Add vLLMEmbeddings (#920)

* Add vLLMEmbeddings to work with multiple GPUs

* Add mocked tests

src/distilabel/embeddings/__init__.py

@@ -14,8 +14,10 @@
 
 from distilabel.embeddings.base import Embeddings
 from distilabel.embeddings.sentence_transformers import SentenceTransformerEmbeddings
+from distilabel.embeddings.vllm import vLLMEmbeddings
 
 __all__ = [
     "Embeddings",
     "SentenceTransformerEmbeddings",
+    "vLLMEmbeddings",
 ]

src/distilabel/embeddings/vllm.py

@@ -0,0 +1,130 @@
+# Copyright 2023-present, Argilla, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+
+from pydantic import Field, PrivateAttr
+
+from distilabel.embeddings.base import Embeddings
+from distilabel.llms.mixins.cuda_device_placement import CudaDevicePlacementMixin
+from distilabel.mixins.runtime_parameters import RuntimeParameter
+
+if TYPE_CHECKING:
+    from vllm import LLM as _vLLM
+
+
+class vLLMEmbeddings(Embeddings, CudaDevicePlacementMixin):
+    """`vllm` library implementation for embedding generation.
+
+    Attributes:
+        model: the model Hugging Face Hub repo id or a path to a directory containing the
+            model weights and configuration files.
+        dtype: the data type to use for the model. Defaults to `auto`.
+        trust_remote_code: whether to trust the remote code when loading the model. Defaults
+            to `False`.
+        quantization: the quantization mode to use for the model. Defaults to `None`.
+        revision: the revision of the model to load. Defaults to `None`.
+        enforce_eager: whether to enforce eager execution. Defaults to `True`.
+        seed: the seed to use for the random number generator. Defaults to `0`.
+        extra_kwargs: additional dictionary of keyword arguments that will be passed to the
+            `LLM` class of `vllm` library. Defaults to `{}`.
+        _model: the `vLLM` model instance. This attribute is meant to be used internally
+            and should not be accessed directly. It will be set in the `load` method.
+
+    References:
+        - [Offline inference embeddings](https://docs.vllm.ai/en/latest/getting_started/examples/offline_inference_embedding.html)
+
+    Examples:
+
+        Generating sentence embeddings:
+
+        ```python
+        from distilabel.embeddings import vLLMEmbeddings
+
+        embeddings = vLLMEmbeddings(model="intfloat/e5-mistral-7b-instruct")
+
+        embeddings.load()
+
+        results = embeddings.encode(inputs=["distilabel is awesome!", "and Argilla!"])
+        # [
+        #   [-0.05447685346007347, -0.01623094454407692, ...],
+        #   [4.4889533455716446e-05, 0.044016145169734955, ...],
+        # ]
+        ```
+    """
+
+    model: str
+    dtype: str = "auto"
+    trust_remote_code: bool = False
+    quantization: Optional[str] = None
+    revision: Optional[str] = None
+
+    enforce_eager: bool = True
+
+    seed: int = 0
+
+    extra_kwargs: Optional[RuntimeParameter[Dict[str, Any]]] = Field(
+        default_factory=dict,
+        description="Additional dictionary of keyword arguments that will be passed to the"
+        " `vLLM` class of `vllm` library. See all the supported arguments at: "
+        "https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py",
+    )
+
+    _model: "_vLLM" = PrivateAttr(None)
+
+    def load(self) -> None:
+        """Loads the `vLLM` model using either the path or the Hugging Face Hub repository id."""
+        super().load()
+
+        CudaDevicePlacementMixin.load(self)
+
+        try:
+            from vllm import LLM as _vLLM
+
+        except ImportError as ie:
+            raise ImportError(
+                "vLLM is not installed. Please install it using `pip install vllm`."
+            ) from ie
+
+        self._model = _vLLM(
+            self.model,
+            dtype=self.dtype,
+            trust_remote_code=self.trust_remote_code,
+            quantization=self.quantization,
+            revision=self.revision,
+            enforce_eager=self.enforce_eager,
+            seed=self.seed,
+            **self.extra_kwargs,  # type: ignore
+        )
+
+    def unload(self) -> None:
+        """Unloads the `vLLM` model."""
+        CudaDevicePlacementMixin.unload(self)
+        super().unload()
+
+    @property
+    def model_name(self) -> str:
+        """Returns the name of the model."""
+        return self.model
+
+    def encode(self, inputs: List[str]) -> List[List[Union[int, float]]]:
+        """Generates embeddings for the provided inputs.
+
+        Args:
+            inputs: a list of texts for which an embedding has to be generated.
+
+        Returns:
+            The generated embeddings.
+        """
+        return [output.outputs.embedding for output in self._model.encode(inputs)]

tests/unit/embeddings/test_vllm.py

@@ -0,0 +1,50 @@
+# Copyright 2023-present, Argilla, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from unittest.mock import MagicMock, Mock
+
+from distilabel.embeddings.vllm import vLLMEmbeddings
+
+
+# @patch("vllm.entrypoints.LLM")
+class TestSentenceTransformersEmbeddings:
+    model_name = "group/model-name"
+
+    def test_model_name(self) -> None:
+        embeddings = vLLMEmbeddings(model=self.model_name)
+
+        assert embeddings.model_name == self.model_name
+
+    def test_encode(self) -> None:
+        embeddings = vLLMEmbeddings(model=self.model_name)
+
+        # the loading should be done here, it's just mocked
+        # embeddings.load()
+        embeddings._model = MagicMock()
+
+        mocked_response = Mock(outputs=Mock(embedding=[0.1] * 10))
+        embeddings._model.encode = Mock(
+            side_effect=lambda x: [mocked_response for _ in range(len(x))]
+        )
+
+        results = embeddings.encode(
+            inputs=[
+                "Hello, how are you?",
+                "What a nice day!",
+                "I hear that llamas are very popular now.",
+            ]
+        )
+
+        for result in results:
+            assert len(result) == 10