pass
-class TaskFromFile(Task):
- def tensorize(self, pairs, shuffle):
- len_max = max([len(x[0]) for x in pairs])
-
- input = torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s[0] + "#" * (len_max - len(s[0]))]
- for s in pairs
- ]
- )
- ],
- 0,
- ).to("cpu")
-
- pred_mask = torch.cat(
- [
- torch.tensor(
- [
- [int(c) for c in s[1] + "0" * (len_max - len(s[1]))]
- for s in pairs
- ]
- )
- ],
- 0,
- ).to("cpu")
-
- if shuffle:
- i = torch.randperm(input.size(0))
- input = input[i].contiguous()
- pred_mask = pred_mask[i].contiguous()
-
- return input, pred_mask
-
- # trim all the tensors in the tuple z to remove as much token from
- # left and right in the first tensor. If z is a tuple, all its
- # elements are trimed according to the triming for the first
- def trim(self, z, token="#"):
- n = self.char2id[token]
- if type(z) == tuple:
- x = z[0]
- i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return tuple([t[:, a:b] for t in z])
- else:
- i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return z[:, a:b]
-
- def __init__(
- self,
- train_filename,
- test_filename,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- shuffle=False,
- device=torch.device("cpu"),
- ):
- self.batch_size = batch_size
- self.device = device
-
- def read_file(filename, nb=-1):
- pairs = []
- with open(filename, "r") as f:
- while True:
- sequence = f.readline().strip()
- if not sequence:
- break
- pred_mask = f.readline().strip()
- assert len(sequence) == len(pred_mask)
- assert set(pred_mask).issubset({"0", "1", "2"}), f"{set(pred_mask)}"
- pairs.append((sequence, pred_mask))
- if len(pairs) == nb:
- break
-
- if nb > 0:
- pairs = pairs[:nb]
- assert len(pairs) == nb
-
- return pairs
-
- train_pairs = read_file(train_filename, nb_train_samples)
- test_pairs = read_file(test_filename, nb_test_samples)
-
- symbols = ["#"] + list(
- set("".join([x[0] for x in train_pairs + test_pairs])) - set(["#"])
- )
- self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
- self.id2char = dict([(n, c) for c, n in self.char2id.items()])
-
- self.train_input, self.train_pred_masks = self.tensorize(
- train_pairs, shuffle=shuffle
- )
- self.test_input, self.test_pred_masks = self.tensorize(
- test_pairs, shuffle=shuffle
- )
-
- 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 self.trim(batch).to(self.device)
-
- def vocabulary_size(self):
- return len(self.char2id)
-
- def tensor2str(self, t):
- return ["".join([self.id2char[x.item()] for x in s]) for s in t]
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- correct = self.trim(self.test_input[:1000]).to(self.device)
- result = correct.clone()
- pred_mask = self.test_pred_masks[:1000, : result.size(1)].to(self.device)
- ar_mask = (pred_mask > 0).long()
- result *= 1 - ar_mask # paraaaaanoiaaaaaaa
-
- logger(f"----------------------------------------------------------")
-
- for e in self.tensor2str(result[:50]):
- logger(f"test_before {e}")
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- logger(f"----------------------------------------------------------")
-
- for e, c in zip(self.tensor2str(result[:50]), self.tensor2str(correct[:50])):
- logger(f"test_after {e}")
- logger(f"correct {c}")
-
- logger(f"----------------------------------------------------------")
-
- err_mask = (pred_mask == 2).long()
- nb_total = err_mask.sum().item()
- nb_correct = ((correct == result).long() * err_mask).sum().item()
-
- logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
- logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
-
-
-####################
-
-import problems
-
-
-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
- assert self.train_input.min() >= 0
- assert self.test_input.min() >= 0
- assert tuple(x.item() for x in self.train_ar_mask.unique()) in {
- (0,),
- (1,),
- (0, 1),
- }
- assert tuple(x.item() for x in self.test_ar_mask.unique()) in {
- (0,),
- (1,),
- (0, 1),
- }
-
- if logger is not None:
- for s, a in zip(self.train_input[:100], self.train_ar_mask[:100]):
- logger(f"train_sequences {self.problem.seq2str(s)}")
- a = "".join(["01"[x.item()] for x in a])
- logger(f" {a}")
-
- 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,
- )
-
- log_ground_truth = ar_mask.min() == 0
-
- 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)}"
- )
- if log_ground_truth:
- logger(
- f" {n_epoch} ground truth {self.problem.seq2str(st)}"
- )
-
- nb_total, nb_correct = self.problem.compute_nb_correct(
- input, ar_mask, result
- )
-
- # 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}%"
- )
-
- logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
-
- if save_attention_image is not None:
- for k in range(10):
- ns = torch.randint(self.test_input.size(0), (1,)).item()
- input = self.test_input[ns : ns + 1].clone()
-
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
- # model.record_attention(True)
- model(BracketedSequence(input))
- model.train(t)
- # ram = model.retrieve_attention()
- # model.record_attention(False)
-
- # tokens_output = [c for c in self.problem.seq2str(input[0])]
- # tokens_input = ["n/a"] + tokens_output[:-1]
- # for n_head in range(ram[0].size(1)):
- # filename = os.path.join(
- # result_dir, f"sandbox_attention_{k}_h{n_head}.pdf"
- # )
- # attention_matrices = [m[0, n_head] for m in ram]
- # save_attention_image(
- # filename,
- # tokens_input,
- # tokens_output,
- # attention_matrices,
- # k_top=10,
- ##min_total_attention=0.9,
- # token_gap=12,
- # layer_gap=50,
- # )
- # logger(f"wrote {filename}")
-
-
-######################################################################
-
-import picoclvr
-
-
-class PicoCLVR(Task):
- # Make a tensor from a list of strings
- def tensorize(self, descr):
- token_descr = [s.strip().split(" ") for s in descr]
- l = max([len(s) for s in token_descr])
- token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
- id_descr = [[self.token2id[u] for u in s] for s in token_descr]
- return torch.tensor(id_descr, device=self.device)
-
- # Make a list of strings from a tensor
- def detensorize(self, x):
- return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
-
- # trim all the tensors in the tuple z to remove as much token from
- # left and right in the first tensor. If z is a tuple, all its
