forward_to_backward = torch.cat(
[
quizzes[:, 0:1],
- quizzes[:, 2 + self.prompt_len :],
- quizzes[:, 1 + self.prompt_len : 2 + self.prompt_len],
+ 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
problem,
nb_train_samples,
nb_test_samples,
+ back_accuracy,
batch_size,
result_dir,
logger,
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
if result_dir is not None:
self.save_quizzes(
- result_dir, "culture_w_quizzes", self.train_w_quizzes[:72]
+ result_dir,
+ "culture_w_quizzes",
+ self.train_w_quizzes[:72],
+ prediction=True,
)
- # toto = self.reverse_time(self.train_w_quizzes[:72])
- # self.save_quizzes(result_dir, "toto", toto)
- # exit(0)
-
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]
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 = input.size(0)
- nb_correct = (input == result).long().min(dim=1).values.sum()
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ self.save_quizzes(
+ result_dir,
+ f"DEBUG_input_{n_epoch}_{result.size(0):04d}",
+ quizzes=input[:72],
+ prediction=True,
+ )
+ self.save_quizzes(
+ result_dir,
+ f"DEBUG_result_{n_epoch}_{result.size(0):04d}",
+ quizzes=result[:72],
+ prediction=True,
+ )
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
+ if self.back_accuracy:
+ 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()
+ )
+
+ self.logger(
+ f"back_accuracy {n_epoch=} {model.id=} {nb_correct=} {nb_total=}"
+ )
+
+ 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"back_accuracy {n_epoch=} {model.id=} {back_nb_correct=} {back_nb_total=}"
+ )
+ nb_total += back_nb_total
+ nb_correct += back_nb_correct
+
+ else:
+ nb_total = input.size(0)
+ nb_correct = (input == result).long().min(dim=1).values.sum()
+
+ exit(0)
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}%"