self,
input,
ar_mask,
+ temperature=1.0,
deterministic_synthesis=False,
forbidden_tokens=None,
forced_biases=None,
):
+ sum_logits = 0
to_generate = (ar_mask.sum(0) > 0).nonzero()
if to_generate.min() > 0:
self(
else:
dist = torch.distributions.categorical.Categorical(logits=logits)
t_next = dist.sample()
+ sum_logits += logits.log_softmax(dim=-1)[
+ torch.arange(t_next.size(0)), t_next
+ ].sum()
input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
+ return sum_logits
+
def record_attention(self, v=True):
for m in self.modules():
if isinstance(m, QKVAttention):