# Written by Francois Fleuret <francois@fleuret.org>
-import math, os, tqdm, warnings
+import math, os, tqdm, warnings, sys
import torch, torchvision
input,
ar_mask,
seq_logproba,
- temperature,
- deterministic_synthesis,
+ deterministic_synthesis=False,
):
+ if input.size(0) == 0:
+ return
+
to_generate = (ar_mask.sum(0) > 0).nonzero()
if to_generate.min() > 0:
logits = output[:, s]
- logits = (logits / temperature).log_softmax(dim=-1)
-
if deterministic_synthesis:
t_next = logits.argmax(-1)
else:
input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
-def masked_inplace_autoregression(
- model,
- batch_size,
- input,
- ar_mask,
- seq_logproba,
- temperature,
- deterministic_synthesis,
- forbidden_tokens=None,
- logit_biases=None,
- progress_bar_desc=None,
- device=torch.device("cpu"),
-):
- assert input.size() == ar_mask.size()
-
- batches = zip(
- input.split(batch_size),
- ar_mask.split(batch_size),
- seq_logproba.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 - 1) // batch_size,
- )
-
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- for input, ar_mask, seq_logproba in batches:
- one_batch_masked_inplace_autoregression(
- model=model,
- input=input,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=deterministic_synthesis,
- )
-
- model.train(t)
-
-
######################################################################
class QuizMachine:
- def indices_forward_and_backward(self, quizzes):
- i_forward = quizzes[:, 0] == self.token_forward
- j_forward = quizzes[:, 1 + self.prompt_len] == self.token_forward
- i_backward = quizzes[:, 0] == self.token_backward
- j_backward = quizzes[:, 1 + self.answer_len] == self.token_backward
- assert torch.logical_or(
- torch.logical_and(i_forward, j_forward),
- torch.logical_and(i_backward, j_backward),
- ).all()
- return i_forward, i_backward
-
- def non_trivial(self, quizzes):
- quizzes = quizzes.clone()
- n_forward = quizzes[quizzes[:, 0] == self.token_forward]
- n_backward = quizzes[:, 0] == self.token_backward
- backward = quizzes[n_backward]
- quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
- return torch.logical_not(
- self.problem.trivial_prompts_and_answers(
- quizzes[:, 1 : 1 + self.prompt_len],
- quizzes[:, 2 + self.prompt_len :],
- )
- )
-
- def reverse_time(self, quizzes):
- i_forward, i_backward = self.indices_forward_and_backward(quizzes)
-
- forward_to_backward = torch.cat(
- [
- quizzes[:, 0:1],
- quizzes[:, 2 + self.prompt_len : 2 + self.prompt_len + self.answer_len],
- quizzes[:, 1 + self.prompt_len : 1 + self.prompt_len + 1],
- quizzes[:, 1 : 1 + self.prompt_len],
- ],
- dim=1,
- )
-
- forward_to_backward[:, 0] = self.token_backward
- forward_to_backward[:, 1 + self.answer_len] = self.token_backward
-
- backward_to_forward = torch.cat(
- [
- quizzes[:, 0:1],
- quizzes[:, 2 + self.answer_len :],
- quizzes[:, 1 + self.answer_len : 2 + self.answer_len],
- quizzes[:, 1 : 1 + self.answer_len],
- ],
- dim=1,
- )
-
- backward_to_forward[:, 0] = self.token_forward
- backward_to_forward[:, 1 + self.prompt_len] = self.token_forward
-
- m = i_forward.long()[:, None]
-
- return m * forward_to_backward + (1 - m) * backward_to_forward
-
- def reverse_random_half_in_place(self, quizzes):
- i = torch.rand(quizzes.size(0)) < 0.5
- if i.any():
- quizzes[i] = self.reverse_time(quizzes[i])
-
- def make_ar_mask(self, quizzes, first=False):
- i_forward, i_backward = self.indices_forward_and_backward(quizzes)
-
- t = torch.arange(quizzes.size(1), device=quizzes.device)
-
- if first:
- m_forward = (t >= 1).long() * (t < 1 + self.prompt_len).long()
- m_backward = (t >= 1).long() * (t < 1 + self.answer_len).long()
- else:
- m_forward = (t >= 2 + self.prompt_len).long()
- m_backward = (t >= 2 + self.answer_len).long()
-
- m = i_forward.long()[:, None]
-
- return m * m_forward + (1 - m) * m_backward
-
- def generate_token_sequences(self, nb):
- prompts, answers = self.problem.generate_prompts_and_answers(nb)
-
- if self.prompt_len is None:
- self.prompt_len = prompts.size(1)
-
- if self.answer_len is None:
- self.answer_len = answers.size(1)
-
- assert prompts.size(1) == self.prompt_len and answers.size(1) == self.answer_len
-
- result = []
-
- for prompt, answer in zip(prompts, answers):
- a = [
- torch.tensor([self.token_forward]),
- prompt,
- torch.tensor([self.token_forward]),
- answer,
- ]
-
- result.append(torch.cat(a, dim=0)[None, :])
-
- return torch.cat(result, dim=0)
-
def __init__(
self,
problem,
- nb_train_samples,
- nb_test_samples,
- back_accuracy,
batch_size,
result_dir,
+ prompt_noise,
logger,
device=torch.device("cpu"),
):
super().__init__()
- v = problem.nb_token_values()
- self.token_forward = v
- self.token_backward = v + 1
- self.nb_token_values = v + 2
-
self.problem = problem
- self.back_accuracy = back_accuracy
self.batch_size = batch_size
self.device = device
self.logger = logger
self.prompt_len = None
self.answer_len = None
+ self.prompt_noise = prompt_noise
+
+ # struct, mask_generate, mask_noise, mask_loss
+ self.train_structures = [
+ (("A", "f_A", "B", "f_B"), (0, 0, 0, 1), (0, 0, 1, 0), (1, 1, 1, 1)),
+ (("f_A", "A", "f_B", "B"), (0, 0, 0, 1), (0, 0, 1, 0), (1, 1, 1, 1)),
+ (("B", "f_B", "A", "f_A"), (0, 0, 0, 1), (0, 0, 0, 0), (1, 1, 1, 1)),
+ (("f_B", "B", "f_A", "A"), (0, 0, 0, 1), (0, 0, 0, 0), (1, 1, 1, 1)),
+ (("f_B", "f_A", "A", "B"), (0, 1, 1, 1), (0, 0, 0, 0), (1, 1, 1, 1)),
+ # (("A", "f_A", "B", "f_B"), (0, 0, 0, 1), (0, 0, 0, 0), (1, 1, 1, 0)),
+ # (("A", "f_A", "B", "f_B"), (0, 0, 0, 1), (0, 0, 1, 0), (1, 1, 0, 1)),
+ # (("f_A", "A", "f_B", "B"), (0, 0, 0, 1), (0, 0, 0, 0), (1, 1, 1, 0)),
+ # (("f_A", "A", "f_B", "B"), (0, 0, 0, 1), (0, 0, 1, 0), (1, 1, 0, 1)),
+ # (("f_B", "f_A", "A", "B"), (0, 1, 1, 1), (0, 0, 0, 0), (1, 1, 1, 1)),
+ ]
+
+ self.test_structures = self.train_structures
self.LOCK_C_QUIZZES = threading.Lock()
self.train_c_quizzes = []
self.test_c_quizzes = []
- def save_quizzes(
+ def vocabulary_size(self):
+ return self.problem.nb_token_values
+
+ ######################################################################
+
+ def autoregression(
self,
- result_dir,
- filename_prefix,
- quizzes,
- mistakes=None,
+ model,
+ input,
+ ar_mask,
+ seq_logproba=None,
+ progress_bar_desc=None,
):
- quizzes = quizzes.clone().to("cpu")
- n_forward = quizzes[quizzes[:, 0] == self.token_forward]
- n_backward = quizzes[:, 0] == self.token_backward
- backward = quizzes[n_backward]
- assert n_forward.size(0) + backward.size(0) == quizzes.size(0)
- quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
-
- predicted_prompts = n_backward.long()
- predicted_answers = 1 - predicted_prompts
- if mistakes is not None:
- # 0/-1/+1 ~ not-to-predict / predicted wrong / predicted correct
- predicted_prompts *= mistakes.to("cpu")
- predicted_answers *= mistakes.to("cpu")
- else:
- # 0/2 ~ not-to-predict / to predict
- predicted_prompts *= 2
- predicted_answers *= 2
+ assert input.size() == ar_mask.size()
- self.problem.save_quizzes(
- result_dir,
- filename_prefix,
- quizzes[:, 1 : 1 + self.prompt_len],
- quizzes[:, 2 + self.prompt_len :],
- predicted_prompts,
- predicted_answers,
+ if seq_logproba is None:
+ seq_logproba = torch.empty(input.size(0), device=self.device)
+
+ batches = zip(
+ input.split(self.batch_size),
+ ar_mask.split(self.batch_size),
+ seq_logproba.split(self.batch_size),
)
- def vocabulary_size(self):
- return self.nb_token_values
+ if progress_bar_desc is not None:
+ batches = tqdm.tqdm(
+ batches,
+ dynamic_ncols=True,
+ desc=progress_bar_desc,
+ total=(input.size(0) + self.batch_size - 1) // self.batch_size,
+ )
+
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ for input, ar_mask, seq_logproba in batches:
+ one_batch_masked_inplace_autoregression(
+ model=model,
+ input=input,
+ ar_mask=ar_mask,
+ seq_logproba=seq_logproba,
+ deterministic_synthesis=False,
+ )
+
+ model.train(t)
######################################################################
- def batches(self, model, split="train", desc=None):
+ def data_input(self, model, split="train"):
assert split in {"train", "test"}
with self.LOCK_C_QUIZZES:
if len(c_quizzes) > 0:
c_quizzes = torch.cat(c_quizzes, dim=0)
+
if c_quizzes.size(0) > w_quizzes.size(0) // 2:
i = torch.randperm(c_quizzes.size(0))[: w_quizzes.size(0) // 2]
c_quizzes = c_quizzes[i]
]
w_quizzes = w_quizzes[i]
- self.nb_batch_w_quizzes = w_quizzes.size(0)
- self.nb_batch_c_quizzes = c_quizzes.size(0)
+ quizzes = torch.cat([w_quizzes, c_quizzes], dim=0)
+ from_w = torch.arange(
+ quizzes.size(0), device=quizzes.device
+ ) < w_quizzes.size(0)
- input = torch.cat([w_quizzes, c_quizzes], dim=0)
else:
- input = w_quizzes
- self.nb_batch_w_quizzes = w_quizzes.size(0)
- self.nb_batch_c_quizzes = 0
+ quizzes = w_quizzes.clone()
+ from_w = torch.full((quizzes.size(0),), True, device=quizzes.device)
+
+ i = torch.randperm(quizzes.size(0), device=quizzes.device)
+ quizzes, from_w = quizzes[i], from_w[i]
+
+ self.randomize_configuations_inplace(
+ quizzes, structs=[s for s, _, _, _ in self.train_structures]
+ )
+
+ quiz_mask_loss = quizzes.new_full(quizzes.size(), 1)
- # Shuffle
- input = input[torch.randperm(input.size(0))]
+ if self.prompt_noise > 0.0:
+ for struct, _, mask_noise, mask_loss in self.train_structures:
+ i = self.problem.indices_select(quizzes=quizzes, struct=struct)
+ if i.any():
+ quizzes[i] = self.problem.inject_noise(
+ quizzes[i], self.prompt_noise, struct=struct, mask=mask_noise
+ )
+ quiz_mask_loss[i] = self.make_quiz_mask(
+ quizzes=quizzes[i], struct=struct, mask=mask_loss
+ )
- 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
+ return quizzes, quiz_mask_loss
######################################################################
- def produce_results(
- self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
- ):
- def compute_accuracy(input, log_prefix=None):
- input = input.to(self.device)
- ar_mask = self.make_ar_mask(input)
- result = input.clone() * (1 - ar_mask)
- seq_logproba = torch.empty(input.size(0), device=self.device)
+ def make_quiz_mask(self, quizzes, struct, mask):
+ assert struct in [s for s, _, _, _ in self.train_structures]
+ return self.problem.make_quiz_mask(quizzes, struct=struct, mask=mask)
- masked_inplace_autoregression(
- model=model,
- batch_size=self.batch_size,
- input=result,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba,
- temperature=1.0,
- deterministic_synthesis=deterministic_synthesis,
- progress_bar_desc=None,
- device=self.device,
- )
+ ######################################################################
- correct = torch.empty(input.size(0), dtype=torch.int64, device=input.device)
+ def predict(self, model, quizzes, struct, mask):
+ ar_mask = self.make_quiz_mask(quizzes=quizzes, struct=struct, mask=mask)
+ result = quizzes * (1 - ar_mask)
- n_forward = input[:, 0] == self.token_forward
- n_backward = input[:, 0] == self.token_backward
+ seq_logproba = torch.empty(quizzes.size(0), device=self.device)
- correct[n_forward] = (
- (input[n_forward] == result[n_forward]).long().min(dim=1).values
- )
+ self.autoregression(
+ model=model,
+ input=result,
+ ar_mask=ar_mask,
+ seq_logproba=seq_logproba,
+ progress_bar_desc="accuracy",
+ )
- if self.back_accuracy and n_backward.any():
- # accuracy of B->A*->B*=B instead of B->A*=A
- back_input = self.reverse_time(result[n_backward])
- back_input[:, 2 + self.prompt_len :] = input[
- n_backward, 1 : 1 + self.answer_len
- ]
- _, correct[n_backward] = compute_accuracy(back_input)
+ correct = (result == quizzes).min(dim=1).values.long()
- if log_prefix is not None:
- forward_nb_correct = correct[n_forward].sum()
- forward_nb_total = correct[n_forward].size(0)
- backward_nb_correct = correct[n_backward].sum()
- backward_nb_total = correct[n_backward].size(0)
+ return result, correct
- self.logger(
- f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total} ({forward_nb_correct*100/forward_nb_total} %)"
- )
+ ######################################################################
- self.logger(
- f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total} ({backward_nb_correct*100/backward_nb_total} %)"
- )
+ def produce_results(self, n_epoch, model, input, result_dir):
+ input = input.to(self.device)
+ result = input.new(input.size())
+ correct = input.new(input.size(0))
+ predicted_parts = input.new(input.size(0), 4)
+
+ nb = 0
- return result, correct
+ # We consider all the configurations that we train for
+ for struct, mask_generate, _, _ in self.test_structures:
+ i = self.problem.indices_select(quizzes=input, struct=struct)
+ nb += i.long().sum()
+ result[i], correct[i] = self.predict(
+ model=model, quizzes=input[i], struct=struct, mask=mask_generate
+ )
+ predicted_parts[i] = torch.tensor(mask_generate, device=self.device)[
+ None, :
+ ]
+ solution_is_deterministic = predicted_parts[i].sum(dim=-1) == 1
+ correct[i] = (2 * correct[i] - 1) * (solution_is_deterministic).long()
- compute_accuracy(model.train_w_quizzes[:nmax], log_prefix="train")
+ assert nb == input.size(0)
- test_result, test_correct = compute_accuracy(
- model.test_w_quizzes[:nmax], log_prefix="test"
+ nb_correct = (correct == 1).long().sum()
+ nb_total = (correct != 0).long().sum()
+ self.logger(
+ f"test_accuracy {n_epoch} model {model.id} val {nb_correct} / {nb_total}"
)
- main_test_accuracy = test_correct.sum() / test_correct.size(0)
- self.logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
+ main_test_accuracy = nb_correct / nb_total
##############################
- self.save_quizzes(
+ correct_parts = predicted_parts * correct[:, None]
+
+ result = result[:128]
+ predicted_parts = predicted_parts[:128]
+ correct_parts = correct_parts[:128]
+
+ self.problem.save_quizzes_as_image(
result_dir,
- f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
- quizzes=test_result[:72],
- mistakes=test_correct[:72] * 2 - 1,
+ f"culture_prediction_{n_epoch:04d}_{model.id:02d}.png",
+ quizzes=result,
+ predicted_parts=predicted_parts,
+ correct_parts=correct_parts,
)
return main_test_accuracy
######################################################################
- def renew_w_quizzes(self, model, nb, for_train=True):
- input = model.train_w_quizzes if for_train else model.test_w_quizzes
- nb = min(nb, input.size(0))
- input[:-nb] = input[nb:].clone()
