Sample the next token from a probability distribution using top-k and/or nucleus (top-p) sampling

top-k-top-p.py

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)
    """
    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)

 # 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

What is the significance of these lines? Cannot get my head around them. Thanks

can anybody please tell this?
is it to keep at least one value?

@not-hermione
Consider this example:

x = torch.arange(5,0,-1) 
cumulative_probs = torch.cumsum(x, dim=0) # tensor([ 5,  9, 12, 14, 15])
sorted_indices_to_remove = cumulative_probs > 13 # tensor([False, False, False,  True,  True])

We want to create a boolean mask called sorted_indices_to_remove to identify which indices in cumulative_probs need to be removed. Specifically, we want to remove indices where the corresponding value in cumulative_probs is greater than 13.

Notice the index corresponding to value 12 is also marked as True in sorted_indices_to_remove, which we don't want to remove.

To address this issue, we use the following two lines of code:

sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0 

These 2 lines of code shift the values in sorted_indices_to_remove to the right by 1 along the last dimension and then set the first value along the last dimension to False.
This ensures the index corresponding to value 12 in cumulative_probs is not marked as True in sorted_indices_to_remove.

@LeeSinLiang
thanks alot for your time and answer.

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.

Note that this implementation does not just take the top_p probability mass. It also includes the probability mass of the token that straddles the top_p boundary. Here is a (numpy, not pytorch) implementation which always samples exactly from the top_p probability mass: https://gist.github.com/calvinmccarter/eaa9ee398606352e6e1df4b50e62881c .