[Frontend] support matryoshka representation / support embedding API dimensions (#16331)

Author: noooop

examples/offline_inference/embed_matryoshka_fy.py

@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from argparse import Namespace
+
+from vllm import LLM, EngineArgs, PoolingParams
+from vllm.utils import FlexibleArgumentParser
+
+
+def main(args: Namespace):
+    # Sample prompts.
+    prompts = [
+        "Follow the white rabbit.",  # English
+        "Sigue al conejo blanco.",  # Spanish
+        "Suis le lapin blanc.",  # French
+        "跟着白兔走。",  # Chinese
+        "اتبع الأرنب الأبيض.",  # Arabic
+        "Folge dem weißen Kaninchen.",  # German
+    ]
+
+    # Create an LLM.
+    # You should pass task="embed" for embedding models
+    model = LLM(**vars(args))
+
+    # Generate embedding. The output is a list of EmbeddingRequestOutputs.
+    outputs = model.embed(prompts, pooling_params=PoolingParams(dimensions=32))
+
+    # Print the outputs.
+    print("\nGenerated Outputs:")
+    print("-" * 60)
+    for prompt, output in zip(prompts, outputs):
+        embeds = output.outputs.embedding
+        embeds_trimmed = ((str(embeds[:16])[:-1] +
+                           ", ...]") if len(embeds) > 16 else embeds)
+        print(f"Prompt: {prompt!r} \n"
+              f"Embeddings: {embeds_trimmed} "
+              f"(size={len(embeds)})")
+        print("-" * 60)
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser()
+    parser = EngineArgs.add_cli_args(parser)
+    # Set example specific arguments
+    parser.set_defaults(model="jinaai/jina-embeddings-v3",
+                        task="embed",
+                        trust_remote_code=True)
+    args = parser.parse_args()
+    main(args)

tests/conftest.py

@@ -960,19 +960,19 @@ def classify(self, prompts: list[str]) -> list[list[float]]:
         req_outputs = self.model.classify(prompts)
         return [req_output.outputs.probs for req_output in req_outputs]
 
-    def encode(
-        self,
-        prompts: list[str],
-        images: Optional[PromptImageInput] = None,
-        videos: Optional[PromptVideoInput] = None,
-        audios: Optional[PromptAudioInput] = None,
-    ) -> list[list[float]]:
+    def encode(self,
+               prompts: list[str],
+               images: Optional[PromptImageInput] = None,
+               videos: Optional[PromptVideoInput] = None,
+               audios: Optional[PromptAudioInput] = None,
+               *args,
+               **kwargs) -> list[list[float]]:
         inputs = self.get_inputs(prompts,
                                  images=images,
                                  videos=videos,
                                  audios=audios)
 
-        req_outputs = self.model.embed(inputs)
+        req_outputs = self.model.embed(inputs, *args, **kwargs)
         return [req_output.outputs.embedding for req_output in req_outputs]
 
     def score(

tests/entrypoints/openai/test_embedding_dimensions.py

@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Run `pytest tests/entrypoints/openai/test_embedding_dimensions.py`.
+"""
+
+from typing import NamedTuple
+
+import openai
+import pytest
+
+from vllm.entrypoints.openai.protocol import EmbeddingResponse
+
+from ...utils import RemoteOpenAIServer
+
+
+class ModelInfo(NamedTuple):
+    name: str
+    is_matryoshka: bool
+
+
+MODELS = [
+    ModelInfo(name="BAAI/bge-m3", is_matryoshka=False),
+    ModelInfo(name="jinaai/jina-embeddings-v3", is_matryoshka=True),
+]
+
+input_texts = [
+    "The chef prepared a delicious meal.",
+] * 3
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+async def test_validating_dimensions(model: ModelInfo):
+    args = [
+        "--task",
+        "embed",
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "bfloat16",
+        "--enforce-eager",
+        "--max-model-len",
+        "512",
+        "--trust_remote_code"
+    ]
+    with RemoteOpenAIServer(model.name, args) as remote_server:
+        client = remote_server.get_async_client()
+
+        async def make_request(dimensions):
+            embedding_response = await client.embeddings.create(
+                model=model.name,
+                input=input_texts,
+                dimensions=dimensions,
+                encoding_format="float",
+            )
+            embeddings = EmbeddingResponse.model_validate(
+                embedding_response.model_dump(mode="json"))
+
+            assert embeddings.id is not None
+            assert len(embeddings.data) == 3
+            assert len(embeddings.data[0].embedding) > 0
+            assert embeddings.usage.completion_tokens == 0
+            assert embeddings.usage.prompt_tokens > 0
+            assert embeddings.usage.total_tokens > 0
+
+            if dimensions is not None:
+                assert len(embeddings.data[0].embedding) == dimensions
+
+        if model.is_matryoshka:
+            for dimensions in [None, 16]:
+                await make_request(dimensions)
+
+            with pytest.raises(openai.BadRequestError):
+                for dimensions in [-1]:
+                    await make_request(dimensions)
+
+        else:
+            for dimensions in [None]:
+                await make_request(dimensions)
+
+            with pytest.raises(openai.BadRequestError):
+                for dimensions in [-1, 16]:
+                    await make_request(dimensions)

tests/models/embedding/language/test_jina.py

@@ -8,7 +8,8 @@
 
 import pytest
 
-from tests.models.embedding.utils import check_embeddings_close
+from tests.models.embedding.utils import check_embeddings_close, matryoshka_fy
+from vllm import PoolingParams
 
 SCORING_MODELS = [
     "jinaai/jina-reranker-v2-base-multilingual",  # Roberta
@@ -126,3 +127,40 @@ def test_embeddings(
         name_1="vllm",
         tol=1e-2,
     )
+
+
+@pytest.mark.parametrize("model", EMBEDDING_MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("dimensions", [16, 32])
+def test_matryoshka(
+    hf_runner,
+    vllm_runner,
+    model,
+    dtype: str,
+    dimensions: int,
+    monkeypatch,
+) -> None:
+
+    example_prompts = EMBEDDING_PROMPTS
+
+    with hf_runner(
+            model,
+            dtype=dtype,
+            is_sentence_transformer=True,
+    ) as hf_model:
+        hf_outputs = hf_model.encode(example_prompts, task="text-matching")
+        hf_outputs = matryoshka_fy(hf_outputs, dimensions)
+
+    with vllm_runner(model, task="embed", dtype=dtype,
+                     max_model_len=None) as vllm_model:
+        vllm_outputs = vllm_model.encode(
+            example_prompts,
+            pooling_params=PoolingParams(dimensions=dimensions))
+
+    check_embeddings_close(
+        embeddings_0_lst=hf_outputs,
+        embeddings_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+        tol=1e-2,
+    )

tests/models/embedding/utils.py

@@ -30,3 +30,10 @@ def check_embeddings_close(
                     f"\n{name_1}:\t{embeddings_1[:16]!r}")
 
         assert sim >= 1 - tol, fail_msg
+
+
+def matryoshka_fy(tensor, dimensions):
+    tensor = torch.tensor(tensor)
+    tensor = tensor[..., :dimensions]
+    tensor = F.normalize(tensor, p=2, dim=1)
+    return tensor

vllm/config.py

@@ -583,6 +583,15 @@ def _init_pooler_config(
                     if getattr(user_config, k) is None:
                         setattr(user_config, k, v)
 
+            if self.is_matryoshka:
+                if user_config.normalize is None:
+                    user_config.normalize = True
+                elif not user_config.normalize:
+                    raise ValueError(
+                        "`normalize` must be enabled (set to True) "
+                        "for models that are compatible with "
+                        "Matryoshka Representation.")
+
             return user_config
 
         return None

vllm/entrypoints/llm.py

@@ -921,6 +921,11 @@ def encode(
         if pooling_params is None:
             # Use default pooling params.
             pooling_params = PoolingParams()
+        elif isinstance(pooling_params, PoolingParams):
+            pooling_params.verify(self.llm_engine.model_config)
+        else:
+            for pooling_param in pooling_params:
+                pooling_param.verify(self.llm_engine.model_config)
 
         self._validate_and_add_requests(
             prompts=parsed_prompts,
@@ -939,6 +944,8 @@ def embed(
         /,
         *,
         use_tqdm: bool = True,
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
         lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
     ) -> list[EmbeddingRequestOutput]:
@@ -953,6 +960,8 @@ def embed(
             prompts: The prompts to the LLM. You may pass a sequence of prompts
                 for batch inference. See :class:`~vllm.inputs.PromptType`
                 for more details about the format of each prompts.
+            pooling_params: The pooling parameters for pooling. If None, we
+                use the default pooling parameters.
             use_tqdm: Whether to use tqdm to display the progress bar.
             lora_request: LoRA request to use for generation, if any.
             prompt_adapter_request: Prompt Adapter request to use for
@@ -968,6 +977,7 @@ def embed(
 
         items = self.encode(prompts,
                             use_tqdm=use_tqdm,
+                            pooling_params=pooling_params,
                             lora_request=lora_request,
                             prompt_adapter_request=prompt_adapter_request)
 

vllm/entrypoints/openai/protocol.py

@@ -1006,7 +1006,8 @@ class EmbeddingCompletionRequest(OpenAIBaseModel):
     # doc: end-embedding-extra-params
 
     def to_pooling_params(self):
-        return PoolingParams(additional_data=self.additional_data)
+        return PoolingParams(dimensions=self.dimensions,
+                             additional_data=self.additional_data)
 
 
 class EmbeddingChatRequest(OpenAIBaseModel):
@@ -1068,7 +1069,8 @@ def check_generation_prompt(cls, data):
         return data
 
     def to_pooling_params(self):
-        return PoolingParams(additional_data=self.additional_data)
+        return PoolingParams(dimensions=self.dimensions,
+                             additional_data=self.additional_data)
 
 
 EmbeddingRequest = Union[EmbeddingCompletionRequest, EmbeddingChatRequest]

vllm/entrypoints/openai/serving_embedding.py

@@ -80,9 +80,6 @@ async def create_embedding(
             return error_check_ret
 
         encoding_format = request.encoding_format
-        if request.dimensions is not None:
-            return self.create_error_response(
-                "dimensions is currently not supported")
 
         model_name = self._get_model_name(request.model)
         request_id = f"embd-{self._base_request_id(raw_request)}"
@@ -99,6 +96,13 @@ async def create_embedding(
                     "greater than max_model_len."
                     " Please, select a smaller truncation size.")
 
+        pooling_params = request.to_pooling_params()
+
+        try:
+            pooling_params.verify(self.model_config)
+        except ValueError as e:
+            return self.create_error_response(str(e))
+
         try:
             (
                 lora_request,
@@ -146,8 +150,6 @@ async def create_embedding(
         # Schedule the request and get the result generator.
         generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
         try:
-            pooling_params = request.to_pooling_params()
-
             for i, engine_prompt in enumerate(engine_prompts):
                 request_id_item = f"{request_id}-{i}"
 

vllm/model_executor/layers/pooler.py

@@ -97,7 +97,7 @@ def forward(
         pooling_metadata: PoolingMetadata,
     ) -> PoolerOutput:
         pooled_data = self.extract_states(hidden_states, pooling_metadata)
-        pooled_data = self.head(pooled_data)
+        pooled_data = self.head(pooled_data, pooling_metadata)
         pooled_outputs = [self.build_output(data) for data in pooled_data]
         return PoolerOutput(outputs=pooled_outputs)
 
@@ -217,14 +217,28 @@ def __init__(self, *, normalize: bool, softmax: bool) -> None:
         self.normalize = normalize
         self.softmax = softmax
 
-    def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor]):
+    def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
+                pooling_metadata: PoolingMetadata):
+
+        dimensions_list = [
+            pooling_param.dimensions
+            for _, pooling_param in pooling_metadata.seq_groups
+        ]
+        if any(d is not None for d in dimensions_list):
+            # change the output dimension
+            assert len(pooled_data) == len(dimensions_list)
+            pooled_data = [
+                vecs if d is None else vecs[..., :d]
+                for vecs, d in zip(pooled_data, dimensions_list)
+            ]
+
         if self.normalize:
             if isinstance(pooled_data, list):
                 pooled_data = [
-                    F.normalize(data, p=2, dim=1) for data in pooled_data
+                    F.normalize(data, p=2, dim=-1) for data in pooled_data
                 ]
             else:
-                pooled_data = F.normalize(pooled_data, p=2, dim=1)
+                pooled_data = F.normalize(pooled_data, p=2, dim=-1)
 
         if self.softmax:
             if isinstance(pooled_data, list):