- fresh_w_quizzes = self.generate_token_sequences(nb)
- self.reverse_random_half_in_place(fresh_w_quizzes)
- input[-nb:] = fresh_w_quizzes.to("cpu")
+ def randomize_configuations_inplace(self, quizzes, structs):
+ r = torch.randint(len(structs), (quizzes.size(0),), device=quizzes.device)
+ for c in range(len(structs)):
+ quizzes[r == c] = self.problem.reconfigure(
+ quizzes[r == c], struct=structs[c]
+ )
+
+ ######################################################################
+
+ def renew_train_w_quizzes(self, model):
+ if hasattr(model, "hard_w_quizzes"):
+ hard_w_quizzes = self.problem.reconfigure(
+ model.hard_w_quizzes, struct=("A", "f_A", "B", "f_B")
+ )
+ self.logger(
+ f"re-using {hard_w_quizzes.size(0)} hard world quizzes from model {model.id}"
+ )
+ if hard_w_quizzes.size(0) >= model.train_w_quizzes.size(0):
+ nb_to_generate = 0
+ model.train_w_quizzes[...] = hard_w_quizzes[
+ torch.randperm(hard_w_quizzes.size(0))[
+ model.train_w_quizzes.size(0)
+ ]
+ ]
+ else:
+ nb_to_generate = model.train_w_quizzes.size(0) - hard_w_quizzes.size(0)
+ model.train_w_quizzes[...] = torch.cat(
+ [
+ hard_w_quizzes,
+ self.problem.generate_w_quizzes(nb_to_generate),
+ ],
+ dim=0,
+ )
+ else:
+ nb_to_generate = 0
+ model.train_w_quizzes[...] = self.problem.generate_w_quizzes(
+ model.train_w_quizzes.size(0)
+ )
######################################################################
else:
self.test_c_quizzes.append(new_c_quizzes.to("cpu"))
- ######################################################################
-
- def logproba_of_solutions(self, models, c_quizzes):
- logproba = c_quizzes.new_zeros(
- c_quizzes.size(0), len(models), device=self.device
- )
-
- for model in models:
- for input, l in zip(
- c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
- ):
- input = input.to(self.device)
- ar_mask = self.make_ar_mask(input)
- output = model(mygpt.BracketedSequence(input)).x
- ce = (
- F.cross_entropy(output.transpose(1, 2), input, reduction="none")
- * ar_mask
- )
- l[:, model.id] = -ce.sum(dim=-1)
+ def save_c_quizzes(self, filename):
+ torch.save((self.train_c_quizzes, self.test_c_quizzes), filename)
- return logproba.to("cpu")
+ def load_c_quizzes(self, filename):
+ self.train_c_quizzes, self.test_c_quizzes = torch.load(filename)
- ###############################################################
+ ######################################################################
- def compute_correctness(
+ def models_logprobas(
self,
- c_quizzes,
models_for_validation,
- bidirectional_validation=False,
- deterministic_validation=True,
+ c_quizzes,
+ struct,
+ mask_loss,
+ mask_noise=None,
+ device=None,
):
- if bidirectional_validation:
- backward_c_quizzes = self.forward_to_backward(c_quizzes)
+ if device is None:
+ device = self.device
+
+ c_quizzes = self.problem.reconfigure(c_quizzes, struct)
seq_logproba = torch.zeros(
c_quizzes.size(0),
max([m.id for m in models_for_validation]) + 1,
- device=self.device,
+ device=device,
)
- nb_correct = 0
-
- seq_logproba[...] = 0.0
+ # if self.prompt_noise > 0.0 and mask_noise is not None:
+ # c_quizzes = self.problem.inject_noise(
+ # c_quizzes, self.prompt_noise, struct=struct, mask=mask_noise
+ # )
for model in models_for_validation:
- result = c_quizzes.clone()
-
- ar_mask = self.make_ar_mask(result)
-
- masked_inplace_autoregression(
- model=model,
- batch_size=self.batch_size,
- input=result,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba[:, model.id],
- temperature=1.0,
- deterministic_synthesis=deterministic_validation,
- # progress_bar_desc="solving c_quizzes",
