-######################################################################
-
-
-# ra_mask is boolean, with 1s on the values to generate
-
-
-def masked_inplace_autoregression(
- model,
- batch_size,
- input,
- ar_mask,
- forbidden_tokens=None,
- progress_bar_desc="autoregression",
- device=torch.device("cpu"),
-):
-
- batches = zip(input.split(batch_size), ar_mask.split(batch_size))
-
- if progress_bar_desc is not None:
- batches = tqdm.tqdm(
- batches,
- dynamic_ncols=True,
- desc=progress_bar_desc,
- total=input.size(0) // batch_size,
- )
-
- for input, ar_mask in batches:
- i = (ar_mask.sum(0) > 0).nonzero()
- if i.min() > 0:
- model(
- mygpt.BracketedSequence(input, 0, i.min())
- ) # Needed to initialize the model's cache
- for s in range(i.min(), i.max() + 1):
- output = model(mygpt.BracketedSequence(input, s, 1)).x
- logits = output[:, s]
- if forbidden_tokens is not None:
- logits = logits.masked_fill(forbidden_tokens, float("-inf"))
- if args.deterministic_synthesis:
- t_next = logits.argmax(1)
- else:
- dist = torch.distributions.categorical.Categorical(logits=logits)
- t_next = dist.sample()
- input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
-
-
-######################################################################
-
-
-class Task:
- def batches(self, split="train"):
- pass
-
- def vocabulary_size(self):
- pass
-
- def produce_results(self, n_epoch, model):
- pass
-
-
-######################################################################
-
-import picoclvr
-
-
-class TaskPicoCLVR(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]
-
- ######################
- # Not the cleanest part of the code
-
- # Extract the last image of each sequence, from the last <img>
- # included, and set to <nul> all the tokens from the beginning of
- # that image to the end
- def excise_last_image(self, input):
- t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
-
- input = input.clone()
- t = (input == t_img).long()
- tail_masks = (t.cumsum(dim=1) == t.sum(dim=1, keepdim=True)).long()
- i = (t * tail_masks).nonzero(as_tuple=True)
- j = (
- i[0][:, None],
- i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
- )
- images = self.trim(input[j])
- input[j] = t_nul
- loss_masks = 1 - tail_masks
- input, loss_masks = self.trim((input, loss_masks))
- return input, loss_masks, images
-
- def add_true_image(self, input, images, loss_masks):
- t_nul = self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
- input = F.pad(input, (0, nb_img_tokens), value=t_nul)
- loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
- t = (input == t_nul).long()
- i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
- j = (
- i[0][:, None],
- i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
- )
- input[j] = images
- loss_masks[j] = 1
- input, loss_masks = self.trim((input, loss_masks))
- return input, loss_masks
-
- def add_generated_image(self, input, loss_masks, model):
- t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
- nb_img_tokens = self.height * self.width + 1
-
- input = F.pad(input, (0, nb_img_tokens), value=t_nul)
- loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
- t = (input == t_nul).long()
- i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
- input[i] = t_img
-
- j = (
- i[0][:, None],
- i[1][:, None]
- + 1
- + torch.arange(nb_img_tokens - 1, device=input.device)[None, :],
- )
- ar_masks = input.new_zeros(input.size(), dtype=torch.int64)
- ar_masks[j] = 1
- forbidden_tokens = (
- torch.arange(self.vocabulary_size(), device=input.device) == t_nul
- )
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
- masked_inplace_autoregression(
- model,
- self.batch_size,
- input,
- ar_masks,
- forbidden_tokens,
- progress_bar_desc=None,
- device=self.device,
- )
- model.train(t)
-
- input, loss_masks = self.trim((input, loss_masks))
-
- return input, loss_masks
-
- ######################
-
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_colors=5,
- device=torch.device("cpu"),
- pruner_train=None,
- pruner_eval=None,
- ):
- 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
-
- param = {
- "nb_train_samples": nb_train_samples,
- "nb_test_samples": nb_test_samples,
- "height": height,
- "width": width,
- "nb_colors": nb_colors,
- "batch_size": batch_size,
- "rng_state": list(torch.get_rng_state()),
- }
-
- log_string(
- 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)])
-
- # 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"):
- 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, 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",
- ):
- tape, loss_masks, _ = self.excise_last_image(input)
- tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
- result_descr = self.detensorize(tape)
- 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_"
- log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- log_string(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- log_string(
- f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
- )
-
- ######################################################################
-
- def produce_results(self, n_epoch, model):
- self.compute_missing_properties(n_epoch, model)
-
- 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] * nb_per_primer
-
- tape = self.tensorize(primer)
