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 # torch.backends.cuda.matmul.allow_tf23
   9 # torch.autocast(torch.bfloat16)
  10
  11 import math, sys, argparse, time, tqdm, os
  12
  13 import torch, torchvision
  14 from torch import nn
  15 from torch.nn import functional as F
  16
  17 import ffutils
  18 import mygpt, tasks
  19
  20 ######################################################################
  21
  22 if torch.cuda.is_available():
  23     device = torch.device("cuda")
  24     torch.backends.cuda.matmul.allow_tf32 = True
  25 else:
  26     device = torch.device("cpu")
  27
  28 ######################################################################
  29
  30 parser = argparse.ArgumentParser(
  31     description="An implementation of GPT with cache.",
  32     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
  33 )
  34
  35 parser.add_argument(
  36     "--task",
  37     type=str,
  38     default="sandbox",
  39     help="sandbox, picoclvr, mnist, maze, snake, stack, expr, world",
  40 )
  41
  42 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
  43
  44 parser.add_argument("--result_dir", type=str, default=None)
  45
  46 parser.add_argument("--seed", type=int, default=0)
  47
  48 parser.add_argument("--nb_epochs", type=int, default=None)
  49
  50 parser.add_argument("--batch_size", type=int, default=None)
  51
  52 parser.add_argument("--nb_train_samples", type=int, default=None)
  53
  54 parser.add_argument("--nb_test_samples", type=int, default=None)
  55
  56 parser.add_argument("--optim", type=str, default="adam")
  57
  58 parser.add_argument("--learning_rate", type=float, default=1e-4)
  59
  60 parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
  61
  62 parser.add_argument("--model", type=str, default="37M")
  63
  64 parser.add_argument("--dim_model", type=int, default=None)
  65
  66 parser.add_argument("--dim_keys", type=int, default=None)
  67
  68 parser.add_argument("--dim_hidden", type=int, default=None)
  69
  70 parser.add_argument("--nb_heads", type=int, default=None)
  71
  72 parser.add_argument("--nb_blocks", type=int, default=None)
  73
  74 parser.add_argument("--dropout", type=float, default=0.1)
  75
  76 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
  77
  78 parser.add_argument("--no_checkpoint", action="store_true", default=False)
  79
  80 parser.add_argument("--overwrite_results", action="store_true", default=False)
  81
  82 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
  83
  84 ##############################
  85 # picoclvr options
  86
  87 parser.add_argument("--sandbox_level", type=int, default=0)
  88
  89 parser.add_argument("--sandbox_levels_nb_items", type=int, default=25)
  90
  91 parser.add_argument("--sandbox_levels_len_source", type=int, default=6)
  92
  93 parser.add_argument("--sandbox_levels_len_result", type=int, default=8)
  94
  95 ##############################
  96 # picoclvr options
  97
  98 parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
  99
 100 parser.add_argument("--picoclvr_height", type=int, default=12)
 101
 102 parser.add_argument("--picoclvr_width", type=int, default=16)
 103
 104 parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
 105
 106 ##############################
 107 # Maze options
 108
 109 parser.add_argument("--maze_height", type=int, default=23)
 110
 111 parser.add_argument("--maze_width", type=int, default=39)
 112
 113 parser.add_argument("--maze_nb_walls", type=int, default=45)
 114
 115 ##############################
 116 # Snake options
 117
 118 parser.add_argument("--snake_height", type=int, default=6)
 119
 120 parser.add_argument("--snake_width", type=int, default=8)
 121
 122 parser.add_argument("--snake_nb_colors", type=int, default=5)
 123
 124 parser.add_argument("--snake_length", type=int, default=200)
 125
 126 ##############################
 127 # Stack options
 128
 129 parser.add_argument("--stack_nb_steps", type=int, default=100)
 130
 131 parser.add_argument("--stack_nb_stacks", type=int, default=3)
 132
 133 parser.add_argument("--stack_nb_digits", type=int, default=3)
 134
 135 parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.75)
 136
 137 ##############################
 138 # Expr options
 139
 140 parser.add_argument("--expr_nb_variables", type=int, default=5)
 141
 142 parser.add_argument("--expr_sequence_length", type=int, default=40)
 143
 144 parser.add_argument("--expr_operand_max", type=int, default=9)
 145
 146 parser.add_argument("--expr_result_max", type=int, default=99)
 147
 148 parser.add_argument("--expr_input_file", type=str, default=None)
 149
 150 ##############################
 151 # World options
 152
 153 parser.add_argument("--world_vqae_nb_epochs", type=int, default=25)
 154
 155 ######################################################################
 156
 157 args = parser.parse_args()
 158
 159 assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
 160
 161 if args.result_dir is None:
 162     args.result_dir = f"results_{args.task}"
 163
 164 ######################################################################
 165
 166 default_task_args = {
 167     "sandbox": {
 168         "nb_epochs": 50,
 169         "batch_size": 25,
 170         "nb_train_samples": 100000,
 171         "nb_test_samples": 10000,
 172     },
 173     "picoclvr": {
 174         "nb_epochs": 25,
 175         "batch_size": 25,
 176         "nb_train_samples": 250000,
 177         "nb_test_samples": 10000,
 178     },
 179     "mnist": {
 180         "nb_epochs": 25,
 181         "batch_size": 10,
 182         "nb_train_samples": 250000,
 183         "nb_test_samples": 10000,
 184     },
 185     "maze": {
 186         "nb_epochs": 25,
 187         "batch_size": 5,
 188         "nb_train_samples": 250000,
 189         "nb_test_samples": 10000,
 190     },
 191     "snake": {
 192         "nb_epochs": 5,
 193         "batch_size": 25,
 194         "nb_train_samples": 250000,
 195         "nb_test_samples": 10000,
 196     },
 197     "stack": {
 198         "nb_epochs": 5,
 199         "batch_size": 25,
 200         "nb_train_samples": 100000,
 201         "nb_test_samples": 1000,
 202     },
 203     "expr": {
 204         "nb_epochs": 40,
 205         "batch_size": 25,
 206         "nb_train_samples": 1000000,
 207         "nb_test_samples": 10000,
 208     },
 209     "world": {
 210         "nb_epochs": 10,
 211         "batch_size": 25,
 212         "nb_train_samples": 25000,
 213         "nb_test_samples": 1000,
 214     },
 215 }
 216
 217 if args.task in default_task_args:
 218     for k, v in default_task_args[args.task].items():
 219         if getattr(args, k) is None:
 220             setattr(args, k, v)
 221
 222 ######################################################################
 223
 224 default_model_args = {
 225     "17K": {
 226         "dim_model": 32,
 227         "dim_keys": 32,
 228         "dim_hidden": 32,
 229         "nb_heads": 2,
 230         "nb_blocks": 2,
 231     },
 232     "37M": {
 233         "dim_model": 512,
 234         "dim_keys": 64,
 235         "dim_hidden": 2048,
 236         "nb_heads": 8,
 237         "nb_blocks": 12,
 238     },
 239     "122M": {
 240         "dim_model": 768,
 241         "dim_keys": 64,
 242         "dim_hidden": 2048,
 243         "nb_heads": 8,
 244         "nb_blocks": 24,
 245     },
 246     "352M": {
 247         "dim_model": 1024,
 248         "dim_keys": 64,
 249         "dim_hidden": 2048,
 250         "nb_heads": 8,
 251         "nb_blocks": 48,
 252     },
 253 }
 254
 255 if args.model in default_model_args:
 256     for k, v in default_model_args[args.model].items():
 257         if getattr(args, k) is None:
