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]
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(