tasks.py

   1 #!/usr/bin/env python
   2
   3 import math, os, tqdm
   4
   5 import torch, torchvision
   6
   7 from torch import nn
   8 from torch.nn import functional as F
   9
  10 ######################################################################
  11
  12
  13 def masked_inplace_autoregression(
  14     model,
  15     batch_size,
  16     input,
  17     ar_mask,
  18     deterministic_synthesis,
  19     forbidden_tokens=None,
  20     progress_bar_desc="autoregression",
  21     device=torch.device("cpu"),
  22 ):
  23     assert input.size() == ar_mask.size()
  24
  25     batches = zip(input.split(batch_size), ar_mask.split(batch_size))
  26
  27     if progress_bar_desc is not None:
  28         batches = tqdm.tqdm(
  29             batches,
  30             dynamic_ncols=True,
  31             desc=progress_bar_desc,
  32             # total=input.size(0) // batch_size,
  33         )
  34
  35     with torch.autograd.no_grad():
  36         t = model.training
  37         model.eval()
  38
  39         for input, ar_mask in batches:
  40             model.masked_inplace_autoregression(
  41                 input, ar_mask, forbidden_tokens, deterministic_synthesis
  42             )
  43
  44         model.train(t)
  45
  46
  47 ######################################################################
  48
  49
  50 class Task:
  51     def batches(self, split="train"):
  52         pass
  53
  54     def vocabulary_size(self):
  55         pass
  56
  57     def produce_results(
  58         self, n_epoch, model, result_dir, logger, deterministic_synthesis
  59     ):
  60         pass
  61
  62
  63 ######################################################################
  64
  65
  66 class Problem:
  67     def generate_sequences(self, nb):
  68         pass
  69
  70     def log_performance(self, sequences, logger):
  71         pass
  72
  73
  74 class ProblemByheart(Problem):
  75     def __init__(self):
  76         nb_seq, len_prompt, len_result = 100, 5, 5
  77         self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
  78         self.seq[:, len_prompt] = 10
  79
  80     def generate_sequences(self, nb):
  81         sequences = self.seq[torch.randint(self.seq.size(0), (nb,))]
  82         ar_mask = (sequences == 10).long()
  83         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
  84         return sequences, ar_mask
  85
  86         # problems = [ProblemByheart()]
  87         # nb_common_codes = 100
  88
  89         # def generate_sequences(nb_samples):
  90         # problem_indexes = torch.randint(len(problems), (nb_samples,))
  91         # nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
  92         # print(f"{nb_samples_per_problem}")
  93         # all_seq = []
  94         # for nb, p in zip(nb_samples_per_problem, problems):
  95         # all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
  96         # return all_seq
  97
  98         # for strain, stest in zip(train_seq, test_seq):
  99         # s = torch.cat((strain, stest), 0)
 100
 101
 102 class SandBox(Task):
 103     def __init__(
 104         self,
 105         problem,
 106         nb_train_samples,
 107         nb_test_samples,
 108         batch_size,
 109         logger=None,
 110         device=torch.device("cpu"),
 111         max_nb_codes=1024,
 112     ):
 113         super().__init__()
 114
 115         self.batch_size = batch_size
 116         self.device = device
 117
 118         self.train_input, self.train_ar_mask = problem.generate_sequences(
 119             nb_train_samples
 120         )
 121         self.test_input, self.test_ar_mask = problem.generate_sequences(nb_test_samples)
 122
 123         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 124
 125         # A bit of paranoia never hurts
 126         assert (
 127             self.nb_codes <= max_nb_codes
 128             and self.train_input.min() >= 0
 129             and self.test_input.min() >= 0
 130             and tuple(self.train_ar_mask.unique()) == (0, 1)
 131             and tuple(self.test_ar_mask.unique()) == (0, 1)
 132         )
 133
 134     def batches(self, split="train", nb_to_use=-1, desc=None):
 135         assert split in {"train", "test"}
 136         input = self.train_input if split == "train" else self.test_input
 137         if nb_to_use > 0:
 138             input = input[:nb_to_use]
 139         if desc is None:
 140             desc = f"epoch-{split}"
 141         for batch in tqdm.tqdm(
 142             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 143         ):
 144             yield batch
 145
 146     def vocabulary_size(self):
 147         return self.nb_codes
 148
 149     def produce_results(
 150         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 151     ):
 152         def compute_accuracy(input, ar_mask):
 153             result = input.clone() * (1 - ar_mask)
 154             masked_inplace_autoregression(
 155                 model,
 156                 self.batch_size,
 157                 result,
 158                 ar_mask,
 159                 deterministic_synthesis,
 160                 progress_bar_desc=None,
 161                 device=self.device,
 162             )
 163
 164             nb_total = ar_mask.sum().item()
 165             nb_correct = ((result == input).long() * ar_mask).sum().item()
 166
 167             return nb_total, nb_correct
 168
