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 seq2str(self, seq):
  71         return "[NOT IMPLEMENTED]"
  72
  73
  74 ####################
  75
  76
  77 class ProblemLevel0(Problem):
  78     def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
  79         self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
  80         self.seq[:, len_prompt] = 10
  81
  82     def generate_sequences(self, nb):
  83         sequences = self.seq[torch.randint(self.seq.size(0), (nb,))]
  84         ar_mask = (sequences == 10).long()
  85         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
  86         return sequences, ar_mask
  87
  88
  89 class ProblemLevel1(Problem):
  90     def __init__(self, nb_operators=100, len_source=5, len_result=8):
  91         self.len_source = len_source
  92         self.len_result = len_result
  93         self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1
  94         self.operators = F.one_hot(
  95             torch.rand(nb_operators, len_result, len_source).argmax(-1),
  96             num_classes=len_source,
  97         )
  98
  99     def generate_sequences(self, nb):
 100         nb_operators = torch.randint(self.operators.size(0), (nb,))
 101         operators = self.operators[nb_operators]
 102         nb_operators = (
 103             nb_operators[:, None]
 104             // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1)
 105         ) % 10
 106         marker1 = torch.full((nb, 1), 10)
 107         source = torch.randint(10, (nb, self.len_source))
 108         marker2 = torch.full((nb, 1), 11)
 109         result = operators.bmm(source[:, :, None]).squeeze(-1)
 110         print(f"{nb_operators.dtype=} {marker1.dtype=}")
 111         sequences = torch.cat((nb_operators, marker1, source, marker2, result), 1)
 112         print(f"{sequences.size()=}")
 113         ar_mask = (sequences == 11).long()
 114         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
 115         return sequences, ar_mask
 116
 117     def seq2str(self, seq):
 118         return "".join("0123456789|>"[x.item()] for x in seq)
 119
 120
 121 class ProblemLevel2(Problem):
 122     def __init__(self, len_source=5, len_result=8):
 123         self.len_source = len_source
 124         self.len_result = len_result
 125
 126     def generate_sequences(self, nb):
 127         operators = F.one_hot(
 128             torch.rand(nb, self.len_result, self.len_source).argmax(-1),
 129             num_classes=self.len_source,
 130         )
 131         source1 = torch.randint(10, (nb, self.len_source))
 132         marker1 = torch.full((nb, 1), 10)
 133         result1 = operators.bmm(source1[:, :, None]).squeeze(-1)
 134         marker2 = torch.full((nb, 1), 11)
 135         source2 = torch.randint(10, (nb, self.len_source))
 136         marker3 = torch.full((nb, 1), 12)
 137         result2 = operators.bmm(source2[:, :, None]).squeeze(-1)
 138
 139         sequences = torch.cat(
 140             (source1, marker1, result1, marker2, source2, marker3, result2), 1
 141         )
 142         ar_mask = (sequences == 12).long()
 143         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
 144         return sequences, ar_mask
 145
 146     def seq2str(self, seq):
 147         return "".join("0123456789>|~"[x.item()] for x in seq)
 148
 149
 150 ####################
 151
 152
 153 class ProblemAddition(Problem):
 154     def __init__(self, nb_digits=10, zero_padded=False, inverted_result=False):
 155         self.nb_digits = nb_digits
 156         self.zero_padded = zero_padded
 157         self.inverted_result = inverted_result
 158         self.char2id = dict([(c, n) for n, c in enumerate("0123456789+=$")])
 159         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
 160
 161     def tensorize(self, strings):
 162         len_max = max([len(x) for x in strings])
 163         return torch.cat(
 164             [
 165                 torch.tensor(
 166                     [
 167                         [self.char2id[c] for c in s + "$" * (len_max - len(s))]
 168                         for s in strings
 169                     ]
 170                 )
 171             ],
 172             0,
 173         )
 174
 175     def generate_sequences(self, nb):
 176         sequences = []
 177         for k in range(nb):
 178             a, b = torch.randint(10**self.nb_digits, (2,))
 179             c = a + b
 180             a, b, c = str(a.item()), str(b.item()), str(c.item())
 181             if self.zero_padded:
 182                 a = "0" * (self.nb_digits - len(a)) + a
 183                 b = "0" * (self.nb_digits - len(b)) + b
 184                 c = "0" * (self.nb_digits + 1 - len(c)) + c
 185             if self.inverted_result:
 186                 c = c[::-1]
 187             sequences.append(f"{a}+{b}={c}$")
 188
 189         sequences = self.tensorize(sequences)
 190         ar_mask = (sequences == self.char2id["="]).long()
 191         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
 192         return sequences, ar_mask
 193
 194     def seq2str(self, seq):
 195         return "".join(self.id2char[x.item()] for x in seq)
 196
 197
 198 # class ProblemUnion(Problem):
 199 # problems = [ProblemByheart()]
 200 # nb_common_codes = 100
 201
 202 # def generate_sequences(nb_samples):
 203 # problem_indexes = torch.randint(len(problems), (nb_samples,))
 204 # nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
 205 # print(f"{nb_samples_per_problem}")
 206 # all_seq = []
 207 # for nb, p in zip(nb_samples_per_problem, problems):
 208 # all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
 209 # return all_seq
 210
 211 # for strain, stest in zip(train_seq, test_seq):
 212 # s = torch.cat((strain, stest), 0)
 213
 214 ####################
 215
 216
 217 class SandBox(Task):
 218     def __init__(
 219         self,
