thomwolf/top-k-top-p.py

## 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)
	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)