- # elements are trimed according to the triming for the first
- def trim(self, z, token="<nul>"):
- n = self.token2id[token]
- if type(z) == tuple:
- x = z[0]
- i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return tuple([t[:, a:b] for t in z])
- else:
- i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return z[:, a:b]
-
- ######################
-
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_colors=5,
- logger=None,
- device=torch.device("cpu"),
- pruner_train=None,
- pruner_eval=None,
- ):
- super().__init__()
-
- def generate_descr(nb, cache_suffix, pruner):
- return picoclvr.generate(
- nb,
- height=self.height,
- width=self.width,
- nb_colors=nb_colors,
- pruner=pruner,
- )
-
- self.height = height
- self.width = width
- self.batch_size = batch_size
- self.device = device
- self.pruner_train = pruner_train
- self.pruner_eval = pruner_eval
-
- if logger is not None:
- logger(
- f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
- )
-
- self.train_descr = generate_descr(
- nb_train_samples, "train", pruner=self.pruner_train
- )
- self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
-
- # Build the tokenizer
- tokens = {"<nul>", "<img>"}
- for d in [self.train_descr, self.test_descr]:
- for s in d:
- for t in s.strip().split(" "):
- tokens.add(t)
- # make this set a sorted list to get the same tensors given
- # the same descr
- tokens = list(tokens)
- tokens.sort()
- self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
- self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
- self.t_img, self.t_nul = self.token2id["<img>"], self.token2id["<nul>"]
-
- # Tokenize the train and test sets
- self.train_input = self.tensorize(self.train_descr)
- self.test_input = self.tensorize(self.test_descr)
-
- 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
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
- ):
- yield self.trim(batch)
-
- def vocabulary_size(self):
- return len(self.token2id)
-
- def compute_missing_properties(
- self, n_epoch, model, logger, deterministic_synthesis, pruner=None
- ):
- acc_nb_requested_properties = []
- acc_nb_missing_properties = []
- acc_nb_results = 0
-
- for input in tqdm.tqdm(
- self.test_input.split(self.batch_size),
- dynamic_ncols=True,
- desc=f"test-properties",
- ):
- result = input.clone()
- ar_mask = (result == self.t_img).long().cumsum(dim=1).clamp(max=1)
- result = (1 - ar_mask) * result + ar_mask * self.t_nul
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- progress_bar_desc=None,
- device=self.device,
- )
-
- result_descr = self.detensorize(result)
- np = picoclvr.nb_properties(
- result_descr,
- height=self.height,
- width=self.width,
- pruner=pruner,
- )
- nb_requested_properties, _, nb_missing_properties = zip(*np)
- acc_nb_requested_properties += nb_requested_properties
- acc_nb_missing_properties += nb_missing_properties
- acc_nb_results += len(result_descr)
-
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
-
- prefix = "" if pruner is None else "pruned_"
- logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- logger(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- logger(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
- )
-
- logger(
- f"main_test_accuracy {n_epoch} {1-nb_missing_properties/nb_requested_properties}"
- )
-
- ######################################################################
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- self.compute_missing_properties(n_epoch, model, logger, deterministic_synthesis)
-
- if self.pruner_eval is not None:
- self.compute_missing_properties(n_epoch, model, self.pruner_eval)
-
- nb_tokens_to_generate = self.height * self.width + 3
- result_descr = []
- nb_per_primer = 8
- primer = []
-
- for primer_descr in [
- "red above green <sep> green top <sep> blue right of red",
- "there is red <sep> there is yellow <sep> there is blue",
- "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
- "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
- ]:
- primer += [primer_descr + " <img>"] * nb_per_primer
-
- result = self.tensorize(primer)
- fill = result.new_full(
- result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
- )
- result = torch.cat((result, fill), 1)
- ar_mask = (result == self.t_nul).long()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
- result_descr = self.detensorize(result)
-
- np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
-
- acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
- acc_nb_results = len(result_descr)
-
- nb_requested_properties = sum(acc_nb_requested_properties)
- nb_missing_properties = sum(acc_nb_missing_properties)
-
- prefix = "demo_"
- logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- logger(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- logger(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
- )
-
- img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
-
- if img.dim() == 5:
- if img.size(1) == 1:
- img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
- else:
- img = torch.cat(
- [
- torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
- for x in img
- ],
- 0,
- )
-
- image_name = os.path.join(result_dir, f"picoclvr_result_{n_epoch:04d}.png")
- torchvision.utils.save_image(
- img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
- )
- logger(f"wrote {image_name}")
-
-
-######################################################################
-
-
-class MNIST(Task):
- 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
- self.device = device
- data_set = torchvision.datasets.MNIST(root="./data", train=True, download=True)
- self.train_input = data_set.data[:nb_train_samples].view(-1, 28 * 28).long()
- data_set = torchvision.datasets.MNIST(root="./data", train=False, download=True)
- self.test_input = data_set.data[:nb_test_samples].view(-1, 28 * 28).long()
-
- 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 256
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
- ar_mask = torch.full_like(results, 1)
- masked_inplace_autoregression(
- model,
- self.batch_size,
- results,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
- image_name = os.path.join(result_dir, f"mnist_result_{n_epoch:04d}.png")
- torchvision.utils.save_image(
- 1 - results.reshape(-1, 1, 28, 28) / 255.0,
- image_name,
- nrow=16,
- pad_value=0.8,
- )
- logger(f"wrote {image_name}")
-
-
-######################################################################
-
-import maze
-
-
-class Maze(Task):
- def map2seq(self, *m):
- return torch.cat([x.flatten(1) for x in m], 1)
-
- def seq2map(self, s):
- s = s.reshape(s.size(0), -1, self.height, self.width)
- return (s[:, k] for k in range(s.size(1)))
-
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_walls,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.height = height
- self.width = width
- self.device = device
-
- train_mazes, train_paths, _ = maze.create_maze_data(
- nb_train_samples,
- height=height,
- width=width,
- nb_walls=nb_walls,
- progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
- )
- self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
-
- test_mazes, test_paths, _ = maze.create_maze_data(
- nb_test_samples,
- height=height,
- width=width,
- nb_walls=nb_walls,
- progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
- )
- self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
-
- 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
- ):
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def compute_error(
- self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
- ):
- model_device = next(model.parameters()).device
- nb_total, nb_correct = 0, 0
- count = torch.zeros(
- self.width * self.height,
- self.width * self.height,
- device=model_device,
- dtype=torch.int64,
- )
-
- for input in self.batches(split, nb_to_use):
- input = input.to(model_device)
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
- ar_mask[:, self.height * self.width :] = 1
- result *= 1 - ar_mask
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- progress_bar_desc=None,
- device=self.device,
- )
- mazes, paths = self.seq2map(result)
- path_correctness = maze.path_correctness(mazes, paths)
- nb_correct += path_correctness.long().sum()
- nb_total += mazes.size(0)
-
- optimal_path_lengths = (
- (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
- )
- predicted_path_lengths = (
- (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
- )
- optimal_path_lengths = optimal_path_lengths[path_correctness]
- predicted_path_lengths = predicted_path_lengths[path_correctness]
- count[optimal_path_lengths, predicted_path_lengths] += 1
-
- if count.max() == 0:
- count = None
- else:
- count = count[
- : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
- ]
-
- return nb_total, nb_correct, count
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- train_nb_total, train_nb_correct, count = self.compute_error(
- model,
- "train",
- nb_to_use=1000,
- deterministic_synthesis=deterministic_synthesis,
- )
- 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, count = self.compute_error(
- model,
- "test",
- nb_to_use=1000,
- deterministic_synthesis=deterministic_synthesis,
- )
- 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}%"
- )
-
- logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
-
- if count is not None:
- proportion_optimal = count.diagonal().sum().float() / count.sum()
- logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
- with open(
- os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
- ) as f:
- for i in range(count.size(0)):
- for j in range(count.size(1)):
- eol = " " if j < count.size(1) - 1 else "\n"
- f.write(f"{count[i,j]}{eol}")
-
- input = self.test_input[:48].to(next(model.parameters()).device)
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
- ar_mask[:, self.height * self.width :] = 1
- result *= 1 - ar_mask
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- mazes, paths = self.seq2map(input)
- _, predicted_paths = self.seq2map(result)
-
- filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
- maze.save_image(
- filename,
- mazes=mazes,
- target_paths=paths,
- predicted_paths=predicted_paths,
- path_correct=maze.path_correctness(mazes, predicted_paths),
- path_optimal=maze.path_optimality(paths, predicted_paths),
- )
- logger(f"wrote {filename}")
-
-
-######################################################################
-
-
-import snake
-
-
-class Snake(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.height = height
- self.width = width
- self.device = device
- self.prompt_length = prompt_length
-
- self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
- nb_train_samples,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- self.device,
- )
- self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
- nb_test_samples,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- self.device,
- )
-
- 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
- ):
- 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_correct(input, prior_visits):
- result = input.clone()
- i = torch.arange(result.size(1), device=result.device)[None, :]
- ar_mask = (
- torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
- .long()
- .expand_as(result)
- )
- result *= 1 - ar_mask
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- nb_total = ((prior_visits > 0) * ar_mask).sum()
-
- nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
-
- return nb_total, nb_correct
-
- test_nb_total, test_nb_correct = compute_nb_correct(
- self.test_input[:1000], self.test_prior_visits[:1000]
- )
-
- 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}%"
- )
-
- logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
-
-
-######################################################################
-
-
-import stack
-
-
-class Stack(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- logger,
- nb_steps,
- nb_stacks,
- nb_digits,
- 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
- self.nb_digits = nb_digits
- self.device = device
-
- if fraction_values_for_train is None:
- values_for_train = None
- values_for_test = None
- else:
- all = torch.randperm(10**nb_digits)
- nb_for_train = int(all.size(0) * fraction_values_for_train)
- values_for_train = all[:nb_for_train]
- values_for_test = all[nb_for_train:]
-
- self.train_input, self.train_stack_counts = stack.generate_sequences(
- nb_train_samples,
- nb_steps,
- nb_stacks,
- nb_digits,
- values_for_train,
- self.device,
- )
-
- self.test_input, self.test_stack_counts = stack.generate_sequences(
