diff --git a/internlm/core/naive_amp.py b/internlm/core/naive_amp.py
index 9e0fd5a..2f4d832 100644
--- a/internlm/core/naive_amp.py
+++ b/internlm/core/naive_amp.py
@@ -150,28 +150,21 @@ class NaiveAMPModel(nn.Module):
     def _register_fp32_parameters_hook(self) -> None:
         dtype = torch.float32
 
+        def to_fp32(x, dtype=dtype):
+            if isinstance(x, Tensor) and x.dtype != dtype:
+                return x.to(dtype)
+            return x
+
         def _pre_forward_hook(model: nn.Module, inputs: tuple):  # pylint: disable=W0613
-            inputs_fp32 = []
             assert isinstance(inputs, tuple)
-            for input_data_ in inputs:
-                if isinstance(input_data_, Tensor) and input_data_.dtype is not dtype:
-                    inputs_fp32.append(input_data_.to(dtype))
-                else:
-                    inputs_fp32.append(input_data_)
-            return tuple(inputs_fp32)
+            return tuple(map(to_fp32, inputs))
 
         def _post_forward_hook(model: nn.Module, inputs: tuple, outputs: Union[tuple, Tensor]):  # pylint: disable=W0613
-            outputs_ = []
-            assert isinstance(outputs, (Tensor, tuple))
+            assert isinstance(inputs, Union[tuple, Tensor])
             if isinstance(outputs, tuple):
-                for output_data_ in outputs:
-                    if isinstance(output_data_, Tensor) and output_data_.dtype is not self.dtype:
-                        outputs_.append(output_data_.to(self.dtype))
-                    else:
-                        outputs_.append(output_data_)
-                return tuple(outputs_)
+                return tuple(map(to_fp32, outputs, self.dtype))
             else:
-                return outputs.to(self.dtype) if outputs.dtype is not self.dtype else outputs
+                return to_fp32(outputs, self.dtype)
 
         # just want to share same for loop for ModuleList and Module
         if isinstance(self.model, nn.ModuleList):