fix mm tokentypeids splitting

src/twinkle/model/megatron/megatron.py

@@ -1186,13 +1186,10 @@ def _save_hf_format(self, output_dir: str, adapter_name: str, lora_converter=Non
             self.hf_config.save_pretrained(output_dir)
             if isinstance(model[0], PeftModel):
                 config = model[0].peft_config[adapter_name]
-                target_modules = None
-                if getattr(config, 'origin_target_modules', None) == 'all-linear':
-                    target_modules = config.target_modules
-                    config.target_modules = 'all-linear'
+                target_modules = config.target_modules
+                config.target_modules = 'all-linear'
                 model[0].peft_config[adapter_name].save_pretrained(output_dir)
-                if getattr(config, 'origin_target_modules', None) == 'all-linear':
-                    config.target_modules = target_modules
+                config.target_modules = target_modules
 
     def _save_megatron_format(self, output_dir: str, adapter_name: str, lora_converter=None):
         """Save in Megatron checkpoint format."""
@@ -1296,9 +1293,6 @@ def _patch_adapter(self, adapter_name: str, config_or_dir: Union[PeftConfig, str
                     config_or_dir = LoraConfig(**config_or_dir)
                 config = config_or_dir
 
-                if config.target_modules == 'all-linear':
-                    config.origin_target_modules = 'all-linear'
-
                 # Expand target_modules (e.g., 'all-linear' -> actual module names)
                 if config.target_modules:
                     if isinstance(config.target_modules, str):
@@ -1564,7 +1558,11 @@ def get_peft_config_dict(self, adapter_name: str = None) -> Optional[Dict[str, A
         config = optimizer_config.adapter_config
         if isinstance(config, dict):
             config = config.get(adapter_name, next(iter(config.values())))
-        return config.to_dict() if hasattr(config, 'to_dict') else dict(config)
+        target_modules = config.target_modules
+        config.target_modules = 'all-linear'
+        _peft_config = config.to_dict() if hasattr(config, 'to_dict') else dict(config)
+        _peft_config['target_modules'] = target_modules
+        return _peft_config
 
     @staticmethod
     def get_target_modules(model: 'torch.nn.Module', target_modules: List[str]) -> List[str]:

src/twinkle/model/multi_lora.py

@@ -419,8 +419,6 @@ def _patch_peft(_module):
             def _patch_megatron(_module):
                 # Expand target_modules (e.g., 'all-linear' -> actual module names)
                 _config = deepcopy(config)
-                if _config.target_modules == 'all-linear':
-                    _config.origin_target_modules = 'all-linear'
                 if isinstance(_module, PeftModel):
                     _module.add_adapter(lora_tenant.adapter_name, _config)
                 else:

src/twinkle/processor/base.py

@@ -101,6 +101,8 @@ def to_tensor(_input):
                 elif (isinstance(value, list) and isinstance(value[0],
                                                              (int, float, np.number))) or key == 'position_ids':
                     value = torch.tensor(value)
+                elif (isinstance(value, list)) and key in ('completion_mask', 'mm_token_type_ids'):
+                    value = torch.tensor(value)
                 elif key in self.VLM_CONCAT_FIELDS:
                     if not isinstance(value[0], torch.Tensor):
                         value = [torch.tensor(v) for v in value]