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| # Copyright (c) 2024 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| import torch.nn.functional as F | |
| 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 logits | |
| def topk_sampling(logits, top_k=50, top_p=1.0, temperature=1.0): | |
| """ | |
| Perform top-k and top-p sampling on logits. | |
| Args: | |
| logits (torch.Tensor): The logits to sample from. | |
| top_k (int, optional): The number of highest probability tokens to keep for top-k filtering. | |
| Must be a positive integer. Defaults to 50. | |
| top_p (float, optional): The cumulative probability threshold for nucleus sampling. | |
| Must be between 0 and 1. Defaults to 1.0. | |
| temperature (float, optional): The scaling factor to adjust the logits distribution. | |
| Must be strictly positive. Defaults to 1.0. | |
| Returns: | |
| torch.Tensor: The sampled token. | |
| """ | |
| # Adjust logits using temperature | |
| 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 from the filtered distribution | |
| token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) | |
| return token | |