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Use same temperature and top_k, top_p in validation as from generation #27

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Use same temperature and top_k, top_p in validation as from generation #27

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20 changes: 20 additions & 0 deletions grail/grail.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1088,9 +1088,29 @@ def _collect_chosen_token_probs(self, commit: dict) -> Optional[list[float]]:
start_t = max(1, prompt_len)
end_t = min(start_t + completion_len, logits.size(0))

from mining.rollout_generator import RolloutGenerator
dummy_rolloutgenerator = RolloutGenerator()
probs_list: list[float] = []
for t in range(start_t, end_t):
step_logits = logits[t - 1]
temperature = dummy_rolloutgenerator.get_temperature()
step_logits = step_logits / temperature
top_k = 50
if top_k > 0:
top_k_logits, top_k_indices = torch.topk(step_logits, k=min(top_k, step_logits.size(-1)))
step_logits = torch.full_like(step_logits, float("-inf"))
step_logits.scatter_(-1, top_k_indices, top_k_logits)

top_p = 0.95
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(step_logits, descending=True)
cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].clone()
sorted_indices_to_remove[0] = 0
indices_to_remove = sorted_indices_to_remove.scatter(0, sorted_indices, sorted_indices_to_remove)
step_logits[indices_to_remove] = float("-inf")

step_probs = torch.softmax(step_logits, dim=-1)
tok_id = int(tokens[t])
probs_list.append(float(step_probs[tok_id].item()))
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