feat: Add ONNX & OpenVINO backend support, and torch dtype kwargs in Sentence Transformers Components

haystack/components/embedders/backends/sentence_transformers_backend.py

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Literal, Optional
 
 from haystack.lazy_imports import LazyImport
 from haystack.utils.auth import Secret
@@ -28,8 +28,9 @@ def get_embedding_backend(  # pylint: disable=too-many-positional-arguments
         model_kwargs: Optional[Dict[str, Any]] = None,
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
         config_kwargs: Optional[Dict[str, Any]] = None,
+        backend: Literal["torch", "onnx", "openvino"] = "torch",
     ):
-        embedding_backend_id = f"{model}{device}{auth_token}{truncate_dim}"
+        embedding_backend_id = f"{model}{device}{auth_token}{truncate_dim}{backend}"
 
         if embedding_backend_id in _SentenceTransformersEmbeddingBackendFactory._instances:
             return _SentenceTransformersEmbeddingBackendFactory._instances[embedding_backend_id]
@@ -42,6 +43,7 @@ def get_embedding_backend(  # pylint: disable=too-many-positional-arguments
             model_kwargs=model_kwargs,
             tokenizer_kwargs=tokenizer_kwargs,
             config_kwargs=config_kwargs,
+            backend=backend,
         )
         _SentenceTransformersEmbeddingBackendFactory._instances[embedding_backend_id] = embedding_backend
         return embedding_backend
@@ -62,8 +64,10 @@ def __init__(  # pylint: disable=too-many-positional-arguments
         model_kwargs: Optional[Dict[str, Any]] = None,
         tokenizer_kwargs: Optional[Dict[str, Any]] = None,
         config_kwargs: Optional[Dict[str, Any]] = None,
+        backend: Literal["torch", "onnx", "openvino"] = "torch",
     ):
         sentence_transformers_import.check()
+
         self.model = SentenceTransformer(
             model_name_or_path=model,
             device=device,
@@ -73,6 +77,7 @@ def __init__(  # pylint: disable=too-many-positional-arguments
             model_kwargs=model_kwargs,
             tokenizer_kwargs=tokenizer_kwargs,
             config_kwargs=config_kwargs,
+            backend=backend,
         )
 
     def embed(self, data: List[str], **kwargs) -> List[List[float]]:

haystack/components/embedders/sentence_transformers_document_embedder.py

@@ -57,6 +57,7 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         config_kwargs: Optional[Dict[str, Any]] = None,
         precision: Literal["float32", "int8", "uint8", "binary", "ubinary"] = "float32",
         encode_kwargs: Optional[Dict[str, Any]] = None,
+        backend: Literal["torch", "onnx", "openvino"] = "torch",
     ):
         """
         Creates a SentenceTransformersDocumentEmbedder component.
@@ -109,6 +110,10 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
             Additional keyword arguments for `SentenceTransformer.encode` when embedding documents.
             This parameter is provided for fine customization. Be careful not to clash with already set parameters and
             avoid passing parameters that change the output type.
+        :param backend:
+            The backend to use for the Sentence Transformers model. Choose from "torch", "onnx", or "openvino".
+            Refer to the [Sentence Transformers documentation](https://sbert.net/docs/sentence_transformer/usage/efficiency.html)
+            for more information on acceleration and quantization options.
         """
 
         self.model = model
@@ -129,6 +134,7 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         self.encode_kwargs = encode_kwargs
         self.embedding_backend = None
         self.precision = precision
+        self.backend = backend
 
     def _get_telemetry_data(self) -> Dict[str, Any]:
         """
@@ -162,6 +168,7 @@ def to_dict(self) -> Dict[str, Any]:
             config_kwargs=self.config_kwargs,
             precision=self.precision,
             encode_kwargs=self.encode_kwargs,
+            backend=self.backend,
         )
         if serialization_dict["init_parameters"].get("model_kwargs") is not None:
             serialize_hf_model_kwargs(serialization_dict["init_parameters"]["model_kwargs"])
@@ -199,6 +206,7 @@ def warm_up(self):
                 model_kwargs=self.model_kwargs,
                 tokenizer_kwargs=self.tokenizer_kwargs,
                 config_kwargs=self.config_kwargs,
+                backend=self.backend,
             )
             if self.tokenizer_kwargs and self.tokenizer_kwargs.get("model_max_length"):
                 self.embedding_backend.model.max_seq_length = self.tokenizer_kwargs["model_max_length"]

haystack/components/embedders/sentence_transformers_text_embedder.py

@@ -51,6 +51,7 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         config_kwargs: Optional[Dict[str, Any]] = None,
         precision: Literal["float32", "int8", "uint8", "binary", "ubinary"] = "float32",
         encode_kwargs: Optional[Dict[str, Any]] = None,
+        backend: Literal["torch", "onnx", "openvino"] = "torch",
     ):
         """
         Create a SentenceTransformersTextEmbedder component.
@@ -99,6 +100,10 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
             Additional keyword arguments for `SentenceTransformer.encode` when embedding texts.
             This parameter is provided for fine customization. Be careful not to clash with already set parameters and
             avoid passing parameters that change the output type.
+        :param backend:
+            The backend to use for the Sentence Transformers model. Choose from "torch", "onnx", or "openvino".
+            Refer to the [Sentence Transformers documentation](https://sbert.net/docs/sentence_transformer/usage/efficiency.html)
+            for more information on acceleration and quantization options.
         """
 
         self.model = model
@@ -117,6 +122,7 @@ def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         self.encode_kwargs = encode_kwargs
         self.embedding_backend = None
         self.precision = precision
+        self.backend = backend
 
     def _get_telemetry_data(self) -> Dict[str, Any]:
         """
@@ -148,6 +154,7 @@ def to_dict(self) -> Dict[str, Any]:
             config_kwargs=self.config_kwargs,
             precision=self.precision,
             encode_kwargs=self.encode_kwargs,
+            backend=self.backend,
         )
         if serialization_dict["init_parameters"].get("model_kwargs") is not None:
             serialize_hf_model_kwargs(serialization_dict["init_parameters"]["model_kwargs"])
@@ -185,6 +192,7 @@ def warm_up(self):
                 model_kwargs=self.model_kwargs,
                 tokenizer_kwargs=self.tokenizer_kwargs,
                 config_kwargs=self.config_kwargs,
+                backend=self.backend,
             )
             if self.tokenizer_kwargs and self.tokenizer_kwargs.get("model_max_length"):
                 self.embedding_backend.model.max_seq_length = self.tokenizer_kwargs["model_max_length"]

haystack/components/rankers/sentence_transformers_diversity.py

@@ -3,11 +3,12 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from enum import Enum
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
 from haystack import Document, component, default_from_dict, default_to_dict, logging
 from haystack.lazy_imports import LazyImport
 from haystack.utils import ComponentDevice, Secret, deserialize_secrets_inplace
+from haystack.utils.hf import deserialize_hf_model_kwargs, serialize_hf_model_kwargs
 
 logger = logging.getLogger(__name__)
 
@@ -111,7 +112,7 @@ class SentenceTransformersDiversityRanker:
     ```
     """  # noqa: E501
 
-    def __init__(
+    def __init__(  # noqa: PLR0913 # pylint: disable=too-many-positional-arguments
         self,
         model: str = "sentence-transformers/all-MiniLM-L6-v2",
         top_k: int = 10,
@@ -126,7 +127,11 @@ def __init__(
         embedding_separator: str = "\n",
         strategy: Union[str, DiversityRankingStrategy] = "greedy_diversity_order",
         lambda_threshold: float = 0.5,
-    ):  # pylint: disable=too-many-positional-arguments
+        model_kwargs: Optional[Dict[str, Any]] = None,
+        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        config_kwargs: Optional[Dict[str, Any]] = None,
+        backend: Literal["torch", "onnx", "openvino"] = "torch",
+    ):
         """
         Initialize a SentenceTransformersDiversityRanker.
 