 169         train_nb_total, train_nb_correct = compute_accuracy(
 170             self.train_input, self.train_ar_mask
 171         )
 172
 173         logger(
 174             f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 175         )
 176
 177         test_nb_total, test_nb_correct = compute_accuracy(
 178             self.test_input, self.test_ar_mask
 179         )
 180
 181         logger(
 182             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 183         )
 184
 185
 186 ######################################################################
 187
 188 import picoclvr
 189
 190
 191 class PicoCLVR(Task):
 192     # Make a tensor from a list of strings
 193     def tensorize(self, descr):
 194         token_descr = [s.strip().split(" ") for s in descr]
 195         l = max([len(s) for s in token_descr])
 196         token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
 197         id_descr = [[self.token2id[u] for u in s] for s in token_descr]
 198         return torch.tensor(id_descr, device=self.device)
 199
 200     # Make a list of strings from a tensor
 201     def detensorize(self, x):
 202         return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
 203
 204     # trim all the tensors in the tuple z to remove as much token from
 205     # left and right in the first tensor. If z is a tuple, all its
 206     # elements are trimed according to the triming for the first
 207     def trim(self, z, token="<nul>"):
 208         n = self.token2id[token]
 209         if type(z) == tuple:
 210             x = z[0]
 211             i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
 212             a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
 213             return tuple([t[:, a:b] for t in z])
 214         else:
 215             i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
 216             a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
 217             return z[:, a:b]
 218
 219     ######################
 220
 221     def __init__(
 222         self,
 223         nb_train_samples,
 224         nb_test_samples,
 225         batch_size,
 226         height,
 227         width,
 228         nb_colors=5,
 229         logger=None,
 230         device=torch.device("cpu"),
 231         pruner_train=None,
 232         pruner_eval=None,
 233     ):
 234         super().__init__()
 235
 236         def generate_descr(nb, cache_suffix, pruner):
 237             return picoclvr.generate(
 238                 nb,
 239                 height=self.height,
 240                 width=self.width,
 241                 nb_colors=nb_colors,
 242                 pruner=pruner,
 243             )
 244
 245         self.height = height
 246         self.width = width
 247         self.batch_size = batch_size
 248         self.device = device
 249         self.pruner_train = pruner_train
 250         self.pruner_eval = pruner_eval
 251
 252         if logger is not None:
 253             logger(
 254                 f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
 255             )
 256
 257         self.train_descr = generate_descr(
 258             nb_train_samples, "train", pruner=self.pruner_train
 259         )
 260         self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
 261
 262         # Build the tokenizer
 263         tokens = {"<nul>", "<img>"}
 264         for d in [self.train_descr, self.test_descr]:
 265             for s in d:
 266                 for t in s.strip().split(" "):
 267                     tokens.add(t)
 268         # make this set a sorted list to get the same tensors given
 269         # the same descr
 270         tokens = list(tokens)
 271         tokens.sort()
 272         self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
 273         self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
 274         self.t_img, self.t_nul = self.token2id["<img>"], self.token2id["<nul>"]
 275
 276         # Tokenize the train and test sets
 277         self.train_input = self.tensorize(self.train_descr)
 278         self.test_input = self.tensorize(self.test_descr)
 279
 280     def batches(self, split="train"):
 281         assert split in {"train", "test"}
 282         input = self.train_input if split == "train" else self.test_input
 283         for batch in tqdm.tqdm(
 284             input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
 285         ):
 286             yield self.trim(batch)
 287
 288     def vocabulary_size(self):
 289         return len(self.token2id)
 290
 291     def compute_missing_properties(
 292         self, n_epoch, model, logger, deterministic_synthesis, pruner=None
 293     ):
 294         acc_nb_requested_properties = []
 295         acc_nb_missing_properties = []
 296         acc_nb_results = 0
 297
 298         for input in tqdm.tqdm(
 299             self.test_input.split(self.batch_size),
 300             dynamic_ncols=True,
 301             desc=f"test-properties",
 302         ):
 303             result = input.clone()
 304             ar_mask = (result == self.t_img).long().cumsum(dim=1).clamp(max=1)
 305             result = (1 - ar_mask) * result + ar_mask * self.t_nul
 306             masked_inplace_autoregression(
 307                 model,
 308                 self.batch_size,
 309                 result,
 310                 ar_mask,
 311                 deterministic_synthesis,
 312                 progress_bar_desc=None,
