mirror of
https://github.com/jasonppy/VoiceCraft.git
synced 2024-12-12 08:36:01 +01:00
63 lines
3.0 KiB
Python
63 lines
3.0 KiB
Python
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 |