fix

optimum_benchmark/backends/base.py

@@ -44,6 +44,8 @@ class Backend(Generic[BackendConfigT], ABC):
     pretrained_processor: Optional[PretrainedProcessor]
 
     def __init__(self, config: BackendConfigT):
+        assert config.name == self.NAME, f"Backend name {self.NAME} doesn't match config name {config.name}"
+
         self.config = config
 
         self.logger = getLogger(self.NAME)
@@ -70,14 +72,13 @@ def __init__(self, config: BackendConfigT):
 
         elif self.config.library == "llama_cpp":
             self.logger.info("\t+ Benchmarking a LlamaCpp model")
-            # TOD: need a custom method to extract shapes from gguf
+            self.automodel_loader = None
+            self.pretrained_config = None
+            self.generation_config = None
+            self.pretrained_processor = None
             self.model_shapes = extract_transformers_shapes_from_artifacts(
                 self.pretrained_config, self.pretrained_processor
             )
-            self.pretrained_processor = None
-            self.generation_config = None
-            self.pretrained_config = None
-            self.automodel_loader = None
 
         else:
             self.logger.info("\t+ Benchmarking a Transformers model")
@@ -109,18 +110,11 @@ def create_no_weights_model(self) -> None:
         self.logger.info("\t+ Saving no weights model's config")
         self.pretrained_config.save_pretrained(save_directory=self.no_weights_model)
 
-    def prepare_input_shapes(self, input_shapes: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        This method is used to prepare and register the input shapes before using them by the model.
-        It can be used to pad the inputs to the correct shape, or compile it to the correct format.
-        """
-        return input_shapes
+    @property
+    def split_between_processes(self) -> bool:
+        return False
 
     def prepare_inputs(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        This method is used to prepare and register the inputs before passing them to the model.
-        It can be used to move the inputs to the correct device, or rename their keys.
-        """
         return inputs
 
     def load(self) -> None: