diff --git a/internlm/solver/optimizer/hybrid_zero_optim.py b/internlm/solver/optimizer/hybrid_zero_optim.py
index 108bb58..491e59c 100644
--- a/internlm/solver/optimizer/hybrid_zero_optim.py
+++ b/internlm/solver/optimizer/hybrid_zero_optim.py
@@ -633,10 +633,10 @@ class HybridZeroOptimizer(BaseOptimizer):
 
         # compute norm for gradients in the last bucket
         total_norms = {}
-        total_param_norms = {}
+        total_param_grad_norms = {}
+        total_layer_grad_norms = {}
         total_param_zero_grad_count = {}
         total_layer_zero_grad_count = {}
-        total_layer_norms = {}
         for group_id in range(self.num_param_groups):
             group_name = self.param_groups[group_id]["name"] if "name" in self.param_groups[group_id] else "default"
             group_name = f"{group_id}_{group_name}"
@@ -653,7 +653,7 @@ class HybridZeroOptimizer(BaseOptimizer):
                     last_stage=True,
                     previous_param_norms=groups_param_norms[group_id],
                 )
-                total_layer_norms[group_name], total_param_norms[group_name] = compute_layer_norm(
+                total_layer_grad_norms[group_name], total_param_grad_norms[group_name] = compute_layer_norm(
                     param_norms=param_norms, loss_scale=self.loss_scale.item()
                 )
             if grad_profiling_config.get("zero_grad_profiling", False):
@@ -674,8 +674,8 @@ class HybridZeroOptimizer(BaseOptimizer):
 
         state, global_norms = self._step(closure=closure, norms=total_norms)
         if grad_profiling_config.get("grad_norm_profiling", False):
-            global_norms["layer_norm"] = total_layer_norms
-            global_norms["param_norm"] = total_param_norms
+            global_norms["layer_grad_norm"] = total_layer_grad_norms
+            global_norms["param_grad_norm"] = total_param_grad_norms
         if grad_profiling_config.get("zero_grad_profiling", False):
             global_norms["layer_zero_grad"] = total_layer_zero_grad_count
             global_norms["param_zero_grad"] = total_param_zero_grad_count
diff --git a/internlm/train/training_internlm.py b/internlm/train/training_internlm.py
index 7010ca5..f79606a 100644
--- a/internlm/train/training_internlm.py
+++ b/internlm/train/training_internlm.py
@@ -533,8 +533,8 @@ def record_current_batch_training_metrics(
         if grad_profiling_config.get("grad_norm_profiling", False) or grad_profiling_config.get(
             "zero_grad_profiling", False
         ):
-            layer_metrics = ["layer_norm", "layer_zero_grad"]
-            param_metrics = ["param_norm", "param_zero_grad"]
+            layer_metrics = ["layer_grad_norm", "layer_zero_grad"]
+            param_metrics = ["param_grad_norm", "param_zero_grad"]
             layer_names = grad_profiling_config.get("layers", [])
             for layer_metric_name in layer_metrics:
                 layer_metric = grad_norm.get(layer_metric_name, {})