rename weights_variant to variant

Author: eaidova

optimum/commands/export/openvino.py

@@ -106,7 +106,7 @@ def parse_args_openvino(parser: "ArgumentParser"):
         ),
     )
     optional_group.add_argument(
-        "--weights-variant",
+        "--variant",
         type=str,
         default=None,
         help=("If specified load weights from variant filename."),
@@ -452,7 +452,7 @@ def run(self):
                 quantization_config=quantization_config,
                 stateful=not self.args.disable_stateful,
                 trust_remote_code=self.args.trust_remote_code,
-                variant=self.args.weights_variant,
+                variant=self.args.variant,
                 cache_dir=self.args.cache_dir,
             )
             model.save_pretrained(self.args.output)
@@ -475,6 +475,6 @@ def run(self):
                 stateful=not self.args.disable_stateful,
                 convert_tokenizer=not self.args.disable_convert_tokenizer,
                 library_name=library_name,
-                weights_variant=self.args.weights_variant,
+                variant=self.args.variant,
                 # **input_shapes,
             )

optimum/exporters/openvino/__main__.py

@@ -122,7 +122,7 @@ def main_export(
     convert_tokenizer: bool = False,
     library_name: Optional[str] = None,
     model_loading_kwargs: Optional[Dict[str, Any]] = None,
-    weights_variant: Optional[str] = None,
+    variant: Optional[str] = None,
     **kwargs_shapes,
 ):
     """
@@ -238,8 +238,8 @@ def main_export(
     custom_architecture = False
     patch_16bit = False
     loading_kwargs = model_loading_kwargs or {}
-    if weights_variant is not None:
-        loading_kwargs["variant"] = weights_variant
+    if variant is not None:
+        loading_kwargs["variant"] = variant
     if library_name == "transformers":
         config = AutoConfig.from_pretrained(
             model_name_or_path,
@@ -350,7 +350,7 @@ class StoreAttr(object):
 
                 GPTQQuantizer.post_init_model = post_init_model
     elif library_name == "diffusers" and is_openvino_version(">=", "2024.6"):
-        _loading_kwargs = {} if weights_variant is None else {"variant": weights_variant}
+        _loading_kwargs = {} if variant is None else {"variant": variant}
         dtype = deduce_diffusers_dtype(
             model_name_or_path,
             revision=revision,

optimum/intel/openvino/modeling_base.py

@@ -609,7 +609,7 @@ def _from_transformers(
             trust_remote_code=trust_remote_code,
             ov_config=ov_config,
             library_name=cls._library_name,
-            weights_variant=variant,
+            variant=variant,
         )
 
         return cls._from_pretrained(

optimum/intel/openvino/modeling_base_seq2seq.py

@@ -423,7 +423,7 @@ def _from_transformers(
             trust_remote_code=trust_remote_code,
             ov_config=ov_config,
             stateful=stateful,
-            model_variant=variant,
+            variant=variant,
         )
 
         return cls._from_pretrained(

optimum/intel/openvino/modeling_diffusion.py

@@ -590,7 +590,7 @@ def _from_transformers(
             force_download=force_download,
             ov_config=ov_config,
             library_name=cls._library_name,
-            weights_variant=variant,
+            variant=variant,
         )
 
         return cls._from_pretrained(

optimum/intel/openvino/modeling_visual_language.py

@@ -630,7 +630,7 @@ def _from_transformers(
             trust_remote_code=trust_remote_code,
             ov_config=ov_config,
             stateful=stateful,
-            weights_variant=variant,
+            variant=variant,
         )
         config = AutoConfig.from_pretrained(save_dir_path, trust_remote_code=trust_remote_code)
         return cls._from_pretrained(