64,
 311         "dim_hidden": 2048,
 312         "nb_heads": 8,
 313         "nb_blocks": 24,
 314     },
 315     "352M": {
 316         "dim_model": 1024,
 317         "dim_keys": 64,
 318         "dim_hidden": 2048,
 319         "nb_heads": 8,
 320         "nb_blocks": 48,
 321     },
 322 }
 323
 324 if args.model in default_model_args:
 325     for k, v in default_model_args[args.model].items():
 326         if getattr(args, k) is None:
 327             setattr(args, k, v)
 328 else:
 329     raise ValueError(f"Unknown model {args.model}")
 330
 331 ######################################################################
 332
 333 try:
 334     os.mkdir(args.result_dir)
 335 except FileExistsError:
 336     if not args.overwrite_results:
 337         print(f"result directory {args.result_dir} already exists")
 338         exit(1)
 339
 340 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 341
 342 if args.seed >= 0:
 343     # torch.backends.cudnn.deterministic = True
 344     # torch.backends.cudnn.benchmark = False
 345     # torch.use_deterministic_algorithms(True)
 346     torch.manual_seed(args.seed)
 347     if torch.cuda.is_available():
 348         torch.cuda.manual_seed_all(args.seed)
 349
 350 ######################################################################
 351
 352
 353 def log_string(s):
 354     t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
 355
 356     if log_file is not None:
 357         log_file.write(t + s + "\n")
 358         log_file.flush()
 359
 360     print(t + s)
 361     sys.stdout.flush()
 362
 363
 364 log_string(f"argv {' '.join(sys.argv)}")
 365
 366 for n in vars(args):
 367     log_string(f"args.{n} {getattr(args, n)}")
 368
 369
 370 ######################################################################
 371
 372
 373 def picoclvr_pruner_horizontal_green(p):
 374     return not ("green" in p and ("left" in p or "right" in p))
 375
 376
 377 picoclvr_pruner_train = (
 378     picoclvr_pruner_horizontal_green
 379     if args.picocvlr_prune_properties in {"train+eval"}
 380     else None
 381 )
 382
 383 picoclvr_pruner_eval = (
 384     (lambda p: not picoclvr_pruner_horizontal_green(p))
 385     if args.picocvlr_prune_properties in {"train+eval", "eval"}
 386     else None
 387 )
 388
 389 ######################################################################
 390
 391 if args.task == "byheart":
 392     task = tasks.SandBox(
 393         problem=problems.ProblemByHeart(),
 394         nb_train_samples=args.nb_train_samples,
 395         nb_test_samples=args.nb_test_samples,
 396         batch_size=args.batch_size,
 397         logger=log_string,
 398         device=device,
 399     )
 400     args.max_percents_of_test_in_train = -1
 401
 402 elif args.task == "learnop":
 403     task = tasks.SandBox(
 404         problem=problems.ProblemLearnOperator(),
 405         nb_train_samples=args.nb_train_samples,
 406         nb_test_samples=args.nb_test_samples,
 407         batch_size=args.batch_size,
 408         logger=log_string,
 409         device=device,
 410     )
 411
 412
 413 elif args.task == "guessop":
 414     task = tasks.SandBox(
 415         problem=problems.ProblemGuessOperator(),
 416         nb_train_samples=args.nb_train_samples,
 417         nb_test_samples=args.nb_test_samples,
 418         batch_size=args.batch_size,
 419         logger=log_string,
 420         device=device,
 421     )
 422
 423
 424 elif args.task == "twotargets":
 425     task = tasks.SandBox(
 426         problem=problems.ProblemTwoTargets(),
 427         nb_train_samples=args.nb_train_samples,
 428         nb_test_samples=args.nb_test_samples,
 429         batch_size=args.batch_size,
 430         logger=log_string,
 431         device=device,
 432     )
 433
 434 elif args.task == "memory":
 435     task = tasks.SandBox(
 436         problem=problems.ProblemMemory(),
 437         nb_train_samples=args.nb_train_samples,
 438         nb_test_samples=args.nb_test_samples,
 439         batch_size=args.batch_size,
 440         logger=log_string,
 441         device=device,
 442     )
 443
 444 elif args.task == "mixing":
 445     task = tasks.SandBox(
 446         problem=problems.ProblemMixing(
 447             hard=args.mixing_hard, random_start=not args.mixing_deterministic_start
 448         ),
 449         nb_train_samples=args.nb_train_samples,
 450         nb_test_samples=args.nb_test_samples,
 451         batch_size=args.batch_size,
 452         logger=log_string,
 453         device=device,
 454     )
 455
 456 elif args.task == "addition":
 457     task = tasks.SandBox(
 458         problem=problems.ProblemAddition(),
 459         nb_train_samples=args.nb_train_samples,
 460         nb_test_samples=args.nb_test_samples,
 461         batch_size=args.batch_size,
 462         logger=log_string,
 463         device=device,
 464     )
 465
 466 elif args.task == "picoclvr":
 467     task = tasks.PicoCLVR(
 468         nb_train_samples=args.nb_train_samples,
 469         nb_test_samples=args.nb_test_samples,
 470         batch_size=args.batch_size,
 471         height=args.picoclvr_height,
 472         width=args.picoclvr_width,
 473         nb_colors=args.picoclvr_nb_colors,
 474         logger=log_string,
 475         device=device,
 476         pruner_train=picoclvr_pruner_train,
 477         pruner_eval=picoclvr_pruner_eval,
 478     )
 479
 480 elif args.task == "mnist":
 481     task = tasks.MNIST(
 482         nb_train_samples=args.nb_train_samples,
 483         nb_test_samples=args.nb_test_samples,
 484         batch_size=args.batch_size,
 485         device=device,
 486     )
 487
 488 elif args.task == "maze":
 489     task = tasks.Maze(
 490         nb_train_samples=args.nb_train_samples,
 491         nb_test_samples=args.nb_test_samples,
 492         batch_size=args.batch_size,
 493         height=args.maze_height,
 494         width=args.maze_width,
 495         nb_walls=args.maze_nb_walls,
 496         device=device,
 497     )
 498
 499 elif args.task == "snake":
 500     task = tasks.Snake(
 501         nb_train_samples=args.nb_train_samples,
 502         nb_test_samples=args.nb_test_samples,
 503         batch_size=args.batch_size,
 504         height=args.snake_height,
 505         width=args.snake_width,
 506         nb_colors=args.snake_nb_colors,
 507         length=args.snake_length,
 508         prompt_length=args.snake_length // 2,
 509         device=device,
 510     )
 511
 512 elif args.task == "stack":
 513     task = tasks.Stack(
 514         nb_train_samples=args.nb_train_samples,
 515         nb_test_samples=args.nb_test_samples,
 516         batch_size=args.batch_size,
 517         logger=log_string,
 518         nb_steps=args.stack_nb_steps,
 519         nb_stacks=args.stack_nb_stacks,
 520         nb_digits=args.stack_nb_digits,
 521         fraction_values_for_train=args.stack_fraction_values_for_train,
 522         device=device,
 523     )
 524
 525 elif args.task == "expr":
 526     task = tasks.Expr(
 527         nb_train_samples=args.nb_train_samples,
 528         nb_test_samples=args.nb_test_samples,
 529         nb_variables=args.expr_nb_variables,
 530         sequence_length=args.expr_sequence_length,
 531         operand_max=args.expr_operand_max,
 532         result_max=args.expr_result_max,
 533         batch_size=args.batch_size,
 534         device=device,
 535     )
 536
 537 elif args.task == "rpl":
 538     task = tasks.RPL(
 539         nb_train_samples=args.nb_train_samples,
 540         nb_test_samples=args.nb_test_samples,
 541         batch_size=args.batch_size,
 542         nb_starting_values=args.rpl_nb_starting_values,
 543         max_input=args.rpl_max_input,
 544         prog_len=args.rpl_prog_len,
 545         nb_runs=args.rpl_nb_runs,
 546         no_prog=args.rpl_no_prog,
 547         logger=log_string,
 548         device=device,
 549     )
 550
 551 elif args.task == "grid":
 552     task = tasks.Grid(
 553         nb_train_samples=args.nb_train_samples,
 554         nb_test_samples=args.nb_test_samples,
 555         batch_size=args.batch_size,
 556         size=args.grid_size,
 557         logger=log_string,
 558         device=device,
 559     )
 560
 561 elif args.task == "qmlp":
 562     task = tasks.QMLP(
 563         nb_train_samples=args.nb_train_samples,
 564         nb_test_samples=args.nb_test_samples,
 565         batch_size=args.batch_size,
 566         result_dir=args.result_dir,
 567         logger=log_string,
 568         device=device,
 569     )
 570
 571 else:
 572     raise ValueError(f"Unknown task {args.task}")
 573
 574 ######################################################################
 575
 576 log_string(f"device {device}")
 577
 578 vocabulary_size = task.vocabulary_size()
 579
 580 log_string(f"vocabulary_size {vocabulary_size}")
 581
 582 ##############################
 583
 584 model = mygpt.MyGPT(
 585     vocabulary_size=vocabulary_size,
 586     dim_model=args.dim_model,
 587     dim_keys=args.dim_keys,
 588     dim_hidden=args.dim_hidden,
 589     nb_heads=args.nb_heads,
 590     nb_blocks=args.nb_blocks,
 591     causal=True,
 592     dropout=args.dropout,
 593 )
 594
 595 model.to(device)
 596
 597 nb_parameters = sum(p.numel() for p in model.parameters())
 598 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 599
 600 ######################################################################
 601
 602 nb_epochs_finished = 0
 603
 604 if args.no_checkpoint:
 605     log_string(f"not trying to load checkpoint.")
 606
 607 else:
 608     try:
 609         checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 610         checkpoint = torch.load(checkpoint_name)
 611         nb_epochs_finished = checkpoint["nb_epochs_finished"]
 612         model.load_state_dict(checkpoint["model_state"])
 613         torch.set_rng_state(checkpoint["rng_state"])
 614         if torch.cuda.is_available():
 615             torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
 616
 617         log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
 618
 619     except FileNotFoundError:
 620         log_string("starting from scratch.")
 621
 622     except:
 623         log_string("error when loading the checkpoint.")
 624         exit(1)
 625
 626 ######################################################################
 627
 628 if args.task == "expr" and args.expr_input_file is not None:
 629     task.produce_results(
 630         n_epoch=nb_epochs_finished,
 631         model=model,
 632         result_dir=args.result_dir,
 633         logger=log_string,
 634         deterministic_synthesis=args.deterministic_synthesis,
 635         input_file=args.expr_input_file,
 636     )
 637
 638     exit(0)
 639
 640 ######################################################################
 641
 642 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
 643
 644 # Compute the entropy of the training tokens
 645
 646 token_count = 0
 647 for input in task.batches(split="train"):
 648     token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
 649 token_probas = token_count / token_count.sum()
 650 entropy = -torch.xlogy(token_probas, token_probas).sum()
 651 train_set_perplexity = math.exp(entropy)
 652
 653 ######################################################################
 654 # A bit of paranoia never hurts
 655
 656 if args.max_percents_of_test_in_train >= 0:
 657
 658     def subsets_as_tuples(batches, cs):
 659         s = set()
 660         for batch in batches:
 661             for x in batch:
 662                 s.add(tuple([v.item() for v in x]))
 663                 if len(s) == cs:
 664                     yield s
 665                     s = set()
 666         yield s
 667
 668     nb_test, nb_in_train = 0, 0
 669     for test_subset in subsets_as_tuples(task.batches(split="test"), 25000):
 670         in_train = set()
 671         for train_subset in subsets_as_tuples(task.batches(split="train"), 25000):
 672             in_train.update(test_subset.intersection(train_subset))
 673         nb_in_train += len(in_train)
 674         nb_test += len(test_subset)
 675
 676     log_string(
 677         f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
 678     )
 679
 680     assert (
 681         nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
 682     ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
 683
 684 ##############################
 685
 686 if args.learning_rate_schedule == "cos":
 687     learning_rate_schedule = {}
 688     for n_epoch in range(args.nb_epochs):
 689         u = n_epoch / args.nb_epochs * math.pi
 690         learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
 691 else:
 692     u = {
 693         int(k): float(v)
 694         for k, v in [
 695             tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
 696         ]
 697     }
 698
 699     learning_rate_schedule = {}
 700     learning_rate = args.learning_rate
 701     for n_epoch in range(args.nb_epochs):
 702         if n_epoch in u:
 703             learning_rate = u[n_epoch]
 704         learning_rate_schedule[n_epoch] = learning_rate
 705
 706 log_string(f"learning_rate_schedule {learning_rate_schedule}")
 707
 708 ##############################
 709
 710 nb_samples_seen = 0
 711
 712 if nb_epochs_finished >= nb_epochs:
 713     task.produce_results(
 714         n_epoch=nb_epochs_finished,
 715         model=model,
 716         result_dir=args.result_dir,
 717         logger=log_string,
 718         deterministic_synthesis=args.deterministic_synthesis,
 719     )
 720
 721 for n_epoch in range(nb_epochs_finished, nb_epochs):
 722     learning_rate = learning_rate_schedule[n_epoch]
 723
 724     log_string(f"learning_rate {learning_rate}")
 725
 726     if args.optim == "sgd":
 727         optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
 728     elif args.optim == "adam":
 729         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 730     elif args.optim == "adamw":
 731         optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 732     else:
 733         raise ValueError(f"Unknown optimizer {args.optim}.")
 734
 735     model.train()
 736
 737     nb_train_samples, acc_train_loss = 0, 0.0
 738
 739     for input in task.batches(split="train"):
 740         input = input.to(device)
 741         output = model(mygpt.BracketedSequence(input)).x
 742         loss = F.cross_entropy(output.transpose(1, 2), input)
 743         acc_train_loss += loss.item() * input.size(0)
 744         nb_train_samples += input.size(0)
 745         nb_samples_seen += input.size(0)
 746
 747         optimizer.zero_grad()
 748         loss.backward()
 749         optimizer.step()
 750
 751     with torch.autograd.no_grad():
 752         model.eval()
 753
 754         nb_test_samples, acc_test_loss = 0, 0.0
 755
 756         for input in task.batches(split="test"):
 757             input = input.to(device)
 758
 759             output = model(mygpt.BracketedSequence(input)).x
 760             loss = F.cross_entropy(output.transpose(1, 2), input)
 761             acc_test_loss += loss.item() * input.size(0)
 762             nb_test_samples += input.size(0)
 763
 764         train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 765         test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
 766
 767         log_string(
 768             f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 769         )
 770
 771         task.produce_results(
 772             n_epoch=n_epoch,
 773             model=model,
 774             result_dir=args.result_dir,
 775             logger=log_string,
 776             deterministic_synthesis=args.deterministic_synthesis,
 777         )
 778
 779     checkpoint = {
 780         "nb_epochs_finished": n_epoch + 1,
 781         "model_state": model.state_dict(),
 782         "rng_state": torch.get_rng_state(),
 783     }
 784
 785     if torch.cuda.is_available():
 786         checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
 787
 788     checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 789     torch.save(checkpoint, checkpoint_name)
 790     log_string(f"saved checkpoint {checkpoint_name}")
 791
 792 ######################################################################