@@ -152,6 +157,18 @@ def __init__(
                          "maximum_margin_relevance".
         :param lambda_threshold: The trade-off parameter between relevance and diversity. Only used when strategy is
                                  "maximum_margin_relevance".
+        :param model_kwargs:
+            Additional keyword arguments for `AutoModelForSequenceClassification.from_pretrained`
+            when loading the model. Refer to specific model documentation for available kwargs.
+        :param tokenizer_kwargs:
+            Additional keyword arguments for `AutoTokenizer.from_pretrained` when loading the tokenizer.
+            Refer to specific model documentation for available kwargs.
+        :param config_kwargs:
+            Additional keyword arguments for `AutoConfig.from_pretrained` when loading the model configuration.
+        :param backend:
+            The backend to use for the Sentence Transformers model. Choose from "torch", "onnx", or "openvino".
+            Refer to the [Sentence Transformers documentation](https://sbert.net/docs/sentence_transformer/usage/efficiency.html)
+            for more information on acceleration and quantization options.
         """
         torch_and_sentence_transformers_import.check()
 
@@ -172,6 +189,10 @@ def __init__(
         self.strategy = DiversityRankingStrategy.from_str(strategy) if isinstance(strategy, str) else strategy
         self.lambda_threshold = lambda_threshold or 0.5
         self._check_lambda_threshold(self.lambda_threshold, self.strategy)
+        self.model_kwargs = model_kwargs
+        self.tokenizer_kwargs = tokenizer_kwargs
+        self.config_kwargs = config_kwargs
+        self.backend = backend
 
     def warm_up(self):
         """
@@ -182,6 +203,10 @@ def warm_up(self):
                 model_name_or_path=self.model_name_or_path,
                 device=self.device.to_torch_str(),
                 use_auth_token=self.token.resolve_value() if self.token else None,
+                model_kwargs=self.model_kwargs,
+                tokenizer_kwargs=self.tokenizer_kwargs,
+                config_kwargs=self.config_kwargs,
+                backend=self.backend,
             )
 
     def to_dict(self) -> Dict[str, Any]:
@@ -191,7 +216,7 @@ def to_dict(self) -> Dict[str, Any]:
         :returns:
             Dictionary with serialized data.
         """
-        return default_to_dict(
+        serialization_dict = default_to_dict(
             self,
             model=self.model_name_or_path,
             top_k=self.top_k,
@@ -206,7 +231,14 @@ def to_dict(self) -> Dict[str, Any]:
             embedding_separator=self.embedding_separator,
             strategy=str(self.strategy),
             lambda_threshold=self.lambda_threshold,
+            model_kwargs=self.model_kwargs,
+            tokenizer_kwargs=self.tokenizer_kwargs,
+            config_kwargs=self.config_kwargs,
+            backend=self.backend,
         )
+        if serialization_dict["init_parameters"].get("model_kwargs") is not None:
+            serialize_hf_model_kwargs(serialization_dict["init_parameters"]["model_kwargs"])
+        return serialization_dict
 
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "SentenceTransformersDiversityRanker":
@@ -222,6 +254,8 @@ def from_dict(cls, data: Dict[str, Any]) -> "SentenceTransformersDiversityRanker
         if init_params.get("device") is not None:
             init_params["device"] = ComponentDevice.from_dict(init_params["device"])
         deserialize_secrets_inplace(init_params, keys=["token"])
+        if init_params.get("model_kwargs") is not None:
+            deserialize_hf_model_kwargs(init_params["model_kwargs"])
         return default_from_dict(cls, data)
 
     def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:

releasenotes/notes/sentence-transformers-onnx-openvino-backends-756b6a484c1e49d3.yaml

@@ -0,0 +1,5 @@
+---
+features:
+  - |
+    Sentence Transformers components now support ONNX and OpenVINO backends through the "backend" parameter.
+    Supported backends are torch (default), onnx, and openvino. Refer to the [Sentence Transformers documentation](https://sbert.net/docs/sentence_transformer/usage/efficiency.html) for more information.

test/components/embedders/test_sentence_transformers_document_embedder.py

@@ -82,6 +82,7 @@ def test_to_dict(self):
                 "encode_kwargs": None,
                 "config_kwargs": None,
                 "precision": "float32",
+                "backend": "torch",
             },
         }
 
@@ -127,6 +128,7 @@ def test_to_dict_with_custom_init_parameters(self):
                 "config_kwargs": {"use_memory_efficient_attention": True},
                 "precision": "int8",
                 "encode_kwargs": {"task": "clustering"},
+                "backend": "torch",
             },
         }
 
@@ -252,6 +254,7 @@ def test_warmup(self, mocked_factory):
             model_kwargs=None,
             tokenizer_kwargs={"model_max_length": 512},
             config_kwargs={"use_memory_efficient_attention": True},
+            backend="torch",
         )
 
     @patch(
@@ -357,3 +360,82 @@ def test_prefix_suffix(self):
             normalize_embeddings=False,
             precision="float32",
         )
+
+    @patch(
+        "haystack.components.embedders.sentence_transformers_document_embedder._SentenceTransformersEmbeddingBackendFactory"
+    )
+    def test_model_onnx_backend(self, mocked_factory):
+        onnx_embedder = SentenceTransformersDocumentEmbedder(
+            model="sentence-transformers/all-MiniLM-L6-v2",
+            token=None,
+            device=ComponentDevice.from_str("cpu"),
+            model_kwargs={
+                "file_name": "onnx/model.onnx"
+            },  # setting the path isn't necessary if the repo contains a "onnx/model.onnx" file but this is to prevent a HF warning
+            backend="onnx",
+        )
+        onnx_embedder.warm_up()
+
+        mocked_factory.get_embedding_backend.assert_called_once_with(
+            model="sentence-transformers/all-MiniLM-L6-v2",
+            device="cpu",
+            auth_token=None,
+            trust_remote_code=False,
+            truncate_dim=None,
+            model_kwargs={"file_name": "onnx/model.onnx"},
+            tokenizer_kwargs=None,
+            config_kwargs=None,
+            backend="onnx",
+        )
+
+    @patch(
+        "haystack.components.embedders.sentence_transformers_document_embedder._SentenceTransformersEmbeddingBackendFactory"
+    )
+    def test_model_openvino_backend(self, mocked_factory):
+        openvino_embedder = SentenceTransformersDocumentEmbedder(
+            model="sentence-transformers/all-MiniLM-L6-v2",
+            token=None,
+            device=ComponentDevice.from_str("cpu"),
+            model_kwargs={
+                "file_name": "openvino/openvino_model.xml"
+            },  # setting the path isn't necessary if the repo contains a "openvino/openvino_model.xml" file but this is to prevent a HF warning
+            backend="openvino",
+        )
+        openvino_embedder.warm_up()
+
+        mocked_factory.get_embedding_backend.assert_called_once_with(
+            model="sentence-transformers/all-MiniLM-L6-v2",
+            device="cpu",
+            auth_token=None,
+            trust_remote_code=False,
+            truncate_dim=None,
+            model_kwargs={"file_name": "openvino/openvino_model.xml"},
+            tokenizer_kwargs=None,
+            config_kwargs=None,
+            backend="openvino",
+        )
+
+    @patch(
+        "haystack.components.embedders.sentence_transformers_document_embedder._SentenceTransformersEmbeddingBackendFactory"
+    )
+    @pytest.mark.parametrize("model_kwargs", [{"torch_dtype": "bfloat16"}, {"torch_dtype": "float16"}])
+    def test_dtype_on_gpu(self, mocked_factory, model_kwargs):
+        torch_dtype_embedder = SentenceTransformersDocumentEmbedder(
+            model="sentence-transformers/all-MiniLM-L6-v2",
+            token=None,
+            device=ComponentDevice.from_str("cuda:0"),
+            model_kwargs=model_kwargs,
+        )
+        torch_dtype_embedder.warm_up()
+
+        mocked_factory.get_embedding_backend.assert_called_once_with(
+            model="sentence-transformers/all-MiniLM-L6-v2",
+            device="cuda:0",
+            auth_token=None,
+            trust_remote_code=False,
+            truncate_dim=None,
+            model_kwargs=model_kwargs,
+            tokenizer_kwargs=None,
+            config_kwargs=None,
+            backend="torch",
+        )

test/components/embedders/test_sentence_transformers_embedding_backend.py

@@ -33,6 +33,7 @@ def test_model_initialization(mock_sentence_transformer):
         auth_token=Secret.from_token("fake-api-token"),
         trust_remote_code=True,
         truncate_dim=256,
+        backend="torch",
     )
     mock_sentence_transformer.assert_called_once_with(
         model_name_or_path="model",
@@ -43,6 +44,7 @@ def test_model_initialization(mock_sentence_transformer):
         model_kwargs=None,
         tokenizer_kwargs=None,
         config_kwargs=None,
+        backend="torch",
     )