+######################################################################
+
+import rpl
+
+
+class RPL(Task):
+ def tensorize(self, sequences):
+ len_max = max([len(x) for x in sequences])
+ return torch.cat(
+ [
+ torch.tensor(
+ [
+ [
+ self.token2id[str(c)]
+ for c in s + ["<nul>"] * (len_max - len(s))
+ ]
+ for s in sequences
+ ]
+ )
+ ],
+ 0,
+ ).to(self.device)
+
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+
+ train_sequences = [
+ rpl.generate()
+ for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data")
+ ]
+ test_sequences = [
+ rpl.generate() for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data")
+ ]
+
+ symbols = list(
+ set(["<nul>"] + [x for l in train_sequences + test_sequences for x in l])
+ )
+ val_max = max([x if type(x) is int else 0 for x in symbols])
+ symbols = list(filter(lambda x: type(x) is str, symbols))
+ symbols.sort()
+ symbols += [str(n) for n in range(val_max + 1)]
+ print(f"{val_max=}")
+ self.token2id = dict([(c, n) for n, c in enumerate(symbols)])
+ self.id2token = dict([(n, c) for c, n in self.token2id.items()])
+
+ self.t_nul, self.t_prog = self.token2id["<nul>"], self.token2id["<prog>"]
+
+ self.train_input = self.tensorize(train_sequences)
+ self.test_input = self.tensorize(test_sequences)
+
+ self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 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
+ ):
+ last = (batch != self.t_nul).max(0).values.nonzero().max() + 3
+ batch = batch[:, :last]
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ def compute_nb_errors(input, nb_to_log=0):
+ result = input.clone()
+ s = (result == self.t_prog).long()
+ ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
+ result = (1 - ar_mask) * result + ar_mask * self.t_nul
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ if nb_to_log > 0:
+ for x in result[:nb_to_log]:
+ s = " ".join([self.id2token[i.item()] for i in x])
+ logger(f"check {n_epoch} {s}")
+ nb_to_log -= min(nb_to_log, result.size(0))
+
+ sum_nb_total, sum_nb_errors = 0, 0
+ for x in result:
+ seq = [self.id2token[i.item()] for i in x]
+ nb_total, nb_errors = rpl.check(seq)
+ sum_nb_total += nb_total
+ sum_nb_errors += nb_errors
+
+ return sum_nb_total, sum_nb_errors
+
+ test_nb_total, test_nb_errors = compute_nb_errors(self.test_input, nb_to_log=10)
+
+ logger(
+ f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
+ )
+
+