 220         problem,
 221         nb_train_samples,
 222         nb_test_samples,
 223         batch_size,
 224         logger=None,
 225         device=torch.device("cpu"),
 226         max_nb_codes=1024,
 227     ):
 228         super().__init__()
 229
 230         self.batch_size = batch_size
 231         self.device = device
 232         self.problem = problem
 233
 234         self.train_input, self.train_ar_mask = self.problem.generate_sequences(
 235             nb_train_samples
 236         )
 237         self.test_input, self.test_ar_mask = self.problem.generate_sequences(
 238             nb_test_samples
 239         )
 240
 241         self.train_input, self.train_ar_mask = self.train_input.to(
 242             device
 243         ), self.train_ar_mask.to(device)
 244         self.test_input, self.test_ar_mask = self.test_input.to(
 245             device
 246         ), self.test_ar_mask.to(device)
 247
 248         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 249
 250         # A bit of paranoia never hurts
 251         assert (
 252             self.nb_codes <= max_nb_codes
 253             and self.train_input.min() >= 0
 254             and self.test_input.min() >= 0
 255             and tuple(self.train_ar_mask.unique()) == (0, 1)
 256             and tuple(self.test_ar_mask.unique()) == (0, 1)
 257         )
 258
 259     def batches(self, split="train", nb_to_use=-1, desc=None):
 260         assert split in {"train", "test"}
 261         input = self.train_input if split == "train" else self.test_input
 262         if nb_to_use > 0:
 263             input = input[:nb_to_use]
 264         if desc is None:
 265             desc = f"epoch-{split}"
 266         for batch in tqdm.tqdm(
 267             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 268         ):
 269             yield batch
 270
 271     def vocabulary_size(self):
 272         return self.nb_codes
 273
 274     def produce_results(
 275         self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
 276     ):
 277         def compute_accuracy(input, ar_mask, logger=None):
 278             input, ar_mask = input[:nmax], ar_mask[:nmax]
 279             result = input.clone() * (1 - ar_mask)
 280
 281             masked_inplace_autoregression(
 282                 model,
 283                 self.batch_size,
 284                 result,
 285                 ar_mask,
 286                 deterministic_synthesis,
 287                 progress_bar_desc=None,
 288                 device=self.device,
 289             )
 290
 291             if logger is not None:
 292                 for sp, st in zip(result[:10], input[:10]):
 293                     logger(
 294                         f"test_sequences {n_epoch} prediction   {self.problem.seq2str(sp)}"
 295                     )
 296                     logger(
 297                         f"               {n_epoch} ground truth {self.problem.seq2str(st)}"
 298                     )
 299
 300             nb_total = ar_mask.sum().item()
 301             nb_correct = ((result == input).long() * ar_mask).sum().item()
 302
 303             return nb_total, nb_correct
 304
 305         train_nb_total, train_nb_correct = compute_accuracy(
 306             self.train_input, self.train_ar_mask
 307         )
 308
 309         logger(
 310             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}%"
 311         )
 312
 313         test_nb_total, test_nb_correct = compute_accuracy(
 314             self.test_input, self.test_ar_mask, logger
 315         )
 316
 317         logger(
 318             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}%"
 319         )
 320
 321
 322 ######################################################################
 323
 324 import picoclvr
 325
 326
 327 class PicoCLVR(Task):
 328     # Make a tensor from a list of strings
 329     def tensorize(self, descr):
 330         token_descr = [s.strip().split(" ") for s in descr]
 331         l = max([len(s) for s in token_descr])
 332         token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
 333         id_descr = [[self.token2id[u] for u in s] for s in token_descr]
 334         return torch.tensor(id_descr, device=self.device)
 335
 336     # Make a list of strings from a tensor
 337     def detensorize(self, x):
 338         return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
 339
 340     # trim all the tensors in the tuple z to remove as much token from
 341     # left and right in the first tensor. If z is a tuple, all its
 342     # elements are trimed according to the triming for the first
 343     def trim(self, z, token="<nul>"):
 344         n = self.token2id[token]
 345         if type(z) == tuple:
 346             x = z[0]
 347             i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
 348             a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
 349             return tuple([t[:, a:b] for t in z])
 350         else:
 351             i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
 352             a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
 353             return z[:, a:b]
 354
 355     ######################
 356
 357     def __init__(
 358         self,
 359         nb_train_samples,
 360         nb_test_samples,
 361         batch_size,
 362         height,
 363         width,
 364         nb_colors=5,
 365         logger=None,
 366         device=torch.device("cpu"),
 367         pruner_train=None,
 368         pruner_eval=None,
 369     ):
 370         super().__init__()
 371
 372         def generate_descr(nb, cache_suffix, pruner):
 373             return picoclvr.generate(
 374                 nb,
 375                 height=self.height,
 376                 width=self.width,
 377                 nb_colors=nb_colors,
 378                 pruner=pruner,
 379             )
 380
 381         self.height = height
 382         self.width = width
 383         self.batch_size = batch_size
 384         self.device = device
 385         self.pruner_train = pruner_train
 386         self.pruner_eval = pruner_eval
 387
 388         if logger is not None:
 389             logger(
 390                 f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
 391             )
 392
 393         self.train_descr = generate_descr(
 394             nb_train_samples, "train", pruner=self.pruner_train
 395         )
 396         self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
 397
 398         # Build the tokenizer
 399         tokens = {"<nul>", "<img>"}
 400         for d in [self.train_descr, self.test_descr]:
 401             for s in d:
 402                 for t in s.strip().split(" "):
 403                     tokens.add(t)
 404         # make this set a sorted list to get the same tensors given
 405         # the same descr
 406         tokens = list(tokens)
 407         tokens.sort()
 408         self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
 409         self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
 410         self.t_img, self.t_nul = self.token2id["<img>"], self.token2id["<nul>"]
 411
 412         # Tokenize the train and test sets
 413         self.train_input = self.tensorize(self.train_descr)
 414         self.test_input = self.tensorize(self.test_descr)
 415
 416     def batches(self, split="train"):
 417         assert split in {"train", "test"}
 418         input = self.train_input if split == "train" else self.test_input
 419         for batch in tqdm.tqdm(
 420             input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
 421         ):
 422             yield self.trim(batch)
 423
 424     def vocabulary_size(self):
 425         return len(self.token2id)
 426
 427     def compute_missing_properties(
 428         self, n_epoch, model, logger, deterministic_synthesis, pruner=None
 429     ):
 430         acc_nb_requested_properties = []
 431         acc_nb_missing_properties = []
 432         acc_nb_results = 0
 433
 434         for input in tqdm.tqdm(
 435             self.test_input.split(self.batch_size),
 436             dynamic_ncols=True,
 437             desc=f"test-properties",
 438         ):
 439             result = input.clone()
 440             ar_mask = (result == self.t_img).long().cumsum(dim=1).clamp(max=1)
 441             result = (1 - ar_mask) * result + ar_mask * self.t_nul
 442             masked_inplace_autoregression(
 443                 model,
 444                 self.batch_size,
 445                 result,
 446                 ar_mask,
 447                 deterministic_synthesis,
 448                 progress_bar_desc=None,
 449                 device=self.device,
 450             )
 451
 452             result_descr = self.detensorize(result)
 453             np = picoclvr.nb_properties(
 454                 result_descr,
 455                 height=self.height,
 456                 width=self.width,
 457                 pruner=pruner,
 458             )
 459             nb_requested_properties, _, nb_missing_properties = zip(*np)
 460             acc_nb_requested_properties += nb_requested_properties
 461             acc_nb_missing_properties += nb_missing_properties
 462             acc_nb_results += len(result_descr)
 463
 464         nb_requested_properties = sum(acc_nb_requested_properties)
 465         nb_missing_properties = sum(acc_nb_missing_properties)
 466
 467         prefix = "" if pruner is None else "pruned_"
 468         logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
 469         logger(
 470             f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
 471         )
 472         logger(
 473             f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
 474         )
 475
 476     ######################################################################
 477
 478     def produce_results(
 479         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 480     ):
 481         self.compute_missing_properties(n_epoch, model, logger, deterministic_synthesis)
 482
 483         if self.pruner_eval is not None:
 484             self.compute_missing_properties(n_epoch, model, self.pruner_eval)
 485
 486         nb_tokens_to_generate = self.height * self.width + 3
 487         result_descr = []
 488         nb_per_primer = 8
 489         primer = []
 490
 491         for primer_descr in [
 492             "red above green <sep> green top <sep> blue right of red",
 493             "there is red <sep> there is yellow <sep> there is blue",
 494             "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
 495             "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
 496         ]:
 497             primer += [primer_descr + " <img>"] * nb_per_primer
 498
 499         result = self.tensorize(primer)
 500         fill = result.new_full(
 501             result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
 502         )
 503         result = torch.cat((result, fill), 1)
 504         ar_mask = (result == self.t_nul).long()
 505         masked_inplace_autoregression(
 506             model,
 507             self.batch_size,
 508             result,
 509             ar_mask,
 510             deterministic_synthesis,
 511             device=self.device,
 512         )
 513         result_descr = self.detensorize(result)
 514
 515         np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
 516
 517         acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
 518         acc_nb_results = len(result_descr)
 519
 520         nb_requested_properties = sum(acc_nb_requested_properties)
 521         nb_missing_properties = sum(acc_nb_missing_properties)
 522
 523         prefix = "demo_"
 524         logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
