主要记录源码中解决文本生成中词组重复出现的问题，代码中有具体操作解析。

class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
    r"""
    :class:`transformers.LogitsProcessor` enforcing an exponential penalty on repeated sequences.

    Args:
        repetition_penalty (:obj:`float`):
            The parameter for repetition penalty. 1.0 means no penalty. See `this paper
            <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
    """

    def __init__(self, penalty: float):
        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

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
        #scores为cur-step的词表分布[batch,seq,vocab_size]，input_ids为输入decoder的文本序列[batch,seq]，则score则是获取当前已经生成文本序列的token概率
        score = torch.gather(scores, 1, input_ids) 

        # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
        #减少已经出现的token的概率
        score = torch.where(score < 0, score * self.penalty, score / self.penalty) 
        
        #将减少后的概率重分配到原始的cur-step词表分布中
        scores.scatter_(1, input_ids, score) 
        return scores