 313                 device=self.device,
 314             )
 315
 316             result_descr = self.detensorize(result)
 317             np = picoclvr.nb_properties(
 318                 result_descr,
 319                 height=self.height,
 320                 width=self.width,
 321                 pruner=pruner,
 322             )
 323             nb_requested_properties, _, nb_missing_properties = zip(*np)
 324             acc_nb_requested_properties += nb_requested_properties
 325             acc_nb_missing_properties += nb_missing_properties
 326             acc_nb_results += len(result_descr)
 327
 328         nb_requested_properties = sum(acc_nb_requested_properties)
 329         nb_missing_properties = sum(acc_nb_missing_properties)
 330
 331         prefix = "" if pruner is None else "pruned_"
 332         logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
 333         logger(
 334             f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
 335         )
 336         logger(
 337             f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
 338         )
 339
 340     ######################################################################
 341
 342     def produce_results(
 343         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 344     ):
 345         self.compute_missing_properties(n_epoch, model, logger, deterministic_synthesis)
 346
 347         if self.pruner_eval is not None:
 348             self.compute_missing_properties(n_epoch, model, self.pruner_eval)
 349
 350         nb_tokens_to_generate = self.height * self.width + 3
 351         result_descr = []
 352         nb_per_primer = 8
 353         primer = []
 354
 355         for primer_descr in [
 356             "red above green <sep> green top <sep> blue right of red",
 357             "there is red <sep> there is yellow <sep> there is blue",
 358             "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
 359             "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
 360         ]:
 361             primer += [primer_descr + " <img>"] * nb_per_primer
 362
 363         result = self.tensorize(primer)
 364         fill = result.new_full(
 365             result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
 366         )
 367         result = torch.cat((result, fill), 1)
 368         ar_mask = (result == self.t_nul).long()
 369         masked_inplace_autoregression(
 370             model,
 371             self.batch_size,
 372             result,
 373             ar_mask,
 374             deterministic_synthesis,
 375             device=self.device,
 376         )
 377         result_descr = self.detensorize(result)
 378
 379         np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
 380
 381         acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
 382         acc_nb_results = len(result_descr)
 383
 384         nb_requested_properties = sum(acc_nb_requested_properties)
 385         nb_missing_properties = sum(acc_nb_missing_properties)
 386
 387         prefix = "demo_"
 388         logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
 389         logger(
 390             f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
 391         )
 392         logger(
 393             f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
 394         )
 395
 396         img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
 397
 398         if img.dim() == 5:
 399             if img.size(1) == 1:
 400                 img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
 401             else:
 402                 img = torch.cat(
 403                     [
 404                         torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
 405                         for x in img
 406                     ],
 407                     0,
 408                 )
 409
 410         image_name = os.path.join(result_dir, f"picoclvr_result_{n_epoch:04d}.png")
 411         torchvision.utils.save_image(
 412             img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
 413         )
 414         logger(f"wrote {image_name}")
 415
 416
 417 ######################################################################
 418
 419
 420 class MNIST(Task):
 421     def __init__(
 422         self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
 423     ):
 424         super().__init__()
 425
 426         self.nb_train_samples = (nb_train_samples,)
 427         self.nb_test_samples = (nb_test_samples,)
 428         self.batch_size = batch_size
 429         self.device = device
 430         data_set = torchvision.datasets.MNIST(root="./data", train=True, download=True)
 431         self.train_input = data_set.data[:nb_train_samples].view(-1, 28 * 28).long()
 432         data_set = torchvision.datasets.MNIST(root="./data", train=False, download=True)
 433         self.test_input = data_set.data[:nb_test_samples].view(-1, 28 * 28).long()
 434
 435     def batches(self, split="train", nb_to_use=-1, desc=None):
 436         assert split in {"train", "test"}
 437         input = self.train_input if split == "train" else self.test_input
 438         if nb_to_use > 0:
 439             input = input[:nb_to_use]
 440         if desc is None:
 441             desc = f"epoch-{split}"
 442         for batch in tqdm.tqdm(
 443             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 444         ):
 445             yield batch