- nb_test_samples,
- nb_steps,
- nb_stacks,
- nb_digits,
- values_for_test,
- self.device,
- )
-
- i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks)
- counts = self.test_stack_counts.flatten()[i.flatten()]
- counts = F.one_hot(counts).sum(0)
- logger(f"test_pop_stack_counts {counts}")
-
- 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
- ):
- 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_correct(input):
- result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- errors = ((result != input).long() * ar_mask).reshape(
- -1, 1 + self.nb_digits
- )
- ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
-
- nb_total = ar_mask.max(1).values.sum()
- nb_correct = nb_total - errors.max(1).values.sum()
-
- return nb_total, nb_correct
-
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
-
- 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}%"
- )
-
- logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
-
- ##############################################################
- # Log a few generated sequences
- input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
- result = input.clone()
- stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
- ar_mask = (result != input).long()
-
- # for n in range(result.size(0)):
- # logger(
- # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- # )
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
- for label, input in [
- ("train", self.train_input[:32]),
- ("test", self.test_input[:32]),
- ]:
- output = model(BracketedSequence(input)).x
- output = output.log_softmax(dim=-1)
- filename = os.path.join(
- result_dir, f"stack_with_crossentropy_{n_epoch:04d}_{label}.txt"
- )
- with open(filename, "w") as f:
- for n in range(input.size(0)):
- s = stack.seq_to_str(
- input[n], nb_stacks=self.nb_stacks, nb_digits=self.nb_digits
- )
- for t, k, w in zip(range(input[n].size(0)), input[n], s.split(" ")):
- u = (
- " " * (10 - len(w))
- + w
- + " "
- + str(output[n][t][k].exp().item())
- + "\n"
- )
- f.write(u)
- f.write("\n")
- logger(f"wrote {filename}")
- #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-
- for n in range(result.size(0)):
- logger(
- f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- )
- ##############################################################
-
-
-######################################################################
-
-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,
- )
-
- 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,
- no_prog=False,
- logger=None,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.device = device
- self.no_prog = no_prog
-
- train_sequences = [
- rpl.generate(
- nb_starting_values=nb_starting_values,
- nb_result_values_max=4 * 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,
- nb_result_values_max=4 * 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)]
- 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.token2id["<nul>"]
- self.t_input = self.token2id["<in>"]
- self.t_output = self.token2id["<out>"]
- self.t_prog = self.token2id["<prg>"]
- self.t_end = self.token2id["<end>"]
-
- self.train_input = self.tensorize(train_sequences)
- self.test_input = self.tensorize(test_sequences)
-
- if no_prog:
- # Excise the program from every train and test example
- k = torch.arange(self.train_input.size(1), device=self.train_input.device)[
- None, :
- ]
- p = (
- ((self.train_input == self.t_prog).long() * k)
- .max(1, keepdim=True)
- .values
- )
- self.train_input = (
- self.train_input * (k <= p).long()
- + self.t_end * (k == p + 1).long()
- + self.t_nul * (k > p + 1).long()
- )
- k = torch.arange(self.test_input.size(1), device=self.test_input.device)[
- None, :
- ]
- p = (
- ((self.test_input == self.t_prog).long() * k)
- .max(1, keepdim=True)
- .values
- )
- self.test_input = (
- self.test_input * (k <= p).long()
- + self.t_end * (k == p + 1).long()
- + self.t_nul * (k > p + 1).long()
- )
-
- if logger is not None:
- logger(f"value_max {val_max}")
- 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_prog(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 one_input, one_result in zip(input, result):
- seq = [self.id2token[i.item()] for i in one_result]
- 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 one_input]
- _, _, 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
-
- # --------------------------------------------------------------------
- def compute_nb_errors_output(input, nb_to_log=0):
- result = input.clone()
- k = torch.arange(result.size(1), device=result.device)[None, :]
- last_output_idx = (
- ((result == self.t_output) * k).max(dim=1, keepdim=True).values
- )
- first_prog_idx = (
- ((result == self.t_prog) * k).max(dim=1, keepdim=True).values
- )
- ar_mask = (k > last_output_idx).long() * (k < first_prog_idx).long()
- 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 one_input, one_result, i, j in zip(
- input, result, last_output_idx, first_prog_idx
- ):
- seq = [self.id2token[i.item()] for i in one_result]
- sum_nb_total += 1
- correct = (one_input - one_result).abs().max() == 0
- sum_nb_errors += 0 if correct else 1
- if nb_to_log > 0:
- result_stack = [
- self.id2token[i.item()] for i in one_result[i : j + 1]
- ]
- target_stack = [
- self.id2token[i.item()] for i in one_input[i : j + 1]
- ]
- comment = "*" if correct else "-"
- result_stack = " ".join([str(x) for x in result_stack])
- target_stack = " ".join([str(x) for x in target_stack])
- logger(
- f"output_test {comment} [{target_stack}] PREDICTED [{result_stack}]"
- )
- nb_to_log -= 1
-
- return sum_nb_total, sum_nb_errors
-
- # --------------------------------------------------------------------
-
- if not self.no_prog:
- test_nb_total, test_nb_errors = compute_nb_errors_prog(
- self.test_input[:1000].to(self.device), nb_to_log=10
- )
-
- logger(
- f"accuracy_prog_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
- )
-
- logger(f"main_test_accuracy {n_epoch} {1-test_nb_errors/test_nb_total}")
-
- test_nb_total, test_nb_errors = compute_nb_errors_output(
- self.test_input[:1000].to(self.device), nb_to_log=10
- )
-
- logger(
- f"accuracy_output_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
- )
-
- if save_attention_image is None:
- logger("no save_attention_image (is pycairo installed?)")
