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…