def modify_logits_for_top_p_filtering(logits, top_p):
    """Set the logits for none top-p values to -inf. Done in-place."""
    if top_p <= 0.0 or top_p >= 1.0:
        return
    # First sort and calculate cumulative sum of probabilities.
    sorted_logits, sorted_indices = torch.sort(logits, descending=False)
    cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
    # Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
    sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
    # scatter sorted tensors to original indexing
    indices_to_remove = sorted_indices_to_remove.scatter(
        1, sorted_indices, sorted_indices_to_remove
    )
    logits.masked_fill_(indices_to_remove, float("-inf"))
Set the logits for none top-p values to -inf. Done in-place.
mamba/mamba_ssm/utils/generation.py