- else:
- ns = torch.randint(self.test_input.size(0), (1,)).item()
- input = self.test_input[ns : ns + 1].clone()
- last = (input != self.t_nul).max(0).values.nonzero().max() + 3
- input = input[:, :last].to(self.device)
-
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
- model.record_attention(True)
- model(BracketedSequence(input))
- model.train(t)
- ram = model.retrieve_attention()
- model.record_attention(False)
-
- tokens_output = [self.id2token[i.item()] for i in input[0]]
- tokens_input = ["n/a"] + tokens_output[:-1]
- for n_head in range(ram[0].size(1)):
- filename = os.path.join(
- result_dir, f"rpl_attention_{n_epoch}_h{n_head}.pdf"
- )
- attention_matrices = [m[0, n_head] for m in ram]
- save_attention_image(
- filename,
- tokens_input,
- tokens_output,
- attention_matrices,
- k_top=10,
- # min_total_attention=0.9,
- token_gap=12,
- layer_gap=50,
- )
- logger(f"wrote {filename}")
-
-
-######################################################################
-
-
-import expr
-
-
-class Expr(Task):
- def tensorize(self, sequences):
- len_max = max([len(x) for x in sequences])
- return torch.cat(
- [
- torch.tensor(
- [
- [self.char2id[c] for c in s + "#" * (len_max - len(s))]
- for s in sequences
- ]
- )
- ],
- 0,
- ).to(self.device)
-
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- nb_variables,
- sequence_length,
- operand_max,
- result_max,
- batch_size,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.device = device
-
- train_sequences = expr.generate_sequences(
- nb_train_samples,
- nb_variables=nb_variables,
- length=sequence_length,
- operand_max=operand_max,
- result_max=result_max,
- )
-
- test_sequences = expr.generate_sequences(
- nb_test_samples,
- nb_variables=nb_variables,
- length=sequence_length,
- operand_max=operand_max,
- result_max=result_max,
- )
-
- symbols = list(set("#" + "".join(train_sequences + test_sequences)))
- symbols.sort()
-
- self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
- self.id2char = dict([(n, c) for c, n in self.char2id.items()])
-
- self.filler, self.space = self.char2id["#"], self.char2id[" "]
-
- 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.filler).max(0).values.nonzero().max() + 3
- batch = batch[:, :last]
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def seq2str(self, s):
- return "".join([self.id2char[k.item()] for k in s])
-
- def produce_results(
- self,
- n_epoch,
- model,
- result_dir,
- logger,
- deterministic_synthesis,
- input_file=None,
- ):
- def compute_nb_correct(input):
- result = input.clone()
- s = (result == self.space).long()
- ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- nb_total = input.size(0)
- nb_correct = (input == result).long().min(1).values.sum()
-
- #######################################################################
- # Comput predicted vs. true variable values
-
- nb_delta = torch.zeros(5, dtype=torch.int64)
- nb_missed = 0
-
- values_input = expr.extract_results([self.seq2str(s) for s in input])
- values_result = expr.extract_results([self.seq2str(s) for s in result])
-
- filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
-
- with open(filename, "w") as f:
- for i, r in zip(values_input, values_result):
- for n, vi in i.items():
- vr = r.get(n)
- f.write(f"{vi} {-1 if vr is None else vr}\n")
-
- if vr is None or vr < 0:
- nb_missed += 1
- else:
- d = abs(vr - vi)
- if d >= nb_delta.size(0):
- nb_missed += 1
- else:
- nb_delta[d] += 1
-
- ######################################################################
-
- return nb_total, nb_correct, nb_delta, nb_missed
-
- (
- test_nb_total,
- test_nb_correct,
- test_nb_delta,
- test_nb_missed,
- ) = compute_nb_correct(self.test_input[:10000])
-
- 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}%"
- )
-
- logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
-
- nb_total = test_nb_delta.sum() + test_nb_missed
- for d in range(test_nb_delta.size(0)):
- logger(
- f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
- )
- logger(
- f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
- )
-
- ##############################################################
- # Log a few generated sequences
- if input_file is None:
- input = self.test_input[:10]
- else:
- with open(input_file, "r") as f:
- sequences = [e.strip() for e in f.readlines()]
- sequences = [s + " " + "#" * 50 for s in sequences]
- input = self.tensorize(sequences)
-
- result = input.clone()
- s = (result == self.space).long()
- ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
- result = (1 - ar_mask) * result + ar_mask * self.filler
-
- for n in range(result.size(0)):
- logger(f"test_before {self.seq2str(result[n])}")
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- correct = (1 - ar_mask) * self.space + ar_mask * input
- for n in range(result.size(0)):
- comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
- logger(f"test_after {self.seq2str(result[n])} {comment}")
- logger(f"truth {self.seq2str(correct[n])}")
- ##############################################################
-
-
-######################################################################
-
-import grid
-
-
-class Grid(Task):
- # Make a tensor from a list of strings
- def str2tensor(self, descr):
- token_descr = [s.strip().split(" ") for s in descr]
- l = max([len(s) for s in token_descr])
- token_descr = [s + ["#"] * (l - len(s)) for s in token_descr]
- id_descr = [[self.token2id[u] for u in s] for s in token_descr]
- return torch.tensor(id_descr, device=self.device)
-
- # Make a list of strings from a tensor
- def tensor2str(self, x):
- return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
-
- # trim all the tensors in the tuple z to remove as much token from
- # left and right in the first tensor. If z is a tuple, all its
- # elements are trimed according to the triming for the first
- def trim(self, z, token="#"):
- n = self.token2id[token]
- if type(z) == tuple:
- x = z[0]
- i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return tuple([t[:, a:b] for t in z])
- else:
- i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
- a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
- return z[:, a:b]
-
- ######################
-
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- size,
- fraction_play=0.0,
- logger=None,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.device = device
- self.batch_size = batch_size
- self.grid_factory = grid.GridFactory(size=size)
- self.fraction_play = fraction_play
-
- if logger is not None:
- logger(
- f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
- )
-
- self.train_descr = self.grid_factory.generate_samples(
- nb=nb_train_samples,
- fraction_play=fraction_play,
- progress_bar=lambda r: tqdm.tqdm(r),
- )
-
- self.test_descr = self.grid_factory.generate_samples(
- nb=nb_test_samples, fraction_play=0.0, progress_bar=lambda r: tqdm.tqdm(r)
- )
-
- if fraction_play > 0:
- self.play_descr = self.grid_factory.generate_samples(
- nb=25, fraction_play=1.0, progress_bar=lambda r: tqdm.tqdm(r)
- )
- else:
- self.play_descr = []
-
- # Build the tokenizer
- tokens = set()
- for d in [self.train_descr, self.test_descr, self.play_descr]:
- for s in d:
- for t in s.strip().split(" "):
- tokens.add(t)
- # make this set a sorted list to get the same tensors given
- # the same descr
- tokens = list(tokens)
- tokens.sort()
- tokens = ["#"] + tokens
- self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
- self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
- self.t_nul = self.token2id["#"]
- self.t_true = self.token2id["true"]
- self.t_false = self.token2id["false"]
- # self.t_pipe = self.token2id["|"]
-
- # Tokenize the train and test sets
- self.train_input = self.str2tensor(self.train_descr)
- self.test_input = self.str2tensor(self.test_descr)
- self.play_input = (
- None if len(self.play_descr) == 0 else self.str2tensor(self.play_descr)
- )
-
- 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
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
- ):
- yield self.trim(batch)
-
- def vocabulary_size(self):
- return len(self.token2id)
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- correct = self.test_input[:1000]
- result = correct.clone()
- ar_mask = torch.logical_or(result == self.t_true, result == self.t_false).long()
- result *= 1 - ar_mask # paraaaaanoiaaaaaaa
-
- logger(f"----------------------------------------------------------")
-
- for e in self.tensor2str(result[:10]):
- logger(f"test_before {e}")
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- logger(f"----------------------------------------------------------")
-
- for e in self.tensor2str(result[:10]):
- logger(f"test_after {e}")
-
- logger(f"----------------------------------------------------------")
-
- nb_total = ar_mask.sum().item()
- nb_correct = ((correct == result).long() * ar_mask).sum().item()
-
- logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
- logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
-
- if self.play_input is not None:
- result = self.play_input.clone()
- ar_mask = (result == self.t_pipe).long().cumsum(dim=1).clamp(max=1)
- result *= 1 - ar_mask # paraaaaanoiaaaaaaa
-
- logger(f"----------------------------------------------------------")
-
- for e in self.tensor2str(result[:10]):
- logger(f"play_before {e}")
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- logger(f"----------------------------------------------------------")
-
- for e in self.tensor2str(result[:10]):
- logger(f"play_after {e}")
-
- logger(f"----------------------------------------------------------")
-
-
-######################################################################
-
-import qmlp
-
-
-class QMLP(Task):
- ######################
-
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- result_dir,
- logger=None,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.device = device
- self.batch_size = batch_size
- self.nb_samples_per_mlp = 256
-
- if logger is not None:
- logger(
- f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
- )
-
- seq, q_test_set, test_error = qmlp.generate_sequence_and_test_set(
- nb_mlps=nb_train_samples + nb_test_samples,
- nb_samples=self.nb_samples_per_mlp,
- device=self.device,
- batch_size=64,
- nb_epochs=250,
- nb_mlps_per_batch=1024,
- )
-
- self.train_input = seq[:nb_train_samples]
- self.train_q_test_set = q_test_set[:nb_train_samples]
- self.train_ref_test_errors = test_error[:nb_train_samples]
- self.test_input = seq[nb_train_samples:]
- self.test_q_test_set = q_test_set[nb_train_samples:]
- self.test_ref_test_errors = test_error[nb_train_samples:]
-
- filename = os.path.join(result_dir, f"train_errors_ref.dat")
- with open(filename, "w") as f:
- for e in self.train_ref_test_errors:
- f.write(f"{e}\n")
-
- filename = os.path.join(result_dir, f"test_errors_ref.dat")
- with open(filename, "w") as f:
- for e in self.test_ref_test_errors:
- f.write(f"{e}\n")
-
- 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
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
- ):
- yield batch
-
- def vocabulary_size(self):
- return self.nb_codes
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
