update ipex

optimum_benchmark/backends/ipex/backend.py

@@ -45,41 +45,29 @@ def load(self) -> None:
 
         self.tmpdir.cleanup()
 
-    def _load_automodel_from_pretrained(self) -> None:
-        self.pretrained_model = self.automodel_loader.from_pretrained(self.config.model, **self.config.model_kwargs)
-
-    def _load_automodel_with_no_weights(self) -> None:
-        original_model, self.config.model = self.config.model, self.no_weights_model
-
-        with fast_weights_init():
-            self._load_automodel_from_pretrained()
-
-        self.logger.info("\t+ Tying model weights")
-        self.pretrained_model.tie_weights()
-
-        self.config.model = original_model
-
     def _load_ipexmodel_from_pretrained(self) -> None:
         self.pretrained_model = self.ipexmodel_class.from_pretrained(
             self.config.model,
-            export=self.config.export,
             **self.config.model_kwargs,
-            **self.automodel_kwargs,
+            **self.ipexmodel_kwargs,
         )
 
     def _load_ipexmodel_with_no_weights(self) -> None:
         with fast_weights_init():
+            self.logger.info("\t+ Loading no weights IPEXModel")
             original_model, self.config.model = self.config.model, self.no_weights_model
             original_export, self.config.export = self.config.export, True
-            self.logger.info("\t+ Loading no weights IPEXModel")
             self._load_ipexmodel_from_pretrained()
             self.config.export = original_export
             self.config.model = original_model
 
     @property
-    def automodel_kwargs(self) -> Dict[str, Any]:
+    def ipexmodel_kwargs(self) -> Dict[str, Any]:
         kwargs = {}
 
+        if self.config.export:
+            kwargs["export"] = self.config.export
+
         if self.config.torch_dtype is not None:
             kwargs["torch_dtype"] = getattr(torch, self.config.torch_dtype)
 
@@ -89,7 +77,7 @@ def automodel_kwargs(self) -> Dict[str, Any]:
     def split_between_processes(self) -> bool:
         return is_torch_distributed_available() and torch.distributed.is_initialized()
 
-    def prepare_inputs(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+    def prepare_inputs_before_load(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         if self.split_between_processes:
             with Accelerator().split_between_processes(inputs=inputs, apply_padding=False) as process_inputs:
                 inputs = process_inputs

optimum_benchmark/backends/ipex/config.py

@@ -13,17 +13,17 @@ class IPEXConfig(BackendConfig):
     version: Optional[str] = ipex_version()
     _target_: str = "optimum_benchmark.backends.ipex.backend.IPEXBackend"
 
-    # load options
     no_weights: bool = False
-    torch_dtype: Optional[str] = None
 
-    # export options
-    export: bool = True
+    # ipexmodel kwargs
+    export: Optional[bool] = None
+    torch_dtype: Optional[str] = None
 
     def __post_init__(self):
         super().__post_init__()
 
         self.device = self.device.lower()
+
         if self.device not in ["cpu", "gpu"]:
             raise ValueError(f"IPEXBackend only supports CPU devices, got {self.device}")