fix typing

optimum/gptq/quantizer.py

@@ -77,7 +77,7 @@ def __init__(
         exllama_config: Dict[str, Any] = None,
         max_input_length: Optional[int] = None,
         cache_block_outputs: Optional[bool] = True,
-        inside_layer_modules: Optional[list] = None,
+        inside_layer_modules: Optional[List[List[str]]] = None,
         *args,
         **kwargs,
     ):
@@ -124,7 +124,7 @@ def __init__(
             cache_block_outputs (`bool`, defaults to `True`):
                 Whether to cache block outputs to reuse as inputs for the succeeding block. It allows optimization of non-standard models
                 (e.g. ChatGLM) but can require more time.
-            inside_layer_modules (`List`, *optional*, defaults to `None`):
+            inside_layer_modules (`List[List[str]]`, *optional*, defaults to `None`):
                 List of module names to quantize inside block_name_to_quantize. If not set, we will quantize all the linear layers.
         """
 
@@ -215,7 +215,7 @@ def convert_model(self, model: nn.Module):
         block_name = self.block_name_to_quantize
         layers_to_be_replaced = get_layers(model, prefix=block_name)
         if self.inside_layer_modules is not None:
-            layers_to_keep = sum(self.inside_layer_modules,[])
+            layers_to_keep = sum(self.inside_layer_modules, [])
             for name in list(layers_to_be_replaced.keys()):
                 if not any(name.endswith(layer) for layer in layers_to_keep):
                     logger.info(f"{name} has not been quantized. We don't convert it")
@@ -453,7 +453,7 @@ def store_input_hook(_, input, *args):
             if not has_device_map or get_device(block) == torch.device("cpu"):
                 block = block.to(0)
             layers = get_layers(block)
-            if isinstance(self.inside_layer_modules,list) and len(self.inside_layer_modules)>0:
+            if isinstance(self.inside_layer_modules, list) and len(self.inside_layer_modules) > 0:
                 if self.true_sequential:
                     layers_name_list = [sum(self.inside_layer_modules, [])]
                 else: