utils.py

import torch
import transformers
import gc
from queue import Queue
from threading import Thread


class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):

    def __init__(self, sentinel_token_ids: list, starting_idx: int):
        transformers.StoppingCriteria.__init__(self)
        self.sentinel_token_ids = sentinel_token_ids
        self.starting_idx = starting_idx
        self.shortest = min([x.shape[-1] for x in sentinel_token_ids])

    def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool:
        for sample in input_ids:
            trimmed_sample = sample[self.starting_idx:]
            trimmed_len = trimmed_sample.shape[-1]
            if trimmed_len < self.shortest:
                continue

            for sentinel in self.sentinel_token_ids:
                sentinel_len = sentinel.shape[-1]
                if trimmed_len < sentinel_len:
                    continue

                window = trimmed_sample[-sentinel_len:]
                if torch.all(torch.eq(sentinel, window)):
                    return True

        return False




class Iteratorize:
    def __init__(self, func, kwargs=None, callback=None):
        self.mfunc = func
        self.c_callback = callback
        self.q = Queue()
        self.sentinel = object()
        self.kwargs = kwargs or {}
        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
            except:
                traceback.print_exc()
                pass

            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):
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.stop_now = True
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()



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