[Frontend] Separate pooling APIs in offline inference (#11129)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

.buildkite/test-pipeline.yaml

@@ -181,14 +181,14 @@ steps:
   commands:
     - VLLM_USE_V1=1 pytest -v -s v1
 
-- label: Examples Test # 15min
+- label: Examples Test # 25min
   working_dir: "/vllm-workspace/examples"
   #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/entrypoints
   - examples/
   commands:
-    - pip install awscli tensorizer # for llava example and tensorizer test
+    - pip install tensorizer # for tensorizer test
     - python3 offline_inference.py
     - python3 cpu_offload.py
     - python3 offline_inference_chat.py
@@ -198,6 +198,9 @@ steps:
     - python3 offline_inference_vision_language_multi_image.py
     - python3 tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference_encoder_decoder.py
+    - python3 offline_inference_classification.py
+    - python3 offline_inference_embedding.py
+    - python3 offline_inference_scoring.py
     - python3 offline_profile.py --model facebook/opt-125m
 
 - label: Prefix Caching Test # 9min

docs/source/models/pooling_models.rst

@@ -6,7 +6,7 @@ Pooling Models
 vLLM also supports pooling models, including embedding, reranking and reward models.
 
 In vLLM, pooling models implement the :class:`~vllm.model_executor.models.VllmModelForPooling` interface.
-These models use a :class:`~vllm.model_executor.layers.Pooler` to aggregate the final hidden states of the input
+These models use a :class:`~vllm.model_executor.layers.Pooler` to extract the final hidden states of the input
 before returning them.
 
 .. note::
@@ -45,20 +45,48 @@ which takes priority over both the model's and Sentence Transformers's defaults.
 ^^^^^^^^^^^^^^
 
 The :class:`~vllm.LLM.encode` method is available to all pooling models in vLLM.
-It returns the aggregated hidden states directly.
+It returns the extracted hidden states directly, which is useful for reward models.
+
+.. code-block:: python
+
+    llm = LLM(model="Qwen/Qwen2.5-Math-RM-72B", task="reward")
+    output, = llm.encode("Hello, my name is")
+
+    data = output.outputs.data
+    print(f"Prompt: {prompt!r} | Data: {data!r}")
+
+``LLM.embed``
+^^^^^^^^^^^^^
+
+The :class:`~vllm.LLM.embed` method outputs an embedding vector for each prompt.
+It is primarily designed for embedding models.
 
 .. code-block:: python
 
     llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed")
-    outputs = llm.encode("Hello, my name is")
+    output, = llm.embed("Hello, my name is")
 
-    outputs = model.encode(prompts)
-    for output in outputs:
-        embeddings = output.outputs.embedding
-        print(f"Prompt: {prompt!r}, Embeddings (size={len(embeddings)}: {embeddings!r}")
+    embeds = output.outputs.embedding
+    print(f"Embeddings: {embeds!r} (size={len(embeds)})")
 
 A code example can be found in `examples/offline_inference_embedding.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_embedding.py>`_.
 
+``LLM.classify``
+^^^^^^^^^^^^^^^^
+
+The :class:`~vllm.LLM.classify` method outputs a probability vector for each prompt.
+It is primarily designed for classification models.
+
+.. code-block:: python
+
+    llm = LLM(model="jason9693/Qwen2.5-1.5B-apeach", task="classify")
+    output, = llm.classify("Hello, my name is")
+
+    probs = output.outputs.probs
+    print(f"Class Probabilities: {probs!r} (size={len(probs)})")
+
+A code example can be found in `examples/offline_inference_classification.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_classification.py>`_.
+
 ``LLM.score``
 ^^^^^^^^^^^^^
 
@@ -71,7 +99,16 @@ These types of models serve as rerankers between candidate query-document pairs
     vLLM can only perform the model inference component (e.g. embedding, reranking) of RAG.
     To handle RAG at a higher level, you should use integration frameworks such as `LangChain <https://github.com/langchain-ai/langchain>`_.
 
-You can use `these tests <https://github.com/vllm-project/vllm/blob/main/tests/models/embedding/language/test_scoring.py>`_ as reference.
+.. code-block:: python
+
+    llm = LLM(model="BAAI/bge-reranker-v2-m3", task="score")
+    output, = llm.score("What is the capital of France?",
+                        "The capital of Brazil is Brasilia.")
+
+    score = output.outputs.score
+    print(f"Score: {score}")
+
+A code example can be found in `examples/offline_inference_scoring.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_scoring.py>`_.
 
 Online Inference
 ----------------

examples/offline_inference_classification.py

+from vllm import LLM
+
+# Sample prompts.
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+# Create an LLM.
+# You should pass task="classify" for classification models
+model = LLM(
+    model="jason9693/Qwen2.5-1.5B-apeach",
+    task="classify",
+    enforce_eager=True,
+)
+
+# Generate logits. The output is a list of ClassificationRequestOutputs.
+outputs = model.classify(prompts)
+
+# Print the outputs.
+for prompt, output in zip(prompts, outputs):
+    probs = output.outputs.probs
+    probs_trimmed = ((str(probs[:16])[:-1] +
+                      ", ...]") if len(probs) > 16 else probs)
+    print(f"Prompt: {prompt!r} | "
+          f"Class Probabilities: {probs_trimmed} (size={len(probs)})")

examples/offline_inference_embedding.py

@@ -9,14 +9,20 @@ prompts = [
 ]
 
 # Create an LLM.
+# You should pass task="embed" for embedding models
 model = LLM(
     model="intfloat/e5-mistral-7b-instruct",
-    task="embed",  # You should pass task="embed" for embedding models
+    task="embed",
     enforce_eager=True,
 )
 
