- # ar_mask is a tensor with 0s and 1s, of same shape as input, with
- # 1s where tokens should be generated. The others are kept
- # unchanged.
-
- def masked_inplace_autoregression(
- self, input, ar_mask, forbidden_tokens=None, deterministic_synthesis=False
- ):
- to_generate = (ar_mask.sum(0) > 0).nonzero()
- 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
- logits = output[:, s]
- if forbidden_tokens is not None:
- logits = logits.masked_fill(forbidden_tokens, float("-inf"))
- if deterministic_synthesis:
- t_next = logits.argmax(1)
- else:
- dist = torch.distributions.categorical.Categorical(logits=logits)
- t_next = dist.sample()
- input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
-