- ):
- correct = self.test_input[:1000]
- result = correct.clone()
- ar_mask = (
- torch.arange(result.size(1), device=result.device)
- > self.nb_samples_per_mlp * 3 + 1
- ).long()[None, :]
- ar_mask = ar_mask.expand_as(result)
- result *= 1 - ar_mask # paraaaaanoiaaaaaaa
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- q_train_set = result[:, : self.nb_samples_per_mlp * 3]
- q_params = result[:, self.nb_samples_per_mlp * 3 + 1 :]
- error_test = qmlp.evaluate_q_params(q_params, self.test_q_test_set)
-
- filename = os.path.join(result_dir, f"test_errors_{n_epoch:04d}.dat")
- with open(filename, "w") as f:
- for e in error_test:
- f.write(f"{e}\n")
-
-
-######################################################################
-
-import greed
-
-
-class Greed(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- T,
- nb_walls,
- nb_coins,
- logger=None,
- device=torch.device("cpu"),
- ):
- super().__init__()
-
- self.batch_size = batch_size
- self.device = device
-
- self.world = greed.GreedWorld(height, width, T, nb_walls, nb_coins)
-
- states, actions, rewards = self.world.generate_episodes(
- nb_train_samples + nb_test_samples
- )
- seq = self.world.episodes2seq(states, actions, rewards)
- self.train_input = seq[:nb_train_samples].to(self.device)
- self.test_input = seq[nb_train_samples:].to(self.device)
-
- def wipe_lookahead_rewards(self, batch):
- t = torch.arange(batch.size(1), device=batch.device)[None, :]
- u = torch.randint(batch.size(1), (batch.size(0), 1), device=batch.device)
- lr_mask = (t <= u).long() * (
- t % self.world.it_len == self.world.index_lookahead_reward
- ).long()
-
- return (
- lr_mask * self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
- + (1 - lr_mask) * batch
- )
-
- 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 self.wipe_lookahead_rewards(batch)
-
- def vocabulary_size(self):
- return self.world.nb_codes
-
- def thinking_autoregression(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
- ):
- snapshots = []
-
- def ar(result, ar_mask, logit_biases=None):
- ar_mask = ar_mask.expand_as(result)
- result *= 1 - ar_mask
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis=deterministic_synthesis,
- logit_biases=logit_biases,
- device=self.device,
- progress_bar_desc=None,
- )
- warnings.warn("keeping thinking snapshots", RuntimeWarning)
- snapshots.append(result[:100].detach().clone())
-
- # Generate iteration after iteration
-
- result = self.test_input[:250].clone()
- # Erase all the content but that of the first iteration
- result[:, self.world.it_len :] = -1
- # Set the lookahead_reward of the firs to UNKNOWN
- result[:, self.world.index_lookahead_reward] = self.world.lookahead_reward2code(
- greed.REWARD_UNKNOWN
- )
-
- t = torch.arange(result.size(1), device=result.device)[None, :]
-
- for u in tqdm.tqdm(
- range(0, result.size(1), self.world.it_len),
- desc="thinking",
- ):
- # Generate the next state but keep the initial one, the
- # lookahead_reward of previous iterations are set to
- # UNKNOWN
- if u > 0:
- result[
- :, u + self.world.index_lookahead_reward
- ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
- ar_mask = (t >= u + self.world.index_states).long() * (
- t < u + self.world.index_states + self.world.state_len
- ).long()
- ar(result, ar_mask)
-
- # Generate the action and reward with lookahead_reward to +1
- result[
- :, u + self.world.index_lookahead_reward
- ] = self.world.lookahead_reward2code(greed.REWARD_PLUS)
- ar_mask = (t >= u + self.world.index_reward).long() * (
- t <= u + self.world.index_action
- ).long()
- ar(result, ar_mask)
-
- # Set the lookahead_reward to UNKNOWN for the next iterations
- result[
- :, u + self.world.index_lookahead_reward
- ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
-
- filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
- with open(filename, "w") as f:
- for n in range(snapshots[0].size(0)):
- for s in snapshots:
- lr, s, a, r = self.world.seq2episodes(
- s[n : n + 1],
- )
- str = self.world.episodes2str(
- lr, s, a, r, unicode=True, ansi_colors=True
- )
- f.write(str)
- f.write("\n\n")
-
- # Saving the generated sequences
-
- lr, s, a, r = self.world.seq2episodes(result)
- str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
-
- filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt")
- with open(filename, "w") as f:
- f.write(str)
- logger(f"wrote {filename}")
-
- def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
- ):
- result = self.wipe_lookahead_rewards(self.test_input[:250].clone())
-
- # Saving the ground truth
-
- lr, s, a, r = self.world.seq2episodes(
- result,
- )
- str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
-
- filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
- with open(filename, "w") as f:
- f.write(str)
- logger(f"wrote {filename}")
-
- # Re-generating from the first frame
-
- ar_mask = (
- torch.arange(result.size(1), device=result.device) >= self.world.it_len
- ).long()[None, :]
- ar_mask = ar_mask.expand_as(result)
- result *= 1 - ar_mask # paraaaaanoiaaaaaaa
-
- masked_inplace_autoregression(
- model,
- self.batch_size,
- result,
- ar_mask,
- deterministic_synthesis,
- device=self.device,
- )
-
- # Saving the generated sequences
-
- lr, s, a, r = self.world.seq2episodes(
- result,
- )
- str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
-
- filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt")
- with open(filename, "w") as f:
- f.write(str)
- logger(f"wrote {filename}")
-
- self.thinking_autoregression(
- n_epoch, model, result_dir, logger, deterministic_synthesis, nmax
