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("--random_regression_order", action="store_true", default=False)
  68
  69 parser.add_argument("--no_checkpoint", action="store_true", default=False)
  70
  71 parser.add_argument("--overwrite_results", action="store_true", default=False)
  72
  73 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
  74
  75 ##############################
  76 # maze options
  77
  78 parser.add_argument("--maze_height", type=int, default=13)
  79
  80 parser.add_argument("--maze_width", type=int, default=21)
  81
  82 parser.add_argument("--maze_nb_walls", type=int, default=15)
  83
  84 ##############################
  85 # one-shot prediction
  86
  87 parser.add_argument("--oneshot", action="store_true", default=False)
  88
  89 parser.add_argument("--oneshot_input", type=str, default="head")
  90
  91 parser.add_argument("--oneshot_output", type=str, default="trace")
  92
  93 ######################################################################
  94
  95 args = parser.parse_args()
  96
  97 try:
  98     os.mkdir(args.result_dir)
  99 except FileExistsError:
 100     if not args.overwrite_results:
 101         print(f"result directory {args.result_dir} already exists")
 102         exit(1)
 103
 104 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 105
 106 if args.seed >= 0:
 107     # torch.backends.cudnn.deterministic = True
 108     # torch.backends.cudnn.benchmark = False
 109     # torch.use_deterministic_algorithms(True)
 110     torch.manual_seed(args.seed)
 111     if torch.cuda.is_available():
 112         torch.cuda.manual_seed_all(args.seed)
 113
 114 ######################################################################
 115
 116
 117 def log_string(s):
 118     t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
 119
 120     if log_file is not None:
 121         log_file.write(t + s + "\n")
 122         log_file.flush()
 123
 124     print(t + s)
 125     sys.stdout.flush()
 126
 127
 128 for n in vars(args):
 129     log_string(f"args.{n} {getattr(args, n)}")
 130
 131 ######################################################################
 132
 133
 134 def generation_order(x, fixed_len=0):
 135     if args.random_regression_order:
 136         order = torch.rand(x.size(), device=x.device)
 137         order[:, :fixed_len] = torch.arange(-fixed_len, 0, device=x.device)
 138         order = order.sort(1).indices
 139     else:
 140         order = (
 141             torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1)
 142         )
 143     return order
 144
 145
 146 def reorder(x, order, reverse=False):  # x is NxTxD1x...xDk, order is NxT'
 147     u = x.reshape(x.size()[:2] + (-1,))
 148     order = order.unsqueeze(-1).expand(-1, -1, u.size(-1))
 149     if reverse:
 150         v = u.new(u.size()).scatter_(1, order, u)
 151     else:
 152         v = u.gather(1, order)
 153     v = v.reshape(v.size()[:2] + x.size()[2:])
 154     return v
 155
 156
 157 def shuffle(x, fixed_len):
 158     order = generation_order(x, fixed_len)
 159     return reorder(x, order), order
 160
 161
 162 def eval_mygpt(model, input, mode="standard", fixed_len=0):
 163     x, order = shuffle(input, fixed_len)
 164     x = model(mygpt.BracketedSequence(x), mode=mode, order=order).x
 165     return reorder(x, order, reverse=True)
 166
 167
 168 ######################################################################
 169
 170 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
 171 # tokens that should be generated
 172
 173
 174 def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None):
 175     for input, ar_mask, order in zip(
 176         input.split(batch_size), ar_mask.split(batch_size), order.split(batch_size)
 177     ):
 178         i = (ar_mask.sum(0) > 0).nonzero()
 179         if i.min() > 0:
 180             # Needed to initialize the model's cache
 181             model(mygpt.BracketedSequence(input, 0, i.min()), order=order)
 182         for s in range(i.min(), i.max() + 1):
 183             output = model(mygpt.BracketedSequence(input, s, 1), order=order).x
 184             logits = output[:, s]
 185             if args.deterministic_synthesis:
 186                 t_next = logits.argmax(1)
 187             else:
 188                 dist = torch.distributions.categorical.Categorical(logits=logits)
 189                 t_next = dist.sample()
 190             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 191
 192
 193 ######################################################################
 194
 195
 196 def compute_perplexity(model, task, fixed_len, split="train"):
 197     with torch.autograd.no_grad():
 198         t = model.training
 199         model.eval()
 200
 201         nb_samples, acc_loss = 0, 0.0
 202
 203         for input in task.batches(split=split):
 204             input = input.to(device)
 205             output = eval_mygpt(model, input, fixed_len=fixed_len)
 206             loss = F.cross_entropy(output.transpose(1, 2), input)
 207             acc_loss += loss.item() * input.size(0)
 208             nb_samples += input.size(0)
 209
 210         model.train(t)
 211
 212         return math.exp(min(100, acc_loss / nb_samples))
 213
 214
 215 ######################################################################
 216
 217
 218 def oneshot_policy_loss(mazes, output, policies, height, width):
 219     masks = (mazes == maze.v_empty).unsqueeze(-1)
 220     targets = policies.permute(0, 2, 1) * masks
 221     output = output * masks
 222     return -(output.log_softmax(-1) * targets).sum() / masks.sum()
 223
 224
 225 def oneshot_trace_loss(mazes, output, policies, height, width):
 226     masks = mazes == maze.v_empty
 227     targets = maze.stationary_densities(
 228         mazes.view(-1, height, width), policies.view(-1, 4, height, width)
 229     ).flatten(-2)
 230     targets = targets * masks
 231     output = output.squeeze(-1) * masks
 232     return (output - targets).abs().sum() / masks.sum()
 233
 234
 235 def oneshot(gpt, task):
 236     t = gpt.training
 237     gpt.eval()
 238
 239     if args.oneshot_input == "head":
 240         dim_in = args.dim_model
 241     elif args.oneshot_input == "deep":
 242         dim_in = args.dim_model * args.nb_blocks * 2
 243     else:
 244         raise ValueError(f"{args.oneshot_input=}")
 245
 246     if args.oneshot_output == "policy":
 247         dim_out = 4
 248         compute_loss = oneshot_policy_loss
 249     elif args.oneshot_output == "trace":
 250         dim_out = 1
 251         compute_loss = oneshot_trace_loss
 252     else:
 253         raise ValueError(f"{args.oneshot_output=}")
 254
 255     model = nn.Sequential(
 256         nn.Linear(dim_in, args.dim_model),
 257         nn.ReLU(),
 258         nn.Linear(args.dim_model, args.dim_model),
 259         nn.ReLU(),
 260         nn.Linear(args.dim_model, dim_out),
 261     ).to(device)
 262
 263     for n_epoch in range(args.nb_epochs):
 264         learning_rate = learning_rate_schedule[n_epoch]
 265         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 266
 267         acc_train_loss, nb_train_samples = 0, 0
 268         for mazes, policies in task.policy_batches(split="train"):
 269             output_gpt = eval_mygpt(
 270                 gpt, mazes, mode=args.oneshot_input, fixed_len=task.height * task.width
 271             )
 272             output = model(output_gpt)
 273
 274             loss = compute_loss(mazes, output, policies, task.height, task.width)
 275             acc_train_loss += loss.item() * mazes.size(0)
 276             nb_train_samples += mazes.size(0)
 277
 278             optimizer.zero_grad()
 279             loss.backward()
 280             optimizer.step()
 281
 282         acc_test_loss, nb_test_samples = 0, 0
 283         for mazes, policies in task.policy_batches(split="test"):
 284             output_gpt = eval_mygpt(
 285                 gpt, mazes, mode=args.oneshot_input, fixed_len=task.height * task.width
 286             )
 287             output = model(output_gpt)
 288             loss = compute_loss(mazes, output, policies, task.height, task.width)
 289             acc_test_loss += loss.item() * mazes.size(0)
 290             nb_test_samples += mazes.size(0)
 291
 292         log_string(
 293             f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
 294         )
 295
 296         # -------------------
 297         mazes = task.test_input[:32, : task.height * task.width]
 298         policies = task.test_policies[:32]
 299         output_gpt = eval_mygpt(
 300             gpt, mazes, mode=args.oneshot_input, fixed_len=task.height * task.width
 301         )
