diff --git a/graph_net/torch/dtype_gen_passes/dtype_generalization_pass.py b/graph_net/torch/dtype_gen_passes/dtype_generalization_pass.py
index 91400d1e0..46b77b5a4 100644
--- a/graph_net/torch/dtype_gen_passes/dtype_generalization_pass.py
+++ b/graph_net/torch/dtype_gen_passes/dtype_generalization_pass.py
@@ -32,6 +32,12 @@
     "bmm",
 }
 
+FP32_SENSITIVE_OPS = {
+    torch.nn.functional.layer_norm: ({2, 3, 4}, {"weight", "bias", "eps"}),
+    torch.nn.functional.group_norm: ({2, 3, 4}, {"weight", "bias", "eps"}),
+    torch.nn.functional.batch_norm: ({1, 2, 3, 4}, {"running_mean", "running_var", "weight", "bias"}),
+    torch.nn.functional.embedding:  ({0}, {"weight"}), 
+}
 
 class ConcretePass(DtypeGeneralizationPass):
     """
@@ -78,6 +84,27 @@ def _node_need_rewrite(self, node: fx.Node) -> bool:
 
         return False
 
+    def _analyze_preserved_nodes(self, graph: fx.Graph) -> set[fx.Node]:
+        """预扫描图：找到所有被 FP32 敏感算子使用的参数节点。"""
+        preserved_nodes = set()
+
+        for node in graph.nodes:
+            if node.op != "call_function":
+                continue
+
+            if node.target in FP32_SENSITIVE_OPS:
+                target_indices, target_kwargs = FP32_SENSITIVE_OPS[node.target]
+
+                for i, arg in enumerate(node.args):
+                    if i in target_indices and isinstance(arg, fx.Node):
+                        preserved_nodes.add(arg)
+
+                for k, v in node.kwargs.items():
+                    if k in target_kwargs and isinstance(v, fx.Node):
+                        preserved_nodes.add(v)
+
+        return preserved_nodes
+
     def rewrite(self, gm: fx.GraphModule) -> fx.GraphModule:
         """
         Rewrite the graph to convert dtypes.
@@ -89,27 +116,41 @@ def rewrite(self, gm: fx.GraphModule) -> fx.GraphModule:
         """
         new_graph = fx.Graph()
         val_map = {}
-
+        preserved_nodes = self._analyze_preserved_nodes(gm.graph)
+        
         def create_placeholder(node: fx.Node) -> fx.Node:
             """Create a placeholder node with dtype conversion if needed."""
             new_node = new_graph.node_copy(node, lambda x: val_map.get(x, x))
-            if self._is_float32_tensor(node):
-                attr_name = str(node.target)
-                if self.should_preserve_weight(attr_name):
-                    return new_node
-
-                return new_graph.call_method("to", args=(new_node, self.torch_dtype))
-            return new_node
+            # if self._is_float32_tensor(node):
+            #     attr_name = str(node.target)
+            #     if self.should_preserve_weight(attr_name):
+            #         return new_node
+            #     return new_graph.call_method("to", args=(new_node, self.torch_dtype))
+            # return new_node
+            if not self._is_float32_tensor(node):
+                return new_node
+
+            if node in preserved_nodes:
+                return new_node 
+            
+            return new_graph.call_method("to", args=(new_node, self.torch_dtype))
 
         def create_get_attr(node: fx.Node) -> fx.Node:
             """Create a get_attr node with dtype conversion if needed."""
             new_node = new_graph.node_copy(node, lambda x: val_map.get(x, x))
-            attr_name = str(node.target)
-            if self._is_float32_tensor(node) and not self.should_preserve_weight(
-                attr_name
-            ):
-                return new_graph.call_method("to", args=(new_node, self.torch_dtype))
-            return new_node
+            # attr_name = str(node.target)
+            # if self._is_float32_tensor(node) and not self.should_preserve_weight(
+            #     attr_name
+            # ):
+            #     return new_graph.call_method("to", args=(new_node, self.torch_dtype))
+            # return new_node
+            if not self._is_float32_tensor(node):
+                return new_node
+
+            if node in preserved_nodes:
+                return new_node 
+            
+            return new_graph.call_method("to", args=(new_node, self.torch_dtype))
 
         def create_new_args(node: fx.Node) -> list:
             """new_args of node with dtype conversion if needed."""
@@ -128,16 +169,16 @@ def create_new_args(node: fx.Node) -> list:
         def create_new_kwargs(node: fx.Node) -> dict:
             """new_kwargs of node with dtype conversion if needed."""
             new_kwargs = {}
-
+            
             for k, v in node.kwargs.items():
                 if isinstance(v, fx.Node):
                     mapped = val_map[v]
                     if self._is_float32_tensor(v):
                         mapped = new_graph.call_method("to", (mapped, self.torch_dtype))
-                    else:
+                    else:                         
                         new_kwargs[k] = mapped
                 else:
-                    new_kwargs[k] = v
+                    new_kwargs[k] = v      
             return new_kwargs
 
         def create_call_function(node: fx.Node) -> fx.Node:
@@ -186,6 +227,9 @@ def create_call_method(node: fx.Node) -> fx.Node:
         gm.graph = new_graph
         gm.recompile()
 
+        with open("output.txt", "w", encoding="utf-8") as f:
+            print(gm.graph, file=f)
+            
         return gm
 
     def _is_float32_tensor(self, node: fx.Node) -> bool: