+######################################################################
+
+
+class Problem:
+ def generate_sequences(self, nb):
+ pass
+
+ def seq2str(self, seq):
+ return "[NOT IMPLEMENTED]"
+
+
+####################
+
+
+class ProblemLevel0(Problem):
+ def __init__(self, nb_sentences=100, len_prompt=5, len_result=5):
+ self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result))
+ self.seq[:, len_prompt] = 10
+
+ def generate_sequences(self, nb):
+ sequences = self.seq[torch.randint(self.seq.size(0), (nb,))]
+ ar_mask = (sequences == 10).long()
+ ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+ return sequences, ar_mask
+
+
+class ProblemLevel1(Problem):
+ def __init__(self, nb_operators=100, len_source=5, len_result=8):
+ self.len_source = len_source
+ self.len_result = len_result
+ self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1
+ self.operators = F.one_hot(
+ torch.rand(nb_operators, len_result, len_source).argmax(-1),
+ num_classes=len_source,
+ )
+
+ def generate_sequences(self, nb):
+ nb_operators = torch.randint(self.operators.size(0), (nb,))
+ operators = self.operators[nb_operators]
+ nb_operators = (
+ nb_operators[:, None]
+ // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1)
+ ) % 10
+ marker1 = torch.full((nb, 1), 10)
+ # source = torch.randint(10, (nb, self.len_source))
+ source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
+ marker2 = torch.full((nb, 1), 11)
+ result = operators.bmm(source[:, :, None]).squeeze(-1)
+ print(f"{nb_operators.dtype=} {marker1.dtype=}")
+ sequences = torch.cat((nb_operators, marker1, source, marker2, result), 1)
+ print(f"{sequences.size()=}")
+ ar_mask = (sequences == 11).long()
+ ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+ return sequences, ar_mask
+
+ def seq2str(self, seq):
+ return "".join("0123456789|>"[x.item()] for x in seq)
+
+
+class ProblemLevel2(Problem):
+ def __init__(self, len_source=5, len_result=8):
+ self.len_source = len_source
+ self.len_result = len_result
+
+ def generate_sequences(self, nb):
+ operators = F.one_hot(
+ torch.rand(nb, self.len_result, self.len_source).argmax(-1),
+ num_classes=self.len_source,
+ )
+ source1 = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source]
+ # source1 = torch.randint(10, (nb, self.len_source))
+ marker1 = torch.full((nb, 1), 10)
+ result1 = operators.bmm(source1[:, :, None]).squeeze(-1)
+ marker2 = torch.full((nb, 1), 11)
+ source2 = torch.randint(10, (nb, self.len_source))
+ marker3 = torch.full((nb, 1), 12)
+ result2 = operators.bmm(source2[:, :, None]).squeeze(-1)
+
+ sequences = torch.cat(
+ (source1, marker1, result1, marker2, source2, marker3, result2), 1
+ )
+ ar_mask = (sequences == 12).long()
+ ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+ return sequences, ar_mask
+
+ def seq2str(self, seq):
+ return "".join("0123456789>|~"[x.item()] for x in seq)
+
+
+####################
+
+
+class ProblemAddition(Problem):
+ def __init__(self, nb_digits=10, zero_padded=False, inverted_result=False):
+ self.nb_digits = nb_digits
+ self.zero_padded = zero_padded
+ self.inverted_result = inverted_result
+ self.char2id = dict([(c, n) for n, c in enumerate("0123456789+=$")])
+ self.id2char = dict([(n, c) for c, n in self.char2id.items()])
+
+ def tensorize(self, strings):
+ len_max = max([len(x) for x in strings])
+ return torch.cat(
+ [
+ torch.tensor(
+ [
+ [self.char2id[c] for c in s + "$" * (len_max - len(s))]
+ for s in strings
+ ]
+ )
+ ],
+ 0,
+ )
+
+ def generate_sequences(self, nb):
+ sequences = []
