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("--noncausal_prompt", action="store_true", default=False)
  70
  71 parser.add_argument("--no_checkpoint", action="store_true", default=False)
  72
  73 parser.add_argument("--overwrite_results", action="store_true", default=False)
  74
  75 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
  76
  77 ##############################
  78 # maze options
  79
  80 parser.add_argument("--maze_height", type=int, default=13)
  81
  82 parser.add_argument("--maze_width", type=int, default=21)
  83
  84 parser.add_argument("--maze_nb_walls", type=int, default=15)
  85
  86 ##############################
  87 # one-shot prediction
  88
  89 parser.add_argument("--oneshot", action="store_true", default=False)
  90
  91 parser.add_argument("--oneshot_input", type=str, default="head")
  92
  93 parser.add_argument("--oneshot_output", type=str, default="trace")
  94
  95 ######################################################################
  96
  97 args = parser.parse_args()
  98
  99 try:
 100     os.mkdir(args.result_dir)
 101 except FileExistsError:
 102     if not args.overwrite_results:
 103         print(f"result directory {args.result_dir} already exists")
 104         exit(1)
 105
 106 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 107
 108 if args.seed >= 0:
 109     # torch.backends.cudnn.deterministic = True
 110     # torch.backends.cudnn.benchmark = False
 111     # torch.use_deterministic_algorithms(True)
 112     torch.manual_seed(args.seed)
 113     if torch.cuda.is_available():
 114         torch.cuda.manual_seed_all(args.seed)
 115
 116 ######################################################################
 117
 118
 119 def log_string(s):
 120     t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
 121
 122     if log_file is not None:
 123         log_file.write(t + s + "\n")
 124         log_file.flush()
 125
 126     print(t + s)
 127     sys.stdout.flush()
 128
 129
 130 for n in vars(args):
 131     log_string(f"args.{n} {getattr(args, n)}")
 132
 133 ######################################################################
 134
 135
 136 def generation_order(x, prompt_len=0):
 137     if args.random_regression_order:
 138         order = torch.rand(x.size(), device=x.device)
 139         order[:, :prompt_len] = torch.arange(-prompt_len, 0, device=x.device)
 140         order = order.sort(1).indices
 141     else:
 142         order = (
 143             torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1)
 144         )
 145     return order
 146
 147
 148 def reorder(x, order, reverse=False):  # x is NxTxD1x...xDk, order is NxT'
 149     u = x.reshape(x.size()[:2] + (-1,))
 150     order = order.unsqueeze(-1).expand(-1, -1, u.size(-1))
 151     if reverse:
 152         v = u.new(u.size()).scatter_(1, order, u)
 153     else:
 154         v = u.gather(1, order)
 155     v = v.reshape(v.size()[:2] + x.size()[2:])
 156     return v
 157
 158
 159 def shuffle(x, prompt_len):
 160     order = generation_order(x, prompt_len)
 161     return reorder(x, order), order
 162
 163
 164 def eval_mygpt(model, input, mode="standard", prompt_len=0):
 165     x, order = shuffle(input, prompt_len)
 166     x = model(mygpt.BracketedSequence(x), mode=mode, order=order).x
 167     return reorder(x, order, reverse=True)
 168
 169
 170 ######################################################################
 171
 172 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
 173 # tokens that should be generated
 174
 175
 176 def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None):
 177     for input, ar_mask, order in zip(
 178         input.split(batch_size), ar_mask.split(batch_size), order.split(batch_size)
 179     ):
 180         i = (ar_mask.sum(0) > 0).nonzero()
 181         if i.min() > 0:
 182             # Needed to initialize the model's cache
 183             model(mygpt.BracketedSequence(input, 0, i.min()), order=order)
 184         for s in range(i.min(), i.max() + 1):
 185             output = model(mygpt.BracketedSequence(input, s, 1), order=order).x
 186             logits = output[:, s]
 187             if args.deterministic_synthesis:
 188                 t_next = logits.argmax(1)
 189             else:
 190                 dist = torch.distributions.categorical.Categorical(logits=logits)
 191                 t_next = dist.sample()
 192             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 193
 194
