X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=0a4dd6fa2f880e93aecbbd494621fae26b7dcdbb;hb=a291e213a152364b74e833200191c08a36451a90;hp=706e1d913c20be0bf9449f94bf3346658a5a1bdc;hpb=8d2ebe29b48e3cf2f0a3937ab1e44d0e12a4924e;p=picoclvr.git diff --git a/tasks.py b/tasks.py index 706e1d9..0a4dd6f 100755 --- a/tasks.py +++ b/tasks.py @@ -76,7 +76,7 @@ class Problem: class ProblemLevel0(Problem): def __init__(self, nb_sentences=100, len_prompt=5, len_result=5): - self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result)) + self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result)) self.seq[:, len_prompt] = 10 def generate_sequences(self, nb): @@ -96,18 +96,20 @@ class ProblemLevel1(Problem): 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)) - marker2 = torch.full((nb,1),11) + 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) + 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) @@ -117,6 +119,36 @@ class ProblemLevel1(Problem): 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) + + #################### @@ -989,6 +1021,166 @@ class Stack(Task): ############################################################## +###################################################################### + +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 + [""] * (len_max - len(s)) + ] + for s in sequences + ] + ) + ], + 0, + ) + + def seq2str(self, seq): + return " ".join([self.id2token[i] for i in seq]) + + def __init__( + self, + nb_train_samples, + nb_test_samples, + batch_size, + nb_starting_values=3, + max_input=9, + prog_len=6, + nb_runs=5, + logger=None, + device=torch.device("cpu"), + ): + super().__init__() + + self.batch_size = batch_size + self.device = device + + train_sequences = [ + rpl.generate( + nb_starting_values=nb_starting_values, + max_input=max_input, + prog_len=prog_len, + nb_runs=nb_runs, + ) + for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data") + ] + + test_sequences = [ + rpl.generate( + nb_starting_values=nb_starting_values, + max_input=max_input, + prog_len=prog_len, + nb_runs=nb_runs, + ) + for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data") + ] + + symbols = list( + set([""] + [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[""], self.token2id[""] + + self.train_input = self.tensorize(train_sequences) + self.test_input = self.tensorize(test_sequences) + + if logger is not None: + for x in self.train_input[:25]: + end = (x != self.t_nul).nonzero().max().item() + 1 + seq = [self.id2token[i.item()] for i in x[:end]] + s = " ".join(seq) + logger(f"example_seq {s}") + + 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].to(self.device) + 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, + ) + + sum_nb_total, sum_nb_errors = 0, 0 + for x, y in zip(input, result): + seq = [self.id2token[i.item()] for i in y] + nb_total, nb_errors, prog, stacks = rpl.compute_nb_errors(seq) + sum_nb_total += 1 + sum_nb_errors += 0 if nb_errors == 0 else 1 + if nb_to_log > 0: + gt_seq = [self.id2token[i.item()] for i in x] + _, _, gt_prog, _ = rpl.compute_nb_errors(gt_seq) + gt_prog = " ".join([str(x) for x in gt_prog]) + prog = " ".join([str(x) for x in prog]) + comment = "*" if nb_errors == 0 else "-" + logger(f"{comment} PROG [{gt_prog}] PREDICTED [{prog}]") + for start_stack, target_stack, result_stack, correct in stacks: + comment = "*" if correct else "-" + start_stack = " ".join([str(x) for x in start_stack]) + target_stack = " ".join([str(x) for x in target_stack]) + result_stack = " ".join([str(x) for x in result_stack]) + logger( + f" {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]" + ) + nb_to_log -= 1 + + return sum_nb_total, sum_nb_errors + + # -------------------------------------------------------------------- + + test_nb_total, test_nb_errors = compute_nb_errors( + self.test_input[:1000].to(self.device), 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}%" + ) + + ######################################################################