 302         output = model(output_gpt)
 303         if args.oneshot_output == "policy":
 304             targets = policies.permute(0, 2, 1)
 305             scores = (
 306                 (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
 307             ).float()
 308         elif args.oneshot_output == "trace":
 309             targets = maze.stationary_densities(
 310                 mazes.view(-1, task.height, task.width),
 311                 policies.view(-1, 4, task.height, task.width),
 312             ).flatten(-2)
 313             scores = output
 314         else:
 315             raise ValueError(f"{args.oneshot_output=}")
 316
 317         scores = scores.reshape(-1, task.height, task.width)
 318         mazes = mazes.reshape(-1, task.height, task.width)
 319         targets = targets.reshape(-1, task.height, task.width)
 320         filename = (
 321             f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png"
 322         )
 323         maze.save_image(
 324             os.path.join(args.result_dir, filename),
 325             mazes=mazes,
 326             score_paths=scores,
 327             score_truth=targets,
 328         )
 329         log_string(f"wrote {filename}")
 330
 331         # -------------------
 332
 333     gpt.train(t)
 334
 335
 336 ######################################################################
 337
 338
 339 class Task:
 340     def batches(self, split="train", nb_to_use=-1, desc=None):
 341         pass
 342
 343     def vocabulary_size(self):
 344         pass
 345
 346     def produce_results(self, n_epoch, model):
 347         pass
 348
 349
 350 ######################################################################
 351
 352 import maze
 353
 354
 355 class TaskMaze(Task):
 356     def map2seq(self, *m):
 357         return torch.cat([x.flatten(1) for x in m], 1)
 358
 359     def seq2map(self, s):
 360         s = s.reshape(s.size(0), -1, self.height, self.width)
 361         return (s[:, k] for k in range(s.size(1)))
 362
 363     def __init__(
 364         self,
 365         nb_train_samples,
 366         nb_test_samples,
 367         batch_size,
 368         height,
 369         width,
 370         nb_walls,
 371         device=torch.device("cpu"),
 372     ):
 373         self.batch_size = batch_size
 374         self.height = height
 375         self.width = width
 376         self.device = device
 377
 378         train_mazes, train_paths, train_policies = maze.create_maze_data(
 379             nb_train_samples,
 380             height=height,
 381             width=width,
 382             nb_walls=nb_walls,
 383             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
 384         )
 385         self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
 386         self.train_policies = train_policies.flatten(-2).to(device)
 387
 388         test_mazes, test_paths, test_policies = maze.create_maze_data(
 389             nb_test_samples,
 390             height=height,
 391             width=width,
 392             nb_walls=nb_walls,
 393             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
 394         )
 395         self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
 396         self.test_policies = test_policies.flatten(-2).to(device)
 397
 398         self.nb_codes = self.train_input.max() + 1
 399
 400     def batches(self, split="train", nb_to_use=-1, desc=None):
 401         assert split in {"train", "test"}
 402         input = self.train_input if split == "train" else self.test_input
 403         if nb_to_use > 0:
 404             input = input[:nb_to_use]
 405         if desc is None:
 406             desc = f"epoch-{split}"
 407         for batch in tqdm.tqdm(
 408             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 409         ):
 410             yield batch
 411
 412     def policy_batches(self, split="train", nb_to_use=-1, desc=None):
 413         assert split in {"train", "test"}
 414         input = self.train_input if split == "train" else self.test_input
 415         policies = self.train_policies if split == "train" else self.test_policies
 416         input = input[:, : self.height * self.width]
 417         policies = policies * (input != maze.v_wall)[:, None]
 418
 419         if nb_to_use > 0:
 420             input = input[:nb_to_use]