 195 ######################################################################
 196
 197
 198 def compute_perplexity(model, task, prompt_len, split="train"):
 199     with torch.autograd.no_grad():
 200         t = model.training
 201         model.eval()
 202
 203         nb_samples, acc_loss = 0, 0.0
 204
 205         for input in task.batches(split=split):
 206             input = input.to(device)
 207             output = eval_mygpt(model, input, prompt_len=prompt_len)
 208             if args.noncausal_prompt:
 209                 d = input.size(1) // 2
 210                 loss = F.cross_entropy(output[:, d:].transpose(1, 2), input[:, d:])
 211             else:
 212                 loss = F.cross_entropy(output.transpose(1, 2), input)
 213             acc_loss += loss.item() * input.size(0)
 214             nb_samples += input.size(0)
 215
 216         model.train(t)
 217
 218         return math.exp(min(100, acc_loss / nb_samples))
 219
 220
 221 ######################################################################
 222
 223
 224 def oneshot_policy_loss(mazes, output, policies, height, width):
 225     masks = (mazes == maze.v_empty).unsqueeze(-1)
 226     targets = policies.permute(0, 2, 1) * masks
 227     output = output * masks
 228     return -(output.log_softmax(-1) * targets).sum() / masks.sum()
 229
 230
 231 def oneshot_trace_loss(mazes, output, policies, height, width):
 232     masks = mazes == maze.v_empty
 233     targets = maze.stationary_densities(
 234         mazes.view(-1, height, width), policies.view(-1, 4, height, width)
 235     ).flatten(-2)
 236     targets = targets * masks
 237     output = output.squeeze(-1) * masks
 238     return (output - targets).abs().sum() / masks.sum()
 239
 240
 241 def oneshot(gpt, task):
 242     t = gpt.training
 243     gpt.eval()
 244
 245     if args.oneshot_input == "head":
 246         dim_in = args.dim_model
 247     elif args.oneshot_input == "deep":
 248         dim_in = args.dim_model * args.nb_blocks * 2
 249     else:
 250         raise ValueError(f"{args.oneshot_input=}")
 251
 252     if args.oneshot_output == "policy":
 253         dim_out = 4
 254         compute_loss = oneshot_policy_loss
 255     elif args.oneshot_output == "trace":
 256         dim_out = 1
 257         compute_loss = oneshot_trace_loss
 258     else:
 259         raise ValueError(f"{args.oneshot_output=}")
 260
 261     model = nn.Sequential(
 262         nn.Linear(dim_in, args.dim_model),
 263         nn.ReLU(),
 264         nn.Linear(args.dim_model, args.dim_model),
 265         nn.ReLU(),
 266         nn.Linear(args.dim_model, dim_out),
 267     ).to(device)
 268
 269     for n_epoch in range(args.nb_epochs):
 270         learning_rate = learning_rate_schedule[n_epoch]
 271         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 272
 273         acc_train_loss, nb_train_samples = 0, 0
 274         for mazes, policies in task.policy_batches(split="train"):
 275             output_gpt = eval_mygpt(
 276                 gpt, mazes, mode=args.oneshot_input, prompt_len=task.height * task.width
 277             )
 278             output = model(output_gpt)
 279
 280             loss = compute_loss(mazes, output, policies, task.height, task.width)
 281             acc_train_loss += loss.item() * mazes.size(0)
 282             nb_train_samples += mazes.size(0)
 283
 284             optimizer.zero_grad()
 285             loss.backward()
 286             optimizer.step()
 287
 288         acc_test_loss, nb_test_samples = 0, 0
 289         for mazes, policies in task.policy_batches(split="test"):
 290             output_gpt = eval_mygpt(
 291                 gpt, mazes, mode=args.oneshot_input, prompt_len=task.height * task.width
 292             )
 293             output = model(output_gpt)
 294             loss = compute_loss(mazes, output, policies, task.height, task.width)
 295             acc_test_loss += loss.item() * mazes.size(0)
 296             nb_test_samples += mazes.size(0)
 297
 298         log_string(
 299             f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
 300         )
 301
 302         # -------------------
 303         mazes = task.test_input[:32, : task.height * task.width]
 304         policies = task.test_policies[:32]
 305         output_gpt = eval_mygpt(
 306             gpt, mazes, mode=args.oneshot_input, prompt_len=task.height * task.width
 307         )
 308         output = model(output_gpt)
 309         if args.oneshot_output == "policy":
 310             targets = policies.permute(0, 2, 1)
