Skip to content

Instantly share code, notes, and snippets.

Last active May 14, 2024 00:20
Show Gist options
  • Save thomwolf/1a5a29f6962089e871b94cbd09daf317 to your computer and use it in GitHub Desktop.
Save thomwolf/1a5a29f6962089e871b94cbd09daf317 to your computer and use it in GitHub Desktop.
Sample the next token from a probability distribution using top-k and/or nucleus (top-p) sampling
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
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. (
assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear
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)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
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
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
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
return logits
# Here is how to use this function for top-p sampling
temperature = 1.0
top_k = 0
top_p = 0.9
# Get logits with a forward pass in our model (input is pre-defined)
logits = model(input)
# Keep only the last token predictions of the first batch item (batch size 1), apply a temperature coefficient and filter
logits = logits[0, -1, :] / temperature
filtered_logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
# Sample from the filtered distribution
probabilities = F.softmax(filtered_logits, dim=-1)
next_token = torch.multinomial(probabilities, 1)
Copy link

yuchenlin commented Jul 5, 2023

does anyone have this error? RuntimeError: scatter(): Expected self.dtype to be equal to src.dtype?

I changed the line to be

indices_to_remove = torch.zeros_like(logits, dtype=sorted_indices_to_remove.dtype).scatter_(
            dim=-1, index=sorted_indices, src=sorted_indices_to_remove )

such that it works now.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment