optimize minicpm-v-2_6 repetition penalty (#11763)

python/llm/src/ipex_llm/transformers/models/minicpmv.py

@@ -19,23 +19,14 @@ import torch
 from transformers.generation.logits_process import RepetitionPenaltyLogitsProcessor
 
 
-# todo
 def patched_repetition_penalty_call(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
-    score = torch.gather(scores, 1, input_ids)
-
-    # if score < 0 then repetition penalty has to be
-    # multiplied to reduce the token probabilities
-    score = torch.where(score < 0, score * self.penalty, score / self.penalty)
-
-    # ipex llm changes start: call scatter on CPU
-    device = scores.device
-    scores = scores.to('cpu')
-    input_ids = input_ids.to('cpu')
-    score = score.to('cpu')
-    scores.scatter_(1, input_ids, score)
-    scores = scores.to(device)
-    # ipex llm changes end
-
+    if scores.device.type == "xpu":
+        import xe_addons
+        xe_addons.repetition_penalty_logits_process_inplaced(scores, input_ids, self.penalty)
+    else:
+        score = torch.gather(scores, 1, input_ids)
+        score = torch.where(score < 0, score * self.penalty, score / self.penalty)
+        scores.scatter_(1, input_ids, score)
     return scores