nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
)
- ar_mask = torch.full(c_quizzes.size(), 1, device=self.device)
+ ar_mask_prompt = torch.zeros(c_quizzes.size(), device=self.device)
+ ar_mask_prompt[:, ar_mask_prompt.size(1) // 2 + 1] = 1
+ ar_mask_solve = 1 - ar_mask_prompt
seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
# bracketing of the temperature to get the target logproba
model=model_for_generation,
batch_size=self.batch_size,
input=c_quizzes,
- ar_mask=ar_mask,
+ ar_mask=ar_mask_prompt,
seq_logproba=seq_logproba,
temperature=temperature,
deterministic_synthesis=False,
ave_seq_logproba = seq_logproba.mean()
+ masked_inplace_autoregression(
+ model=model_for_generation,
+ batch_size=self.batch_size,
+ input=c_quizzes,
+ ar_mask=ar_mask_solve,
+ seq_logproba=seq_logproba,
+ temperature=temperature,
+ deterministic_synthesis=True,
+ # progress_bar_desc="sampling c_quizzes",
+ device=self.device,
+ )
+
# If we do not have target logprobs, get out now
if min_ave_seq_logproba is None:
break