import gc from queue import Queue from threading import Thread import torch import transformers import modules.shared as shared # Copied from https://github.com/PygmalionAI/gradio-ui/ class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria): def __init__(self, sentinel_token_ids: list[torch.LongTensor], starting_idx: int): transformers.StoppingCriteria.__init__(self) self.sentinel_token_ids = sentinel_token_ids self.starting_idx = starting_idx def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool: for sample in input_ids: trimmed_sample = sample[self.starting_idx:] for i in range(len(self.sentinel_token_ids)): # Can't unfold, output is still too tiny. Skip. if trimmed_sample.shape[-1] < self.sentinel_token_ids[i].shape[-1]: continue for window in trimmed_sample.unfold(0, self.sentinel_token_ids[i].shape[-1], 1): if torch.all(torch.eq(self.sentinel_token_ids[i][0], window)): return True return False class Stream(transformers.StoppingCriteria): def __init__(self, callback_func=None): self.callback_func = callback_func def __call__(self, input_ids, scores) -> bool: if self.callback_func is not None: self.callback_func(input_ids[0]) return False class Iteratorize: """ Transforms a function that takes a callback into a lazy iterator (generator). """ def __init__(self, func, kwargs={}, callback=None): self.mfunc=func self.c_callback=callback self.q = Queue() self.sentinel = object() self.kwargs = kwargs self.stop_now = False def _callback(val): if self.stop_now: raise ValueError self.q.put(val) def gentask(): try: ret = self.mfunc(callback=_callback, **self.kwargs) except ValueError: pass clear_torch_cache() self.q.put(self.sentinel) if self.c_callback: self.c_callback(ret) self.thread = Thread(target=gentask) self.thread.start() def __iter__(self): return self def __next__(self): obj = self.q.get(True,None) if obj is self.sentinel: raise StopIteration else: return obj def __del__(self): clear_torch_cache() def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.stop_now = True clear_torch_cache() def clear_torch_cache(): gc.collect() if not shared.args.cpu: torch.cuda.empty_cache()