Delete stopping.py

stopping.py

@@ -1,72 +0,0 @@
-import torch
-from transformers import StoppingCriteria, StoppingCriteriaList
-
-from prompter import PromptType
-
-
-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 [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)])
-    else:
-        stopping_criteria = StoppingCriteriaList()
-    return stopping_criteria