- loss_masks = 1 - (tape == self.token2id["<nul>"]).long()
- tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
- result_descr = self.detensorize(tape)
-
- 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_"
- log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
- log_string(
- f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
- )
- log_string(
- 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(args.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
- )
- log_string(f"wrote {image_name}")
-
-
-######################################################################
-
-
-class TaskMNIST(Task):
- def __init__(self, batch_size, device=torch.device("cpu")):
- self.device = device
- self.batch_size = batch_size
-
- def batches(self, split="train"):
- assert split in {"train", "test"}
- data_set = torchvision.datasets.MNIST(
- root="./data", train=(split == "train"), download=True
- )
- data_input = data_set.data.view(-1, 28 * 28).long()
- if args.nb_train_samples is not None:
- data_input = data_input[: args.nb_train_samples]
- for batch in tqdm.tqdm(
- data_input.split(self.batch_size), desc=f"epoch-{split}"
- ):
- yield batch
-
- def vocabulary_size(self):
- return 256
-
- def produce_results(self, n_epoch, model):
- 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, device=self.device
- )
- image_name = os.path.join(args.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,
- )
- log_string(f"wrote {image_name}")
-
-
-######################################################################
-
-import maze
-
-
-class TaskMaze(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"),
- ):
- 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):
- nb_total, nb_correct = 0, 0
- count = torch.zeros(
- self.width * self.height,
- self.width * self.height,
- device=self.device,
- dtype=torch.int64,
- )
- for input in tqdm.tqdm(
- task.batches(split, nb_to_use),
- dynamic_ncols=True,
- desc=f"test-mazes",
- ):
- 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,
- 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):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- train_nb_total, train_nb_correct, count = self.compute_error(
- model, "train", nb_to_use=1000
- )
- log_string(
- 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
- )
- log_string(
- 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}%"
- )
-
- if count is not None:
- proportion_optimal = count.diagonal().sum().float() / count.sum()
- log_string(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
- with open(
- os.path.join(args.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]
- 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, device=self.device
- )
-
- mazes, paths = self.seq2map(input)
- _, predicted_paths = self.seq2map(result)
-
- filename = os.path.join(args.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),
- )
- log_string(f"wrote {filename}")
-
- model.train(t)
-
-
-######################################################################
-
-
-import snake
-
-
-class TaskSnake(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- height,
- width,
- nb_colors,
- length,
- prompt_length,
- device=torch.device("cpu"),
- ):
- 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):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- 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
-
- # snake.solver(result,ar_mask)
-
- masked_inplace_autoregression(
- model, self.batch_size, result, ar_mask, device=self.device
- )
-
- nb_total = ((prior_visits > 0) * ar_mask).sum()
-
- nb_correct = (
- (result == input).long() * (prior_visits > 0) * ar_mask
- ).sum()
-
- # nb_total = result.size(0)
- # nb_correct = ((result - input).abs().sum(1) == 0).sum()
-
- return nb_total, nb_correct
-
- # train_nb_total, train_nb_correct = compute_nb_correct(
- # self.train_input, self.train_prior_visits
- # )
-
- # log_string(
- # f"accuracy_train 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_nb_correct(
- self.test_input[:1000], self.test_prior_visits[:1000]
- )
-
- log_string(
- 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}%"
- )
-
- model.train(t)
-
-
-######################################################################
-
-
-import stack
-
-
-class TaskStack(Task):
- def __init__(
- self,
- nb_train_samples,
- nb_test_samples,
- batch_size,
- nb_steps,
- nb_stacks,
- nb_digits,
- fraction_values_for_train=None,
- device=torch.device("cpu"),
- ):
- 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)
- log_string(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):
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- 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, 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])
-
- log_string(
- 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}%"
- )
-
- ##############################################################
- # 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)):
- log_string(
- 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, device=self.device
- )
- for n in range(result.size(0)):
- log_string(
- f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
- )
- ##############################################################
-
- model.train(t)
-