 311             scores = (
 312                 (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
 313             ).float()
 314         elif args.oneshot_output == "trace":
 315             targets = maze.stationary_densities(
 316                 mazes.view(-1, task.height, task.width),
 317                 policies.view(-1, 4, task.height, task.width),
 318             ).flatten(-2)
 319             scores = output
 320         else:
 321             raise ValueError(f"{args.oneshot_output=}")
 322
 323         scores = scores.reshape(-1, task.height, task.width)
 324         mazes = mazes.reshape(-1, task.height, task.width)
 325         targets = targets.reshape(-1, task.height, task.width)
 326         filename = (
 327             f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png"
 328         )
 329         maze.save_image(
 330             os.path.join(args.result_dir, filename),
 331             mazes=mazes,
 332             score_paths=scores,
 333             score_truth=targets,
 334         )
 335         log_string(f"wrote {filename}")
 336
 337         # -------------------
 338
 339     gpt.train(t)
 340
 341
 342 ######################################################################
 343
 344
 345 class Task:
 346     def batches(self, split="train", nb_to_use=-1, desc=None):
 347         pass
 348
 349     def vocabulary_size(self):
 350         pass
 351
 352     def produce_results(self, n_epoch, model):
 353         pass
 354
 355
 356 ######################################################################
 357
 358 import maze
 359
 360
 361 class TaskMaze(Task):
 362     def map2seq(self, *m):
 363         return torch.cat([x.flatten(1) for x in m], 1)
 364
 365     def seq2map(self, s):
 366         s = s.reshape(s.size(0), -1, self.height, self.width)
 367         return (s[:, k] for k in range(s.size(1)))
 368
 369     def __init__(
 370         self,
 371         nb_train_samples,
 372         nb_test_samples,
 373         batch_size,
 374         height,
 375         width,
 376         nb_walls,
 377         device=torch.device("cpu"),
 378     ):
 379         self.batch_size = batch_size
 380         self.height = height
 381         self.width = width
 382         self.device = device
 383
 384         train_mazes, train_paths, train_policies = maze.create_maze_data(
 385             nb_train_samples,
 386             height=height,
 387             width=width,
 388             nb_walls=nb_walls,
 389             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
 390         )
 391         self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
 392         self.train_policies = train_policies.flatten(-2).to(device)
 393
 394         test_mazes, test_paths, test_policies = maze.create_maze_data(
 395             nb_test_samples,
 396             height=height,
 397             width=width,
 398             nb_walls=nb_walls,
 399             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
 400         )
 401         self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
 402         self.test_policies = test_policies.flatten(-2).to(device)
 403
 404         self.nb_codes = self.train_input.max() + 1
 405
 406     def batches(self, split="train", nb_to_use=-1, desc=None):
 407         assert split in {"train", "test"}
 408         input = self.train_input if split == "train" else self.test_input
 409         if nb_to_use > 0:
 410             input = input[:nb_to_use]
 411         if desc is None:
 412             desc = f"epoch-{split}"
 413         for batch in tqdm.tqdm(
 414             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 415         ):
 416             yield batch
 417
 418     def policy_batches(self, split="train", nb_to_use=-1, desc=None):
 419         assert split in {"train", "test"}
 420         input = self.train_input if split == "train" else self.test_input
 421         policies = self.train_policies if split == "train" else self.test_policies
 422         input = input[:, : self.height * self.width]
 423         policies = policies * (input != maze.v_wall)[:, None]
 424
 425         if nb_to_use > 0:
 426             input = input[:nb_to_use]
 427             policies = policies[:nb_to_use]
 428
 429         if desc is None:
 430             desc = f"epoch-{split}"
 431         for batch in tqdm.tqdm(
 432             zip(input.split(self.batch_size), policies.split(self.batch_size)),
 433             dynamic_ncols=True,
 434             desc=desc,
 435         ):
 436             yield batch
 437
 438     def vocabulary_size(self):
 439         return self.nb_codes
 440
 441     def compute_error(self, model, split="train", nb_to_use=-1):
 442         nb_total, nb_correct = 0, 0
 443         for input in task.batches(split, nb_to_use):
 444             result = input.clone()
 445             ar_mask = result.new_zeros(result.size())
 446             ar_mask[:, self.height * self.width :] = 1
 447             result *= 1 - ar_mask
 448             x, order = shuffle(result, self.height * self.width)
 449             masked_inplace_autoregression(
 450                 model, self.batch_size, x, ar_mask, order=order
 451             )
 452             result = reorder(x, order, reverse=True)
 453             mazes, paths = self.seq2map(result)
 454             nb_correct += maze.path_correctness(mazes, paths).long().sum()
 455             nb_total += mazes.size(0)
 456
 457         return nb_total, nb_correct
 458
 459     def produce_results(self, n_epoch, model):
 460         with torch.autograd.no_grad():
 461             t = model.training
 462             model.eval()
 463
 464             train_nb_total, train_nb_correct = self.compute_error(
 465                 model, "train", nb_to_use=1000
 466             )
 467             log_string(
 468                 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 469             )
 470
 471             test_nb_total, test_nb_correct = self.compute_error(
 472                 model, "test", nb_to_use=1000
 473             )
 474             log_string(
 475                 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 476             )
 477
 478             input = self.test_input[:32]
 479             result = input.clone()
 480             ar_mask = result.new_zeros(result.size())
 481             ar_mask[:, self.height * self.width :] = 1
 482             result *= 1 - ar_mask
 483             x, order = shuffle(result, self.height * self.width)
 484             masked_inplace_autoregression(
 485                 model, self.batch_size, x, ar_mask, order=order
 486             )
 487             result = reorder(x, order, reverse=True)
 488
 489             mazes, paths = self.seq2map(input)
 490             _, predicted_paths = self.seq2map(result)
 491             filename = f"result_{n_epoch:04d}.png"
 492             maze.save_image(
 493                 os.path.join(args.result_dir, filename),
 494                 mazes=mazes,
 495                 target_paths=paths,
 496                 predicted_paths=predicted_paths,
 497                 path_correct=maze.path_correctness(mazes, predicted_paths),
 498             )
 499             log_string(f"wrote {filename}")
 500
 501             model.train(t)
 502
 503
 504 ######################################################################
 505
 506 log_string(f"device {device}")
 507
 508
 509 task = TaskMaze(
 510     nb_train_samples=args.nb_train_samples,
 511     nb_test_samples=args.nb_test_samples,
 512     batch_size=args.batch_size,
 513     height=args.maze_height,
 514     width=args.maze_width,
 515     nb_walls=args.maze_nb_walls,
 516     device=device,
 517 )
 518
 519
 520 vocabulary_size = task.vocabulary_size()
 521
 522 log_string(f"vocabulary_size {vocabulary_size}")
 523
 524 ##############################
 525
 526
 527 def noncausal_prompt_amm_generator(d):
 528     q = torch.arange(d)[:, None]
 529     k = torch.arange(d)[None, :]
 530     s = args.maze_height * args.maze_width
 531     #    return torch.logical_and(q < k, torch.logical_or(q >= s, k >= s))
 532     return q < k
 533
 534
 535 amm_generator = None
 536
 537 if args.noncausal_prompt:
 538     amm_generator = noncausal_prompt_amm_generator
 539
 540 model = mygpt.MyGPT(
 541     vocabulary_size=vocabulary_size,
 542     dim_model=args.dim_model,
 543     dim_keys=args.dim_keys,
 544     dim_hidden=args.dim_hidden,
 545     nb_heads=args.nb_heads,
 546     nb_blocks=args.nb_blocks,
 547     causal=True,
 548     dropout=args.dropout,
 549     amm_generator=amm_generator,
 550 )
 551
 552 model.to(device)
 553
 554 nb_parameters = sum(p.numel() for p in model.parameters())
 555 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 556
 557 ######################################################################
 558
 559 nb_epochs_finished = 0
 560
 561 if args.no_checkpoint:
 562     log_string(f"not trying to load checkpoint.")
