Delete CHATTS/utils/infer_utils.py

CHATTS/utils/infer_utils.py

@@ -1,45 +0,0 @@
-
-import torch
-import torch.nn.functional as F
-
-    
-class CustomRepetitionPenaltyLogitsProcessorRepeat():
-
-    def __init__(self, penalty: float, max_input_ids, past_window):
-        if not isinstance(penalty, float) or not (penalty > 0):
-            raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
-
-        self.penalty = penalty
-        self.max_input_ids = max_input_ids
-        self.past_window = past_window
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-        
-        input_ids = input_ids[:, -self.past_window:]
-        freq = F.one_hot(input_ids, scores.size(1)).sum(1)
-        freq[self.max_input_ids:] = 0
-        alpha = self.penalty**freq
-        scores = torch.where(scores < 0, scores*alpha, scores/alpha)
-
-        return scores
-    
-class CustomRepetitionPenaltyLogitsProcessor():
-
-    def __init__(self, penalty: float, max_input_ids, past_window):
-        if not isinstance(penalty, float) or not (penalty > 0):
-            raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
-
-        self.penalty = penalty
-        self.max_input_ids = max_input_ids
-        self.past_window = past_window
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-        
-        input_ids = input_ids[:, -self.past_window:]
-        score = torch.gather(scores, 1, input_ids)
-        _score = score.detach().clone()
-        score = torch.where(score < 0, score * self.penalty, score / self.penalty)
-        score[input_ids>=self.max_input_ids] = _score[input_ids>=self.max_input_ids]
-        scores.scatter_(1, input_ids, score)
-        
-        return scores