+ for k in range(nb):
+ a, b = torch.randint(10**self.nb_digits, (2,))
+ c = a + b
+ a, b, c = str(a.item()), str(b.item()), str(c.item())
+ if self.zero_padded:
+ a = "0" * (self.nb_digits - len(a)) + a
+ b = "0" * (self.nb_digits - len(b)) + b
+ c = "0" * (self.nb_digits + 1 - len(c)) + c
+ if self.inverted_result:
+ c = c[::-1]
+ sequences.append(f"{a}+{b}={c}$")
+
+ sequences = self.tensorize(sequences)
+ ar_mask = (sequences == self.char2id["="]).long()
+ ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+ return sequences, ar_mask
+
+ def seq2str(self, seq):
+ return "".join(self.id2char[x.item()] for x in seq)
+
+
+# class ProblemUnion(Problem):
+# problems = [ProblemByheart()]
+# nb_common_codes = 100
+
+# def generate_sequences(nb_samples):
+# problem_indexes = torch.randint(len(problems), (nb_samples,))
+# nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
+# print(f"{nb_samples_per_problem}")
+# all_seq = []
+# for nb, p in zip(nb_samples_per_problem, problems):
+# all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
+# return all_seq
+
+# for strain, stest in zip(train_seq, test_seq):
+# s = torch.cat((strain, stest), 0)
+
+####################
+
+
+class SandBox(Task):
+ def __init__(
+ self,
+ problem,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ logger=None,
+ device=torch.device("cpu"),
+ max_nb_codes=1024,
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+ self.problem = problem
+
+ self.train_input, self.train_ar_mask = self.problem.generate_sequences(
+ nb_train_samples
+ )
+ self.test_input, self.test_ar_mask = self.problem.generate_sequences(
+ nb_test_samples
+ )
+
+ self.train_input, self.train_ar_mask = self.train_input.to(
+ device
+ ), self.train_ar_mask.to(device)
+ self.test_input, self.test_ar_mask = self.test_input.to(
+ device
+ ), self.test_ar_mask.to(device)
+
+ self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+
+ # A bit of paranoia never hurts
+ assert (
+ self.nb_codes <= max_nb_codes
+ and self.train_input.min() >= 0
+ and self.test_input.min() >= 0
+ and tuple(self.train_ar_mask.unique()) == (0, 1)
+ and tuple(self.test_ar_mask.unique()) == (0, 1)
+ )
+
+ def batches(self, split="train", nb_to_use=-1, desc=None):
+ assert split in {"train", "test"}
+ input = self.train_input if split == "train" else self.test_input
+ if nb_to_use > 0:
+ input = input[:nb_to_use]
+ if desc is None:
+ desc = f"epoch-{split}"
+ for batch in tqdm.tqdm(
+ input.split(self.batch_size), dynamic_ncols=True, desc=desc
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+ ):
+ def compute_accuracy(input, ar_mask, logger=None):
+ input, ar_mask = input[:nmax], ar_mask[:nmax]
+ result = input.clone() * (1 - ar_mask)
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ progress_bar_desc=None,
+ device=self.device,
+ )
+
+ if logger is not None:
+ for sp, st in zip(result[:10], input[:10]):
+ logger(
+ f"test_sequences {n_epoch} prediction {self.problem.seq2str(sp)}"
+ )
+ logger(
+ f" {n_epoch} ground truth {self.problem.seq2str(st)}"
+ )
+
+ nb_total = ar_mask.sum().item()
+ nb_correct = ((result == input).long() * ar_mask).sum().item()
+
+ return nb_total, nb_correct
+
+ train_nb_total, train_nb_correct = compute_accuracy(
+ self.train_input, self.train_ar_mask
+ )
+
+ logger(
+ 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}%"
+ )
+
+ test_nb_total, test_nb_correct = compute_accuracy(
+ self.test_input, self.test_ar_mask, logger
+ )
+
+ logger(
+ 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}%"
+ )
+
+