diff --git a/PyTorch/built-in/rl/OpenRLHF_v0.5.7_for_PyTorch/transformers_need/modeling_qwen2_vl.py b/PyTorch/built-in/rl/OpenRLHF_v0.5.7_for_PyTorch/transformers_need/modeling_qwen2_vl.py
index 660bca2c3af04b887a78cbebadfaf659f097d879..b6860bcf14c6e88e1c0013e3309d5fc228ad2cef 100644
--- a/PyTorch/built-in/rl/OpenRLHF_v0.5.7_for_PyTorch/transformers_need/modeling_qwen2_vl.py
+++ b/PyTorch/built-in/rl/OpenRLHF_v0.5.7_for_PyTorch/transformers_need/modeling_qwen2_vl.py
@@ -24,6 +24,7 @@ from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
+import torch_npu
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils.checkpoint
@@ -178,8 +179,8 @@ def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim
         unsqueeze_dim
     )
 
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
+    q_embed = torch_npu.npu_rotary_mul(q, cos ,sin)
+    k_embed = torch_npu.npu_rotary_mul(k, cos ,sin)
     return q_embed, k_embed
 
 
@@ -189,9 +190,19 @@ def apply_rotary_pos_emb_vision(
     orig_q_dtype = q.dtype
     orig_k_dtype = k.dtype
     q, k = q.float(), k.float()
-    cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
+    
+    cos = cos.unsqueeze(1).unsqueeze(2).float()
+    sin = sin.unsqueeze(1).unsqueeze(2).float()
+
+    q = q.unsqueeze(2)
+    k = k.unsqueeze(2)
+
+    q_embed = torch_npu.npu_rotary_mul(q, cos ,sin)
+    k_embed = torch_npu.npu_rotary_mul(k, cos ,sin)
+
+    q_embed = q_embed.squeeze(2)
+    k_embed = k_embed.squeeze(2)
+
     q_embed = q_embed.to(orig_q_dtype)
     k_embed = k_embed.to(orig_k_dtype)
     return q_embed, k_embed
@@ -380,7 +391,7 @@ class VisionSdpaAttention(nn.Module):
             cos, sin = position_embeddings
         q, k = apply_rotary_pos_emb_vision(q, k, cos, sin)
 
-        attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool)
+        attention_mask = torch.zeros([1, 1, seq_length, seq_length], device=q.device, dtype=torch.bool)
         for i in range(1, len(cu_seqlens)):
             attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True
         q = q.transpose(0, 1)
@@ -442,11 +453,7 @@ class Qwen2RMSNorm(nn.Module):
         self.variance_epsilon = eps
 
     def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
+        return torch_npu.npu_rms_norm(hidden_states, self.weight, epsilon=self.variance_epsilon)[0]
 
     def extra_repr(self):
         return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"