if to_generate.min() > 0:
self(
BracketedSequence(input, 0, to_generate.min())
) # Needed to initialize the model's cache
for s in range(to_generate.min(), to_generate.max() + 1):
output = self(BracketedSequence(input, s, 1)).x
if to_generate.min() > 0:
self(
BracketedSequence(input, 0, to_generate.min())
) # Needed to initialize the model's cache
for s in range(to_generate.min(), to_generate.max() + 1):
output = self(BracketedSequence(input, s, 1)).x
if forbidden_tokens is not None:
logits = logits.masked_fill(forbidden_tokens, float("-inf"))
if forbidden_tokens is not None:
logits = logits.masked_fill(forbidden_tokens, float("-inf"))
if forced_biases is not None:
logits = logits + forced_biases[None, :]
if forced_biases is not None:
logits = logits + forced_biases[None, :]
- sum_logits += logits.log_softmax(dim=-1)[
- torch.arange(t_next.size(0)), t_next
- ]
- 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)
+
+ input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]