-# Generate embedding. The output is a list of PoolingRequestOutputs.
-outputs = model.encode(prompts)
+# Generate embedding. The output is a list of EmbeddingRequestOutputs.
+outputs = model.embed(prompts)
+
 # Print the outputs.
-for output in outputs:
-    print(output.outputs.embedding)  # list of 4096 floats
+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} | "
+          f"Embeddings: {embeds_trimmed} (size={len(embeds)})")

examples/offline_inference_scoring.py

+from vllm import LLM
+
+# Sample prompts.
+text_1 = "What is the capital of France?"
+texts_2 = [
+    "The capital of Brazil is Brasilia.", "The capital of France is Paris."
+]
+
+# Create an LLM.
+# You should pass task="score" for cross-encoder models
+model = LLM(
+    model="BAAI/bge-reranker-v2-m3",
+    task="score",
+    enforce_eager=True,
+)
+
+# Generate scores. The output is a list of ScoringRequestOutputs.
+outputs = model.score(text_1, texts_2)
+
+# Print the outputs.
+for text_2, output in zip(texts_2, outputs):
+    score = output.outputs.score
+    print(f"Pair: {[text_1, text_2]!r} | Score: {score}")

examples/offline_inference_vision_language_embedding.py

@@ -133,7 +133,7 @@ def run_encode(model: str, modality: QueryModality):
     if req_data.image is not None:
         mm_data["image"] = req_data.image
 
-    outputs = req_data.llm.encode({
+    outputs = req_data.llm.embed({
         "prompt": req_data.prompt,
         "multi_modal_data": mm_data,
     })

tests/conftest.py

@@ -719,14 +719,6 @@ class VllmRunner:
 
         return inputs
 
-    def classify(self, prompts: List[str]) -> List[str]:
-        req_outputs = self.model.encode(prompts)
-        outputs = []
-        for req_output in req_outputs:
-            embedding = req_output.outputs.embedding
-            outputs.append(embedding)
-        return outputs
-
     def generate(
         self,
         prompts: List[str],
@@ -897,6 +889,10 @@ class VllmRunner:
             returned_outputs.append((token_ids, texts))
         return returned_outputs
 
+    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],
@@ -909,16 +905,16 @@ class VllmRunner:
                                  videos=videos,
                                  audios=audios)
 
-        req_outputs = self.model.encode(inputs)
+        req_outputs = self.model.embed(inputs)
         return [req_output.outputs.embedding for req_output in req_outputs]
 
     def score(
         self,
         text_1: Union[str, List[str]],
         text_2: Union[str, List[str]],
-    ) -> List[List[float]]:
+    ) -> List[float]:
         req_outputs = self.model.score(text_1, text_2)
-        return [req_output.outputs.embedding for req_output in req_outputs]
+        return [req_output.outputs.score for req_output in req_outputs]
 
     def __enter__(self):
         return self

tests/entrypoints/openai/test_score.py

@@ -39,8 +39,8 @@ async def test_text_1_str_text_2_list(server: RemoteOpenAIServer,
     assert score.id is not None
     assert score.data is not None
     assert len(score.data) == 2
-    assert score.data[0].score[0] <= 0.01
-    assert score.data[1].score[0] >= 0.9
+    assert score.data[0].score <= 0.01
+    assert score.data[1].score >= 0.9
 
 
 @pytest.mark.asyncio
@@ -67,8 +67,8 @@ async def test_text_1_list_text_2_list(server: RemoteOpenAIServer,
     assert score.id is not None
     assert score.data is not None
     assert len(score.data) == 2
-    assert score.data[0].score[0] <= 0.01
-    assert score.data[1].score[0] >= 0.9
+    assert score.data[0].score <= 0.01
+    assert score.data[1].score >= 0.9
 
 
 @pytest.mark.asyncio
@@ -90,4 +90,4 @@ async def test_text_1_str_text_2_str(server: RemoteOpenAIServer,
     assert score.id is not None
     assert score.data is not None
     assert len(score.data) == 1
-    assert score.data[0].score[0] >= 0.9
+    assert score.data[0].score >= 0.9

tests/models/embedding/language/test_scoring.py

@@ -42,7 +42,7 @@ def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str):
     assert len(vllm_outputs) == 1
     assert len(hf_outputs) == 1
 
-    assert math.isclose(hf_outputs[0], vllm_outputs[0][0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
 
 
 @pytest.mark.parametrize("dtype", ["half"])
@@ -63,8 +63,8 @@ def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str):
     assert len(vllm_outputs) == 2
     assert len(hf_outputs) == 2
 
-    assert math.isclose(hf_outputs[0], vllm_outputs[0][0], rel_tol=0.01)
-    assert math.isclose(hf_outputs[1], vllm_outputs[1][0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)
 
 
 @pytest.mark.parametrize("dtype", ["half"])
@@ -85,5 +85,5 @@ def test_llm_N_to_N(vllm_runner, hf_runner, model_name, dtype: str):
     assert len(vllm_outputs) == 2
     assert len(hf_outputs) == 2
 
-    assert math.isclose(hf_outputs[0], vllm_outputs[0][0], rel_tol=0.01)
-    assert math.isclose(hf_outputs[1], vllm_outputs[1][0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[0], vllm_outputs[0], rel_tol=0.01)
+    assert math.isclose(hf_outputs[1], vllm_outputs[1], rel_tol=0.01)

tests/models/test_oot_registration.py

@@ -2,7 +2,7 @@ import os
 
 import pytest
 
-from vllm import LLM, PoolingParams, SamplingParams
+from vllm import LLM, SamplingParams
 from vllm.assets.image import ImageAsset
 
 from ..utils import fork_new_process_for_each_test
@@ -36,9 +36,8 @@ def test_oot_registration_text_generation(dummy_opt_path):
 def test_oot_registration_embedding(dummy_gemma2_embedding_path):
     os.environ["VLLM_PLUGINS"] = "register_dummy_model"
     prompts = ["Hello, my name is", "The text does not matter"]
-    sampling_params = PoolingParams()
     llm = LLM(model=dummy_gemma2_embedding_path, load_format="dummy")
-    outputs = llm.encode(prompts, sampling_params)
+    outputs = llm.embed(prompts)
 
     for output in outputs:
         assert all(v == 0 for v in output.outputs.embedding)

vllm/__init__.py

@@ -7,8 +7,11 @@ from vllm.entrypoints.llm import LLM
 from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import PromptType, TextPrompt, TokensPrompt
 from vllm.model_executor.models import ModelRegistry
-from vllm.outputs import (CompletionOutput, PoolingOutput,
-                          PoolingRequestOutput, RequestOutput)
+from vllm.outputs import (ClassificationOutput, ClassificationRequestOutput,
+                          CompletionOutput, EmbeddingOutput,
+                          EmbeddingRequestOutput, PoolingOutput,
+                          PoolingRequestOutput, RequestOutput, ScoringOutput,
+                          ScoringRequestOutput)
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 
@@ -27,6 +30,12 @@ __all__ = [
     "CompletionOutput",
     "PoolingOutput",
     "PoolingRequestOutput",
+    "EmbeddingOutput",
+    "EmbeddingRequestOutput",
+    "ClassificationOutput",
+    "ClassificationRequestOutput",
+    "ScoringOutput",
+    "ScoringRequestOutput",
     "LLMEngine",
     "EngineArgs",
     "AsyncLLMEngine",
@@ -34,26 +43,3 @@ __all__ = [
     "initialize_ray_cluster",
     "PoolingParams",
 ]
-
-
-def __getattr__(name: str):
-    import warnings
-
-    if name == "EmbeddingOutput":
-        msg = ("EmbeddingOutput has been renamed to PoolingOutput. "
-               "The original name will be removed in an upcoming version.")
-
-        warnings.warn(DeprecationWarning(msg), stacklevel=2)
-
-        return PoolingOutput
-
-    if name == "EmbeddingRequestOutput":
-        msg = ("EmbeddingRequestOutput has been renamed to "
-               "PoolingRequestOutput. "
-               "The original name will be removed in an upcoming version.")
-
-        warnings.warn(DeprecationWarning(msg), stacklevel=2)
-
-        return PoolingRequestOutput
-
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/engine/llm_engine.py

@@ -46,11 +46,10 @@ from vllm.outputs import (PoolingRequestOutput, RequestOutput,
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import RequestOutputKind, SamplingParams
-from vllm.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest,
-                           ParallelSampleSequenceGroup, Sequence,
-                           SequenceGroup, SequenceGroupBase,
-                           SequenceGroupMetadata, SequenceGroupOutput,
-                           SequenceStatus)
+from vllm.sequence import (ExecuteModelRequest, ParallelSampleSequenceGroup,
+                           PoolingSequenceGroupOutput, Sequence, SequenceGroup,
+                           SequenceGroupBase, SequenceGroupMetadata,
+                           SequenceGroupOutput, SequenceStatus)
 from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context,
                           init_tracer)
 from vllm.transformers_utils.config import try_get_generation_config
@@ -966,9 +965,9 @@ class LLMEngine:
     @staticmethod
     def _process_sequence_group_outputs(
         seq_group: SequenceGroup,
-        outputs: List[EmbeddingSequenceGroupOutput],
+        outputs: List[PoolingSequenceGroupOutput],
     ) -> None:
-        seq_group.embeddings = outputs[0].embeddings
+        seq_group.pooled_data = outputs[0].data
 
         for seq in seq_group.get_seqs():
             seq.status = SequenceStatus.FINISHED_STOPPED
@@ -1784,8 +1783,8 @@ class LLMEngine:
                                num_prompt_tokens_iter)
         # Spec decode, if enabled, emits specialized metrics from the worker in
         # sampler output.
-        if model_output and (model_output[0].spec_decode_worker_metrics
-                             is not None):
+        if model_output and isinstance(model_output[0], SamplerOutput) and (
+                model_output[0].spec_decode_worker_metrics is not None):
             spec_decode_metrics = model_output[0].spec_decode_worker_metrics
         else:
             spec_decode_metrics = None

vllm/entrypoints/llm.py

@@ -26,7 +26,9 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.guided_decoding.guided_fields import (
     GuidedDecodingRequest, LLMGuidedOptions)
-from vllm.outputs import PoolingRequestOutput, RequestOutput
+from vllm.outputs import (ClassificationRequestOutput, EmbeddingRequestOutput,
+                          PoolingRequestOutput, RequestOutput,
+                          ScoringRequestOutput)
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
@@ -120,7 +122,7 @@ class LLM:
         serving, use the :class:`~vllm.AsyncLLMEngine` class instead.
     """
 
-    DEPRECATE_LEGACY: ClassVar[bool] = False
+    DEPRECATE_LEGACY: ClassVar[bool] = True
     """A flag to toggle whether to deprecate the legacy generate/encode API."""
 
     DEPRECATE_INIT_POSARGS: ClassVar[bool] = True
@@ -257,11 +259,14 @@ class LLM:
         self,
         prompts: Union[PromptType, Sequence[PromptType]],
         /,
-        *,
         sampling_params: Optional[Union[SamplingParams,
                                         Sequence[SamplingParams]]] = None,
+        *,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
     ) -> List[RequestOutput]:
         ...
 
@@ -275,6 +280,9 @@ class LLM:
         prompt_token_ids: Optional[List[int]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
     ) -> List[RequestOutput]:
         ...
 
@@ -288,6 +296,9 @@ class LLM:
         prompt_token_ids: Optional[List[List[int]]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
     ) -> List[RequestOutput]:
         ...
 
@@ -302,6 +313,9 @@ class LLM:
         prompt_token_ids: List[int],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
     ) -> List[RequestOutput]:
         ...
 
@@ -316,6 +330,9 @@ class LLM:
         prompt_token_ids: List[List[int]],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
     ) -> List[RequestOutput]:
         ...
 
@@ -328,6 +345,9 @@ class LLM:
         prompt_token_ids: Union[List[int], List[List[int]]],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
     ) -> List[RequestOutput]:
         ...
 
@@ -678,11 +698,12 @@ class LLM:
         self,
         prompts: Union[PromptType, Sequence[PromptType]],
         /,
-        *,
         pooling_params: Optional[Union[PoolingParams,
                                        Sequence[PoolingParams]]] = None,
+        *,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
     ) -> List[PoolingRequestOutput]:
         ...
 
@@ -696,6 +717,7 @@ class LLM:
         prompt_token_ids: Optional[List[int]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
     ) -> List[PoolingRequestOutput]:
         ...
 
@@ -709,6 +731,7 @@ class LLM:
         prompt_token_ids: Optional[List[List[int]]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
     ) -> List[PoolingRequestOutput]:
         ...
 
@@ -723,6 +746,7 @@ class LLM:
         prompt_token_ids: List[int],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
     ) -> List[PoolingRequestOutput]:
         ...
 
@@ -737,6 +761,7 @@ class LLM:
         prompt_token_ids: List[List[int]],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
     ) -> List[PoolingRequestOutput]:
         ...
 
@@ -749,6 +774,7 @@ class LLM:
         prompt_token_ids: Union[List[int], List[List[int]]],
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
     ) -> List[PoolingRequestOutput]:
         ...
 
