I’ve successfully implemented and used my own StoppingCriteria.
However I came across one limitation : the stopping criteria is applied to the whole batch.
i.e. I can’t mark specific sample of the batch as “stopped” and other as “not stopped”, I have to return a single boolean for the whole batch.
But I want to have more fine-grained control over which sample is stopped or not, so when used with beam search, I can early-terminate some beam, and keep generating for other beams.
How can I achieve that (if it’s possible) ?
__call__() function of my custom stopping criteria look like :
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
return all(self.is_stop(sample) for sample in input_ids)
Instead of returning a boolean, I’d like to return a tensor of boolean.
From what I saw in the source code, it seems it’s not implemented yet…
Have you managed to solve this problem?
I am facing the same problem and would also be interested in a solution
Nope I didn’t solve this problem.
My work-around is to keep generating until the whole batch match the stopping criteria, and then post-process the generated text to fit what I wanted.
So in my case I wanted to stop generation whenever a space is generated. I stopped the generation once ALL samples generated a space. And then post-processed them to keep only the first word (anything after the first space is generated was discarded).
I meet the problem too. It’s really make me confusion.
How about using a
LogitsProcessor instead of a
StoppingCriteria ? The idea is to give the
<pad> tokens an
inf logit while giving all other tokens a
-inf logit when the stopping criteria is met. This way, tokens generated after the stopping criteria is met will only be the
<eos> token. However, this approach does not affect other sequences that have not yet met the stopping criteria.
Thanks for your reply ! I tried it and it works perfectly, much nicer than my custom StoppingCriteria.
For future reference, this is what my code look like (in my case I needed to stop generation once the model generated a space, but force at least one character generation at the beginning (I’m using a character-based transformer)) :
"""Logits processor (to use with HuggingFace `generate()` method :
This logit processor simply ensure that we generate at least one letter
other than space, and that we don't generate anything after generating
a space (in order to generate single word).
base_len (int): Size of the given context. Used to know if this is
the first character to generate.
sp_token_id (int): ID of the space token.
eos_token_id (int): ID of the EOS token.
def __init__(self, base_len: int, eos_token_id: int):
self.base_len = base_len
self.sp_token_id = sp_token_id
self.eos_token_id = eos_token_id
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
if input_ids.size(1) > self.base_len:
forced_eos = torch.full((scores.size(1),), -float("inf"))
forced_eos[self.eos_token_id] = 0
# Force generation of EOS after a space
scores[input_ids[:, -1] == self.sp_token_id] = forced_eos
Then it can be used like this :
from transformers import LogitsProcessorList
logits_processor = LogitsProcessorList([StopAfterSpaceIsGenerated(base_len, 35, self.model.config.eos_token_id)])
@heekang how do I use
inf with sampling (
do_sample=True). The approach works fine without sampling, but errors with sampling enabled. See Invalidate beam in do_sample mode with LogitsProcessor by setting it to -inf for a reproducible example