 258             setattr(args, k, v)
 259 else:
 260     raise ValueError(f"Unknown model {args.model}")
 261
 262 ######################################################################
 263
 264 try:
 265     os.mkdir(args.result_dir)
 266 except FileExistsError:
 267     if not args.overwrite_results:
 268         print(f"result directory {args.result_dir} already exists")
 269         exit(1)
 270
 271 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 272
 273 if args.seed >= 0:
 274     # torch.backends.cudnn.deterministic = True
 275     # torch.backends.cudnn.benchmark = False
 276     # torch.use_deterministic_algorithms(True)
 277     torch.manual_seed(args.seed)
 278     if torch.cuda.is_available():
 279         torch.cuda.manual_seed_all(args.seed)
 280
 281 ######################################################################
 282
 283
 284 def log_string(s):
 285     t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
 286
 287     if log_file is not None:
 288         log_file.write(t + s + "\n")
 289         log_file.flush()
 290
 291     print(t + s)
 292     sys.stdout.flush()
 293
 294
 295 for n in vars(args):
 296     log_string(f"args.{n} {getattr(args, n)}")
 297
 298
 299 ######################################################################
 300
 301
 302 def picoclvr_pruner_horizontal_green(p):
 303     return not ("green" in p and ("left" in p or "right" in p))
 304
 305
 306 picoclvr_pruner_train = (
 307     picoclvr_pruner_horizontal_green
 308     if args.picocvlr_prune_properties in {"train+eval"}
 309     else None
 310 )
 311
 312 picoclvr_pruner_eval = (
 313     (lambda p: not picoclvr_pruner_horizontal_green(p))
 314     if args.picocvlr_prune_properties in {"train+eval", "eval"}
 315     else None
 316 )
 317
 318 ######################################################################
 319
 320 if args.task == "sandbox":
 321     if args.sandbox_level == 0:
 322         problem = tasks.ProblemLevel0(
 323             nb_sentences=args.sandbox_levels_nb_items,
 324             len_prompt=args.sandbox_levels_len_source,
 325             len_result=args.sandbox_levels_len_result,
 326         )
 327     elif args.sandbox_level == 1:
 328         problem = tasks.ProblemLevel1(
 329             nb_operators=args.sandbox_levels_nb_items,
 330             len_source=args.sandbox_levels_len_source,
 331             len_result=args.sandbox_levels_len_result,
 332         )
 333     elif args.sandbox_level == 2:
 334         problem = tasks.ProblemLevel2(
 335             len_source=args.sandbox_levels_len_source,
 336             len_result=args.sandbox_levels_len_result,
 337         )
 338     else:
 339         raise ValueError(f"Unknown sandbox level {args.sandbox_level}")
 340
 341     task = tasks.SandBox(
 342         problem,
 343         # tasks.ProblemAddition(zero_padded=False, inverted_result=False),
 344         nb_train_samples=args.nb_train_samples,
 345         nb_test_samples=args.nb_test_samples,
 346         batch_size=args.batch_size,
 347         logger=log_string,
 348         device=device,
 349     )
 350
 351 elif args.task == "picoclvr":
 352     task = tasks.PicoCLVR(
 353         nb_train_samples=args.nb_train_samples,
 354         nb_test_samples=args.nb_test_samples,
 355         batch_size=args.batch_size,
 356         height=args.picoclvr_height,
 357         width=args.picoclvr_width,
 358         nb_colors=args.picoclvr_nb_colors,
 359         logger=log_string,
 360         device=device,
 361         pruner_train=picoclvr_pruner_train,
 362         pruner_eval=picoclvr_pruner_eval,
 363     )
 364
 365 elif args.task == "mnist":
 366     task = tasks.MNIST(
 367         nb_train_samples=args.nb_train_samples,
 368         nb_test_samples=args.nb_test_samples,
 369         batch_size=args.batch_size,
 370         device=device,
 371     )
 372
 373 elif args.task == "maze":
 374     task = tasks.Maze(
 375         nb_train_samples=args.nb_train_samples,
 376         nb_test_samples=args.nb_test_samples,
 377         batch_size=args.batch_size,
 378         height=args.maze_height,
 379         width=args.maze_width,
 380         nb_walls=args.maze_nb_walls,
 381         device=device,
 382     )
 383
 384 elif args.task == "snake":
 385     task = tasks.Snake(
 386         nb_train_samples=args.nb_train_samples,
 387         nb_test_samples=args.nb_test_samples,
 388         batch_size=args.batch_size,
 389         height=args.snake_height,
 390         width=args.snake_width,
 391         nb_colors=args.snake_nb_colors,
 392         length=args.snake_length,
 393         prompt_length=args.snake_length // 2,
 394         device=device,
 395     )
 396
 397 elif args.task == "stack":
 398     task = tasks.Stack(
 399         nb_train_samples=args.nb_train_samples,
 400         nb_test_samples=args.nb_test_samples,
 401         batch_size=args.batch_size,
 402         logger=log_string,
 403         nb_steps=args.stack_nb_steps,
 404         nb_stacks=args.stack_nb_stacks,
 405         nb_digits=args.stack_nb_digits,
 406         fraction_values_for_train=args.stack_fraction_values_for_train,
 407         device=device,
 408     )
 409
 410 elif args.task == "expr":
 411     task = tasks.Expr(
 412         nb_train_samples=args.nb_train_samples,
 413         nb_test_samples=args.nb_test_samples,
 414         nb_variables=args.expr_nb_variables,
 415         sequence_length=args.expr_sequence_length,
 416         operand_max=args.expr_operand_max,
 417         result_max=args.expr_result_max,
 418         batch_size=args.batch_size,
 419         device=device,
 420     )
 421
 422 elif args.task == "world":
 423     task = tasks.World(
 424         nb_train_samples=args.nb_train_samples,
 425         nb_test_samples=args.nb_test_samples,
 426         batch_size=args.batch_size,
 427         vqae_nb_epochs=args.world_vqae_nb_epochs,
 428         logger=log_string,
 429         device=device,
 430     )
 431
 432 else:
 433     raise ValueError(f"Unknown task {args.task}")
 434
 435 ######################################################################
 436
 437 log_string(f"device {device}")
 438
 439 vocabulary_size = task.vocabulary_size()
 440
 441 log_string(f"vocabulary_size {vocabulary_size}")
 442
 443 ##############################
 444
 445 model = mygpt.MyGPT(
 446     vocabulary_size=vocabulary_size,
 447     dim_model=args.dim_model,
 448     dim_keys=args.dim_keys,
 449     dim_hidden=args.dim_hidden,
 450     nb_heads=args.nb_heads,
 451     nb_blocks=args.nb_blocks,
 452     causal=True,
 453     dropout=args.dropout,
 454 )
 455
 456 model.to(device)
 457
 458 nb_parameters = sum(p.numel() for p in model.parameters())
 459 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 460
 461 ######################################################################
 462
 463 nb_epochs_finished = 0
 464
 465 if args.no_checkpoint:
 466     log_string(f"not trying to load checkpoint.")
 467
 468 else:
 469     try:
 470         checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 471         checkpoint = torch.load(checkpoint_name)
 472         nb_epochs_finished = checkpoint["nb_epochs_finished"]
 473         model.load_state_dict(checkpoint["model_state"])
 474         torch.set_rng_state(checkpoint["rng_state"])
 475         if torch.cuda.is_available():
 476             torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
 477
 478         log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
 479
 480     except FileNotFoundError:
 481         log_string("starting from scratch.")
 482
 483     except:
 484         log_string("error when loading the checkpoint.")
