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]
)
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 = (
.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, 2 + self.prompt_len :
+ 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()