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