import torch import torch.nn.functional as F def top_k_top_p_filtering( logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1 ): """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (batch size, vocabulary size) if top_k > 0: keep only top k tokens with highest probability (top-k filtering). if top_p < 1.0: 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) Make sure we keep at least min_tokens_to_keep per batch example in the output From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317 """ if top_k > 0: top_k = min( max(top_k, min_tokens_to_keep), logits.size(-1) ) # Safety check # Remove all tokens with a probability less than the last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value if top_p < 1.0: 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 (token with 0 are kept) sorted_indices_to_remove = cumulative_probs > top_p if min_tokens_to_keep > 1: # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below) sorted_indices_to_remove[..., :min_tokens_to_keep] = 0 # 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 # scatter sorted tensors to original indexing indices_to_remove = sorted_indices_to_remove.scatter( 1, sorted_indices, sorted_indices_to_remove ) logits[indices_to_remove] = filter_value return logits def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0): # temperature: (`optional`) float # The value used to module the next token probabilities. Must be strictly positive. Default to 1.0. # top_k: (`optional`) int # The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50. # top_p: (`optional`) float # The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1. # Temperature (higher temperature => more likely to sample low probability tokens) if temperature != 1.0: logits = logits / temperature # Top-p/top-k filtering logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p) # Sample token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) return token