 525         logger(
 526             f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
 527         )
 528         logger(
 529             f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
 530         )
 531
 532         img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
 533
 534         if img.dim() == 5:
 535             if img.size(1) == 1:
 536                 img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
 537             else:
 538                 img = torch.cat(
 539                     [
 540                         torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
 541                         for x in img
 542                     ],
 543                     0,
 544                 )
 545
 546         image_name = os.path.join(result_dir, f"picoclvr_result_{n_epoch:04d}.png")
 547         torchvision.utils.save_image(
 548             img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
 549         )
 550         logger(f"wrote {image_name}")
 551
 552
 553 ######################################################################
 554
 555
 556 class MNIST(Task):
 557     def __init__(
 558         self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
 559     ):
 560         super().__init__()
 561
 562         self.nb_train_samples = (nb_train_samples,)
 563         self.nb_test_samples = (nb_test_samples,)
 564         self.batch_size = batch_size
 565         self.device = device
 566         data_set = torchvision.datasets.MNIST(root="./data", train=True, download=True)
 567         self.train_input = data_set.data[:nb_train_samples].view(-1, 28 * 28).long()
 568         data_set = torchvision.datasets.MNIST(root="./data", train=False, download=True)
 569         self.test_input = data_set.data[:nb_test_samples].view(-1, 28 * 28).long()
 570
 571     def batches(self, split="train", nb_to_use=-1, desc=None):
 572         assert split in {"train", "test"}
 573         input = self.train_input if split == "train" else self.test_input
 574         if nb_to_use > 0:
 575             input = input[:nb_to_use]
 576         if desc is None:
 577             desc = f"epoch-{split}"
 578         for batch in tqdm.tqdm(
 579             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 580         ):
 581             yield batch
 582
 583     def vocabulary_size(self):
 584         return 256
 585
 586     def produce_results(
 587         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 588     ):
 589         results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
 590         ar_mask = torch.full_like(results, 1)
 591         masked_inplace_autoregression(
 592             model,
 593             self.batch_size,
 594             results,
 595             ar_mask,
 596             deterministic_synthesis,
 597             device=self.device,
 598         )
 599         image_name = os.path.join(result_dir, f"mnist_result_{n_epoch:04d}.png")
 600         torchvision.utils.save_image(
 601             1 - results.reshape(-1, 1, 28, 28) / 255.0,
 602             image_name,
 603             nrow=16,
 604             pad_value=0.8,
 605         )
 606         logger(f"wrote {image_name}")
 607
 608
 609 ######################################################################
 610
 611 import maze
 612
 613
 614 class Maze(Task):
 615     def map2seq(self, *m):
 616         return torch.cat([x.flatten(1) for x in m], 1)
 617
 618     def seq2map(self, s):
 619         s = s.reshape(s.size(0), -1, self.height, self.width)
 620         return (s[:, k] for k in range(s.size(1)))
 621
 622     def __init__(
 623         self,
 624         nb_train_samples,
 625         nb_test_samples,
 626         batch_size,
 627         height,
 628         width,
 629         nb_walls,
 630         device=torch.device("cpu"),
 631     ):
 632         super().__init__()
 633
 634         self.batch_size = batch_size
 635         self.height = height
 636         self.width = width
 637         self.device = device
 638
 639         train_mazes, train_paths, _ = maze.create_maze_data(
 640             nb_train_samples,
 641             height=height,
 642             width=width,
 643             nb_walls=nb_walls,
 644             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
 645         )
 646         self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
 647
 648         test_mazes, test_paths, _ = maze.create_maze_data(
 649             nb_test_samples,
 650             height=height,
 651             width=width,
 652             nb_walls=nb_walls,
 653             progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
 654         )
 655         self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
 656
 657         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 658
 659     def batches(self, split="train", nb_to_use=-1, desc=None):
 660         assert split in {"train", "test"}
 661         input = self.train_input if split == "train" else self.test_input
 662         if nb_to_use > 0:
 663             input = input[:nb_to_use]
 664         if desc is None:
 665             desc = f"epoch-{split}"
 666         for batch in tqdm.tqdm(
 667             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 668         ):
 669             yield batch
 670
 671     def vocabulary_size(self):
 672         return self.nb_codes
 673
 674     def compute_error(
 675         self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
 676     ):
 677         nb_total, nb_correct = 0, 0
 678         count = torch.zeros(
 679             self.width * self.height,
 680             self.width * self.height,
 681             device=self.device,
 682             dtype=torch.int64,
 683         )
 684
 685         for input in self.batches(split, nb_to_use):
 686             result = input.clone()
 687             ar_mask = result.new_zeros(result.size())
 688             ar_mask[:, self.height * self.width :] = 1
 689             result *= 1 - ar_mask
 690             masked_inplace_autoregression(
 691                 model,
 692                 self.batch_size,
 693                 result,
 694                 ar_mask,
 695                 deterministic_synthesis,
 696                 progress_bar_desc=None,
 697                 device=self.device,
 698             )
 699             mazes, paths = self.seq2map(result)
 700             path_correctness = maze.path_correctness(mazes, paths)
 701             nb_correct += path_correctness.long().sum()
 702             nb_total += mazes.size(0)
 703
 704             optimal_path_lengths = (
 705                 (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
 706             )
 707             predicted_path_lengths = (
 708                 (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
 709             )
 710             optimal_path_lengths = optimal_path_lengths[path_correctness]
 711             predicted_path_lengths = predicted_path_lengths[path_correctness]
 712             count[optimal_path_lengths, predicted_path_lengths] += 1
 713
 714         if count.max() == 0:
 715             count = None
 716         else:
 717             count = count[
 718                 : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
 719             ]
 720
 721         return nb_total, nb_correct, count
 722
 723     def produce_results(
 724         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 725     ):
 726         train_nb_total, train_nb_correct, count = self.compute_error(
 727             model,
 728             "train",
 729             nb_to_use=1000,
 730             deterministic_synthesis=deterministic_synthesis,
 731         )
 732         logger(
 733             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}%"
 734         )
 735
 736         test_nb_total, test_nb_correct, count = self.compute_error(
 737             model,
 738             "test",
 739             nb_to_use=1000,
 740             deterministic_synthesis=deterministic_synthesis,
 741         )
 742         logger(
 743             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}%"
 744         )
 745
 746         if count is not None:
 747             proportion_optimal = count.diagonal().sum().float() / count.sum()
 748             logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
 749             with open(
 750                 os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
 751             ) as f:
 752                 for i in range(count.size(0)):
 753                     for j in range(count.size(1)):
 754                         eol = " " if j < count.size(1) - 1 else "\n"
 755                         f.write(f"{count[i,j]}{eol}")
 756
 757         input = self.test_input[:48]
 758         result = input.clone()
 759         ar_mask = result.new_zeros(result.size())
 760         ar_mask[:, self.height * self.width :] = 1
 761         result *= 1 - ar_mask
 762         masked_inplace_autoregression(
 763             model,
 764             self.batch_size,
 765             result,
 766             ar_mask,
 767             deterministic_synthesis,
 768             device=self.device,
 769         )
 770
 771         mazes, paths = self.seq2map(input)
 772         _, predicted_paths = self.seq2map(result)
 773
 774         filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
 775         maze.save_image(
 776             filename,
 777             mazes=mazes,
 778             target_paths=paths,
 779             predicted_paths=predicted_paths,
 780             path_correct=maze.path_correctness(mazes, predicted_paths),
 781             path_optimal=maze.path_optimality(paths, predicted_paths),
 782         )
 783         logger(f"wrote {filename}")
 784
 785
 786 ######################################################################
 787
 788
 789 import snake
 790
 791
 792 class Snake(Task):
 793     def __init__(
 794         self,
 795         nb_train_samples,
 796         nb_test_samples,
 797         batch_size,
 798         height,
 799         width,
 800         nb_colors,
 801         length,
 802         prompt_length,
 803         device=torch.device("cpu"),
 804     ):
 805         super().__init__()
 806
 807         self.batch_size = batch_size
 808         self.height = height
 809         self.width = width
 810         self.device = device
 811         self.prompt_length = prompt_length
 812
 813         self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
 814             nb_train_samples,
 815             height,
 816             width,
 817             nb_colors,
 818             length,
 819             prompt_length,
 820             self.device,
 821         )
 822         self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
 823             nb_test_samples,
 824             height,
 825             width,
 826             nb_colors,
 827             length,
 828             prompt_length,
 829             self.device,
 830         )
 831
 832         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 833
 834     def batches(self, split="train", nb_to_use=-1, desc=None):
 835         assert split in {"train", "test"}
 836         input = self.train_input if split == "train" else self.test_input
 837         if nb_to_use > 0:
 838             input = input[:nb_to_use]
 839         if desc is None:
 840             desc = f"epoch-{split}"
 841         for batch in tqdm.tqdm(
 842             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 843         ):
 844             yield batch
 845
 846     def vocabulary_size(self):
 847         return self.nb_codes
 848
 849     def produce_results(
 850         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 851     ):
