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
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]
device=self.device,
)
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ 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)
.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, 2 + self.prompt_len :
+ 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[:nmax])