Upload stopping.py

stopping.py

@@ -0,0 +1,70 @@
+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