removed alm hypothesis

Signed-off-by: BAAI-OpenPlatform <[email protected]>

flagai/model/predictor/utils.py

@@ -299,54 +299,6 @@ def is_done(self, best_sum_logprobs: float, cur_len: int) -> bool:
             ret = self.worst_score >= cur_score
             return ret
 
-
-class BeamHypothesesALM:
-    def __init__(self, num_beams: int, max_length: int, length_penalty: float, early_stopping: bool):
-        """
-        Initialize n-best list of hypotheses.
-        """
-        self.max_length = max_length - 1  # ignoring bos_token
-        self.length_penalty = length_penalty
-        self.early_stopping = early_stopping
-        self.num_beams = num_beams
-        self.beams = []
-        self.worst_score = 1e9
-
-    def __len__(self):
-        """
-        Number of hypotheses in the list.
-        """
-        return len(self.beams)
-
-    def add(self, hyp: torch.LongTensor, sum_logprobs: float, mems=None):
-        """
-        Add a new hypothesis to the list.
-        """
-        score = sum_logprobs / (max(hyp.shape[-1], 1) ** self.length_penalty)
-        if len(self) < self.num_beams or score > self.worst_score:
-            self.beams.append((score, hyp, mems))
-            if len(self) > self.num_beams:
-                sorted_next_scores = sorted([(s, idx) for idx, (s, _, _) in enumerate(self.beams)])
-                del self.beams[sorted_next_scores[0][1]]
-                self.worst_score = sorted_next_scores[1][0]
-            else:
-                self.worst_score = min(score, self.worst_score)
-
-    def is_done(self, best_sum_logprobs: float, cur_len: int) -> bool:
-        """
-        If there are enough hypotheses and that none of the hypotheses being generated can become better than the worst
-        one in the heap, then we are done with this sentence.
-        """
-
-        if len(self) < self.num_beams:
-            return False
-        elif self.early_stopping:
-            return True
-        else:
-            cur_score = best_sum_logprobs / cur_len ** self.length_penalty
-            ret = self.worst_score >= cur_score
-            return ret
-
 
 def viterbi_decode(nodes, trans):
     """
@@ -566,7 +518,7 @@ def __init__(
 
         self._is_init = False
         self._beam_hyps = [
-            BeamHypothesesALM(
+            BeamHypotheses(
                 num_beams=self.num_beams,
                 max_length=self.max_length,
                 length_penalty=self.length_penalty,