diff --git a/llm/fastdeploy_llm/engine.py b/llm/fastdeploy_llm/engine.py
index 8661893668..7208fed7de 100644
--- a/llm/fastdeploy_llm/engine.py
+++ b/llm/fastdeploy_llm/engine.py
@@ -376,40 +376,64 @@ def dy_input_preprocess(inputs):
     """
     stop_flags = inputs["dyinput_flags"]
     dec_length = inputs["seq_len_decoder"]
+    enc_length = inputs["seq_len_encoder"]
     bsz = len(stop_flags)
 
     tgt_pos = paddle.ones(shape=(bsz, 2, 1), dtype="int64")
 
     for i in range(bsz):
         if stop_flags[i] == 1:
-            length = int(dec_length[i, 0])
+            length = int(enc_length[i, 0])
             if args.is_ptuning:
                 model_id = inputs['model_id'][i]
-                if not model_id:
-                    attention_mask[i, 0, :length, :
-                                   max_prefix_len] = paddle.zeros(
-                                       [1, length, max_prefix_len],
-                                       dtype=args.dtype)
+                if "chatglm" in args.architecture:
+                    attention_mask[i, 0, :length, : length+max_prefix_len] = 1
+                    attention_mask[i, 0, :length - 1,  length+max_prefix_len - 1] = 0
+                    tgt_pos[i, 0, 0] = paddle.to_tensor(
+                        [length], dtype="int64")
+                    
+                    if not model_id:
+                        tgt_generation_mask[i, 0, 0, max_prefix_len : length + max_prefix_len] = paddle.ones(
+                        shape=[1, length], dtype=args.dtype
+                    )
+                    else:
+                        tgt_generation_mask[i, 0, 0, : length + max_prefix_len] = paddle.ones(
+                        shape=[1, length + max_prefix_len], dtype=args.dtype
+                    )
                 else:
-                    attention_mask[i, 0, :length, :
-                                   max_prefix_len] = paddle.ones(
-                                       [1, length, max_prefix_len],
-                                       dtype=args.dtype)
-                attention_mask[i, 0, :length, max_prefix_len:max_prefix_len +
-                               length] = paddle.tril(
-                                   paddle.ones(
-                                       shape=[length, length],
-                                       dtype=args.dtype))
-                position_ids[i, :max_prefix_len] = 0
-                position_ids[i, max_prefix_len:max_prefix_len + inputs[
-                    "input_ids"].shape[1]] = paddle.arange(inputs["input_ids"]
-                                                           .shape[1])
-                tgt_generation_mask[i, 0, 0, :max_prefix_len +
-                                    length] = paddle.ones(
-                                        shape=[1, max_prefix_len + length],
+                    if not model_id:
+                        attention_mask[i, 0, :length, :
+                                    max_prefix_len] = paddle.zeros(
+                                        [1, length, max_prefix_len],
+                                        dtype=args.dtype)
+                        tgt_generation_mask[i, 0, 0, max_prefix_len : length + max_prefix_len] = paddle.ones(
+                        shape=[1, length], dtype=args.dtype
+                    )
+                    else:
+                        attention_mask[i, 0, :length, :
+                                    max_prefix_len] = paddle.ones(
+                                        [1, length, max_prefix_len],
                                         dtype=args.dtype)
+                        tgt_generation_mask[i, 0, 0, :max_prefix_len +
+                                            length] = paddle.ones(
+                                                shape=[1, max_prefix_len + length],
+                                                dtype=args.dtype)
+                    
+                    attention_mask[i, 0, :length, max_prefix_len:max_prefix_len +
+                                length] = paddle.tril(
+                                    paddle.ones(
+                                        shape=[length, length],
+                                        dtype=args.dtype))
+                    position_ids[i, :max_prefix_len] = 0
+                    position_ids[i, max_prefix_len:max_prefix_len + inputs[
+                        "input_ids"].shape[1]] = paddle.arange(inputs["input_ids"]
+                                                            .shape[1])
+                        
             else:
                 if "chatglm" in args.architecture:
+                    # todo： check alignment with paddlenlp: 
+                    # attention_mask[i, 0, :length, :length] = 1 
+                    # attention_mask[i, 0, :length - 1, length - 1] = 0
                     attention_mask[i, 0, :length, :length] = 0
                     attention_mask[i, 0, :length - 1, length - 1] = 1
                     tgt_pos[i, 0, 0] = paddle.to_tensor(
@@ -434,7 +458,7 @@ def dy_input_preprocess(inputs):
     inputs["tgt_generation_mask"] = tgt_generation_mask
     if "chatglm" in args.architecture:
         inputs["tgt_pos"] = tgt_pos
-        inputs["position_ids"] = generate_position_ids_for_chatglm(dec_length)
+        inputs["position_ids"] = generate_position_ids_for_chatglm(enc_length)
     if args.is_ptuning:
         prefix_caches = []
         for model_id in inputs['model_id']: