pass
+######################################################################
+
+
+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}%"
+ )
+
+
######################################################################
import picoclvr
pruner_train=None,
pruner_eval=None,
):
+ super().__init__()
+
def generate_descr(nb, cache_suffix, pruner):
return picoclvr.generate(
nb,
def __init__(
self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
):
+ super().__init__()
+
self.nb_train_samples = (nb_train_samples,)
self.nb_test_samples = (nb_test_samples,)
self.batch_size = batch_size
nb_walls,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.height = height
self.width = width
prompt_length,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.height = height
self.width = width
fraction_values_for_train=None,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.nb_steps = nb_steps
self.nb_stacks = nb_stacks
##############################################################
+######################################################################
+
+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 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(["<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)
+
+ if logger is not None:
+ for x in self.train_input[:10]:
+ 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]
+ 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], 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}%"
+ )
+
+
######################################################################
batch_size,
device=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.device = device
device=torch.device("cpu"),
device_storage=torch.device("cpu"),
):
+ super().__init__()
+
self.batch_size = batch_size
self.device = device
device_storage=device_storage,
)
- print(f"{train_action_seq.size()=}")
-
train_frame_seq = self.frame2seq(train_frames).to(device_storage)
test_frame_seq = self.frame2seq(test_frames).to(device_storage)
self.nb_codes = nb_frame_codes + nb_action_codes
train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
- print(f"{train_action_seq.device=} {nb_frame_codes.device=}")
+
train_action_seq += nb_frame_codes
self.train_input = torch.cat(
(train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
(seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
)
result = result.reshape(-1, result.size(-1))
- print(f"{result.size()=}")
frames = self.seq2frame(result)
image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")