 421             policies = policies[:nb_to_use]
 422
 423         if desc is None:
 424             desc = f"epoch-{split}"
 425         for batch in tqdm.tqdm(
 426             zip(input.split(self.batch_size), policies.split(self.batch_size)),
 427             dynamic_ncols=True,
 428             desc=desc,
 429         ):
 430             yield batch
 431
 432     def vocabulary_size(self):
 433         return self.nb_codes
 434
 435     def compute_error(self, model, split="train", nb_to_use=-1):
 436         nb_total, nb_correct = 0, 0
 437         for input in task.batches(split, nb_to_use):
 438             result = input.clone()
 439             ar_mask = result.new_zeros(result.size())
 440             ar_mask[:, self.height * self.width :] = 1
 441             result *= 1 - ar_mask
 442             x, order = shuffle(result, self.height * self.width)
 443             masked_inplace_autoregression(
 444                 model, self.batch_size, x, ar_mask, order=order
 445             )
 446             result = reorder(x, order, reverse=True)
 447             mazes, paths = self.seq2map(result)
 448             nb_correct += maze.path_correctness(mazes, paths).long().sum()
 449             nb_total += mazes.size(0)
 450
 451         return nb_total, nb_correct
 452
 453     def produce_results(self, n_epoch, model):
 454         with torch.autograd.no_grad():
 455             t = model.training
 456             model.eval()
 457
 458             train_nb_total, train_nb_correct = self.compute_error(
 459                 model, "train", nb_to_use=1000
 460             )
 461             log_string(
 462                 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 463             )
 464
 465             test_nb_total, test_nb_correct = self.compute_error(
 466                 model, "test", nb_to_use=1000
 467             )
 468             log_string(
 469                 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 470             )
 471
 472             input = self.test_input[:32]
 473             result = input.clone()
 474             ar_mask = result.new_zeros(result.size())
 475             ar_mask[:, self.height * self.width :] = 1
 476             result *= 1 - ar_mask
 477             x, order = shuffle(result, self.height * self.width)
 478             masked_inplace_autoregression(
 479                 model, self.batch_size, x, ar_mask, order=order
 480             )
 481             result = reorder(x, order, reverse=True)
 482
 483             mazes, paths = self.seq2map(input)
 484             _, predicted_paths = self.seq2map(result)
 485             filename = f"result_{n_epoch:04d}.png"
 486             maze.save_image(
 487                 os.path.join(args.result_dir, filename),
 488                 mazes=mazes,
 489                 target_paths=paths,
 490                 predicted_paths=predicted_paths,
 491                 path_correct=maze.path_correctness(mazes, predicted_paths),
 492             )
 493             log_string(f"wrote {filename}")
 494
 495             model.train(t)
 496
 497
 498 ######################################################################
 499
 500 log_string(f"device {device}")
 501
 502
 503 task = TaskMaze(
 504     nb_train_samples=args.nb_train_samples,
 505     nb_test_samples=args.nb_test_samples,
 506     batch_size=args.batch_size,
 507     height=args.maze_height,
 508     width=args.maze_width,
 509     nb_walls=args.maze_nb_walls,
 510     device=device,
 511 )
 512
 513
 514 vocabulary_size = task.vocabulary_size()
 515
 516 log_string(f"vocabulary_size {vocabulary_size}")
 517
 518 ##############################
 519
 520 model = mygpt.MyGPT(
 521     vocabulary_size=vocabulary_size,
 522     dim_model=args.dim_model,
 523     dim_keys=args.dim_keys,
 524     dim_hidden=args.dim_hidden,
 525     nb_heads=args.nb_heads,
 526     nb_blocks=args.nb_blocks,
 527     causal=True,
 528     dropout=args.dropout,
 529 )
 530
 531 model.to(device)
 532
 533 nb_parameters = sum(p.numel() for p in model.parameters())
 534 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 535
 536 ######################################################################
 537
 538 nb_epochs_finished = 0
 539
 540 if args.no_checkpoint:
 541     log_string(f"not trying to load checkpoint.")