@@ -768,7 +794,8 @@ class LLM:
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
     ) -> List[PoolingRequestOutput]:
-        """Generates the completions for the input prompts.
+        """Apply pooling to the hidden states corresponding to the input
+        prompts.
 
         This class automatically batches the given prompts, considering
         the memory constraint. For the best performance, put all of your prompts
@@ -787,7 +814,7 @@ class LLM:
 
         Returns:
             A list of ``PoolingRequestOutput`` objects containing the
-            generated embeddings in the same order as the input prompts.
+            pooled hidden states in the same order as the input prompts.
 
         Note:
             Using ``prompts`` and ``prompt_token_ids`` as keyword parameters is
@@ -833,28 +860,110 @@ class LLM:
         return self.engine_class.validate_outputs(outputs,
                                                   PoolingRequestOutput)
 
+    def embed(
+        self,
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
+        *,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        """
+        Generate an embedding vector for each prompt.
+
+        This class automatically batches the given prompts, considering
+        the memory constraint. For the best performance, put all of your prompts
+        into a single list and pass it to this method.
+
+        Args:
+            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.
+            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
+                generation, if any.
+
+        Returns:
+            A list of ``EmbeddingRequestOutput`` objects containing the
+            embedding vectors in the same order as the input prompts.
+        """
+        if self.llm_engine.model_config.task != "embed":
+            raise ValueError(
+                "Embedding API is only enabled for `--task embed`")
+
+        items = self.encode(prompts,
+                            use_tqdm=use_tqdm,
+                            lora_request=lora_request,
+                            prompt_adapter_request=prompt_adapter_request)
+
+        return [EmbeddingRequestOutput.from_base(item) for item in items]
+
+    def classify(
+        self,
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
+        *,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[ClassificationRequestOutput]:
+        """
+        Generate class logits for each prompt.
+
+        This class automatically batches the given prompts, considering
+        the memory constraint. For the best performance, put all of your prompts
+        into a single list and pass it to this method.
+
+        Args:
+            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.
+            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
+                generation, if any.
+
+        Returns:
+            A list of ``ClassificationRequestOutput`` objects containing the
+            embedding vectors in the same order as the input prompts.
+        """
+        if self.llm_engine.model_config.task != "classify":
+            raise ValueError(
+                "Classification API is only enabled for `--task classify`")
+
+        items = self.encode(prompts,
+                            use_tqdm=use_tqdm,
+                            lora_request=lora_request,
+                            prompt_adapter_request=prompt_adapter_request)
+
+        return [ClassificationRequestOutput.from_base(item) for item in items]
+
     def score(
         self,
         text_1: Union[SingletonPrompt, Sequence[SingletonPrompt]],
         text_2: Union[SingletonPrompt, Sequence[SingletonPrompt]],
         /,
+        *,
         truncate_prompt_tokens: Optional[int] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
-    ) -> List[PoolingRequestOutput]:
-        """Generates similarity scores for all pairs <text,text_pair>.
+    ) -> List[ScoringRequestOutput]:
+        """Generate similarity scores for all pairs ``<text,text_pair>``.
 
-        The inputs can be 1 -> 1, 1 -> N or N -> N. In the 1 - N case
-        the text_1 sentence will be replicated N times to pair with the text_2
-        sentences. The input pairs are used to build a list of prompts for the
+        The inputs can be ``1 -> 1``, ``1 -> N`` or ``N -> N``.
+        In the ``1 - N`` case the ``text_1`` sentence will be replicated ``N``
+        times to pair with the ``text_2`` sentences.
+        The input pairs are used to build a list of prompts for the
         cross encoder model. This class automatically batches the prompts,
         considering the memory constraint. For the best performance, put all
         of your texts into a single list and pass it to this method.
 
         Args:
             text_1: can be a single prompt or a list of prompts, in which
-                case it has to have the same length as the text_2 list
+                case it has to have the same length as the ``text_2`` list
             text_2: The texts to pair with the query to form the input
                 to the LLM. See :class:`~vllm.inputs.PromptType` for
                 more details about the format of each prompts.
@@ -864,7 +973,7 @@ class LLM:
                 generation, if any.
 
         Returns:
-            A list of ``PoolingRequestOutput`` objects containing the
+            A list of ``ScoringRequestOutput`` objects containing the
             generated scores in the same order as the input prompts.
         """
         runner_type = self.llm_engine.model_config.runner_type
@@ -884,6 +993,8 @@ class LLM:
 
         if not self.llm_engine.model_config.is_cross_encoder:
             raise ValueError("Your model does not support cross encoding")
+        if self.llm_engine.model_config.task != "score":
+            raise ValueError("Score API is only enabled for `--task score`")
 
         tokenizer = self.llm_engine.get_tokenizer()
 
@@ -954,8 +1065,10 @@ class LLM:
         )
 
         outputs = self._run_engine(use_tqdm=use_tqdm)
-        return self.engine_class.validate_outputs(outputs,
-                                                  PoolingRequestOutput)
+        items = self.engine_class.validate_outputs(outputs,
+                                                   PoolingRequestOutput)
+
+        return [ScoringRequestOutput.from_base(item) for item in items]
 
     def start_profile(self) -> None:
         self.llm_engine.start_profile()

vllm/entrypoints/openai/protocol.py

@@ -900,7 +900,7 @@ class EmbeddingResponse(OpenAIBaseModel):
 class ScoreResponseData(OpenAIBaseModel):
     index: int
     object: str = "score"
-    score: Union[List[float], str]
+    score: float
 
 
 class ScoreResponse(OpenAIBaseModel):

vllm/entrypoints/openai/serving_embedding.py

@@ -18,14 +18,15 @@ from vllm.entrypoints.openai.protocol import (EmbeddingChatRequest,
                                               ErrorResponse, UsageInfo)
 from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
 from vllm.logger import init_logger
-from vllm.outputs import PoolingOutput, PoolingRequestOutput
+from vllm.outputs import (EmbeddingOutput, EmbeddingRequestOutput,
+                          PoolingRequestOutput)
 from vllm.utils import merge_async_iterators
 
 logger = init_logger(__name__)
 