- device=self.device,
- )
-
- correct = (c_quizzes == result).long().min(dim=-1).values
-
- if bidirectional_validation:
- backward_result = backward_c_quizzes.clone()
-
- ar_mask = self.make_ar_mask(backward_result)
-
- masked_inplace_autoregression(
- model=model,
- batch_size=self.batch_size,
- input=backward_result,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba[:, model.id],
- temperature=1.0,
- deterministic_synthesis=deterministic_validation,
- # progress_bar_desc="solving backward c_quizzes",
- device=self.device,
- )
-
- backward_correct = (
- (backward_c_quizzes == backward_result).long().min(dim=-1).values
- )
-
- correct *= backward_correct
-
- # endif
-
- nb_correct += correct
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ for input, l in zip(
+ c_quizzes.split(self.batch_size),
+ seq_logproba.split(self.batch_size),
+ ):
+ input = input.to(device)
+ quiz_mask_loss = self.make_quiz_mask(
+ input, struct=struct, mask=mask_loss
+ )
+ output = model(mygpt.BracketedSequence(input)).x
+ l[:, model.id] = (
+ -F.cross_entropy(
+ output.transpose(1, 2), input, reduction="none"
+ )
+ * quiz_mask_loss
+ ).sum(dim=1)
+
+ model.train(t)
+
+ return seq_logproba.to("cpu")
- return nb_correct, seq_logproba
-
- ###############################################################
-
- def generate_quizzes(self, nb, model_for_generation, temperature=1.0):
- c_quizzes = torch.empty(
- nb,
- self.prompt_len + self.answer_len + 2,
- device=self.device,
- dtype=torch.int64,
- )
+ ######################################################################
+ def generate_c_quizzes(self, nb, model_for_generation, procedure, recorder=None):
seq_logproba = torch.zeros(nb, device=self.device)
- # First, we generate the answer at high temperature
+ c_quizzes = None
- c_quizzes[:, 0] = self.token_backward
- c_quizzes[:, 1 + self.answer_len] = self.token_backward
+ for s, m, mt in procedure:
+ if c_quizzes is None:
+ c_quizzes = self.problem.create_empty_quizzes(nb, s)
+ c_quizzes = c_quizzes.to(self.device)
+ elif s != pred_s:
+ c_quizzes = self.problem.reconfigure(c_quizzes, s)
+ pred_s = s
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=self.make_ar_mask(c_quizzes, first=True),
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=False,
- device=self.device,
- )
+ if mt is not None:
+ mt(model_for_generation)
- # Then, we generate the prompt at low temperature
-
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=self.make_ar_mask(c_quizzes),
- seq_logproba=seq_logproba,
- temperature=1 / temperature,
- deterministic_synthesis=False,
- device=self.device,
- )
+ self.autoregression(
+ model=model_for_generation,
+ input=c_quizzes,
+ ar_mask=self.make_quiz_mask(c_quizzes, s, m),
+ seq_logproba=seq_logproba,
+ )
- # Then we return the quizz, and re-generate the response, now
- # at low temperature
+ model_for_generation.reset_transformations()
- c_quizzes = self.reverse_time(c_quizzes)
+ if recorder is not None:
+ x = c_quizzes.clone()
+ t = torch.tensor(m, device=x.device)[None, :].expand(x.size(0), -1)
+ recorder.append(
+ self.problem.reconfigure([x, t], ("A", "f_A", "B", "f_B"))
+ )
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=self.make_ar_mask(c_quizzes),
- seq_logproba=seq_logproba,
- temperature=1 / temperature,
- deterministic_synthesis=False,
- device=self.device,
- )
+ c_quizzes = self.problem.reconfigure(c_quizzes, ("A", "f_A", "B", "f_B"))
return c_quizzes.to("cpu")
+
+ ######################################################################
projects / culture.git / blobdiff