 542
 543 else:
 544     try:
 545         checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 546         checkpoint = torch.load(checkpoint_name)
 547         nb_epochs_finished = checkpoint["nb_epochs_finished"]
 548         model.load_state_dict(checkpoint["model_state"])
 549         torch.set_rng_state(checkpoint["rng_state"])
 550         if torch.cuda.is_available():
 551             torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
 552
 553         log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
 554
 555     except FileNotFoundError:
 556         log_string("starting from scratch.")
 557
 558     except:
 559         log_string("error when loading the checkpoint.")
 560         exit(1)
 561
 562 ######################################################################
 563
 564 token_count = 0
 565 for input in task.batches(split="train"):
 566     token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
 567 token_probas = token_count / token_count.sum()
 568 entropy = -torch.xlogy(token_probas, token_probas).sum()
 569 train_set_perplexity = math.exp(entropy)
 570
 571 ##############################
 572
 573 if args.learning_rate_schedule == "cos":
 574     learning_rate_schedule = {}
 575     for n_epoch in range(args.nb_epochs):
 576         u = n_epoch / args.nb_epochs * math.pi
 577         learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
 578 else:
 579     u = {
 580         int(k): float(v)
 581         for k, v in [
 582             tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
 583         ]
 584     }
 585
 586     learning_rate_schedule = {}
 587     learning_rate = args.learning_rate
 588     for n_epoch in range(args.nb_epochs):
 589         if n_epoch in u:
 590             learning_rate = u[n_epoch]
 591         learning_rate_schedule[n_epoch] = learning_rate
 592
 593 log_string(f"learning_rate_schedule {learning_rate_schedule}")
 594
 595 ##############################
 596
 597 if nb_epochs_finished >= args.nb_epochs:
 598     n_epoch = nb_epochs_finished
 599     train_perplexity = compute_perplexity(
 600         model, task, fixed_len=task.height * task.width, split="train"
 601     )
 602     test_perplexity = compute_perplexity(
 603         model, task, fixed_len=task.height * task.width, split="test"
 604     )
 605
 606     log_string(
 607         f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 608     )
 609
 610     task.produce_results(n_epoch, model)
 611
 612 ##############################
 613
 614 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
 615     learning_rate = learning_rate_schedule[n_epoch]
 616
 617     log_string(f"learning_rate {learning_rate}")
 618
 619     if args.optim == "sgd":
 620         optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
 621     elif args.optim == "adam":
 622         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 623     elif args.optim == "adamw":
 624         optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 625     else:
 626         raise ValueError(f"{args.optim=}")
 627
 628     model.train()
 629
 630     nb_train_samples, acc_train_loss = 0, 0.0
 631
 632     for input in task.batches(split="train"):
 633         input = input.to(device)
 634         output = eval_mygpt(
 635             model, input, mode=args.oneshot_input, fixed_len=task.height * task.width
 636         )
 637         loss = F.cross_entropy(output.transpose(1, 2), input)
 638         acc_train_loss += loss.item() * input.size(0)
 639         nb_train_samples += input.size(0)
 640
 641         optimizer.zero_grad()
 642         loss.backward()
 643         optimizer.step()
 644
 645     train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 646     test_perplexity = compute_perplexity(
 647         model, task, fixed_len=task.height * task.width, split="test"
 648     )
 649
 650     log_string(
 651         f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 652     )
 653
 654     task.produce_results(n_epoch, model)
 655
 656     checkpoint = {
 657         "nb_epochs_finished": n_epoch + 1,
 658         "model_state": model.state_dict(),
 659         "rng_state": torch.get_rng_state(),
 660     }
 661
 662     if torch.cuda.is_available():
 663         checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
 664
 665     checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 666     torch.save(checkpoint, checkpoint_name)
 667     log_string(f"saved checkpoint {checkpoint_name}")
 668
 669 ######################################################################
 670
 671 if args.oneshot:
 672     oneshot(model, task)
 673
 674 ######################################################################