diff --git a/qllm/quantization/vptq/quant_vptq.py b/qllm/quantization/vptq/quant_vptq.py
index 2bdd891..220f897 100644
--- a/qllm/quantization/vptq/quant_vptq.py
+++ b/qllm/quantization/vptq/quant_vptq.py
@@ -28,6 +28,7 @@ def set_tokenizer(self, tokenizer):
 
     def get_level_order_linear(self, model, model_prefix, dev):
         level_map = {}
+        prevent_release_tensors = [] #reserve tensors and prevent tensor release, we need id is unique
         class Catcher(torch.nn.Module):
             def __init__(self, module):
                 super().__init__()
@@ -40,12 +41,19 @@ def fake_forward(hidden_state, *args, **kwargs):
                 nonlocal level_map
                 if len(level_map) == 0:
                     hidden_state *= 0
-                key = int(hidden_state[..., 0].item())
+                input_shape = hidden_state.shape
+                key = id(hidden_state) + hidden_state.data_ptr()
+                prevent_release_tensors.append(hidden_state)
+                if hidden_state.numel() == 0:
+                    value = 2025+len(level_map)
+                    input_shape = tuple(i + 1 if i == 0 else i for i in input_shape)
+                else:
+                    value = int(hidden_state[..., 0].item())
                 if key not in level_map:
                     level_map[key] = []
                 level_map[key].append(name)
-                out = torch.ones(hidden_state.shape[:-1]+(out_fetures,), device=hidden_state.device)
-                return out*key + 1
+                out = torch.ones(input_shape[:-1] + (out_fetures,), device=hidden_state.device)
+                return out * value + 1
             fake_forward.layer_name = name
             return fake_forward
 
@@ -67,6 +75,7 @@ def fake_forward_2(hidden_state, *args, **kwargs):
             model(torch.ones([1, 1], dtype=torch.int64).to(dev))
         except ValueError:
             pass
+
         attention_layers[1] = attention_layers[1].module
         for _, l_layer in old_forwards.items():
             l_layer[0].forward = l_layer[1]
@@ -74,12 +83,13 @@ def fake_forward_2(hidden_state, *args, **kwargs):
         
     def collect_hessian_pre(self, model, model_prefix, dev):
         level_linear_names = self.get_level_order_linear(model, model_prefix, "cpu")
+        logger.info("linear arch:" + str(level_linear_names))
+
         for k in list(level_linear_names.keys()):
             for sub_name in level_linear_names[k]:
                 level_linear_names[sub_name] = level_linear_names[k][0]
             level_linear_names.pop(k)
         self.name2hessian = level_linear_names
-        logger.info("linear arch:"+str(level_linear_names))
         if self.quant_config.hessian_path is not None and self.quant_config.inv_hessian_path is not None:
             logger.info("read cached Hessian data")
             _, attention_layers, layer_input_args = self.hijack_block_inputs(model, [(torch.tensor((1, 1), dtype=torch.int64), )], model_prefix, "cpu")