init is_quantization of load_column_parallel_weight (BasevLLMParameter)

vllm/model_executor/parameter.py

@@ -91,10 +91,10 @@ class BasevLLMParameter(Parameter):
                 or self._is_1d_and_scalar(loaded_weight))
         self.data.copy_(loaded_weight)
 
-    def load_column_parallel_weight(self, loaded_weight: torch.Tensor):
+    def load_column_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = False):
         self._assert_and_load(loaded_weight)
 
-    def load_row_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = None):
+    def load_row_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = False):
         self._assert_and_load(loaded_weight)
 
     def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
@@ -140,7 +140,7 @@ class _ColumnvLLMParameter(BasevLLMParameter):
     def output_dim(self):
         return self._output_dim
 
-    def load_column_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization:Optional[bool]):
+    def load_column_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = False):
         if not envs.VLLM_USE_NN or len( self.data.shape)==1 or is_quantization:
             shard_size = self.data.shape[self.output_dim]
         else:
@@ -238,7 +238,7 @@ class RowvLLMParameter(BasevLLMParameter):
     def input_dim(self):
         return self._input_dim
 
-    def load_row_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = None):
+    def load_row_parallel_weight(self, loaded_weight: torch.Tensor, is_quantization: Optional[bool] = False):
         if not envs.VLLM_USE_NN or is_quantization:
             shard_size = self.data.shape[self.input_dim]
         else: