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import torch
from transformers import StoppingCriteria, StoppingCriteriaList
from enums import PromptType
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=[], device="cuda", model_max_length=None):
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)
self.model_max_length = model_max_length
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
if self.model_max_length is not None and input_ids[0].shape[0] >= self.model_max_length:
# critical limit
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, prompt_dict, tokenizer, device, human='<human>:', bot="<bot>:", model_max_length=None):
# FIXME: prompt_dict unused currently
if prompt_type in [PromptType.human_bot.name, PromptType.instruct_vicuna.name, PromptType.instruct_with_end.name]:
if prompt_type == PromptType.human_bot.name:
# 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 == PromptType.instruct_vicuna.name:
# 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: # use hidden variable to avoid annoying properly logger bug
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,
model_max_length=model_max_length)])
else:
stopping_criteria = StoppingCriteriaList()
return stopping_criteria
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