 
 def _get_embedding(
-    output: PoolingOutput,
+    output: EmbeddingOutput,
     encoding_format: Literal["float", "base64"],
 ) -> Union[List[float], str]:
     if encoding_format == "float":
@@ -46,8 +47,10 @@ def request_output_to_embedding_response(
     data: List[EmbeddingResponseData] = []
     num_prompt_tokens = 0
     for idx, final_res in enumerate(final_res_batch):
+        embedding_res = EmbeddingRequestOutput.from_base(final_res)
         prompt_token_ids = final_res.prompt_token_ids
-        embedding = _get_embedding(final_res.outputs, encoding_format)
+
+        embedding = _get_embedding(embedding_res.outputs, encoding_format)
         embedding_data = EmbeddingResponseData(index=idx, embedding=embedding)
         data.append(embedding_data)
 

vllm/entrypoints/openai/serving_engine.py

@@ -31,7 +31,7 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               ErrorResponse,
                                               LoadLoraAdapterRequest,
                                               ModelCard, ModelList,
-                                              ModelPermission,
+                                              ModelPermission, ScoreRequest,
                                               TokenizeChatRequest,
                                               TokenizeCompletionRequest,
                                               UnloadLoraAdapterRequest)
@@ -73,7 +73,7 @@ class LoRAModulePath:
 
 
 CompletionLikeRequest = Union[CompletionRequest, DetokenizeRequest,
-                              EmbeddingCompletionRequest,
+                              EmbeddingCompletionRequest, ScoreRequest,
                               TokenizeCompletionRequest]
 
 ChatLikeRequest = Union[ChatCompletionRequest, EmbeddingChatRequest,
@@ -567,12 +567,14 @@ class OpenAIServing:
         return None
 
     @staticmethod
-    def _base_request_id(raw_request: Request,
+    def _base_request_id(raw_request: Optional[Request],
                          default: Optional[str] = None) -> Optional[str]:
         """Pulls the request id to use from a header, if provided"""
         default = default or random_uuid()
-        return raw_request.headers.get(
-            "X-Request-Id", default) if raw_request is not None else default
+        if raw_request is None:
+            return default
+
+        return raw_request.headers.get("X-Request-Id", default)
 
     @staticmethod
     def _get_decoded_token(logprob: Logprob,

vllm/entrypoints/openai/serving_score.py

@@ -13,7 +13,7 @@ from vllm.entrypoints.openai.protocol import (ErrorResponse, ScoreRequest,
 from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
 from vllm.inputs.data import TokensPrompt
 from vllm.logger import init_logger
-from vllm.outputs import PoolingRequestOutput
+from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
 from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 from vllm.utils import make_async, merge_async_iterators
 
@@ -24,13 +24,13 @@ def request_output_to_score_response(
         final_res_batch: List[PoolingRequestOutput], request_id: str,
         created_time: int, model_name: str) -> ScoreResponse:
     data: List[ScoreResponseData] = []
-    score = None
     num_prompt_tokens = 0
     for idx, final_res in enumerate(final_res_batch):
-        if final_res is not None:
-            score = final_res.outputs.embedding
-            score_data = ScoreResponseData(index=idx, score=score)
-            data.append(score_data)
+        classify_res = ScoringRequestOutput.from_base(final_res)
+
+        score_data = ScoreResponseData(index=idx,
+                                       score=classify_res.outputs.score)
+        data.append(score_data)
 
     usage = UsageInfo(
         prompt_tokens=num_prompt_tokens,

vllm/model_executor/layers/pooler.py

 from enum import IntEnum
-from typing import List, Optional
+from typing import List, Optional, Union
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from transformers import PretrainedConfig
+from typing_extensions import assert_never
 
 from vllm.config import PoolerConfig
 from vllm.model_executor.pooling_metadata import (PoolingMetadata,
                                                   PoolingTensors)
-from vllm.sequence import EmbeddingSequenceGroupOutput, PoolerOutput
+from vllm.sequence import PoolerOutput, PoolingSequenceGroupOutput
 from vllm.transformers_utils.config import (
     get_cross_encoder_activation_function)
 
@@ -22,7 +24,7 @@ class PoolingType(IntEnum):
     MEAN = 4
 
 
-class Pooler(nn.Module):
+class SimplePooler(nn.Module):
     """A layer that pools specific information from hidden states.
 
     This layer does the following:
@@ -35,21 +37,203 @@ class Pooler(nn.Module):
         normalize: Whether to normalize the pooled data.
     """
 
+    @staticmethod
+    def from_pooling_type(
+        pooling_type: PoolingType,
+        *,
+        normalize: bool,
+        softmax: bool,
+        step_tag_id: Optional[int] = None,
+        returned_token_ids: Optional[List[int]] = None,
+    ) -> "SimplePooler":
+        if pooling_type == PoolingType.LAST:
+            assert step_tag_id is None and returned_token_ids is None
+            return LastPool(normalize=normalize, softmax=softmax)
+        if pooling_type == PoolingType.ALL:
+            assert step_tag_id is None and returned_token_ids is None
+            return AllPool(normalize=normalize, softmax=softmax)
+        if pooling_type == PoolingType.CLS:
+            assert step_tag_id is None and returned_token_ids is None
+            return CLSPool(normalize=normalize, softmax=softmax)
+        if pooling_type == PoolingType.MEAN:
+            assert step_tag_id is None and returned_token_ids is None
+            return MeanPool(normalize=normalize, softmax=softmax)
+        if pooling_type == PoolingType.STEP:
+            return StepPool(normalize=normalize,
+                            softmax=softmax,
+                            step_tag_id=step_tag_id,
+                            returned_token_ids=returned_token_ids)
+
+        assert_never(pooling_type)
+
+    def __init__(self, *, normalize: bool, softmax: bool) -> None:
+        super().__init__()
+
+        self.head = PoolerHead(normalize=normalize, softmax=softmax)
+
+    def get_prompt_lens(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> torch.Tensor:
+        return PoolingTensors.from_pooling_metadata(
+            pooling_metadata, hidden_states.device).prompt_lens
+
+    def extract_states(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[list[torch.Tensor], torch.Tensor]:
+        raise NotImplementedError
+
+    def build_output(self, data: torch.Tensor) -> PoolingSequenceGroupOutput:
+        return PoolingSequenceGroupOutput(data)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> PoolerOutput:
+        pooled_data = self.extract_states(hidden_states, pooling_metadata)
+        pooled_data = self.head(pooled_data)
+        pooled_outputs = [self.build_output(data) for data in pooled_data]
+        return PoolerOutput(outputs=pooled_outputs)
+
+
+class CLSPool(SimplePooler):
+
+    def extract_states(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[list[torch.Tensor], torch.Tensor]:
+        prompt_lens = self.get_prompt_lens(hidden_states, pooling_metadata)
+
+        first_token_flat_indices = torch.zeros_like(prompt_lens)
+        first_token_flat_indices[1:] += torch.cumsum(prompt_lens, dim=0)[:-1]
+        return hidden_states[first_token_flat_indices]
+
+
+class LastPool(SimplePooler):
+
+    def extract_states(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[list[torch.Tensor], torch.Tensor]:
+        prompt_lens = self.get_prompt_lens(hidden_states, pooling_metadata)
+
+        last_token_flat_indices = torch.cumsum(prompt_lens, dim=0) - 1
+        return hidden_states[last_token_flat_indices]
+
+
+class AllPool(SimplePooler):
+
+    def extract_states(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[list[torch.Tensor], torch.Tensor]:
+        prompt_lens = self.get_prompt_lens(hidden_states, pooling_metadata)
+
+        offset = 0
+        pooled_data = list[torch.Tensor]()
+        for prompt_len in prompt_lens:
+            pooled_data.append(hidden_states[offset:offset + prompt_len])
+            offset += prompt_len
+
+        return pooled_data
+
+
+class MeanPool(SimplePooler):
+
+    def extract_states(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[list[torch.Tensor], torch.Tensor]:
+        prompt_lens = self.get_prompt_lens(hidden_states, pooling_metadata)
+
+        cumsum = torch.cumsum(hidden_states, dim=0)
+        start_indices = torch.cat([
+            torch.tensor([0], device=hidden_states.device),
+            torch.cumsum(prompt_lens[:-1], dim=0)
+        ])
+        end_indices = torch.cumsum(prompt_lens, dim=0)
+        return (cumsum[end_indices - 1] - cumsum[start_indices] +
+                hidden_states[start_indices]) / prompt_lens.unsqueeze(1)
+
+
+class StepPool(SimplePooler):
+
     def __init__(
         self,
-        pooling_type: PoolingType,
+        *,
         normalize: bool,
         softmax: bool,
         step_tag_id: Optional[int] = None,
         returned_token_ids: Optional[List[int]] = None,
     ):
+        super().__init__(normalize=normalize, softmax=softmax)
+
+        self.step_tag_id = step_tag_id
+        self.returned_token_ids = returned_token_ids
+
+    def extract_states(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Union[list[torch.Tensor], torch.Tensor]:
+        prompt_lens = self.get_prompt_lens(hidden_states, pooling_metadata)
+
+        returned_token_ids = self.returned_token_ids
+        if returned_token_ids is not None and len(returned_token_ids) > 0:
+            hidden_states = hidden_states[:, returned_token_ids]
+
+        step_tag_id = self.step_tag_id
+
+        offset = 0
+        pooled_data = list[torch.Tensor]()
+        for prompt_len, seq_data_i in zip(prompt_lens,
+                                          pooling_metadata.seq_data.values()):
+            pooled_data_i = hidden_states[offset:offset + prompt_len]
+            if step_tag_id is not None:
+                token_ids = torch.tensor(seq_data_i.prompt_token_ids)
+                pooled_data_i = pooled_data_i[token_ids == step_tag_id]
+
+            offset += prompt_len
+            pooled_data.append(pooled_data_i)
+
+        return pooled_data
+
+
+class PoolerHead(nn.Module):
+
+    def __init__(self, *, normalize: bool, softmax: bool) -> None:
         super().__init__()
 
-        self.pooling_type = pooling_type
         self.normalize = normalize
         self.softmax = softmax
-        self.step_tag_id = step_tag_id
-        self.returned_token_ids = returned_token_ids
+
+    def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor]):
+        if self.normalize:
+            if isinstance(pooled_data, list):
+                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)
+
+        if self.softmax:
+            if isinstance(pooled_data, list):
+                pooled_data = [F.softmax(data, dim=-1) for data in pooled_data]
+            else:
+                pooled_data = F.softmax(pooled_data, dim=-1)
+
+        return pooled_data
+
+
+class Pooler(nn.Module):
 
     @classmethod
     def from_config_with_defaults(
@@ -60,8 +244,8 @@ class Pooler(nn.Module):
         softmax: bool,
         step_tag_id: Optional[int] = None,
         returned_token_ids: Optional[List[int]] = None,
-    ) -> "Pooler":
-        return cls(
+    ) -> SimplePooler:
+        return SimplePooler.from_pooling_type(
             pooling_type=PoolingType[pooler_config.pooling_type]
             if pooler_config.pooling_type is not None else pooling_type,
             normalize=pooler_config.normalize
@@ -75,85 +259,6 @@ class Pooler(nn.Module):
             returned_token_ids,
         )
 
