diff --git a/examples/transformers/prune_hf_bert.py b/examples/transformers/prune_hf_bert.py
index 70915279..1f5f0a9f 100644
--- a/examples/transformers/prune_hf_bert.py
+++ b/examples/transformers/prune_hf_bert.py
@@ -26,7 +26,7 @@
 pruner = tp.pruner.MetaPruner(
     model, 
     example_inputs, 
-    global_pruning=True, # If False, a uniform pruning ratio will be assigned to different layers.
+    global_pruning=False, # If False, a uniform pruning ratio will be assigned to different layers.
     importance=imp, # importance criterion for parameter selection
     iterative_steps=1, # the number of iterations to achieve target pruning ratio
     pruning_ratio=0.5,
diff --git a/torch_pruning/pruner/algorithms/metapruner.py b/torch_pruning/pruner/algorithms/metapruner.py
index ef8143e3..41a2e1e1 100644
--- a/torch_pruning/pruner/algorithms/metapruner.py
+++ b/torch_pruning/pruner/algorithms/metapruner.py
@@ -507,12 +507,14 @@ def prune_global(self) -> typing.Generator:
                                 sub_pruning_idxs = sub_imp_argsort[:n_pruned_per_group]+chg*group_size
                                 pruning_indices.append(sub_pruning_idxs)
                 else:
-                    pruning_indices = (imp <= thres).nonzero().view(-1)
+                    _pruning_indices = (imp <= thres).nonzero().view(-1)
                     if len(pruning_indices)>0 and self.round_to: 
-                        n_pruned = len(pruning_indices)
+                        n_pruned = len(_pruning_indices)
                         current_channels = get_channel_fn(module)
                         n_pruned = self._round_to(n_pruned, current_channels, self.round_to)
-                        pruning_indices = pruning_indices[:n_pruned]
+                        _pruning_indices = _pruning_indices[:n_pruned]
+                    pruning_indices.append(_pruning_indices)
+                        
             # Prune heads
             if len(global_head_importance)>0:
                 if group in global_head_importance and n_heads_removed>0:
@@ -523,11 +525,12 @@ def prune_global(self) -> typing.Generator:
                             pruning_indices.append( torch.arange(head_id*group_size, (head_id+1)*group_size, device=head_imp.device) )
                     for qkv_layer in qkv_layers:
                         self.num_heads[qkv_layer] -= len(head_pruning_indices) # update num heads after pruning
-                    pruning_indices = torch.cat(pruning_indices, 0)
-                    pruning_indices = torch.unique(pruning_indices)    
             
+            if len(pruning_indices)>0:
+                pruning_indices = torch.unique(torch.cat(pruning_indices, 0)).tolist()
+
             # create pruning group
             group = self.DG.get_pruning_group(
-                module, pruning_fn, pruning_indices.tolist())
+                module, pruning_fn, pruning_indices)
             if self.DG.check_pruning_group(group):
                 yield group