diff --git a/python/llm/src/ipex_llm/transformers/models/qwen_vl.py b/python/llm/src/ipex_llm/transformers/models/qwen_vl.py
index 4cdfc7ee..34f79052 100644
--- a/python/llm/src/ipex_llm/transformers/models/qwen_vl.py
+++ b/python/llm/src/ipex_llm/transformers/models/qwen_vl.py
@@ -126,7 +126,13 @@ def qwen_attention_forward_vl(
         query = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
         key = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
         value = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
+        # TODO: speed up
+        # mixed_x_layer = self.c_attn(hidden_states)
+        # query, key, value = mixed_x_layer.split(self.split_size, dim=2)
 
+        # query = self._split_heads(query, self.num_heads, self.head_dim)
+        # key = self._split_heads(key, self.num_heads, self.head_dim)
+        # value = self._split_heads(value, self.num_heads, self.head_dim)
         if rotary_pos_emb is not None:
             cur_len = query.shape[1]
             rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
@@ -183,7 +189,8 @@ def qwen_attention_forward_vl(
         present = None
 
     if decoding_fast_path:
-        query = query.transpose(1, 2) # change to (bsz, q_len, num_heads, head_dim)
+        # change to (bsz, q_len, num_heads, head_dim)
+        query = query.transpose(1, 2)
 
     if self.use_logn_attn and not self.training:
         if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype:
@@ -196,7 +203,7 @@ def qwen_attention_forward_vl(
     query = query.permute(0, 2, 1, 3)
 
     if not self.training and not hidden_states.requires_grad and \
-    use_esimd_sdp(q_len, key.shape[2], self.head_dim, query):
+            use_esimd_sdp(q_len, key.shape[2], self.head_dim, query):
         import linear_fp16_esimd
         attn_output = linear_fp16_esimd.sdp_forward(query,
                                                     key,
@@ -209,7 +216,6 @@ def qwen_attention_forward_vl(
             query, key, value, registered_causal_mask, attention_mask, head_mask
         )
 
-
     context_layer = self._merge_heads(
         attn_output, self.num_heads, self.head_dim
     )