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