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 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
 133 # tokens that should be generated
 134
 135
 136 def masked_inplace_autoregression(model, batch_size, input, ar_mask):
 137     for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
 138         i = (ar_mask.sum(0) > 0).nonzero()
 139         if i.min() > 0:
 140             # Needed to initialize the model's cache
 141             model(mygpt.BracketedSequence(input, 0, i.min()))
 142         for s in range(i.min(), i.max() + 1):
 143             output = model(mygpt.BracketedSequence(input, s, 1)).x
 144             logits = output[:, s]
 145             if args.deterministic_synthesis:
 146                 t_next = logits.argmax(1)
 147             else:
 148                 dist = torch.distributions.categorical.Categorical(logits=logits)
 149                 t_next = dist.sample()
 150             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 151
 152
 153 ######################################################################
 154
 155
 156 def compute_perplexity(model, split="train"):
 157     with torch.autograd.no_grad():
 158         t = model.training
 159         model.eval()
 160
 161         nb_samples, acc_loss = 0, 0.0
 162
 163         for input in task.batches(split=split):
 164             input = input.to(device)
 165
 166             output = model(mygpt.BracketedSequence(input)).x
 167             loss = F.cross_entropy(output.transpose(1, 2), input)
 168             acc_loss += loss.item() * input.size(0)
 169             nb_samples += input.size(0)
 170
 171         model.train(t)
 172
 173         return math.exp(min(100, acc_loss / nb_samples))
 174
 175
 176 ######################################################################
 177
 178
 179 def oneshot_policy_loss(mazes, output, policies, height, width):
 180     masks = (mazes == maze.v_empty).unsqueeze(-1)
 181     targets = policies.permute(0, 2, 1) * masks
 182     output = output * masks
 183     return -(output.log_softmax(-1) * targets).sum() / masks.sum()
 184
 185
 186 def oneshot_trace_loss(mazes, output, policies, height, width):
 187     masks = mazes == maze.v_empty
 188     targets = maze.stationary_densities(
 189         mazes.view(-1, height, width), policies.view(-1, 4, height, width)
 190     ).flatten(-2)
 191     targets = targets * masks
 192     output = output.squeeze(-1) * masks
 193     return (output - targets).abs().sum() / masks.sum()
 194
 195
 196 def oneshot(gpt, task):
 197     t = gpt.training
 198     gpt.eval()
 199
 200     if args.oneshot_input == "head":
 201         dim_in = args.dim_model
 202     elif args.oneshot_input == "deep":
 203         dim_in = args.dim_model * args.nb_blocks * 2
 204     else:
 205         raise ValueError(f"{args.oneshot_input=}")
 206
 207     if args.oneshot_output == "policy":
 208         dim_out = 4
 209         compute_loss = oneshot_policy_loss
 210     elif args.oneshot_output == "trace":
 211         dim_out = 1
 212         compute_loss = oneshot_trace_loss
 213     else:
 214         raise ValueError(f"{args.oneshot_output=}")
 215
 216     model = nn.Sequential(
 217         nn.Linear(dim_in, args.dim_model),
 218         nn.ReLU(),
 219         nn.Linear(args.dim_model, args.dim_model),
 220         nn.ReLU(),
 221         nn.Linear(args.dim_model, dim_out),
 222     ).to(device)
 223
 224     for n_epoch in range(args.nb_epochs):
 225         learning_rate = learning_rate_schedule[n_epoch]
 226         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 227
 228         acc_train_loss, nb_train_samples = 0, 0
 229         for mazes, policies in task.policy_batches(split="train"):
 230             output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
 231             output = model(output_gpt)
 232
 233             loss = compute_loss(mazes, output, policies, task.height, task.width)
 234             acc_train_loss += loss.item() * mazes.size(0)
 235             nb_train_samples += mazes.size(0)
 236
 237             optimizer.zero_grad()
 238             loss.backward()
 239             optimizer.step()
 240
 241         acc_test_loss, nb_test_samples = 0, 0
 242         for mazes, policies in task.policy_batches(split="test"):
 243             output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
 244             output = model(output_gpt)
 245             loss = compute_loss(mazes, output, policies, task.height, task.width)
 246             acc_test_loss += loss.item() * mazes.size(0)
 247             nb_test_samples += mazes.size(0)
 248
 249         log_string(
 250             f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
 251         )
 252
 253         # -------------------
 254         mazes = task.test_input[:32, : task.height * task.width]
 255         policies = task.test_policies[:32]
 256         output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
 257         output = model(output_gpt)
 258         if args.oneshot_output == "policy":
 259             targets = policies.permute(0, 2, 1)
 260             scores = (
 261                 (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
 262             ).float()
 263         elif args.oneshot_output == "trace":
 264             targets = maze.stationary_densities(
 265                 mazes.view(-1, task.height, task.width),
 266                 policies.view(-1, 4, task.height, task.width),
 267             ).flatten(-2)
 268             scores = output
 269         else:
 270             raise ValueError(f"{args.oneshot_output=}")
 271
 272         scores = scores.reshape(-1, task.height, task.width)
 273         mazes = mazes.reshape(-1, task.height, task.width)
 274         targets = targets.reshape(-1, task.height, task.width)
 275         maze.save_image(
 276             os.path.join(
 277                 args.result_dir,
 278                 f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png",
 279             ),
 280             mazes=mazes,
 281             score_paths=scores,
 282             score_truth=targets,
 283         )
 284         # -------------------
 285
 286     gpt.train(t)
 287
 288
 289 ######################################################################
 290
 291
 292 class Task:
 293     def batches(self, split="train"):
 294         pass
 295
 296     def vocabulary_size(self):
 297         pass
 298
 299     def produce_results(self, n_epoch, model):
 300         pass
 301
 302
 303 ######################################################################
 304
 305 import maze
 306
 307
 308 class TaskMaze(Task):
 309     def map2seq(self, *m):
 310         return torch.cat([x.flatten(1) for x in m], 1)
 311
 312     def seq2map(self, s):
 313         s = s.reshape(s.size(0), -1, self.height, self.width)
 314         return (s[:, k] for k in range(s.size(1)))
 315
 316     def __init__(
 317         self,
 318         nb_train_samples,
 319         nb_test_samples,
 320         batch_size,
 321         height,
 322         width,
 323         nb_walls,
 324         device=torch.device("cpu"),
 325     ):
 326         self.batch_size = batch_size
 327         self.height = height
 328         self.width = width
 329         self.device = device
 330
 331         train_mazes, train_paths, train_policies = maze.create_maze_data(
 332             nb_train_samples,
 333             height=height,
 334             width=width,
 335             nb_walls=nb_walls,
 336             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
 337         )
 338         self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
 339         self.train_policies = train_policies.flatten(-2).to(device)
 340
 341         test_mazes, test_paths, test_policies = maze.create_maze_data(
 342             nb_test_samples,
 343             height=height,
 344             width=width,
 345             nb_walls=nb_walls,
 346             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
 347         )
 348         self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
 349         self.test_policies = test_policies.flatten(-2).to(device)
 350
 351         self.nb_codes = self.train_input.max() + 1
 352
 353     def batches(self, split="train", nb_to_use=-1):
 354         assert split in {"train", "test"}
 355         input = self.train_input if split == "train" else self.test_input
 356         if nb_to_use > 0:
 357             input = input[:nb_to_use]
 358         for batch in tqdm.tqdm(
 359             input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
 360         ):
 361             yield batch
 362
 363     def policy_batches(self, split="train", nb_to_use=-1):
 364         assert split in {"train", "test"}
 365         input = self.train_input if split == "train" else self.test_input
 366         policies = self.train_policies if split == "train" else self.test_policies
 367         input = input[:, : self.height * self.width]
 368         policies = policies * (input != maze.v_wall)[:, None]
 369
 370         if nb_to_use > 0:
 371             input = input[:nb_to_use]
 372             policies = policies[:nb_to_use]
 373
 374         for batch in tqdm.tqdm(
 375             zip(input.split(self.batch_size), policies.split(self.batch_size)),
 376             dynamic_ncols=True,
 377             desc=f"epoch-{split}",
 378         ):
 379             yield batch
 380
 381     def vocabulary_size(self):
 382         return self.nb_codes
 383
 384     def compute_error(self, model, split="train", nb_to_use=-1):
 385         nb_total, nb_correct = 0, 0
 386         for input in task.batches(split, nb_to_use):
 387             result = input.clone()
 388             ar_mask = result.new_zeros(result.size())
 389             ar_mask[:, self.height * self.width :] = 1
 390             result *= 1 - ar_mask
 391             masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
 392             mazes, paths = self.seq2map(result)
 393             nb_correct += maze.path_correctness(mazes, paths).long().sum()
 394             nb_total += mazes.size(0)
 395
 396         return nb_total, nb_correct
 397
 398     def produce_results(self, n_epoch, model):
 399         with torch.autograd.no_grad():
 400             t = model.training
 401             model.eval()
 402
 403             train_nb_total, train_nb_correct = self.compute_error(
 404                 model, "train", nb_to_use=1000
 405             )
 406             log_string(
 407                 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 408             )
 409
 410             test_nb_total, test_nb_correct = self.compute_error(
 411                 model, "test", nb_to_use=1000
 412             )
 413             log_string(
 414                 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 415             )
 416
 417             input = self.test_input[:32]
 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             masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
 423
 424             mazes, paths = self.seq2map(input)
 425             _, predicted_paths = self.seq2map(result)
 426             maze.save_image(
 427                 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
 428                 mazes=mazes,
 429                 target_paths=paths,
 430                 predicted_paths=predicted_paths,
 431                 path_correct=maze.path_correctness(mazes, predicted_paths),
 432             )
 433
 434             model.train(t)
 435
 436
 437 ######################################################################
 438
 439 log_string(f"device {device}")
 440
 441
 442 task = TaskMaze(
 443     nb_train_samples=args.nb_train_samples,
 444     nb_test_samples=args.nb_test_samples,
 445     batch_size=args.batch_size,
 446     height=args.maze_height,
 447     width=args.maze_width,
 448     nb_walls=args.maze_nb_walls,
 449     device=device,
 450 )
 451
 452
 453 vocabulary_size = task.vocabulary_size()
 454
 455 log_string(f"vocabulary_size {vocabulary_size}")
 456
 457 ##############################
 458
 459 model = mygpt.MyGPT(
 460     vocabulary_size=vocabulary_size,
 461     dim_model=args.dim_model,
 462     dim_keys=args.dim_keys,
 463     dim_hidden=args.dim_hidden,
 464     nb_heads=args.nb_heads,
 465     nb_blocks=args.nb_blocks,
 466     causal=True,
 467     dropout=args.dropout,
 468 )
 469
 470 model.to(device)
 471
 472 nb_parameters = sum(p.numel() for p in model.parameters())
 473 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 474
 475 ######################################################################
 476
 477 nb_epochs_finished = 0
 478
 479 if args.no_checkpoint:
 480     log_string(f"not trying to load checkpoint.")
 481
 482 else:
 483     try:
 484         checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 485         checkpoint = torch.load(checkpoint_name)
 486         nb_epochs_finished = checkpoint["nb_epochs_finished"]
 487         model.load_state_dict(checkpoint["model_state"])
 488         torch.set_rng_state(checkpoint["rng_state"])
 489         if torch.cuda.is_available():
 490             torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
 491
 492         log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
 493
 494     except FileNotFoundError:
 495         log_string("starting from scratch.")
 496
 497     except:
 498         log_string("error when loading the checkpoint.")
 499         exit(1)
 500
 501 ######################################################################
 502
 503 token_count = 0
 504 for input in task.batches(split="train"):
 505     token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
 506 token_probas = token_count / token_count.sum()
 507 entropy = -torch.xlogy(token_probas, token_probas).sum()
 508 train_set_perplexity = math.exp(entropy)
 509
 510 ##############################
 511
 512 if args.learning_rate_schedule == "cos":
 513     learning_rate_schedule = {}
 514     for n_epoch in range(args.nb_epochs):
 515         u = n_epoch / args.nb_epochs * math.pi
 516         learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
 517 else:
 518     u = {
 519         int(k): float(v)
 520         for k, v in [
 521             tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
 522         ]
 523     }
 524
 525     learning_rate_schedule = {}
 526     learning_rate = args.learning_rate
 527     for n_epoch in range(args.nb_epochs):
 528         if n_epoch in u:
 529             learning_rate = u[n_epoch]
 530         learning_rate_schedule[n_epoch] = learning_rate
 531
 532 log_string(f"learning_rate_schedule {learning_rate_schedule}")
 533
 534 ##############################
 535
 536 if nb_epochs_finished >= args.nb_epochs:
 537     n_epoch = nb_epochs_finished
 538     train_perplexity = compute_perplexity(model, split="train")
 539     test_perplexity = compute_perplexity(model, split="test")
 540
 541     log_string(
 542         f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 543     )
 544
 545     task.produce_results(n_epoch, model)
 546
 547     exit(0)
 548
 549 ##############################
 550
 551 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
 552     learning_rate = learning_rate_schedule[n_epoch]
 553
 554     log_string(f"learning_rate {learning_rate}")
 555
 556     if args.optim == "sgd":
 557         optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
 558     elif args.optim == "adam":
 559         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 560     elif args.optim == "adamw":
 561         optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 562     else:
 563         raise ValueError(f"{args.optim=}")
 564
 565     model.train()
 566
 567     nb_train_samples, acc_train_loss = 0, 0.0
 568
 569     for input in task.batches(split="train"):
 570         input = input.to(device)
 571         output = model(mygpt.BracketedSequence(input)).x
 572         loss = F.cross_entropy(output.transpose(1, 2), input)
 573         acc_train_loss += loss.item() * input.size(0)
 574         nb_train_samples += input.size(0)
 575
 576         optimizer.zero_grad()
 577         loss.backward()
 578         optimizer.step()
 579
 580     train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 581     test_perplexity = compute_perplexity(model, split="test")
 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(n_epoch, model)
 588
 589     checkpoint = {
 590         "nb_epochs_finished": n_epoch + 1,
 591         "model_state": model.state_dict(),
 592         "rng_state": torch.get_rng_state(),
 593     }
 594
 595     if torch.cuda.is_available():
 596         checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
 597
 598     checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 599     torch.save(checkpoint, checkpoint_name)
 600     log_string(f"saved checkpoint {checkpoint_name}")
 601
 602 ######################################################################
 603
 604 if args.oneshot:
 605     oneshot(model, task)
 606
 607 ######################################################################