seq_logproba,
temperature=1.0,
deterministic_synthesis=False,
- forbidden_tokens=None,
- forced_biases=None,
):
to_generate = (ar_mask.sum(0) > 0).nonzero()
logits = (logits / temperature).log_softmax(dim=-1)
- if forbidden_tokens is not None:
- logits = logits.masked_fill(forbidden_tokens, float("-inf"))
-
- if forced_biases is not None:
- logits = logits + forced_biases[None, :]
-
if deterministic_synthesis:
t_next = logits.argmax(-1)
else:
seq_logproba=seq_logproba,
temperature=temperature,
deterministic_synthesis=deterministic_synthesis,
- forbidden_tokens=forbidden_tokens,
- forced_biases=logit_biases,
)
model.train(t)
class QuizzMachine:
- 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 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 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 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):
+ if torch.rand(1) < 0.5:
+ a = [
+ torch.tensor([self.token_forward]),
+ prompt,
+ torch.tensor([self.token_forward]),
+ answer,
+ ]
+ else:
+ a = [
+ torch.tensor([self.token_backward]),
+ answer,
+ torch.tensor([self.token_backward]),
+ prompt,
+ ]
+
+ 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,
logger,
):
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.train_w_quizzes = self.problem.generate_token_sequences(
- nb_train_samples
- ).to(device)
- self.test_w_quizzes = self.problem.generate_token_sequences(nb_test_samples).to(
+ self.train_w_quizzes = self.generate_token_sequences(nb_train_samples).to(
device
)
- self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
+ self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device)
self.train_c_quizzes = []
self.test_c_quizzes = []
if result_dir is not None:
- self.problem.save_quizzes(
- self.train_w_quizzes[:72], result_dir, "culture_w_quizzes"
+ self.save_quizzes(
+ result_dir,
+ "culture_w_quizzes",
+ self.train_w_quizzes[:72],
+ prediction=True,
)
+ def save_quizzes(self, result_dir, filename_prefix, quizzes, prediction=False):
+ quizzes = quizzes.clone()
+ forward = quizzes[quizzes[:, 0] == self.token_forward]
+ ib = quizzes[:, 0] == self.token_backward
+ backward = quizzes[ib]
+ assert forward.size(0) + backward.size(0) == quizzes.size(0)
+ quizzes[ib] = self.reverse_time(quizzes[ib])
+
+ if prediction:
+ predicted_prompts = ib
+ predicted_answers = torch.logical_not(ib)
+ else:
+ predicted_prompts = None
+ predicted_answers = None
+
+ self.problem.save_quizzes(
+ result_dir,
+ filename_prefix,
+ quizzes[:, 1 : 1 + self.prompt_len],
+ quizzes[:, 2 + self.prompt_len :],
+ predicted_prompts,
+ predicted_answers,
+ )
+
def batches(self, split="train", desc=None):
assert split in {"train", "test"}
if split == "train":
yield batch
def vocabulary_size(self):
- return self.nb_codes
+ return self.nb_token_values
def produce_results(
self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
):
def compute_accuracy(input):
- input = input[:nmax]
ar_mask = self.make_ar_mask(input)
result = input.clone() * (1 - ar_mask)
seq_logproba = torch.empty(input.size(0), device=self.device)
device=self.device,
)
- nb_total, nb_correct = (
- input.size(0),
- (input == result).long().min(dim=1).values.sum(),
- )
+ if self.back_accuracy:
+ # If back_accuracy is True, we compute the accuracy on
+ # the backward quizzes not by counting how many time
+ # the real prompt A is equal to the reconstructed
+ # prompt A*, but how many time the answers B* computed
+ # from A* is equal to the correct answer. So we look
+ # for the accuracy of A->B*=B for the forward, but for
+ # the backward we look at B->A*->B*=B instead of B->A*=A
+
+ n_forward = input[:, 0] == self.token_forward
+ nb_total = input[n_forward].size(0)
+ nb_correct = (
+ (input[n_forward] == result[n_forward])
+ .long()
+ .min(dim=1)
+ .values.sum()
+ .item()
+ )
+
+ n_backward = input[:, 0] == self.token_backward
+ back_input = self.reverse_time(result[n_backward])
+
+ if back_input.size(0) > 0:
+ back_input[:, 2 + self.prompt_len :] = input[
+ n_backward, 1 : 1 + self.answer_len
+ ]
+ back_nb_total, back_nb_correct = compute_accuracy(back_input)
+
+ self.logger(
+ f"accuracy {n_epoch=} {model.id=} {nb_correct} / {nb_total}"
+ )
+ self.logger(
+ f"back_accuracy {n_epoch=} {model.id=} {back_nb_correct} / {back_nb_total}"
+ )
+
+ nb_total += back_nb_total
+ nb_correct += back_nb_correct
+ else:
+ self.logger(
+ f"accuracy {n_epoch=} {model.id=} {nb_correct} / {nb_total}"
+ )
+
+ else:
+ nb_total = input.size(0)
+ nb_correct = (input == result).long().min(dim=1).values.sum()
return nb_total, nb_correct
- train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes)
+ train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes[:nmax])
self.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_w_quizzes)
+ test_nb_total, test_nb_correct = compute_accuracy(self.test_w_quizzes[:nmax])
self.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}%"
device=self.device,
)
- self.problem.save_quizzes(
- result[:72], result_dir, f"culture_prediction_{n_epoch:04d}_{model.id:02d}"
