diff --git a/ACL_PyTorch/built-in/nlp/Bert_Base_Uncased_for_Pytorch/ReadMe.md b/ACL_PyTorch/built-in/nlp/Bert_Base_Uncased_for_Pytorch/ReadMe.md
index 18ca26c9de04c940e0f11e288b5bcc4b47aa1f5a..865a190861676cb9ce559a47d9002bbe6ad2dd58 100644
--- a/ACL_PyTorch/built-in/nlp/Bert_Base_Uncased_for_Pytorch/ReadMe.md
+++ b/ACL_PyTorch/built-in/nlp/Bert_Base_Uncased_for_Pytorch/ReadMe.md
@@ -158,11 +158,11 @@
 
 
          ![img](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/turing/resourcecenter/img/public_sys-resources/notice_3.0-zh-cn.png)
-
+    
          使用ATC工具将.onnx文件转换为.om文件，需要.onnx算子版本需为11。在bert_base_pth2onnx.py脚本中torch.onnx.export方法中的输入参数opset_version的值需为11，请勿修改。
-
-      3. 此步可选，根据onnx图里是否存在（0，2，3，1）的transpose进行优化，若存在，根据这类Transpose和紧跟它的MatMul的name更新add_attr_transB.py，也就是更新里面的transpose_nodes和bmm_nodes两个list，然后运行下面命令。
-
+    
+      3. 此步可选，根据onnx图里是否存在（0，2，3，1）的transpose进行优化，若存在，运行下面命令。
+    
          ```
          python3 add_attr_trans_b.py bert_base_batch_8.onnx bert_base_batch_8.onnx
          ```
@@ -180,7 +180,7 @@
          export ASCEND_OPP_PATH=${install_path}/opp
          
          # 使用二进制输入时，执行如下命令
-         atc --input_format=ND --framework=5 --model=bert_base_batch_8.onnx --input_shape="input_ids:8,512;token_type_ids:8,512;attention_mask:8,512" --output=bert_base_batch_8_auto --log=info --soc_version=Ascend710
+         atc --input_format=ND --framework=5 --model=bert_base_batch_8.onnx --input_shape="input_ids:8,512;token_type_ids:8,512;attention_mask:8,512" --output=bert_base_batch_8_auto --log=info --soc_version=Ascend710 --optypelist_for_implmode="Gelu" --op_select_implmode=high_performance --input_fp16_nodes="attention_mask"
          ```
 
          ![img](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/turing/resourcecenter/img/public_sys-resources/note_3.0-zh-cn.png)
diff --git a/ACL_PyTorch/built-in/nlp/Bert_Base_Uncased_for_Pytorch/add_attr_trans_b.py b/ACL_PyTorch/built-in/nlp/Bert_Base_Uncased_for_Pytorch/add_attr_trans_b.py
index 45b2063c18bf1bd62bb74e81cd27d73efbe21fbb..b2b94bbb35b75f6a311e7567bd0e0dc9c70a5e97 100644
--- a/ACL_PyTorch/built-in/nlp/Bert_Base_Uncased_for_Pytorch/add_attr_trans_b.py
+++ b/ACL_PyTorch/built-in/nlp/Bert_Base_Uncased_for_Pytorch/add_attr_trans_b.py
@@ -16,43 +16,22 @@ from gener_core.mod_modify.onnx_graph import OXGraph
 from gener_core.mod_modify.interface import AttrType as AT
 
 
+FUS_NODE_TRANS = "Transpose"
+FUS_NODE_BMM = "MatMul"
 input_model = sys.argv[1]
 output_model = sys.argv[2]
 
 mod = OXGraph(input_model)
-
-transpose_nodes = ['Transpose_60',
-    'Transpose_154',
-    'Transpose_248',
-    'Transpose_342',
-    'Transpose_436',
-    'Transpose_530',
-    'Transpose_624',
-    'Transpose_718',
-    'Transpose_812',
-    'Transpose_906',
-    'Transpose_1000',
-    'Transpose_1094']
-bmm_nodes = ['MatMul_72',
-    'MatMul_166',
-    'MatMul_260',
-    'MatMul_354',
-    'MatMul_448',
-    'MatMul_542',
-    'MatMul_636',
-    'MatMul_730',
-    'MatMul_824',
-    'MatMul_918',
-    'MatMul_1012',
-    'MatMul_1106']
 io_map = mod.get_net_in_out_map()
+trans_nodes = mod.get_nodes_by_optype(FUS_NODE_TRANS)
 
-for transpose_node in transpose_nodes:
-    now_trans = mod.get_node(transpose_node)
-    now_trans.set_attr({"perm": (AT.LIST_INT, [0, 2, 1, 3])})
-for bmm in bmm_nodes:
-    now_bmm = mod.get_node(bmm)
-    now_bmm.set_attr({"transB": (AT.INT, 1)})
+for trans_node in trans_nodes:
+    if trans_node.get_attr("perm", AT.LIST_INT) == [0, 2, 3, 1]:
+        trans_node.set_attr({"perm": (AT.LIST_INT, [0, 2, 1, 3])})
+        bmm = io_map.get(trans_node.name)
+        if FUS_NODE_BMM in bmm[0]:
+            new_bmm = mod.get_node(bmm[0])
+            new_bmm.set_attr({"transB": (AT.INT, 1)})
 
 mod.save_new_model(output_model)
 print("OK")
\ No newline at end of file