beaver.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, itertools, os
  12
  13 import torch, torchvision
  14 from torch import nn
  15 from torch.nn import functional as F
  16
  17 import mygpt, tensorstack
  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(description="A maze shortest path solving with a GPT.")
  30
  31 parser.add_argument("--log_filename", type=str, default="train.log")
  32
  33 parser.add_argument("--result_dir", type=str, default="results_default")
  34
  35 parser.add_argument("--seed", type=int, default=0)
  36
  37 parser.add_argument("--nb_epochs", type=int, default=25)
  38
  39 parser.add_argument("--nb_train_samples", type=int, default=200000)
  40
  41 parser.add_argument("--nb_test_samples", type=int, default=50000)
  42
  43 parser.add_argument("--batch_size", type=int, default=25)
  44
  45 parser.add_argument("--optim", type=str, default="adam")
  46
  47 parser.add_argument("--learning_rate", type=float, default=1e-3)
  48
  49 parser.add_argument(
  50     "--learning_rate_schedule", type=str, default="10: 2e-4,20: 4e-5,30: 8e-6"
  51 )
  52
  53 parser.add_argument("--dim_model", type=int, default=512)
  54
  55 parser.add_argument("--dim_keys", type=int, default=64)
  56
  57 parser.add_argument("--dim_hidden", type=int, default=2048)
  58
  59 parser.add_argument("--nb_heads", type=int, default=8)
  60
  61 parser.add_argument("--nb_blocks", type=int, default=12)
  62
  63 parser.add_argument("--dropout", type=float, default=0.1)
  64
  65 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
  66
  67 parser.add_argument("--no_checkpoint", action="store_true", default=False)
  68
  69 parser.add_argument("--overwrite_results", action="store_true", default=False)
  70
  71 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
  72
  73 ##############################
  74 # maze options
  75
  76 parser.add_argument("--maze_height", type=int, default=13)
  77
  78 parser.add_argument("--maze_width", type=int, default=21)
  79
  80 parser.add_argument("--maze_nb_walls", type=int, default=15)
  81
  82 ##############################
  83 # one-shot prediction
  84
  85 parser.add_argument("--oneshot", action="store_true", default=False)
  86
  87 parser.add_argument("--oneshot_input", type=str, default="head")
  88
  89 parser.add_argument("--oneshot_output", type=str, default="trace")
  90
  91 ######################################################################
  92
  93 args = parser.parse_args()
  94
  95 try:
  96     os.mkdir(args.result_dir)
  97 except FileExistsError:
  98     if not args.overwrite_results:
  99         print(f"result directory {args.result_dir} already exists")
 100         exit(1)
 101
 102 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 103
 104 if args.seed >= 0:
 105     # torch.backends.cudnn.deterministic = True
 106     # torch.backends.cudnn.benchmark = False
 107     # torch.use_deterministic_algorithms(True)
 108     torch.manual_seed(args.seed)
 109     if torch.cuda.is_available():
 110         torch.cuda.manual_seed_all(args.seed)
 111
 112 ######################################################################
 113
 114
 115 def log_string(s):
 116     t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
 117
 118     if log_file is not None:
 119         log_file.write(t + s + "\n")
 120         log_file.flush()
 121
 122     print(t + s)
 123     sys.stdout.flush()
 124
 125
 126 for n in vars(args):
 127     log_string(f"args.{n} {getattr(args, n)}")
 128
 129 ######################################################################
 130
 131
 132 def random_order(result, fixed_len):
 133     order = torch.rand(result.size(), device=result.device)
 134     order[:, :fixed_len] = torch.linspace(-2, -1, fixed_len, device=order.device)
 135     return order.sort(1).indices
 136
 137
 138 def shuffle(x, order, reorder=False):
 139     if x.dim() == 3:
 140         order = order.unsqueeze(-1).expand(-1, -1, x.size(-1))
 141     if reorder:
 142         y = x.new(x.size())
 143         y.scatter_(1, order, x)
 144         return y
 145     else:
 146         return x.gather(1, order)
 147
 148
 149 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
 150 # tokens that should be generated
 151
 152
 153 def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None):
 154     for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
 155         i = (ar_mask.sum(0) > 0).nonzero()
 156         if i.min() > 0:
 157             # Needed to initialize the model's cache
 158             model(mygpt.BracketedSequence(input, 0, i.min()), order=order)
 159         for s in range(i.min(), i.max() + 1):
 160             output = model(mygpt.BracketedSequence(input, s, 1), order=order).x
 161             logits = output[:, s]
 162             if args.deterministic_synthesis:
 163                 t_next = logits.argmax(1)
 164             else:
 165                 dist = torch.distributions.categorical.Categorical(logits=logits)
 166                 t_next = dist.sample()
 167             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 168
 169
 170 ######################################################################
 171
 172
 173 def compute_perplexity(model, split="train"):
 174     with torch.autograd.no_grad():
 175         t = model.training
 176         model.eval()
 177
 178         nb_samples, acc_loss = 0, 0.0
 179
 180         for input in task.batches(split=split):
 181             input = input.to(device)
 182             order = random_order(input, task.height * task.width)
 183             input = shuffle(input, order)
 184             output = model(mygpt.BracketedSequence(input), order=order).x
 185             loss = F.cross_entropy(output.transpose(1, 2), input)
 186             acc_loss += loss.item() * input.size(0)
 187             nb_samples += input.size(0)
 188
 189         model.train(t)
 190
 191         return math.exp(min(100, acc_loss / nb_samples))
 192
 193
 194 ######################################################################
 195
 196
 197 def oneshot_policy_loss(mazes, output, policies, height, width):
 198     masks = (mazes == maze.v_empty).unsqueeze(-1)
 199     targets = policies.permute(0, 2, 1) * masks
 200     output = output * masks
 201     return -(output.log_softmax(-1) * targets).sum() / masks.sum()
 202
 203
 204 def oneshot_trace_loss(mazes, output, policies, height, width):
 205     masks = mazes == maze.v_empty
 206     targets = maze.stationary_densities(
 207         mazes.view(-1, height, width), policies.view(-1, 4, height, width)
 208     ).flatten(-2)
 209     targets = targets * masks
 210     output = output.squeeze(-1) * masks
 211     return (output - targets).abs().sum() / masks.sum()
 212
 213
 214 def oneshot(gpt, task):
 215     t = gpt.training
 216     gpt.eval()
 217
 218     if args.oneshot_input == "head":
 219         dim_in = args.dim_model
 220     elif args.oneshot_input == "deep":
 221         dim_in = args.dim_model * args.nb_blocks * 2
 222     else:
 223         raise ValueError(f"{args.oneshot_input=}")
 224
 225     if args.oneshot_output == "policy":
 226         dim_out = 4
 227         compute_loss = oneshot_policy_loss
 228     elif args.oneshot_output == "trace":
 229         dim_out = 1
 230         compute_loss = oneshot_trace_loss
 231     else:
 232         raise ValueError(f"{args.oneshot_output=}")
 233
 234     model = nn.Sequential(
 235         nn.Linear(dim_in, args.dim_model),
 236         nn.ReLU(),
 237         nn.Linear(args.dim_model, args.dim_model),
 238         nn.ReLU(),
 239         nn.Linear(args.dim_model, dim_out),
 240     ).to(device)
 241
 242     for n_epoch in range(args.nb_epochs):
 243         learning_rate = learning_rate_schedule[n_epoch]
 244         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 245
 246         acc_train_loss, nb_train_samples = 0, 0
 247         for mazes, policies in task.policy_batches(split="train"):
 248             order = random_order(input, task.height * task.width)
 249             x = shuffle(mazes, order)
 250             x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
 251             output_gpt = shuffle(x, order, reorder=True)
 252             output = model(output_gpt)
 253
 254             loss = compute_loss(mazes, output, policies, task.height, task.width)
 255             acc_train_loss += loss.item() * mazes.size(0)
 256             nb_train_samples += mazes.size(0)
 257
 258             optimizer.zero_grad()
 259             loss.backward()
 260             optimizer.step()
 261
 262         acc_test_loss, nb_test_samples = 0, 0
 263         for mazes, policies in task.policy_batches(split="test"):
 264             order = random_order(input, task.height * task.width)
 265             x = shuffle(mazes, order)
 266             x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
 267             output_gpt = shuffle(x, order, reorder=True)
 268             output = model(output_gpt)
 269             loss = compute_loss(mazes, output, policies, task.height, task.width)
 270             acc_test_loss += loss.item() * mazes.size(0)
 271             nb_test_samples += mazes.size(0)
 272
 273         log_string(
 274             f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
 275         )
 276
 277         # -------------------
 278         mazes = task.test_input[:32, : task.height * task.width]
 279         policies = task.test_policies[:32]
 280         order = random_order(input, task.height * task.width)
 281         x = shuffle(mazes, order)
 282         x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
 283         output_gpt = shuffle(x, order, reorder=True)
 284         output = model(output_gpt)
 285         if args.oneshot_output == "policy":
 286             targets = policies.permute(0, 2, 1)
 287             scores = (
 288                 (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
 289             ).float()
 290         elif args.oneshot_output == "trace":
 291             targets = maze.stationary_densities(
 292                 mazes.view(-1, task.height, task.width),
 293                 policies.view(-1, 4, task.height, task.width),
 294             ).flatten(-2)
 295             scores = output
 296         else:
 297             raise ValueError(f"{args.oneshot_output=}")
 298
 299         scores = scores.reshape(-1, task.height, task.width)
 300         mazes = mazes.reshape(-1, task.height, task.width)
 301         targets = targets.reshape(-1, task.height, task.width)
 302         maze.save_image(
 303             os.path.join(
 304                 args.result_dir,
 305                 f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png",
 306             ),
 307             mazes=mazes,
 308             score_paths=scores,
 309             score_truth=targets,
 310         )
 311         # -------------------
 312
 313     gpt.train(t)
 314
 315
 316 ######################################################################
 317
 318
 319 class Task:
 320     def batches(self, split="train", nb_to_use=-1, desc=None):
 321         pass
 322
 323     def vocabulary_size(self):
 324         pass
 325
 326     def produce_results(self, n_epoch, model):
 327         pass
 328
 329
 330 ######################################################################
 331
 332 import maze
 333
 334
 335 class TaskMaze(Task):
 336     def map2seq(self, *m):
 337         return torch.cat([x.flatten(1) for x in m], 1)
 338
 339     def seq2map(self, s):
 340         s = s.reshape(s.size(0), -1, self.height, self.width)
 341         return (s[:, k] for k in range(s.size(1)))
 342
 343     def __init__(
 344         self,
 345         nb_train_samples,
 346         nb_test_samples,
 347         batch_size,
 348         height,
 349         width,
 350         nb_walls,
 351         device=torch.device("cpu"),
 352     ):
 353         self.batch_size = batch_size
 354         self.height = height
 355         self.width = width
 356         self.device = device
 357
 358         train_mazes, train_paths, train_policies = maze.create_maze_data(
 359             nb_train_samples,
 360             height=height,
 361             width=width,
 362             nb_walls=nb_walls,
 363             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
 364         )
 365         self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
 366         self.train_policies = train_policies.flatten(-2).to(device)
 367
 368         test_mazes, test_paths, test_policies = maze.create_maze_data(
 369             nb_test_samples,
 370             height=height,
 371             width=width,
 372             nb_walls=nb_walls,
 373             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
 374         )
 375         self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
 376         self.test_policies = test_policies.flatten(-2).to(device)
 377
 378         self.nb_codes = self.train_input.max() + 1
 379
 380     def batches(self, split="train", nb_to_use=-1, desc=None):
 381         assert split in {"train", "test"}
 382         input = self.train_input if split == "train" else self.test_input
 383         if nb_to_use > 0:
 384             input = input[:nb_to_use]
 385         if desc is None:
 386             desc = f"epoch-{split}"
 387         for batch in tqdm.tqdm(
 388             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 389         ):
 390             yield batch
 391
 392     def policy_batches(self, split="train", nb_to_use=-1, desc=None):
 393         assert split in {"train", "test"}
 394         input = self.train_input if split == "train" else self.test_input
 395         policies = self.train_policies if split == "train" else self.test_policies
 396         input = input[:, : self.height * self.width]
 397         policies = policies * (input != maze.v_wall)[:, None]
 398
 399         if nb_to_use > 0:
 400             input = input[:nb_to_use]
 401             policies = policies[:nb_to_use]
 402
 403         if desc is None:
 404             desc = f"epoch-{split}"
 405         for batch in tqdm.tqdm(
 406             zip(input.split(self.batch_size), policies.split(self.batch_size)),
 407             dynamic_ncols=True,
 408             desc=desc,
 409         ):
 410             yield batch
 411
 412     def vocabulary_size(self):
 413         return self.nb_codes
 414
 415     def compute_error(self, model, split="train", nb_to_use=-1):
 416         nb_total, nb_correct = 0, 0
 417         for input in task.batches(split, nb_to_use):
 418             result = input.clone()
 419             ar_mask = result.new_zeros(result.size())
 420             ar_mask[:, self.height * self.width :] = 1
 421             result *= 1 - ar_mask
 422             order = random_order(result, self.height * self.width)
 423             masked_inplace_autoregression(
 424                 model, self.batch_size, result, ar_mask, order=order
 425             )
 426             result = shuffle(result, order, reorder=True)
 427             mazes, paths = self.seq2map(result)
 428             nb_correct += maze.path_correctness(mazes, paths).long().sum()
 429             nb_total += mazes.size(0)
 430
 431         return nb_total, nb_correct
 432
 433     def produce_results(self, n_epoch, model):
 434         with torch.autograd.no_grad():
 435             t = model.training
 436             model.eval()
 437
 438             train_nb_total, train_nb_correct = self.compute_error(
 439                 model, "train", nb_to_use=1000
 440             )
 441             log_string(
 442                 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 443             )
 444
 445             test_nb_total, test_nb_correct = self.compute_error(
 446                 model, "test", nb_to_use=1000
 447             )
 448             log_string(
 449                 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 450             )
 451
 452             input = self.test_input[:32]
 453             result = input.clone()
 454             ar_mask = result.new_zeros(result.size())
 455             ar_mask[:, self.height * self.width :] = 1
 456             result *= 1 - ar_mask
 457             masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
 458
 459             mazes, paths = self.seq2map(input)
 460             _, predicted_paths = self.seq2map(result)
 461             maze.save_image(
 462                 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
 463                 mazes=mazes,
 464                 target_paths=paths,
 465                 predicted_paths=predicted_paths,
 466                 path_correct=maze.path_correctness(mazes, predicted_paths),
 467             )
 468
 469             model.train(t)
 470
 471
 472 ######################################################################
 473
 474 log_string(f"device {device}")
 475
 476
 477 task = TaskMaze(
 478     nb_train_samples=args.nb_train_samples,
 479     nb_test_samples=args.nb_test_samples,
 480     batch_size=args.batch_size,
 481     height=args.maze_height,
 482     width=args.maze_width,
 483     nb_walls=args.maze_nb_walls,
 484     device=device,
 485 )
 486
 487
 488 vocabulary_size = task.vocabulary_size()
 489
 490 log_string(f"vocabulary_size {vocabulary_size}")
 491
 492 ##############################
 493
 494 model = mygpt.MyGPT(
 495     vocabulary_size=vocabulary_size,
 496     dim_model=args.dim_model,
 497     dim_keys=args.dim_keys,
 498     dim_hidden=args.dim_hidden,
 499     nb_heads=args.nb_heads,
 500     nb_blocks=args.nb_blocks,
 501     causal=True,
 502     dropout=args.dropout,
 503 )
 504
 505 model.to(device)
 506
 507 nb_parameters = sum(p.numel() for p in model.parameters())
 508 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 509
 510 ######################################################################
 511
 512 nb_epochs_finished = 0
 513
 514 if args.no_checkpoint:
 515     log_string(f"not trying to load checkpoint.")
 516
 517 else:
 518     try:
 519         checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 520         checkpoint = torch.load(checkpoint_name)
 521         nb_epochs_finished = checkpoint["nb_epochs_finished"]
 522         model.load_state_dict(checkpoint["model_state"])
 523         torch.set_rng_state(checkpoint["rng_state"])
 524         if torch.cuda.is_available():
 525             torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
 526
 527         log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
 528
 529     except FileNotFoundError:
 530         log_string("starting from scratch.")
 531
 532     except:
 533         log_string("error when loading the checkpoint.")
 534         exit(1)
 535
 536 ######################################################################
 537
 538 token_count = 0
 539 for input in task.batches(split="train"):
 540     token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
 541 token_probas = token_count / token_count.sum()
 542 entropy = -torch.xlogy(token_probas, token_probas).sum()
 543 train_set_perplexity = math.exp(entropy)
 544
 545 ##############################
 546
 547 if args.learning_rate_schedule == "cos":
 548     learning_rate_schedule = {}
 549     for n_epoch in range(args.nb_epochs):
 550         u = n_epoch / args.nb_epochs * math.pi
 551         learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
 552 else:
 553     u = {
 554         int(k): float(v)
 555         for k, v in [
 556             tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
 557         ]
 558     }
 559
 560     learning_rate_schedule = {}
 561     learning_rate = args.learning_rate
 562     for n_epoch in range(args.nb_epochs):
 563         if n_epoch in u:
 564             learning_rate = u[n_epoch]
 565         learning_rate_schedule[n_epoch] = learning_rate
 566
 567 log_string(f"learning_rate_schedule {learning_rate_schedule}")
 568
 569 ##############################
 570
 571 if nb_epochs_finished >= args.nb_epochs:
 572     n_epoch = nb_epochs_finished
 573     train_perplexity = compute_perplexity(model, split="train")
 574     test_perplexity = compute_perplexity(model, split="test")
 575
 576     log_string(
 577         f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 578     )
 579
 580     task.produce_results(n_epoch, model)
 581
 582     exit(0)
 583
 584 ##############################
 585
 586 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
 587     learning_rate = learning_rate_schedule[n_epoch]
 588
 589     log_string(f"learning_rate {learning_rate}")
 590
 591     if args.optim == "sgd":
 592         optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
 593     elif args.optim == "adam":
 594         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 595     elif args.optim == "adamw":
 596         optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 597     else:
 598         raise ValueError(f"{args.optim=}")
 599
 600     model.train()
 601
 602     nb_train_samples, acc_train_loss = 0, 0.0
 603
 604     for input in task.batches(split="train"):
 605         input = input.to(device)
 606         order = random_order(input, task.height * task.width)
 607         input = shuffle(input, order)
 608         output = model(mygpt.BracketedSequence(input), order=order).x
 609         loss = F.cross_entropy(output.transpose(1, 2), input)
 610         acc_train_loss += loss.item() * input.size(0)
 611         nb_train_samples += input.size(0)
 612
 613         optimizer.zero_grad()
 614         loss.backward()
 615         optimizer.step()
 616
 617     train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 618     test_perplexity = compute_perplexity(model, split="test")
 619
 620     log_string(
 621         f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 622     )
 623
 624     task.produce_results(n_epoch, model)
 625
 626     checkpoint = {
 627         "nb_epochs_finished": n_epoch + 1,
 628         "model_state": model.state_dict(),
 629         "rng_state": torch.get_rng_state(),
 630     }
 631
 632     if torch.cuda.is_available():
 633         checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
 634
 635     checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 636     torch.save(checkpoint, checkpoint_name)
 637     log_string(f"saved checkpoint {checkpoint_name}")
 638
 639 ######################################################################
 640
 641 if args.oneshot:
 642     oneshot(model, task)
 643
 644 ######################################################################