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| import torch | |
| from transformers import StoppingCriteria, StoppingCriteriaList | |
| class StoppingCriteriaSub(StoppingCriteria): | |
| def __init__(self, stops=[], encounters=[], device="cuda"): | |
| super().__init__() | |
| assert len(stops) % len(encounters) == 0, "Number of stops and encounters must match" | |
| self.encounters = encounters | |
| self.stops = [stop.to(device) for stop in stops] | |
| self.num_stops = [0] * len(stops) | |
| def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: | |
| for stopi, stop in enumerate(self.stops): | |
| if torch.all((stop == input_ids[0][-len(stop):])).item(): | |
| self.num_stops[stopi] += 1 | |
| if self.num_stops[stopi] >= self.encounters[stopi % len(self.encounters)]: | |
| # print("Stopped", flush=True) | |
| return True | |
| # print("Tokens: %s" % input_ids[0].cpu().numpy(), flush=True) | |
| # print("Stop Tokens: %s" % [x.cpu().numpy() for x in self.stops], flush=True) | |
| return False | |
| def get_stopping(prompt_type, tokenizer, device, human='<human>:', bot="<bot>:"): | |
| if prompt_type in ['human_bot', 'instruct_vicuna', 'instruct_with_end']: | |
| if prompt_type == 'human_bot': | |
| # encounters = [prompt.count(human) + 1, prompt.count(bot) + 1] | |
| # stopping only starts once output is beyond prompt | |
| # 1 human is enough to trigger, but need 2 bots, because very first view back will be bot we added | |
| stop_words = [human, bot, '\n' + human, '\n' + bot] | |
| encounters = [1, 2] | |
| elif prompt_type == 'instruct_vicuna': | |
| # even below is not enough, generic strings and many ways to encode | |
| stop_words = [ | |
| '### Human:', | |
| """ | |
| ### Human:""", | |
| """ | |
| ### Human: | |
| """, | |
| '### Assistant:', | |
| """ | |
| ### Assistant:""", | |
| """ | |
| ### Assistant: | |
| """, | |
| ] | |
| encounters = [1, 2] | |
| else: | |
| # some instruct prompts have this as end, doesn't hurt to stop on it since not common otherwise | |
| stop_words = ['### End'] | |
| encounters = [1] | |
| stop_words_ids = [ | |
| tokenizer(stop_word, return_tensors='pt')['input_ids'].squeeze() for stop_word in stop_words] | |
| # handle single token case | |
| stop_words_ids = [x if len(x.shape) > 0 else torch.tensor([x]) for x in stop_words_ids] | |
| stop_words_ids = [x for x in stop_words_ids if x.shape[0] > 0] | |
| # avoid padding in front of tokens | |
| if tokenizer.pad_token: | |
| stop_words_ids = [x[1:] if x[0] == tokenizer.pad_token_id and len(x) > 1 else x for x in stop_words_ids] | |
| # handle fake \n added | |
| stop_words_ids = [x[1:] if y[0] == '\n' else x for x, y in zip(stop_words_ids, stop_words)] | |
| # build stopper | |
| stopping_criteria = StoppingCriteriaList( | |
| [StoppingCriteriaSub(stops=stop_words_ids, encounters=encounters, device=device)]) | |
| else: | |
| stopping_criteria = StoppingCriteriaList() | |
| return stopping_criteria | |