input,
ar_mask,
seq_logproba,
- temperature=1.0,
- deterministic_synthesis=False,
+ temperature,
+ deterministic_synthesis,
):
to_generate = (ar_mask.sum(0) > 0).nonzero()
t_next = dist.sample()
all_n = torch.arange(t_next.size(0))
- seq_logproba += logits[all_n, t_next].sum(dim=-1)
+
+ seq_logproba += logits[all_n, t_next]
input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
).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)
backward_nb_total = correct[n_backward].size(0)
self.logger(
- f"{log_prefix}_forward_accuracy {n_epoch} {model.id=} {forward_nb_correct} / {forward_nb_total}"
+ 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.id=} {backward_nb_correct} / {backward_nb_total}"
+ 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} %)"
)
return result, correct
nb_correct = 0
+ seq_logproba[...] = 0.0
+
for model in models_for_validation:
result = c_quizzes.clone()
- seq_logproba[...] = 0.0
-
ar_mask = self.make_ar_mask(result)
masked_inplace_autoregression(