 852         def compute_nb_correct(input, prior_visits):
 853             result = input.clone()
 854             i = torch.arange(result.size(1), device=result.device)[None, :]
 855             ar_mask = (
 856                 torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
 857                 .long()
 858                 .expand_as(result)
 859             )
 860             result *= 1 - ar_mask
 861
 862             masked_inplace_autoregression(
 863                 model,
 864                 self.batch_size,
 865                 result,
 866                 ar_mask,
 867                 deterministic_synthesis,
 868                 device=self.device,
 869             )
 870
 871             nb_total = ((prior_visits > 0) * ar_mask).sum()
 872
 873             nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
 874
 875             return nb_total, nb_correct
 876
 877         test_nb_total, test_nb_correct = compute_nb_correct(
 878             self.test_input[:1000], self.test_prior_visits[:1000]
 879         )
 880
 881         logger(
 882             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}%"
 883         )
 884
 885
 886 ######################################################################
 887
 888
 889 import stack
 890
 891
 892 class Stack(Task):
 893     def __init__(
 894         self,
 895         nb_train_samples,
 896         nb_test_samples,
 897         batch_size,
 898         logger,
 899         nb_steps,
 900         nb_stacks,
 901         nb_digits,
 902         fraction_values_for_train=None,
 903         device=torch.device("cpu"),
 904     ):
 905         super().__init__()
 906
 907         self.batch_size = batch_size
 908         self.nb_steps = nb_steps
 909         self.nb_stacks = nb_stacks
 910         self.nb_digits = nb_digits
 911         self.device = device
 912
 913         if fraction_values_for_train is None:
 914             values_for_train = None
 915             values_for_test = None
 916         else:
 917             all = torch.randperm(10**nb_digits)
 918             nb_for_train = int(all.size(0) * fraction_values_for_train)
 919             values_for_train = all[:nb_for_train]
 920             values_for_test = all[nb_for_train:]
 921
 922         self.train_input, self.train_stack_counts = stack.generate_sequences(
 923             nb_train_samples,
 924             nb_steps,
 925             nb_stacks,
 926             nb_digits,
 927             values_for_train,
 928             self.device,
 929         )
 930
 931         self.test_input, self.test_stack_counts = stack.generate_sequences(
 932             nb_test_samples,
 933             nb_steps,
 934             nb_stacks,
 935             nb_digits,
 936             values_for_test,
 937             self.device,
 938         )
 939
 940         i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks)
 941         counts = self.test_stack_counts.flatten()[i.flatten()]
 942         counts = F.one_hot(counts).sum(0)
 943         logger(f"test_pop_stack_counts {counts}")
 944
 945         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 946
 947     def batches(self, split="train", nb_to_use=-1, desc=None):
 948         assert split in {"train", "test"}
 949         input = self.train_input if split == "train" else self.test_input
 950         if nb_to_use > 0:
 951             input = input[:nb_to_use]
 952         if desc is None:
 953             desc = f"epoch-{split}"
 954         for batch in tqdm.tqdm(
 955             input.split(self.batch_size), dynamic_ncols=True, desc=desc
 956         ):
 957             yield batch
 958
 959     def vocabulary_size(self):
 960         return self.nb_codes
 961
 962     def produce_results(
 963         self, n_epoch, model, result_dir, logger, deterministic_synthesis
 964     ):
 965         def compute_nb_correct(input):
 966             result = input.clone()
 967             stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
 968             ar_mask = (result != input).long()
 969             masked_inplace_autoregression(
 970                 model,
 971                 self.batch_size,
 972                 result,
 973                 ar_mask,
 974                 deterministic_synthesis,
 975                 device=self.device,
 976             )
 977
 978             errors = ((result != input).long() * ar_mask).reshape(
 979                 -1, 1 + self.nb_digits
 980             )
 981             ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
 982
 983             nb_total = ar_mask.max(1).values.sum()
 984             nb_correct = nb_total - errors.max(1).values.sum()
 985
 986             return nb_total, nb_correct
 987
 988         test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
 989
 990         logger(
 991             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}%"
 992         )
 993
 994         ##############################################################
 995         # Log a few generated sequences
 996         input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
 997         result = input.clone()
 998         stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
 999         ar_mask = (result != input).long()
1000
1001         # for n in range(result.size(0)):
1002         # logger(
1003         # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
1004         # )
1005
1006         masked_inplace_autoregression(
1007             model,
1008             self.batch_size,
1009             result,
1010             ar_mask,
1011             deterministic_synthesis,
1012             device=self.device,
1013         )
1014
1015         for n in range(result.size(0)):
1016             logger(
1017                 f"test_after  {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
1018             )
1019         ##############################################################
