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# thomwolf/top-k-top-p.py

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

### mataney commented Sep 25, 2019

huggingface/transformers#1333
:)

I am getting this error here, plus can you please elaborate about that "input"?

logits = model.forward(input)
Traceback (most recent call last):

File "", line 1, in
logits = model.forward(input)

TypeError: forward() missing 1 required positional argument: 'mc_token_ids'

### TheEdoardo93 commented Dec 18, 2019

You can refer in the run_generation.py in the Transformers to a full source code working on a real model, i.e. OpenAI GPT-2.

I am getting this error here, plus can you please elaborate about that "input"?

logits = model.forward(input)
Traceback (most recent call last):

File "", line 1, in
logits = model.forward(input)

TypeError: forward() missing 1 required positional argument: 'mc_token_ids'

### aj7tesh commented Feb 14, 2020

sample from the smallest set whose cumulative probability mass exceeds p for next words
what exactly this means? lets say I put p =0.9 then it will filter only those next tokens which have probability of > 0.9 or what ?

### chungyilinxrspace commented Feb 20, 2020

@thomwolf
Hi, I am recently learning the temperature sampling/ Nucleus sampling,
And I read the paper: "The Curious Case of Neural Text Degeneration", they rescaled the original-distribution to a new-distribution,

In the top_k_top_p_filtering function, it set the logit score to zero but doesn't change the probability distribution.
Does "Change the probability distribution" is necessary for top-p sampling?
Thank you ~

@chungyilinxrspace
In the top_k_top_p_filtering function, it set the logit score to zero but doesn't change the probability distribution.
Does "Change the probability distribution" is necessary for top-p sampling?

Hi!
TL;DR: The filtering function provided operates on the logits and not on the probabilities.

After filtering the logits, they are converted to class probabilities via the call to `F.softmax`, which ensures both that the filtered classes have zero probability (since they have logit value `float("-inf)"`) and that the filtered probabilities define a proper, scaled, proability distribution. Hence the probability distribution is indeed "changed".

### nilinykh commented Jun 3, 2020

Hello all!
First, thank you for a very nice piece of code.

I have a more general question about nucleus sampling itself, maybe someone will be willing to clarify several things for me.
How do we choose k and p? As fas as I understand, every time we generate text, it will be different given that k and p are the same (or different). In other words, one cannot get a stable generate output (unlike when using greedy or beam search).
Is there a good approximation of what values for these parameters could be? Or should it based solely on empirical observations for a particular problem? If the latter is the case, can anyone navigate me towards basic ideas on how changing k and/or p would affect generated output in general ?

### Hyman25 commented Oct 27, 2020

@mdda
Line24 exactly produce a list of indices. and your code helps.

### JiyangZhang commented Nov 19, 2020

How can I return multiple sampling sequences?
My understanding is run nucleus sampling for a whole sequence multiple times.

Thanks!

### tapdiego-amzn commented Aug 21, 2021

Thank you for this code. Is it distributed under some Open Source license or are there otherwise any limitations on its use?

### kushalj001 commented May 1, 2022

`````` # 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

### umiswing commented Jul 20, 2022

@thomwolf
Hello! I'm trying to modify the code to support batch size grater than one. I get some problem to make the top_p support 2d input. I didn't find the appropriate pytorch api to index the 2d tensor(line 26~27) in the code and implement it with for loop, which is too slow. Could you provide some suggestions about the implementation?

### nicofirst1 commented Aug 8, 2022

@umiswing I'm also looking for a batched version of this, did you find anything?

### umiswing commented Aug 8, 2022

@umiswing I'm also looking for a batched version of this, did you find anything?

@nicofirst1 I modify it to batch version. But I didn't do much test for it. I hope it can help.

`````` # 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

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

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.

### calvinmccarter commented Aug 31, 2024

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 .