diff --git a/icefall/checkpoint.py b/icefall/checkpoint.py
index 4e02dd382..5069b78e8 100644
--- a/icefall/checkpoint.py
+++ b/icefall/checkpoint.py
@@ -466,8 +466,10 @@ def average_state_dict(
 
     uniqued_names = list(uniqued.values())
     for k in uniqued_names:
-        state_dict_1[k] *= weight_1
-        state_dict_1[k] += (
-            state_dict_2[k].to(device=state_dict_1[k].device) * weight_2
-        )
-        state_dict_1[k] *= scaling_factor
+        v = state_dict_1[k]
+        if torch.is_floating_point(v):
+            v *= weight_1
+            v += (
+                state_dict_2[k].to(device=state_dict_1[k].device) * weight_2
+            )
+            v *= scaling_factor
diff --git a/icefall/diagnostics.py b/icefall/diagnostics.py
index 071d27112..9d2ea46c9 100644
--- a/icefall/diagnostics.py
+++ b/icefall/diagnostics.py
@@ -137,6 +137,8 @@ class TensorDiagnostic(object):
             x = x[0]
         if not isinstance(x, Tensor):
             return
+        if x.numel() == 0:  # for empty tensor
+            return
         x = x.detach().clone()
         if x.ndim == 0:
             x = x.unsqueeze(0)
@@ -185,6 +187,9 @@ class TensorDiagnostic(object):
 
     def print_diagnostics(self):
         """Print diagnostics for each dimension of the tensor."""
+        if self.stats is None:
+            print(f"Warning: the stats of {self.name} is None.")
+            return
         for dim, this_dim_stats in enumerate(self.stats):
             for stats_type, stats_list in this_dim_stats.items():
                 # stats_type could be "rms", "value", "abs", "eigs", "positive".