 563
 564 else:
 565     try:
 566         checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 567         checkpoint = torch.load(checkpoint_name)
 568         nb_epochs_finished = checkpoint["nb_epochs_finished"]
 569         model.load_state_dict(checkpoint["model_state"])
 570         torch.set_rng_state(checkpoint["rng_state"])
 571         if torch.cuda.is_available():
 572             torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
 573
 574         log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
 575
 576     except FileNotFoundError:
 577         log_string("starting from scratch.")
 578
 579     except:
 580         log_string("error when loading the checkpoint.")
 581         exit(1)
 582
 583 ######################################################################
 584
 585 token_count = 0
 586 for input in task.batches(split="train"):
 587     token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
 588 token_probas = token_count / token_count.sum()
 589 entropy = -torch.xlogy(token_probas, token_probas).sum()
 590 train_set_perplexity = math.exp(entropy)
 591
 592 ##############################
 593
 594 if args.learning_rate_schedule == "cos":
 595     learning_rate_schedule = {}
 596     for n_epoch in range(args.nb_epochs):
 597         u = n_epoch / args.nb_epochs * math.pi
 598         learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
 599 else:
 600     u = {
 601         int(k): float(v)
 602         for k, v in [
 603             tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
 604         ]
 605     }
 606
 607     learning_rate_schedule = {}
 608     learning_rate = args.learning_rate
 609     for n_epoch in range(args.nb_epochs):
 610         if n_epoch in u:
 611             learning_rate = u[n_epoch]
 612         learning_rate_schedule[n_epoch] = learning_rate
 613
 614 log_string(f"learning_rate_schedule {learning_rate_schedule}")
 615
 616 ##############################
 617
 618 if nb_epochs_finished >= args.nb_epochs:
 619     n_epoch = nb_epochs_finished
 620     train_perplexity = compute_perplexity(
 621         model, task, prompt_len=task.height * task.width, split="train"
 622     )
 623     test_perplexity = compute_perplexity(
 624         model, task, prompt_len=task.height * task.width, split="test"
 625     )
 626
 627     log_string(
 628         f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 629     )
 630
 631     task.produce_results(n_epoch, model)
 632
 633 ##############################
 634
 635 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
 636     learning_rate = learning_rate_schedule[n_epoch]
 637
 638     log_string(f"learning_rate {learning_rate}")
 639
 640     if args.optim == "sgd":
 641         optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
 642     elif args.optim == "adam":
 643         optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 644     elif args.optim == "adamw":
 645         optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 646     else:
 647         raise ValueError(f"{args.optim=}")
 648
 649     model.train()
 650
 651     nb_train_samples, acc_train_loss = 0, 0.0
 652
 653     for input in task.batches(split="train"):
 654         input = input.to(device)
 655         output = eval_mygpt(model, input, prompt_len=task.height * task.width)
 656         if args.noncausal_prompt:
 657             d = input.size(1) // 2
 658             loss = F.cross_entropy(output[:, d:].transpose(1, 2), input[:, d:])
 659         else:
 660             loss = F.cross_entropy(output.transpose(1, 2), input)
 661         acc_train_loss += loss.item() * input.size(0)
 662         nb_train_samples += input.size(0)
 663
 664         optimizer.zero_grad()
 665         loss.backward()
 666         optimizer.step()
 667
 668     train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 669     test_perplexity = compute_perplexity(
 670         model, task, prompt_len=task.height * task.width, split="test"
 671     )
 672
 673     log_string(
 674         f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
 675     )
 676
 677     task.produce_results(n_epoch, model)
 678
 679     checkpoint = {
 680         "nb_epochs_finished": n_epoch + 1,
 681         "model_state": model.state_dict(),
 682         "rng_state": torch.get_rng_state(),
 683     }
 684
 685     if torch.cuda.is_available():
 686         checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
 687
 688     checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
 689     torch.save(checkpoint, checkpoint_name)
 690     log_string(f"saved checkpoint {checkpoint_name}")
 691
 692 ######################################################################
 693
 694 if args.oneshot:
 695     oneshot(model, task)
 696
 697 ######################################################################