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