reset to resize

app.py

@@ -283,6 +283,7 @@ def buildmodel(**kwargs):
     #     resume,
     #     config=model_args
     # ).to(device)
+    
     model = CrelloModel.from_pretrained(
         "WYBar/LLM_For_Layout_Planning",
         subfolder="checkpoint-26000",      # 加载检查点目录
@@ -303,8 +304,8 @@ def buildmodel(**kwargs):
 def construction_layout():
     params_dict = {  
         # 需要修改
-        "input_model": "WYBar/LLM_For_Layout_Planning", 
-        "resume": "WYBar/LLM_For_Layout_Planning",
+        "input_model": "/openseg_blob/v-sirui/temporary/2024-02-21/Layout_train/COLEv2/Design_LLM/checkpoint/Meta-Llama-3-8B", 
+        "resume": "/openseg_blob/v-sirui/temporary/2024-02-21/SVD/Int2lay_1016/checkpoint/int2lay_1031/1031_test/checkpoint-26000/",
         
         "seed": 0,  
         "mask_values": False,  
@@ -320,10 +321,10 @@ def construction_layout():
     # Init model
     model, quantizer, tokenizer = buildmodel(**params_dict)
     
-    print('resize token embeddings to match the tokenizer', 129423)
-    model.lm.resize_token_embeddings(129423)
-    model.input_embeddings = model.lm.get_input_embeddings()
-    print('after token embeddings to match the tokenizer', 129423)
+    # print('resize token embeddings to match the tokenizer', 129423)
+    # model.lm.resize_token_embeddings(129423)
+    # model.input_embeddings = model.lm.get_input_embeddings()
+    # print('after token embeddings to match the tokenizer', 129423)
     
     print("before .to(device)")
     model = model.to(device)

app_test.py

@@ -283,7 +283,7 @@ def buildmodel(**kwargs):
     #     resume,
     #     config=model_args
     # ).to(device)
-
+    
     model = CrelloModel.from_pretrained(
         "WYBar/LLM_For_Layout_Planning",
         subfolder="checkpoint-26000",      # 加载检查点目录
@@ -321,10 +321,10 @@ def construction_layout():
     # Init model
     model, quantizer, tokenizer = buildmodel(**params_dict)
     
-    print('resize token embeddings to match the tokenizer', 129423)
-    model.lm.resize_token_embeddings(129423)
-    model.input_embeddings = model.lm.get_input_embeddings()
-    print('after token embeddings to match the tokenizer', 129423)
+    # print('resize token embeddings to match the tokenizer', 129423)
+    # model.lm.resize_token_embeddings(129423)
+    # model.input_embeddings = model.lm.get_input_embeddings()
+    # print('after token embeddings to match the tokenizer', 129423)
     
     print("before .to(device)")
     model = model.to(device)

modeling_crello.py

@@ -181,9 +181,10 @@ class CrelloModel(PreTrainedModel):
       self.lm.train()
       self.lm.config.gradient_checkpointing = True
 
-    # print('resize token embeddings to match the tokenizer', config.vocab_size)
-    # self.lm.resize_token_embeddings(config.vocab_size)
-    # self.input_embeddings = self.lm.get_input_embeddings()
+    print('resize token embeddings to match the tokenizer', config.vocab_size)
+    self.lm.resize_token_embeddings(config.vocab_size)
+    self.input_embeddings = self.lm.get_input_embeddings()
+    print('after token embeddings to match the tokenizer', config.vocab_size)
     
   def train(self, mode=True):
     super().train(mode=mode)