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| # Copyright (c) 2023 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 | |
| # This function is modified from https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py | |
| 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 (torch.Tensor): Logits distribution with shape (batch size, vocabulary size). | |
| top_k (int, optional): Keep only top k tokens with highest probability (top-k filtering). | |
| Set to 0 to disable. Defaults to 0. | |
| top_p (float, optional): Keep the top tokens with a cumulative probability >= top_p (nucleus filtering). | |
| Must be between 0 and 1, inclusive. Defaults to 1.0. | |
| filter_value (float, optional): The value to assign to filtered logits. Defaults to -float('Inf'). | |
| min_tokens_to_keep (int, optional): Ensure that at least this number of tokens are kept per batch example. | |
| Defaults to 1. | |
| Returns: | |
| torch.Tensor: The filtered logits. | |
| """ | |
| """ | |
| 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: | |
| # Apply top-k filtering | |
| top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) | |
| indices_to_remove = logits < torch.topk(logits, top_k).values[..., -1, None] | |
| logits[indices_to_remove] = filter_value | |
| if top_p < 1.0: | |
| # Apply top-p filtering | |
| sorted_logits, sorted_indices = torch.sort(logits, descending=True) | |
| cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) | |
| # Create a mask to remove tokens with cumulative probability above the top_p threshold | |
| sorted_indices_to_remove = cumulative_probs > top_p | |
| if min_tokens_to_keep > 1: | |
| sorted_indices_to_remove[..., :min_tokens_to_keep] = 0 | |
| sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() | |
| sorted_indices_to_remove[..., 0] = 0 | |
| # Scatter sorted tensors back to original indexing | |
| indices_to_remove = sorted_indices.scatter( | |
| 1, sorted_indices, sorted_indices_to_remove | |
| ) | |
| logits[indices_to_remove] = filter_value | |
| 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 | |