- )
-
-
-######################################################################
######################################################################
import world
torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=8, padding=2)
logger(f"wrote {image_name}")
+ def make_ar_mask(self, input):
+ b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
+ return b.long()[None, :].expand_as(input)
+
def __init__(
self,
nb_train_samples,
self.train_input = world.generate(
nb_train_samples, height=self.height, width=self.width
- )
- self.train_ar_mask = (
- (torch.arange(self.train_input.size(1)) > self.train_input.size(1) // 2)
- .long()[None, :]
- .expand_as(self.train_input)
- )
+ ).to(device)
self.test_input = world.generate(
nb_test_samples, height=self.height, width=self.width
- )
- self.test_ar_mask = (
- (torch.arange(self.test_input.size(1)) > self.test_input.size(1) // 2)
- .long()[None, :]
- .expand_as(self.test_input)
- )
-
- 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)
+ ).to(device)
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+ self.train_quizzes = []
+ self.test_quizzes = []
+
if result_dir is not None:
self.save_image(
self.train_input[:96], result_dir, f"world_train.png", logger
)
- def batches(self, split="train", nb_to_use=-1, desc=None):
+ def batches(self, split="train", 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 split == "train":
+ input = self.train_input
+ quizzes = self.train_quizzes
+ else:
+ input = self.test_input
+ quizzes = self.test_quizzes
+
+ if len(quizzes) > 0:
+ quizzes = torch.cat(quizzes, dim=0)
+ if quizzes.size(0) > input.size(0) // 2:
+ i = torch.randperm(input.size(0))[: input.size(0) // 2]
+ quizzes = quizzes[i]
+
+ i = torch.randperm(input.size(0))[: input.size(0) - quizzes.size(0)]
+ input = input[i]
+
+ self.nb_batch_samples_world = input.size(0)
+ self.nb_batch_samples_quizzes = quizzes.size(0)
+
+ input = torch.cat([input, quizzes], dim=0)
+ else:
+ self.nb_batch_samples_world = input.size(0)
+ self.nb_batch_samples_quizzes = 0
+
if desc is None:
desc = f"epoch-{split}"
for batch in tqdm.tqdm(
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]
+ def compute_accuracy(input, logger=None):
+ input = input[:nmax]
+ ar_mask = self.make_ar_mask(input)
result = input.clone() * (1 - ar_mask)
masked_inplace_autoregression(
return nb_total, nb_correct
- train_nb_total, train_nb_correct = compute_accuracy(
- self.train_input, self.train_ar_mask
- )
+ train_nb_total, train_nb_correct = compute_accuracy(self.train_input)
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
- )
+ test_nb_total, test_nb_correct = compute_accuracy(self.test_input, 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}%"
##############################
- input, ar_mask = self.test_input[:96], self.test_ar_mask[:96]
+ input = self.test_input[:96]
+ ar_mask = self.make_ar_mask(input)
result = input.clone() * (1 - ar_mask)
masked_inplace_autoregression(
)
self.save_image(
- result[:96], result_dir, f"world_result_{n_epoch:04d}.png", logger
+ result[:96],
+ result_dir,
+ f"world_prediction_{n_epoch:04d}_{model.id:02d}.png",
+ logger,
)
return main_test_accuracy
def store_new_quizzes(self, new_quizzes, for_train=True):
- input = self.train_input if for_train else self.test_input
-
- nb_current = input.size(0)
- nb_new = new_quizzes.size(0)
- if nb_new >= nb_current:
- input[...] = new_quizzes[:nb_current]
+ if for_train:
+ self.train_quizzes.append(new_quizzes)
else:
- nb_kept = nb_current - nb_new
- input[:nb_kept] = input[-nb_kept:].clone()
- input[nb_kept:] = new_quizzes
+ self.test_quizzes.append(new_quizzes)
def create_new_quizzes(
- self, n_epoch, result_dir, logger, nb, models, other_models, nb_runs
+ self,
+ n_epoch,
+ result_dir,
+ logger,
+ nb,
+ model,
+ other_models,
):
new_quizzes = torch.empty(
nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64
device=self.device,
)
- input = (
- new_quizzes[:, None, :]
- .expand(-1, nb_runs, -1)
- .clone()
- .reshape(-1, new_quizzes.size(-1))
- )
- result = input.clone()
+ ar_mask = self.make_ar_mask(new_quizzes)
- ar_mask = (
- (torch.arange(result.size(1), device=self.device) > result.size(1) // 2)
- .long()[None, :]
- .expand_as(result)
- )
+ nb_correct = 0
- dispatch = torch.randint(len(other_models), (result.size(0),))
+ for m in other_models:
+ result = new_quizzes.clone()
- for n, m in enumerate(other_models):
masked_inplace_autoregression(
m,
self.batch_size,
- result[dispatch == n],
- ar_mask[dispatch == n],
- deterministic_synthesis=False,
- progress_bar_desc=None,
+ result,
+ ar_mask,
+ deterministic_synthesis=True,
+ progress_bar_desc="solving quizzes",
device=self.device,
)
- nb_correct = (
- (input == result).long().min(dim=-1).values.reshape(-1, nb_runs).sum(dim=-1)
- )
+ l = self.height * self.width
+ direction = new_quizzes[:, l : l + 1]
+ direction = world.token_forward * (
+ direction == world.token_backward
+ ) + world.token_backward * (direction == world.token_forward)
+ inverted_quizzes = torch.cat(
+ [new_quizzes[:, l + 1 :], direction, new_quizzes[:, :l]], dim=1
+ )
+
+ inverted_result = inverted_quizzes.clone()
+
+ masked_inplace_autoregression(
+ m,
+ self.batch_size,
+ inverted_result,
+ ar_mask,
+ deterministic_synthesis=True,
+ progress_bar_desc="solving reverse quizzes",
+ device=self.device,
+ )
+
+ nb_correct += (new_quizzes == result).long().min(dim=-1).values * (
+ inverted_quizzes == inverted_result
+ ).long().min(dim=-1).values
return new_quizzes, nb_correct
projects / culture.git / blobdiff