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> PoolerOutput:
-        """Pools specific information from hidden states based on metadata."""
-
-        prompt_lens = PoolingTensors.from_pooling_metadata(
-            pooling_metadata, hidden_states.device).prompt_lens
-
-        if self.pooling_type is PoolingType.CLS:
-            first_token_flat_indices = torch.zeros_like(prompt_lens)
-            first_token_flat_indices[1:] += torch.cumsum(prompt_lens,
-                                                         dim=0)[:-1]
-            pooled_data = hidden_states[first_token_flat_indices]
-        elif self.pooling_type == PoolingType.LAST:
-            last_token_flat_indices = torch.cumsum(prompt_lens, dim=0) - 1
-            pooled_data = hidden_states[last_token_flat_indices]
-        elif self.pooling_type == PoolingType.ALL:
-            offset = 0
-            pooled_data = []
-            for prompt_len in prompt_lens:
-                pooled_data.append(hidden_states[offset:offset + prompt_len])
-                offset += prompt_len
-        elif self.pooling_type == PoolingType.MEAN:
-            # Calculate mean pooling
-            cumsum = torch.cumsum(hidden_states, dim=0)
-            start_indices = torch.cat([
-                torch.tensor([0], device=hidden_states.device),
-                torch.cumsum(prompt_lens[:-1], dim=0)
-            ])
-            end_indices = torch.cumsum(prompt_lens, dim=0)
-            pooled_data = (
-                cumsum[end_indices - 1] - cumsum[start_indices] +
-                hidden_states[start_indices]) / prompt_lens.unsqueeze(1)
-        elif self.pooling_type == PoolingType.STEP:
-            returned_token_ids = self.returned_token_ids
-            if returned_token_ids is not None and len(returned_token_ids) > 0:
-                hidden_states = hidden_states[:, returned_token_ids]
-
-            step_tag_id = self.step_tag_id
-
-            offset = 0
-            pooled_data = []
-            for prompt_len, seq_data_i in zip(
-                    prompt_lens, pooling_metadata.seq_data.values()):
-                pooled_data_i = hidden_states[offset:offset + prompt_len]
-                if step_tag_id is not None:
-                    token_ids = torch.tensor(seq_data_i.prompt_token_ids)
-                    pooled_data_i = pooled_data_i[token_ids == step_tag_id]
-
-                offset += prompt_len
-                pooled_data.append(pooled_data_i)
-        else:
-            raise ValueError(f"Invalid pooling type: {self.pooling_type}")
-
-        if self.normalize:
-            if isinstance(pooled_data, list):
-                pooled_data = [
-                    nn.functional.normalize(data, p=2, dim=1)
-                    for data in pooled_data
-                ]
-            else:
-                pooled_data = nn.functional.normalize(pooled_data, p=2, dim=1)
-
-        if self.softmax:
-            if isinstance(pooled_data, list):
-                pooled_data = [
-                    nn.functional.softmax(data, dim=-1) for data in pooled_data
-                ]
-            else:
-                pooled_data = nn.functional.softmax(pooled_data, dim=-1)
-
-        pooled_outputs = [
-            EmbeddingSequenceGroupOutput(data.tolist()) for data in pooled_data
-        ]
-
-        return PoolerOutput(outputs=pooled_outputs)
-
 
 class CrossEncodingPooler(nn.Module):
     """A layer that pools specific information from hidden states.
@@ -208,9 +313,8 @@ class CrossEncodingPooler(nn.Module):
         if self.pooler is not None:
             # apply classifier once on the full batch if possible
             pooled_output = self.classifier(pooled_output)
-        logits = self.default_activation_function(pooled_output)
 
-        pooled_outputs = [
-            EmbeddingSequenceGroupOutput(data.tolist()) for data in logits
-        ]
+        scores = self.default_activation_function(pooled_output).squeeze(-1)
+
+        pooled_outputs = [PoolingSequenceGroupOutput(data) for data in scores]
         return PoolerOutput(outputs=pooled_outputs)

vllm/model_executor/models/gritlm.py

@@ -2,19 +2,20 @@ from array import array
 from typing import List, Optional, Union
 
 import torch
-from torch import nn
+import torch.nn as nn
 from xformers.ops.fmha.attn_bias import BlockDiagonalMask
 
 from vllm.attention import AttentionMetadata
 from vllm.attention.backends.xformers import XFormersImpl
 from vllm.config import ModelConfig, VllmConfig
 from vllm.logger import init_logger
+from vllm.model_executor.layers.pooler import PoolerHead
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.pooling_metadata import (PoolingMetadata,
                                                   PoolingTensors)
 from vllm.multimodal.utils import cached_get_tokenizer
-from vllm.sequence import (EmbeddingSequenceGroupOutput, IntermediateTensors,
-                           PoolerOutput)
+from vllm.sequence import (IntermediateTensors, PoolerOutput,
+                           PoolingSequenceGroupOutput)
 
 logger = init_logger(__name__)
 
@@ -52,6 +53,8 @@ class GritLMPooler(nn.Module):
         self.embed_pattern_ids = tokens_to_ids(
             ["▁<", "|", "embed", "|", ">", "<0x0A>"])
 
+        self.head = PoolerHead(normalize=True, softmax=False)
+
     def _find_array(self, arr: array, target: array, start_idx: int) -> int:
         """
         Find the first occurrence of target in arr starting from start_idx.
@@ -75,7 +78,7 @@ class GritLMPooler(nn.Module):
                 return i
         return -1
 
-    def _get_instruction_len(self, prompt_token_ids: array) -> bool:
+    def _get_instruction_len(self, prompt_token_ids: array) -> int:
         """
         Get the length of the instruction in the prompt.
 
@@ -168,10 +171,10 @@ class GritLMPooler(nn.Module):
         mean_embeddings = sum_embeddings / num_non_instruction_tokens.unsqueeze(
             1)
 
-        pooled_data = nn.functional.normalize(mean_embeddings, p=2, dim=1)
+        pooled_data = self.head(mean_embeddings)
 
         pooled_outputs = [
-            EmbeddingSequenceGroupOutput(data.tolist()) for data in pooled_data
+            PoolingSequenceGroupOutput(data) for data in pooled_data
         ]
 
         return PoolerOutput(outputs=pooled_outputs)

vllm/outputs.py

 import time
+import warnings
 from dataclasses import dataclass
-from typing import Dict, List, Optional
+from typing import Dict, Generic, List, Optional
 from typing import Sequence as GenericSequence
 from typing import Union
 
+import torch
+from typing_extensions import TypeVar
+
 from vllm.lora.request import LoRARequest
 from vllm.multimodal.inputs import MultiModalPlaceholderDict
 from vllm.sampling_params import RequestOutputKind
@@ -57,14 +61,26 @@ class PoolingOutput:
     """The output data of one pooling output of a request.
 
     Args:
-        embedding: The embedding vector, which is a list of floats. The
-        length of vector depends on the model as listed in the embedding guide.
+        data: The extracted hidden states.
     """
-    embedding: List[float]
+    data: torch.Tensor
 
     def __repr__(self) -> str:
-        return (f"PoolingOutput("
-                f"embedding={len(self.embedding)})")
+        return (f"PoolingOutput(data={self.data})")
+
+    def __eq__(self, other: object) -> bool:
+        return (isinstance(other, self.__class__) and bool(
+            (self.data == other.data).all()))
+
+    @property
+    def embedding(self) -> list[float]:
+        msg = ("`LLM.encode()` now returns raw outputs. "
+               "To return embeddings, use `LLM.embed()`. "
+               "To return class probabilities, use `LLM.classify()` "
+               "and access the `probs` attribute. ")
+        warnings.warn(msg, DeprecationWarning, stacklevel=2)
+
+        return self.data.tolist()
 
 
 class RequestOutput:
@@ -316,7 +332,10 @@ class RequestOutput:
                 f"multi_modal_placeholders={self.multi_modal_placeholders})")
 
 
-class PoolingRequestOutput:
+_O = TypeVar("_O", default=PoolingOutput)
+
+
+class PoolingRequestOutput(Generic[_O]):
     """
     The output data of a pooling request to the LLM.
 