 485         exit(1)
 486
 487 ######################################################################
 488
 489 if args.task == "expr" and args.expr_input_file is not None:
 490     task.produce_results(
 491         nb_epochs_finished,
 492         model,
 493         args.result_dir,
 494         log_string,
 495         args.deterministic_synthesis,
 496         args.expr_input_file,
 497     )
 498
 499     exit(0)
 500
 501 ######################################################################
 502
 503 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
 504
 505 # Compute the entropy of the training tokens
 506
 507 token_count = 0
 508 for input in task.batches(split="train"):
 509     token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
 510 token_probas = token_count / token_count.sum()
 511 entropy = -torch.xlogy(token_probas, token_probas).sum()
 512 train_set_perplexity = math.exp(entropy)
 513
 514 ##############################
 515
 516 # A bit of paranoia never hurts
 517
 518 train_examples = {}
 519
 520
 521 for input in task.batches(split="train"):
 522     assert input.dim() == 2 and input.dtype == torch.int64
 523     for x in input:
 524         train_examples[x.sum().item()] = x
 525
 526 nb_total, nb_collisions = 0, 0
 527 for input in task.batches(split="test"):
 528     assert input.dim() == 2 and input.dtype == torch.int64
 529     for x in input:
 530         nb_total += 1
 531         y = train_examples.get(x.sum().item())
 532         if y is not None:
 533             if x.size() == y.size() and (x - y).abs().sum() == 0:
 534                 nb_collisions += 1
 535
 536 del train_examples
 537
 538 log_string(
 539     f"data_check {nb_collisions*100/nb_total:.02f}% ({nb_collisions}/{nb_total}) of test samples are in the train set"
 540 )
 541
 542 ##############################
 543
 544 if args.learning_rate_schedule == "cos":
 545     learning_rate_schedule = {}
 546     for n_epoch in range(args.nb_epochs):
 547         u = n_epoch / args.nb_epochs * math.pi
 548         learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
 549 else:
 550     u = {
 551         int(k): float(v)
 552         for k, v in [
 553             tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
 554         ]
 555     }
 556
 557     learning_rate_schedule = {}
 558     learning_rate = args.learning_rate
 559     for n_epoch in range(args.nb_epochs):
 560         if n_epoch in u:
 561             learning_rate = u[n_epoch]
 562         learning_rate_schedule[n_epoch] = learning_rate
 563
 564 log_string(f"learning_rate_schedule {learning_rate_schedule}")
 565
 566 ##############################
 567
 568 nb_samples_seen = 0
 569
 570 if nb_epochs_finished >= nb_epochs:
 571     task.produce_results(
 572         nb_epochs_finished,
 573         model,
 574         args.result_dir,
 575         log_string,
 576         args.deterministic_synthesis,
 577     )
 578
 579 for n_epoch in range(nb_epochs_finished, nb_epochs):
 580     learning_rate = learning_rate_schedule[n_epoch]
 581
 582     log_string(f"learning_rate {learning_rate}")
 583
 584     if args.optim == "sgd":
 585         optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
 586     elif args.optim == "adam":
 587         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 588     elif args.optim == "adamw":
 589         optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 590     else:
 591         raise ValueError(f"Unknown optimizer {args.optim}.")
 592
 593     model.train()
 594
 595     nb_train_samples, acc_train_loss = 0, 0.0
 596
 597     for input in task.batches(split="train"):
 598         input = input.to(device)
 599         output = model(mygpt.BracketedSequence(input)).x
 600         loss = F.cross_entropy(output.transpose(1, 2), input)
 601         acc_train_loss += loss.item() * input.size(0)
 602         nb_train_samples += input.size(0)
 603         nb_samples_seen += input.size(0)
 604
 605         optimizer.zero_grad()
 606         loss.backward()
 607         optimizer.step()
 608
 609     with torch.autograd.no_grad():
 610         model.eval()
 611
 612         nb_test_samples, acc_test_loss = 0, 0.0
 613
 614         for input in task.batches(split="test"):
 615             input = input.to(device)
 616
 617             output = model(mygpt.BracketedSequence(input)).x
 618             loss = F.cross_entropy(output.transpose(1, 2), input)
 619             acc_test_loss += loss.item() * input.size(0)
 620             nb_test_samples += input.size(0)
 621
 622         train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 623         test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
 624
 625         log_string(
 626             f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 627         )
 628
 629         task.produce_results(
 630             n_epoch, model, args.result_dir, log_string, args.deterministic_synthesis
 631         )
 632
 633     checkpoint = {
 634         "nb_epochs_finished": n_epoch + 1,
 635         "model_state": model.state_dict(),
 636         "rng_state": torch.get_rng_state(),
 637     }
 638
 639     if torch.cuda.is_available():
 640         checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
 641
 642     checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 643     torch.save(checkpoint, checkpoint_name)
 644     log_string(f"saved checkpoint {checkpoint_name}")
 645
 646 ######################################################################