self,
input,
ar_mask,
+ seq_logproba,
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:
logits = output[:, s]
- logits = logits.log_softmax(dim=1) / temperature
+ logits = (logits / temperature).log_softmax(dim=-1)
if forbidden_tokens is not None:
logits = logits.masked_fill(forbidden_tokens, float("-inf"))
logits = logits + forced_biases[None, :]
if deterministic_synthesis:
- t_next = logits.argmax(1)
+ t_next = logits.argmax(-1)
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]
+ if seq_logproba is not None:
+ all_t = torch.arange(t_next.size(0))
+ seq_logproba += logits[all_t, t_next].sum(dim=-1)
- return sum_logits
+ input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
def record_attention(self, v=True):
for m in self.modules():