add ipex backend

.github/workflows/tests.yml

@@ -61,7 +61,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install '.[train, onnx, openvino, dev]'
+          python -m pip install '.[train, onnx, openvino, ipex, dev]'
 
       - name: Install model2vec
         run: python -m pip install model2vec

docs/installation.md

@@ -4,6 +4,7 @@ We recommend **Python 3.9+**, **[PyTorch 1.11.0+](https://pytorch.org/get-starte
 * **Default:** This allows for loading, saving, and inference (i.e., getting embeddings) of models.
 * **ONNX:** This allows for loading, saving, inference, optimizing, and quantizing of models using the ONNX backend.
 * **OpenVINO:** This allows for loading, saving, and inference of models using the OpenVINO backend.
+* **IPEX:** This allows for loading, saving, and inference of models using the IPEX backend.
 * **Default and Training**: Like **Default**, plus training.
 * **Development**: All of the above plus some dependencies for developing Sentence Transformers, see [Editable Install](#editable-install).
 
@@ -37,6 +38,12 @@ Note that you can mix and match the various extras, e.g. ``pip install -U "sente
 
         pip install -U "sentence-transformers[openvino]"
 
+.. tab:: IPEX
+
+    ::
+
+        pip install -U "sentence-transformers[ipex]"
+
 .. tab:: Default and Training
 
     ::
@@ -87,6 +94,12 @@ Note that you can mix and match the various extras, e.g. ``pip install -U "sente
 
         pip install -U "sentence-transformers[openvino]"
 
+.. tab:: IPEX
+
+    ::
+
+        pip install -U "sentence-transformers[ipex]"
+
 .. tab:: Default and Training
 
     ::
@@ -139,6 +152,12 @@ You can install ``sentence-transformers`` directly from source to take advantage
 
         pip install -U "sentence-transformers[openvino] @ git+https://github.com/UKPLab/sentence-transformers.git"
 
+.. tab:: IPEX
+
+    ::
+
+        pip install -U "sentence-transformers[ipex] @ git+https://github.com/UKPLab/sentence-transformers.git"
+
 .. tab:: Default and Training
 
     ::

pyproject.toml

@@ -51,6 +51,7 @@ train = ["datasets", "accelerate>=0.20.3"]
 onnx = ["optimum[onnxruntime]>=1.23.1"]
 onnx-gpu = ["optimum[onnxruntime-gpu]>=1.23.1"]
 openvino = ["optimum-intel[openvino]>=1.20.0"]
+ipex = ["optimum-intel[ipex]>=1.21.0"]
 dev = ["datasets", "accelerate>=0.20.3", "pre-commit", "pytest", "pytest-cov", "peft"]
 
 [build-system]

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

@@ -49,7 +49,7 @@ 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
@@ -187,8 +187,12 @@ def _load_model(
             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: str, config: PeftConfig, cache_dir: str, **model_args) -> None:
         from peft import PeftModel
@@ -254,6 +258,24 @@ def _load_openvino_model(
         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: str, config: PretrainedConfig, cache_dir: str, **model_args
     ) -> None:

tests/test_backends.py

@@ -15,16 +15,18 @@
 except ImportError:
     pytest.skip("OpenVINO and ONNX backends are not available", allow_module_level=True)
 
+try:
+    from optimum.intel import IPEXModel
+except ImportError:
+    pytest.skip("IPEX backend is not available", allow_module_level=True)
+
 from sentence_transformers import SentenceTransformer
 
 
 ## Testing exporting:
 @pytest.mark.parametrize(
     ["backend", "expected_auto_model_class"],
-    [
-        ("onnx", ORTModelForFeatureExtraction),
-        ("openvino", OVModelForFeatureExtraction),
-    ],
+    [("onnx", ORTModelForFeatureExtraction), ("openvino", OVModelForFeatureExtraction), ("ipex", IPEXModel)],
 )
 @pytest.mark.parametrize(
     "model_kwargs", [{}, {"file_name": "wrong_file_name"}]