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