1020
1021
1022 ######################################################################
1023
1024
1025 import expr
1026
1027
1028 class Expr(Task):
1029     def tensorize(self, sequences):
1030         len_max = max([len(x) for x in sequences])
1031         return torch.cat(
1032             [
1033                 torch.tensor(
1034                     [
1035                         [self.char2id[c] for c in s + "#" * (len_max - len(s))]
1036                         for s in sequences
1037                     ]
1038                 )
1039             ],
1040             0,
1041         ).to(self.device)
1042
1043     def __init__(
1044         self,
1045         nb_train_samples,
1046         nb_test_samples,
1047         nb_variables,
1048         sequence_length,
1049         operand_max,
1050         result_max,
1051         batch_size,
1052         device=torch.device("cpu"),
1053     ):
1054         super().__init__()
1055
1056         self.batch_size = batch_size
1057         self.device = device
1058
1059         train_sequences = expr.generate_sequences(
1060             nb_train_samples,
1061             nb_variables=nb_variables,
1062             length=sequence_length,
1063             operand_max=operand_max,
1064             result_max=result_max,
1065         )
1066
1067         test_sequences = expr.generate_sequences(
1068             nb_test_samples,
1069             nb_variables=nb_variables,
1070             length=sequence_length,
1071             operand_max=operand_max,
1072             result_max=result_max,
1073         )
1074
1075         symbols = list(set("#" + "".join(train_sequences + test_sequences)))
1076         symbols.sort()
1077
1078         self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
1079         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
1080
1081         self.filler, self.space = self.char2id["#"], self.char2id[" "]
1082
1083         self.train_input = self.tensorize(train_sequences)
1084         self.test_input = self.tensorize(test_sequences)
1085
1086         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
1087
1088     def batches(self, split="train", nb_to_use=-1, desc=None):
1089         assert split in {"train", "test"}
1090         input = self.train_input if split == "train" else self.test_input
1091         if nb_to_use > 0:
1092             input = input[:nb_to_use]
1093         if desc is None:
1094             desc = f"epoch-{split}"
1095         for batch in tqdm.tqdm(
1096             input.split(self.batch_size), dynamic_ncols=True, desc=desc
1097         ):
1098             last = (batch != self.filler).max(0).values.nonzero().max() + 3
1099             batch = batch[:, :last]
1100             yield batch
1101
1102     def vocabulary_size(self):
1103         return self.nb_codes
1104
1105     def seq2str(self, s):
1106         return "".join([self.id2char[k.item()] for k in s])
1107
1108     def produce_results(
1109         self,
1110         n_epoch,
1111         model,
1112         result_dir,
1113         logger,
1114         deterministic_synthesis,
1115         input_file=None,
1116     ):
1117         def compute_nb_correct(input):
1118             result = input.clone()
1119             s = (result == self.space).long()
1120             ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
1121             result = (1 - ar_mask) * result + ar_mask * self.filler
1122             masked_inplace_autoregression(
1123                 model,
1124                 self.batch_size,
1125                 result,
1126                 ar_mask,
1127                 deterministic_synthesis,
1128                 device=self.device,
1129             )
1130
1131             nb_total = input.size(0)
1132             nb_correct = (input == result).long().min(1).values.sum()
1133
1134             #######################################################################
1135             # Comput predicted vs. true variable values
1136
1137             nb_delta = torch.zeros(5, dtype=torch.int64)
1138             nb_missed = 0
1139
1140             values_input = expr.extract_results([self.seq2str(s) for s in input])
1141             values_result = expr.extract_results([self.seq2str(s) for s in result])
1142
1143             filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
1144
1145             with open(filename, "w") as f:
1146                 for i, r in zip(values_input, values_result):
1147                     for n, vi in i.items():
1148                         vr = r.get(n)
1149                         f.write(f"{vi} {-1 if vr is None else vr}\n")
1150
1151                         if vr is None or vr < 0:
1152                             nb_missed += 1
1153                         else:
1154                             d = abs(vr - vi)
1155                             if d >= nb_delta.size(0):
1156                                 nb_missed += 1
1157                             else:
1158                                 nb_delta[d] += 1
1159
1160             ######################################################################
1161
1162             return nb_total, nb_correct, nb_delta, nb_missed
1163
1164         (
1165             test_nb_total,
1166             test_nb_correct,
1167             test_nb_delta,
1168             test_nb_missed,
1169         ) = compute_nb_correct(self.test_input[:10000])
1170
1171         logger(
1172             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}%"
1173         )
1174
1175         nb_total = test_nb_delta.sum() + test_nb_missed
1176         for d in range(test_nb_delta.size(0)):
1177             logger(
1178                 f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
1179             )
1180         logger(
1181             f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
1182         )
1183
