add ipex backend

Signed-off-by: jiqing-feng <[email protected]>

sentence_transformers/SentenceTransformer.py

@@ -128,7 +128,7 @@ class SentenceTransformer(nn.Sequential, FitMixin, PeftAdapterMixin):
         model_card_data (:class:`~sentence_transformers.model_card.SentenceTransformerModelCardData`, optional): A model
             card data object that contains information about the model. This is used to generate a model card when saving
             the model. If not set, a default model card data object is created.
-        backend (str): The backend to use for inference. Can be one of "torch" (default), "onnx", or "openvino".
+        backend (str): The backend to use for inference. Can be one of "torch" (default), "onnx", "openvino", or "ipex".
             See https://sbert.net/docs/sentence_transformer/usage/efficiency.html for benchmarking information
             on the different backends.
 
@@ -177,7 +177,7 @@ def __init__(
         tokenizer_kwargs: dict[str, Any] | None = None,
         config_kwargs: dict[str, Any] | None = None,
         model_card_data: SentenceTransformerModelCardData | None = None,
-        backend: Literal["torch", "onnx", "openvino"] = "torch",
+        backend: Literal["torch", "onnx", "openvino", "ipex"] = "torch",
     ) -> None:
         # Note: self._load_sbert_model can also update `self.prompts` and `self.default_prompt_name`
         self.prompts = prompts or {}
@@ -382,8 +382,8 @@ def __init__(
         # Pass the model to the model card data for later use in generating a model card upon saving this model
         self.model_card_data.register_model(self)
 
-    def get_backend(self) -> Literal["torch", "onnx", "openvino"]:
-        """Return the backend used for inference, which can be one of "torch", "onnx", or "openvino".
+    def get_backend(self) -> Literal["torch", "onnx", "openvino", "ipex"]:
+        """Return the backend used for inference, which can be one of "torch", "onnx", "openvino" or "ipex".
 
         Returns:
             str: The backend used for inference.

sentence_transformers/models/Transformer.py

@@ -46,7 +46,7 @@ class Transformer(nn.Module):
         tokenizer_name_or_path: Name or path of the tokenizer. When
             None, then model_name_or_path is used
         backend: Backend used for model inference. Can be `torch`, `onnx`,
-            or `openvino`. Default is `torch`.
+            `openvino`, or `ipex`. Default is `torch`.
     """
 
     save_in_root: bool = True
@@ -143,8 +143,10 @@ def _load_model(self, model_name_or_path, config, cache_dir, backend, **model_ar
             self._load_onnx_model(model_name_or_path, config, cache_dir, **model_args)
         elif backend == "openvino":
             self._load_openvino_model(model_name_or_path, config, cache_dir, **model_args)
+        elif backend == "ipex":
+            self._load_ipex_model(model_name_or_path, config, cache_dir, **model_args)
         else:
-            raise ValueError(f"Unsupported backend '{backend}'. `backend` should be `torch`, `onnx`, or `openvino`.")
+            raise ValueError(f"Unsupported backend '{backend}'. `backend` should be `torch`, `onnx`, `openvino`, or `ipex`.")
 
     def _load_peft_model(self, model_name_or_path, config, cache_dir, **model_args) -> None:
         if is_peft_available():
@@ -210,6 +212,24 @@ def _load_openvino_model(self, model_name_or_path, config, cache_dir, **model_ar
         if export:
             self._backend_warn_to_save(model_name_or_path, is_local, backend_name)
 
+    def _load_ipex_model(self, model_name_or_path, config, cache_dir, **model_args) -> None:
+        try:
+            from optimum.intel import IPEXModel
+        except ModuleNotFoundError:
+            raise Exception(
+                "Using the IPEX backend requires installing Optimum and IPEX. "
+                "You can install them with pip: `pip install optimum-intel[ipex]`."
+            )
+
+        self.auto_model: IPEXModel = IPEXModel.from_pretrained(
+            model_name_or_path,
+            config=config,
+            cache_dir=cache_dir,
+            **model_args,
+        )
+        # Wrap the save_pretrained method to save the model in the correct subfolder
+        self.auto_model._save_pretrained = _save_pretrained_wrapper(self.auto_model._save_pretrained, self.backend)
+
     def _load_onnx_model(self, model_name_or_path, config, cache_dir, **model_args) -> None:
         try:
             import onnxruntime as ort