 446
 447     def vocabulary_size(self):
 448         return 256
 449
 450     def produce_results(
 451         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 452     ):
 453         results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
 454         ar_mask = torch.full_like(results, 1)
 455         masked_inplace_autoregression(
 456             model,
 457             self.batch_size,
 458             results,
 459             ar_mask,
 460             deterministic_synthesis,
 461             device=self.device,
 462         )
 463         image_name = os.path.join(result_dir, f"mnist_result_{n_epoch:04d}.png")
 464         torchvision.utils.save_image(
 465             1 - results.reshape(-1, 1, 28, 28) / 255.0,
 466             image_name,
 467             nrow=16,
 468             pad_value=0.8,
 469         )
 470         logger(f"wrote {image_name}")
 471
 472
 473 ######################################################################
 474
 475 import maze
 476
 477
 478 class Maze(Task):
 479     def map2seq(self, *m):
 480         return torch.cat([x.flatten(1) for x in m], 1)
 481
 482     def seq2map(self, s):
 483         s = s.reshape(s.size(0), -1, self.height, self.width)
 484         return (s[:, k] for k in range(s.size(1)))
 485
 486     def __init__(
 487         self,
 488         nb_train_samples,
 489         nb_test_samples,
 490         batch_size,
 491         height,
 492         width,
 493         nb_walls,
 494         device=torch.device("cpu"),
 495     ):
 496         super().__init__()
 497
 498         self.batch_size = batch_size
 499         self.height = height
 500         self.width = width
 501         self.device = device
 502
 503         train_mazes, train_paths, _ = maze.create_maze_data(
 504             nb_train_samples,
 505             height=height,
 506             width=width,
 507             nb_walls=nb_walls,
 508             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
 509         )
 510         self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
 511
 512         test_mazes, test_paths, _ = maze.create_maze_data(
 513             nb_test_samples,
 514             height=height,
 515             width=width,
 516             nb_walls=nb_walls,
 517             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
 518         )
 519         self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
 520
 521         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 522
 523     def batches(self, split="train", nb_to_use=-1, desc=None):
 524         assert split in {"train", "test"}
 525         input = self.train_input if split == "train" else self.test_input
 526         if nb_to_use > 0:
 527             input = input[:nb_to_use]
 528         if desc is None:
 529             desc = f"epoch-{split}"
 530         for batch in tqdm.tqdm(
 531             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 532         ):
 533             yield batch
 534
 535     def vocabulary_size(self):
 536         return self.nb_codes
 537
 538     def compute_error(
 539         self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
 540     ):
 541         nb_total, nb_correct = 0, 0
 542         count = torch.zeros(
 543             self.width * self.height,
 544             self.width * self.height,
 545             device=self.device,
 546             dtype=torch.int64,
 547         )
 548
 549         for input in self.batches(split, nb_to_use):
 550             result = input.clone()
 551             ar_mask = result.new_zeros(result.size())
 552             ar_mask[:, self.height * self.width :] = 1
 553             result *= 1 - ar_mask
 554             masked_inplace_autoregression(
 555                 model,
 556                 self.batch_size,
 557                 result,
 558                 ar_mask,
 559                 deterministic_synthesis,
 560                 progress_bar_desc=None,
 561                 device=self.device,
 562             )
 563             mazes, paths = self.seq2map(result)
 564             path_correctness = maze.path_correctness(mazes, paths)
 565             nb_correct += path_correctness.long().sum()
 566             nb_total += mazes.size(0)
 567
 568             optimal_path_lengths = (
 569                 (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
 570             )
 571             predicted_path_lengths = (
 572                 (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
 573             )
 574             optimal_path_lengths = optimal_path_lengths[path_correctness]
 575             predicted_path_lengths = predicted_path_lengths[path_correctness]
 576             count[optimal_path_lengths, predicted_path_lengths] += 1
 577
 578         if count.max() == 0:
 579             count = None
 580         else:
 581             count = count[
 582                 : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
 583             ]
 584
 585         return nb_total, nb_correct, count
 586
 587     def produce_results(
 588         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 589     ):
 590         train_nb_total, train_nb_correct, count = self.compute_error(
 591             model,
 592             "train",
 593             nb_to_use=1000,
 594             deterministic_synthesis=deterministic_synthesis,
 595         )
 596         logger(
 597             f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
 598         )
 599
 600         test_nb_total, test_nb_correct, count = self.compute_error(
 601             model,
 602             "test",
 603             nb_to_use=1000,
 604             deterministic_synthesis=deterministic_synthesis,
 605         )
 606         logger(
 607             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 608         )
 609
 610         if count is not None:
 611             proportion_optimal = count.diagonal().sum().float() / count.sum()
 612             logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
 613             with open(
 614                 os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
 615             ) as f:
 616                 for i in range(count.size(0)):
 617                     for j in range(count.size(1)):
 618                         eol = " " if j < count.size(1) - 1 else "\n"
 619                         f.write(f"{count[i,j]}{eol}")
 620
 621         input = self.test_input[:48]
 622         result = input.clone()
 623         ar_mask = result.new_zeros(result.size())
 624         ar_mask[:, self.height * self.width :] = 1
 625         result *= 1 - ar_mask
 626         masked_inplace_autoregression(
 627             model,
 628             self.batch_size,
 629             result,
 630             ar_mask,
 631             deterministic_synthesis,
 632             device=self.device,
 633         )
 634
 635         mazes, paths = self.seq2map(input)
 636         _, predicted_paths = self.seq2map(result)
 637
 638         filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
 639         maze.save_image(
 640             filename,
 641             mazes=mazes,
 642             target_paths=paths,
 643             predicted_paths=predicted_paths,
 644             path_correct=maze.path_correctness(mazes, predicted_paths),
 645             path_optimal=maze.path_optimality(paths, predicted_paths),
 646         )
 647         logger(f"wrote {filename}")
 648
 649
 650 ######################################################################
 651
 652
 653 import snake
 654
 655
 656 class Snake(Task):
 657     def __init__(
 658         self,
 659         nb_train_samples,
 660         nb_test_samples,
 661         batch_size,
 662         height,
 663         width,
 664         nb_colors,
 665         length,
 666         prompt_length,
 667         device=torch.device("cpu"),
 668     ):
 669         super().__init__()
 670
 671         self.batch_size = batch_size
 672         self.height = height
 673         self.width = width
 674         self.device = device
 675         self.prompt_length = prompt_length
 676
 677         self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
 678             nb_train_samples,
 679             height,
 680             width,
 681             nb_colors,
 682             length,
 683             prompt_length,
 684             self.device,
 685         )
 686         self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
 687             nb_test_samples,
 688             height,
 689             width,
 690             nb_colors,
 691             length,
 692             prompt_length,
 693             self.device,
 694         )
 695
 696         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 697
 698     def batches(self, split="train", nb_to_use=-1, desc=None):
 699         assert split in {"train", "test"}
 700         input = self.train_input if split == "train" else self.test_input
 701         if nb_to_use > 0:
 702             input = input[:nb_to_use]
 703         if desc is None:
 704             desc = f"epoch-{split}"
 705         for batch in tqdm.tqdm(
 706             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 707         ):
 708             yield batch
 709
 710     def vocabulary_size(self):
 711         return self.nb_codes
 712
 713     def produce_results(
 714         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 715     ):
 716         def compute_nb_correct(input, prior_visits):
 717             result = input.clone()
 718             i = torch.arange(result.size(1), device=result.device)[None, :]
 719             ar_mask = (
 720                 torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
 721                 .long()
 722                 .expand_as(result)
 723             )
 724             result *= 1 - ar_mask
 725
 726             masked_inplace_autoregression(
 727                 model,
 728                 self.batch_size,
 729                 result,
 730                 ar_mask,
 731                 deterministic_synthesis,
 732                 device=self.device,
 733             )
 734
 735             nb_total = ((prior_visits > 0) * ar_mask).sum()
 736
 737             nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
 738
 739             return nb_total, nb_correct
 740
 741         test_nb_total, test_nb_correct = compute_nb_correct(
 742             self.test_input[:1000], self.test_prior_visits[:1000]
 743         )
 744
 745         logger(
 746             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 747         )
 748
 749
 750 ######################################################################
 751
 752
 753 import stack
 754
 755
 756 class Stack(Task):
 757     def __init__(
 758         self,
 759         nb_train_samples,
 760         nb_test_samples,
 761         batch_size,
 762         logger,
 763         nb_steps,
 764         nb_stacks,
 765         nb_digits,
 766         fraction_values_for_train=None,
 767         device=torch.device("cpu"),
 768     ):
 769         super().__init__()
 770
 771         self.batch_size = batch_size
 772         self.nb_steps = nb_steps
 773         self.nb_stacks = nb_stacks
 774         self.nb_digits = nb_digits
 775         self.device = device
 776
 777         if fraction_values_for_train is None:
 778             values_for_train = None
 779             values_for_test = None
 780         else:
 781             all = torch.randperm(10**nb_digits)
 782             nb_for_train = int(all.size(0) * fraction_values_for_train)
 783             values_for_train = all[:nb_for_train]
 784             values_for_test = all[nb_for_train:]
 785
 786         self.train_input, self.train_stack_counts = stack.generate_sequences(
 787             nb_train_samples,
 788             nb_steps,
 789             nb_stacks,
 790             nb_digits,
 791             values_for_train,
 792             self.device,
 793         )
 794
 795         self.test_input, self.test_stack_counts = stack.generate_sequences(
 796             nb_test_samples,
 797             nb_steps,
 798             nb_stacks,
 799             nb_digits,
 800             values_for_test,
 801             self.device,
 802         )
 803
 804         i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks)
 805         counts = self.test_stack_counts.flatten()[i.flatten()]
 806         counts = F.one_hot(counts).sum(0)
 807         logger(f"test_pop_stack_counts {counts}")
 808
 809         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 810
 811     def batches(self, split="train", nb_to_use=-1, desc=None):
 812         assert split in {"train", "test"}
 813         input = self.train_input if split == "train" else self.test_input
 814         if nb_to_use > 0:
 815             input = input[:nb_to_use]
 816         if desc is None:
 817             desc = f"epoch-{split}"
 818         for batch in tqdm.tqdm(
 819             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 820         ):
 821             yield batch
 822
 823     def vocabulary_size(self):
 824         return self.nb_codes
 825
 826     def produce_results(
 827         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 828     ):
 829         def compute_nb_correct(input):
 830             result = input.clone()
 831             stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
 832             ar_mask = (result != input).long()
 833             masked_inplace_autoregression(
 834                 model,
 835                 self.batch_size,
 836                 result,
 837                 ar_mask,
 838                 deterministic_synthesis,
 839                 device=self.device,
 840             )
 841
 842             errors = ((result != input).long() * ar_mask).reshape(
 843                 -1, 1 + self.nb_digits
 844             )
 845             ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
 846
 847             nb_total = ar_mask.max(1).values.sum()
 848             nb_correct = nb_total - errors.max(1).values.sum()
 849
 850             return nb_total, nb_correct
 851
 852         test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
 853
 854         logger(
 855             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
 856         )
 857
 858         ##############################################################
 859         # Log a few generated sequences
 860         input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
 861         result = input.clone()
 862         stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
 863         ar_mask = (result != input).long()
 864
 865         # for n in range(result.size(0)):
 866         # logger(
 867         # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
 868         # )
 869
 870         masked_inplace_autoregression(
 871             model,
 872             self.batch_size,
 873             result,
 874             ar_mask,
 875             deterministic_synthesis,
 876             device=self.device,
 877         )
 878
 879         for n in range(result.size(0)):
 880             logger(
 881                 f"test_after  {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
 882             )
 883         ##############################################################
 884
 885
 886 ######################################################################
 887
 888
 889 import expr
 890
 891
 892 class Expr(Task):
 893     def tensorize(self, sequences):
 894         len_max = max([len(x) for x in sequences])
 895         return torch.cat(
 896             [
 897                 torch.tensor(
 898                     [
 899                         [self.char2id[c] for c in s + "#" * (len_max - len(s))]
 900                         for s in sequences
 901                     ]
 902                 )
 903             ],
 904             0,
 905         ).to(self.device)
 906
 907     def __init__(
 908         self,
 909         nb_train_samples,
 910         nb_test_samples,
 911         nb_variables,
 912         sequence_length,
 913         operand_max,
 914         result_max,
 915         batch_size,
 916         device=torch.device("cpu"),
 917     ):
 918         super().__init__()
 919
 920         self.batch_size = batch_size
 921         self.device = device
 922
 923         train_sequences = expr.generate_sequences(
 924             nb_train_samples,
 925             nb_variables=nb_variables,
 926             length=sequence_length,
 927             operand_max=operand_max,
 928             result_max=result_max,
 929         )
 930
 931         test_sequences = expr.generate_sequences(
 932             nb_test_samples,
 933             nb_variables=nb_variables,
 934             length=sequence_length,
 935             operand_max=operand_max,
 936             result_max=result_max,
 937         )
 938
 939         symbols = list(set("#" + "".join(train_sequences + test_sequences)))
 940         symbols.sort()
 941
 942         self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
 943         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
 944
 945         self.filler, self.space = self.char2id["#"], self.char2id[" "]
 946
 947         self.train_input = self.tensorize(train_sequences)
 948         self.test_input = self.tensorize(test_sequences)
 949
 950         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 951
 952     def batches(self, split="train", nb_to_use=-1, desc=None):
 953         assert split in {"train", "test"}
 954         input = self.train_input if split == "train" else self.test_input
 955         if nb_to_use > 0:
 956             input = input[:nb_to_use]
 957         if desc is None:
 958             desc = f"epoch-{split}"
 959         for batch in tqdm.tqdm(
 960             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 961         ):
 962             last = (batch != self.filler).max(0).values.nonzero().max() + 3
 963             batch = batch[:, :last]
 964             yield batch
 965
 966     def vocabulary_size(self):
 967         return self.nb_codes
 968
 969     def seq2str(self, s):
 970         return "".join([self.id2char[k.item()] for k in s])
 971
 972     def produce_results(
 973         self,
 974         n_epoch,
 975         model,
 976         result_dir,
 977         logger,
 978         deterministic_synthesis,
 979         input_file=None,
 980     ):
 981         def compute_nb_correct(input):
 982             result = input.clone()
 983             s = (result == self.space).long()
 984             ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
 985             result = (1 - ar_mask) * result + ar_mask * self.filler
 986             masked_inplace_autoregression(
 987                 model,
 988                 self.batch_size,
 989                 result,
 990                 ar_mask,
 991                 deterministic_synthesis,
 992                 device=self.device,
 993             )
 994
 995             nb_total = input.size(0)
 996             nb_correct = (input == result).long().min(1).values.sum()
 997
 998             #######################################################################
 999             # Comput predicted vs. true variable values
1000
1001             nb_delta = torch.zeros(5, dtype=torch.int64)
1002             nb_missed = 0
1003
1004             values_input = expr.extract_results([self.seq2str(s) for s in input])
1005             values_result = expr.extract_results([self.seq2str(s) for s in result])
1006
1007             filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
1008
1009             with open(filename, "w") as f:
1010                 for i, r in zip(values_input, values_result):
1011                     for n, vi in i.items():
1012                         vr = r.get(n)
1013                         f.write(f"{vi} {-1 if vr is None else vr}\n")
1014
1015                         if vr is None or vr < 0:
1016                             nb_missed += 1
1017                         else:
1018                             d = abs(vr - vi)
1019                             if d >= nb_delta.size(0):
1020                                 nb_missed += 1
1021                             else:
1022                                 nb_delta[d] += 1
1023
1024             ######################################################################
1025
1026             return nb_total, nb_correct, nb_delta, nb_missed
1027
1028         (
1029             test_nb_total,
1030             test_nb_correct,
1031             test_nb_delta,
1032             test_nb_missed,
1033         ) = compute_nb_correct(self.test_input[:10000])
1034
1035         logger(
1036             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
1037         )
1038
1039         nb_total = test_nb_delta.sum() + test_nb_missed
1040         for d in range(test_nb_delta.size(0)):
1041             logger(
1042                 f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
1043             )
1044         logger(
1045             f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
1046         )
1047
1048         ##############################################################
1049         # Log a few generated sequences
1050         if input_file is None:
1051             input = self.test_input[:10]
1052         else:
1053             with open(input_file, "r") as f:
1054                 sequences = [e.strip() for e in f.readlines()]
1055                 sequences = [s + " " + "#" * 50 for s in sequences]
1056                 input = self.tensorize(sequences)
1057
1058         result = input.clone()
1059         s = (result == self.space).long()
1060         ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
1061         result = (1 - ar_mask) * result + ar_mask * self.filler
1062
1063         for n in range(result.size(0)):
1064             logger(f"test_before {self.seq2str(result[n])}")
1065
1066         masked_inplace_autoregression(
1067             model,
1068             self.batch_size,
1069             result,
1070             ar_mask,
1071             deterministic_synthesis,
1072             device=self.device,
1073         )
1074
1075         correct = (1 - ar_mask) * self.space + ar_mask * input
1076         for n in range(result.size(0)):
1077             comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
1078             logger(f"test_after  {self.seq2str(result[n])} {comment}")
1079             logger(f"truth       {self.seq2str(correct[n])}")
1080         ##############################################################
1081
1082
1083 ######################################################################
1084
1085 import world
1086
1087
1088 class World(Task):
1089     def __init__(
1090         self,
1091         nb_train_samples,
1092         nb_test_samples,
1093         batch_size,
1094         vqae_nb_epochs,
1095         logger=None,
1096         device=torch.device("cpu"),
1097         device_storage=torch.device("cpu"),
1098     ):
1099         super().__init__()
1100
1101         self.batch_size = batch_size
1102         self.device = device
1103
1104         (
1105             train_frames,
1106             train_action_seq,
1107             test_frames,
1108             test_action_seq,
1109             self.frame2seq,
1110             self.seq2frame,
1111         ) = world.create_data_and_processors(
1112             nb_train_samples,
1113             nb_test_samples,
1114             mode="first_last",
1115             nb_steps=30,
1116             nb_epochs=vqae_nb_epochs,
1117             logger=logger,
1118             device=device,
1119             device_storage=device_storage,
1120         )
1121
1122         print(f"{train_action_seq.size()=}")
1123
1124         train_frame_seq = self.frame2seq(train_frames).to(device_storage)
1125         test_frame_seq = self.frame2seq(test_frames).to(device_storage)
1126
1127         nb_frame_codes = max(train_frame_seq.max(), test_frame_seq.max()) + 1
1128         nb_action_codes = max(train_action_seq.max(), test_action_seq.max()) + 1
1129
1130         self.len_frame_seq = train_frame_seq.size(1)
1131         self.len_action_seq = train_action_seq.size(1)
1132         self.nb_codes = nb_frame_codes + nb_action_codes
1133
1134         train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
1135         print(f"{train_action_seq.device=} {nb_frame_codes.device=}")
1136         train_action_seq += nb_frame_codes
1137         self.train_input = torch.cat(
1138             (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
1139         )
1140
1141         test_frame_seq = test_frame_seq.reshape(test_frame_seq.size(0) // 2, 2, -1)
1142         test_action_seq += nb_frame_codes
1143         self.test_input = torch.cat(
1144             (test_frame_seq[:, 0, :], test_action_seq, test_frame_seq[:, 1, :]), 1
1145         )
1146
1147     def batches(self, split="train", nb_to_use=-1, desc=None):
1148         assert split in {"train", "test"}
1149         input = self.train_input if split == "train" else self.test_input
1150         if nb_to_use > 0:
1151             input = input[:nb_to_use]
1152         if desc is None:
1153             desc = f"epoch-{split}"
1154         for batch in tqdm.tqdm(
1155             input.split(self.batch_size), dynamic_ncols=True, desc=desc
1156         ):
1157             yield batch.to(self.device)
1158
1159     def vocabulary_size(self):
1160         return self.nb_codes
1161
1162     def produce_results(
1163         self, n_epoch, model, result_dir, logger, deterministic_synthesis
1164     ):
1165         k = torch.arange(
1166             2 * self.len_frame_seq + self.len_action_seq, device=self.device
1167         )[None, :]
1168
1169         input = self.test_input[:64].to(self.device)
1170         result = input.clone()
1171
1172         ar_mask = (
1173             (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
1174         )
1175         result *= 1 - ar_mask
1176
1177         masked_inplace_autoregression(
1178             model,
1179             self.batch_size,
1180             result,
1181             ar_mask,
1182             deterministic_synthesis,
1183             device=self.device,
1184         )
1185
1186         seq_start = input[:, : self.len_frame_seq]
1187         seq_end = input[:, self.len_frame_seq + self.len_action_seq :]
1188         seq_predicted = result[:, self.len_frame_seq + self.len_action_seq :]
1189
1190         result = torch.cat(
1191             (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
1192         )
1193         result = result.reshape(-1, result.size(-1))
1194         print(f"{result.size()=}")
1195
1196         frames = self.seq2frame(result)
1197         image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
1198         torchvision.utils.save_image(
1199             frames.float() / (world.Box.nb_rgb_levels - 1),
1200             image_name,
1201             nrow=12,
1202             padding=1,
1203             pad_value=0.0,
1204         )
1205         logger(f"wrote {image_name}")
1206
1207
1208 ######################################################################