fix tutorial visualize

docs/source/notes/autodelta.md

@@ -34,7 +34,7 @@ Then use the config to add a delta model to the backbone model
 delta_model = AutoDeltaModel.from_config(delta_config, backbone_model=backbone_model)
 
 # now visualize the modified backbone_model
-from opendelta import Visualization
+from bigmodelvis import Visualization
 Visualizaiton(backbone_model).structure_graph()
 ```
 

docs/source/notes/custom.md

@@ -15,7 +15,7 @@ Delta tuning's core change in the structure of the base model is to decorate (mo
 We should first know the name of the feedforward layer in the BART model by visualization. <img src="../imgs/hint-icon-2.jpg" height="30px"> *For more about visualization, see [Visualization](visualization).*
 
 ```python
-from opendelta import Visualization
+from bigmodelvis import Visualization
 Visualization(model).structure_graph()
 ```
 

docs/source/notes/inspect.md

@@ -30,12 +30,12 @@ name: raw_print
 The original presentation of models is **not tailored for repeated structures, big models, or parameters-centric tasks**.
 
 
-## Using visualization from opendelta.
+## Using visualization from bigmodelvis.
 
 First let's visualize all the parameters in the bert model. As we can see, structure inside a bert model, and the all the paramters location of the model are neatly represented in tree structure. (See [color scheme](color_schema) for the colors)
 
 ```python
-from opendelta import Visualization
+from bigmodelvis import Visualization
 model_vis = Visualization(backbone_model)
 model_vis.structure_graph()
 ```

docs/source/notes/namebasedaddr.md

@@ -35,7 +35,7 @@ print(root.name_b[0].name_a)
 We can visualize the model (For details, see [visualization](visualization))
 
 ```python
-from opendelta import Visualization
+from bigmodelvis import Visualization
 Visualization(root).structure_graph()
 ```
 

examples/tutorial/2_with_bmtrain.py

@@ -28,6 +28,7 @@ from model_center.dataset import DistributedDataLoader
 import opendelta as od
 from opendelta import LoraModel, AdapterModel, CompacterModel, LowRankAdapterModel, BitFitModel, ParallelAdapterModel
 from opendelta.utils.inspect import inspect_optimizer_statistics
+from bigmodelvis import Visualization
 print("before modify")
 
 class BertModel(torch.nn.Module):
@@ -56,7 +57,7 @@ def get_model(args):
         "WiC" : 2,
     }
     model = BertModel(args, num_types[args.dataset_name])
-    # od.Visualization(model).structure_graph()
+    Visualization(model).structure_graph()
 
     if args.delta_type == "lora":
         delta_model = LoraModel(backbone_model=model, modified_modules=['project_q', 'project_k'], backend='bmt')