Fix test bugs (#5484)

Author: tastelikefeet

swift/llm/argument/base_args/base_args.py

@@ -103,7 +103,8 @@ def _prepare_training_args(self, training_args: Dict[str, Any]) -> None:
 
     def _init_lazy_tokenize(self):
         if self.lazy_tokenize is None:
-            if self.model_meta.is_multimodal and not self.streaming and not self.packing:
+            if (self.model_meta is not None and self.model_meta.is_multimodal and not self.streaming
+                    and not self.packing):
                 self.lazy_tokenize = True
             else:
                 self.lazy_tokenize = False

swift/llm/argument/base_args/template_args.py

@@ -47,7 +47,7 @@ class TemplateArguments:
     template_backend: Literal['swift', 'jinja'] = 'swift'
 
     def __post_init__(self):
-        if self.template is None and hasattr(self, 'model_meta'):
+        if self.template is None and getattr(self, 'model_meta', None):
             self.template = self.model_meta.template
         if self.use_chat_template is None:
             self.use_chat_template = True

swift/llm/argument/sampling_args.py

@@ -56,6 +56,9 @@ class SamplingArguments(BaseArguments):
     def _init_model_info(self):
         if self.sampler_engine != 'client':
             return super()._init_model_info()
+        else:
+            self.model_info = None
+            self.model_meta = None
         self.task_type = 'causal_lm'
         return
 

swift/llm/train/tuner.py

@@ -243,7 +243,7 @@ def prepare_adapter(args: TrainArguments, model, *, template=None, train_dataset
         logger.info(f'adalora_config: {adalora_config}')
     elif args.train_type == 'llamapro':
         llamapro_config = LLaMAProConfig(
-            model_type=model.model_meta.model_arch,
+            model_type=model.model_meta.model_arch.arch_name,
             num_new_blocks=args.llamapro_num_new_blocks,
             num_groups=args.llamapro_num_groups)
         model = Swift.prepare_model(model, llamapro_config)