+ self.save_quizzes(
+ result_dir,
+ f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
+ quizzes=result[:72],
+ prediction=True,
)
return main_test_accuracy
input = self.train_w_quizzes if for_train else self.test_w_quizzes
nb = min(nb, input.size(0))
input[:-nb] = input[nb:].clone()
- input[-nb:] = self.problem.generate_token_sequences(nb).to(self.device)
+ input[-nb:] = self.generate_token_sequences(nb).to(self.device)
def store_c_quizzes(self, new_c_quizzes, for_train=True):
if for_train:
else:
self.test_c_quizzes.append(new_c_quizzes)
- def reverse_time(self, c_quizzes):
- token_forward, token_backward = self.problem.direction_tokens()
-
- l = (c_quizzes.size(1) - 1) // 2
- direction = c_quizzes[:, l : l + 1]
- direction = self.problem.token_forward * (
- direction == self.problem.token_backward
- ) + self.problem.token_backward * (direction == self.problem.token_forward)
-
- return torch.cat([c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1)
-
- def comput_correctness(self, c_quizzes, models_for_validation):
- reversed_c_quizzes = self.reverse_time(c_quizzes)
-
- ar_mask = self.make_ar_mask(c_quizzes)
- seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
+ def compute_correctness(
+ self,
+ c_quizzes,
+ models_for_validation,
+ bidirectional_validation=False,
+ deterministic_validation=True,
+ ):
+ if bidirectional_validation:
+ backward_c_quizzes = self.forward_to_backward(c_quizzes)
- # Check how many of models can solve the quizzes in both directions
+ seq_logproba = torch.zeros(
+ c_quizzes.size(0),
+ max([m.id for m in models_for_validation]) + 1,
+ device=self.device,
+ )
nb_correct = 0
for model in models_for_validation:
result = c_quizzes.clone()
+ seq_logproba[...] = 0.0
+
+ 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,
+ seq_logproba=seq_logproba[:, model.id],
temperature=1.0,
- deterministic_synthesis=True,
+ deterministic_synthesis=deterministic_validation,
# progress_bar_desc="solving c_quizzes",
device=self.device,
)
correct = (c_quizzes == result).long().min(dim=-1).values
- reversed_result = reversed_c_quizzes.clone()
+ if bidirectional_validation:
+ backward_result = backward_c_quizzes.clone()
- masked_inplace_autoregression(
- model=model,
- batch_size=self.batch_size,
- input=reversed_result,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba,
- temperature=1.0,
- deterministic_synthesis=True,
- # progress_bar_desc="solving reversed c_quizzes",
- device=self.device,
- )
+ ar_mask = self.make_ar_mask(backward_result)
- reversed_correct = (
- (reversed_c_quizzes == reversed_result).long().min(dim=-1).values
- )
+ 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
- nb_correct += correct * reversed_correct
+ # endif
- return nb_correct
+ nb_correct += correct
+
+ return nb_correct, seq_logproba
###############################################################
- def generate_quizzes(self, nb, model_for_generation, reverse_cleanup=False):
+ def generate_quizzes(self, nb, model_for_generation, temperature=1.0):
c_quizzes = torch.empty(
nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
)
- ar_mask_prompt = torch.zeros(c_quizzes.size(), device=self.device)
- ar_mask_prompt[:, : ar_mask_prompt.size(1) // 2 + 1] = 1
- ar_mask_solve = 1 - ar_mask_prompt
- seq_logproba = torch.empty(ar_mask_prompt.size(0), device=self.device)
-
- if reverse_cleanup:
- warnings.warn("very high temperature with reversed cleanup", RuntimeWarning)
- temperature = 10.0
- else:
- temperature = 1.0
+ seq_logproba = torch.zeros(nb, device=self.device)
- # warnings.warn("noise injection", RuntimeWarning)
- # noise_std = torch.rand(1).item()
- # self.logger(f"{noise_std=}")
+ # First, we generate the answer at high temperature
- # mygpt.set_noise_injection(model_for_generation, noise_std)
+ c_quizzes[:, 0] = self.token_backward
+ c_quizzes[:, 1 + self.answer_len] = self.token_backward
masked_inplace_autoregression(
model=model_for_generation,
batch_size=self.batch_size,
input=c_quizzes,
- ar_mask=ar_mask_prompt,
+ ar_mask=self.make_ar_mask(c_quizzes, first=True),
seq_logproba=seq_logproba,
temperature=temperature,
deterministic_synthesis=False,
device=self.device,
)
- # mygpt.set_noise_injection(model_for_generation, 0.0)
-
- ave_seq_logproba = seq_logproba.mean()
+ # 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=ar_mask_solve,
+ ar_mask=self.make_ar_mask(c_quizzes),
seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=True,
+ temperature=1 / temperature,
+ deterministic_synthesis=False,
device=self.device,
)
- if reverse_cleanup:
- c_quizzes = self.reverse_time(c_quizzes)
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=ar_mask_solve,
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=True,
- device=self.device,
- )
+ # Then we return the quizz, and re-generate the response, now
+ # at low temperature
+
+ c_quizzes = self.reverse_time(c_quizzes)
+
+ 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,
+ )
- return c_quizzes, seq_logproba.mean()
+ return c_quizzes
projects / culture.git / blobdiff