Batched top-k and top-p/nucleus sampling in PyTorch!

top-k-top-p-batched.py

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
        
        Basic outline taken from https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
    """
    assert logits.dim() == 2  # [BATCH_SIZE, VOCAB_SIZE]
    top_k = min(top_k, logits.size(-1))  # Safety check
    if top_k > 0:
        # Remove all tokens with a probability less than the last token of the top-k
        indices_to_remove = logits < torch.topk(logits, top_k, dim=1)[0][..., -1, None]
        logits[indices_to_remove] = filter_value
    
    sorted_logits, sorted_indices = torch.sort(logits, descending=True)
    
    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
    # Remove tokens with cumulative probability above the threshold
    sorted_indices_to_remove = cumulative_probs > top_p
    # Shift the indices to the right to keep also the first token above the threshold
    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
    sorted_indices_to_remove[..., 0] = 0
    
    # Replace logits to be removed with -inf in the sorted_logits
    sorted_logits[sorted_indices_to_remove] = filter_value
    # Then reverse the sorting process by mapping back sorted_logits to their original position
    logits = torch.gather(sorted_logits, 1, sorted_indices.argsort(-1))
    
    pred_token = torch.multinomial(F.softmax(logits, -1), 1) # [BATCH_SIZE, 1]
    return pred_token