1184         ##############################################################
1185         # Log a few generated sequences
1186         if input_file is None:
1187             input = self.test_input[:10]
1188         else:
1189             with open(input_file, "r") as f:
1190                 sequences = [e.strip() for e in f.readlines()]
1191                 sequences = [s + " " + "#" * 50 for s in sequences]
1192                 input = self.tensorize(sequences)
1193
1194         result = input.clone()
1195         s = (result == self.space).long()
1196         ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
1197         result = (1 - ar_mask) * result + ar_mask * self.filler
1198
1199         for n in range(result.size(0)):
1200             logger(f"test_before {self.seq2str(result[n])}")
1201
1202         masked_inplace_autoregression(
1203             model,
1204             self.batch_size,
1205             result,
1206             ar_mask,
1207             deterministic_synthesis,
1208             device=self.device,
1209         )
1210
1211         correct = (1 - ar_mask) * self.space + ar_mask * input
1212         for n in range(result.size(0)):
1213             comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
1214             logger(f"test_after  {self.seq2str(result[n])} {comment}")
1215             logger(f"truth       {self.seq2str(correct[n])}")
1216         ##############################################################
1217
1218
1219 ######################################################################
1220
1221 import world
1222
1223
1224 class World(Task):
1225     def __init__(
1226         self,
1227         nb_train_samples,
1228         nb_test_samples,
1229         batch_size,
1230         vqae_nb_epochs,
1231         logger=None,
1232         device=torch.device("cpu"),
1233         device_storage=torch.device("cpu"),
1234     ):
1235         super().__init__()
1236
1237         self.batch_size = batch_size
1238         self.device = device
1239
1240         (
1241             train_frames,
1242             train_action_seq,
1243             test_frames,
1244             test_action_seq,
1245             self.frame2seq,
1246             self.seq2frame,
1247         ) = world.create_data_and_processors(
1248             nb_train_samples,
1249             nb_test_samples,
1250             mode="first_last",
1251             nb_steps=30,
1252             nb_epochs=vqae_nb_epochs,
1253             logger=logger,
1254             device=device,
1255             device_storage=device_storage,
1256         )
1257
1258         train_frame_seq = self.frame2seq(train_frames).to(device_storage)
1259         test_frame_seq = self.frame2seq(test_frames).to(device_storage)
1260
1261         nb_frame_codes = max(train_frame_seq.max(), test_frame_seq.max()) + 1
1262         nb_action_codes = max(train_action_seq.max(), test_action_seq.max()) + 1
1263
1264         self.len_frame_seq = train_frame_seq.size(1)
1265         self.len_action_seq = train_action_seq.size(1)
1266         self.nb_codes = nb_frame_codes + nb_action_codes
1267
1268         train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
1269
1270         train_action_seq += nb_frame_codes
1271         self.train_input = torch.cat(
1272             (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
1273         )
1274
1275         test_frame_seq = test_frame_seq.reshape(test_frame_seq.size(0) // 2, 2, -1)
1276         test_action_seq += nb_frame_codes
1277         self.test_input = torch.cat(
1278             (test_frame_seq[:, 0, :], test_action_seq, test_frame_seq[:, 1, :]), 1
1279         )
1280
1281     def batches(self, split="train", nb_to_use=-1, desc=None):
1282         assert split in {"train", "test"}
1283         input = self.train_input if split == "train" else self.test_input
1284         if nb_to_use > 0:
1285             input = input[:nb_to_use]
1286         if desc is None:
1287             desc = f"epoch-{split}"
1288         for batch in tqdm.tqdm(
1289             input.split(self.batch_size), dynamic_ncols=True, desc=desc
1290         ):
1291             yield batch.to(self.device)
1292
1293     def vocabulary_size(self):
1294         return self.nb_codes
1295
1296     def produce_results(
1297         self, n_epoch, model, result_dir, logger, deterministic_synthesis
1298     ):
1299         k = torch.arange(
1300             2 * self.len_frame_seq + self.len_action_seq, device=self.device
1301         )[None, :]
1302
1303         input = self.test_input[:64].to(self.device)
1304         result = input.clone()
1305
1306         ar_mask = (
1307             (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
1308         )
1309         result *= 1 - ar_mask
1310
1311         masked_inplace_autoregression(
1312             model,
1313             self.batch_size,
1314             result,
1315             ar_mask,
1316             deterministic_synthesis,
1317             device=self.device,
1318         )
1319
1320         seq_start = input[:, : self.len_frame_seq]
1321         seq_end = input[:, self.len_frame_seq + self.len_action_seq :]
1322         seq_predicted = result[:, self.len_frame_seq + self.len_action_seq :]
1323
1324         result = torch.cat(
1325             (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
1326         )
1327         result = result.reshape(-1, result.size(-1))
1328
1329         frames = self.seq2frame(result)
1330         image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
1331         torchvision.utils.save_image(
1332             frames.float() / (world.Box.nb_rgb_levels - 1),
1333             image_name,
1334             nrow=12,
1335             padding=1,
1336             pad_value=0.0,
1337         )
1338         logger(f"wrote {image_name}")
1339
1340
1341 ######################################################################