@@ -327,24 +346,24 @@ class PoolingRequestOutput:
         finished (bool): A flag indicating whether the pooling is completed.
     """
 
-    def __init__(self, request_id: str, outputs: "PoolingOutput",
+    def __init__(self, request_id: str, outputs: _O,
                  prompt_token_ids: List[int], finished: bool):
         self.request_id = request_id
         self.prompt_token_ids = prompt_token_ids
         self.finished = finished
         self.outputs = outputs
 
-    @classmethod
-    def from_seq_group(cls,
-                       seq_group: 'SequenceGroup') -> "PoolingRequestOutput":
-        if seq_group.embeddings is None:
-            raise ValueError(
-                "Embeddings are missing in seq_group for EmbeddingRequest.")
-        output = PoolingOutput(seq_group.embeddings)
+    @staticmethod
+    def from_seq_group(seq_group: SequenceGroup) -> "PoolingRequestOutput":
+        pooled_data = seq_group.pooled_data
+        assert pooled_data is not None
+
+        output = PoolingOutput(pooled_data)
         prompt_token_ids = seq_group.prompt_token_ids
         finished = seq_group.is_finished()
 
-        return cls(seq_group.request_id, output, prompt_token_ids, finished)
+        return PoolingRequestOutput(seq_group.request_id, output,
+                                    prompt_token_ids, finished)
 
     def __repr__(self):
         """
@@ -356,89 +375,137 @@ class PoolingRequestOutput:
         Returns:
             str: A string representation of the PoolingRequestOutput instance.
         """
-        return (f"PoolingRequestOutput(request_id='{self.request_id}', "
-                f"outputs={repr(self.outputs)}, "
+        return (f"{type(self).__name__}(request_id={self.request_id!r}, "
+                f"outputs={self.outputs!r}, "
                 f"prompt_token_ids={self.prompt_token_ids}, "
                 f"finished={self.finished})")
 
 
+class RequestOutputFactory:
+
+    @staticmethod
+    def create(seq_group: SequenceGroup,
+               seq_id_to_seq_group: Dict[str, SequenceGroupBase],
+               use_cache: bool = False):
+        if seq_group.pooled_data is not None:
+            return PoolingRequestOutput.from_seq_group(seq_group)
+        else:
+            return RequestOutput.from_seq_group(seq_group, use_cache,
+                                                seq_id_to_seq_group)
+
+
 @dataclass
-class ScoreOutput:
-    """The output data of one completion output of a request.
+class EmbeddingOutput:
+    """The output data of one embedding output of a request.
 
     Args:
-        score: The score, which is a list of floats. 
-        index: The correspondent text index of the score.
+        embedding: The embedding vector, which is a list of floats.
+        Its length depends on the hidden dimension of the model.
     """
-    index: int
-    score: List[float]
+    embedding: list[float]
+
+    @staticmethod
+    def from_base(pooling_output: PoolingOutput):
+        pooled_data = pooling_output.data
+        if pooled_data.ndim != 1:
+            raise ValueError("pooled_data should be a 1-D embedding vector")
+
+        return EmbeddingOutput(pooled_data.tolist())
+
+    @property
+    def hidden_size(self) -> int:
+        return len(self.embedding)
 
     def __repr__(self) -> str:
-        return (f"ScoreOutput("
-                f"score={self.score}), "
-                f"index={self.index})")
+        return f"EmbeddingOutput(hidden_size={self.hidden_size})"
 
 
-class ScoreRequestOutput:
-    """
-    The output data of an score request to the LLM.
+class EmbeddingRequestOutput(PoolingRequestOutput[EmbeddingOutput]):
+
+    @staticmethod
+    def from_base(request_output: PoolingRequestOutput):
+        return EmbeddingRequestOutput(
+            request_id=request_output.request_id,
+            outputs=EmbeddingOutput.from_base(request_output.outputs),
+            prompt_token_ids=request_output.prompt_token_ids,
+            finished=request_output.finished,
+        )
+
+
+@dataclass
+class ClassificationOutput:
+    """The output data of one classification output of a request.
 
     Args:
-        request_id (str): A unique identifier for the score request.
-        outputs (score): The embedding results for the given input.
+        probs: The probability vector, which is a list of floats.
+        Its length depends on the number of classes.
     """
+    probs: list[float]
 
-    def __init__(self, request_id: str, outputs: "ScoreOutput"):
-        self.request_id = request_id
-        self.outputs = outputs
+    @staticmethod
+    def from_base(pooling_output: PoolingOutput):
+        pooled_data = pooling_output.data
+        if pooled_data.ndim != 1:
+            raise ValueError("pooled_data should be a 1-D probability vector")
 
-    def __repr__(self):
-        """
-        Returns a string representation of an ScoreRequestOutput instance.
+        return ClassificationOutput(pooled_data.tolist())
 
-        The representation includes the request_id and the number of outputs,
-        providing a quick overview of the embedding request's results.
+    @property
+    def num_classes(self) -> int:
+        return len(self.probs)
 
-        Returns:
-            str: A string representation of the ScoreRequestOutput instance.
-        """
-        return (f"ScoreRequestOutput(request_id='{self.request_id}', "
-                f"outputs={repr(self.outputs)}")
+    def __repr__(self) -> str:
+        return f"ClassificationOutput(num_classes={self.num_classes})"
 
 
-class RequestOutputFactory:
+class ClassificationRequestOutput(PoolingRequestOutput[ClassificationOutput]):
 
     @staticmethod
-    def create(seq_group: SequenceGroup,
-               seq_id_to_seq_group: Dict[str, SequenceGroupBase],
-               use_cache: bool = False):
-        # Determine the type based on a condition, for example:
-        if hasattr(seq_group,
-                   'embeddings') and seq_group.embeddings is not None:
-            return PoolingRequestOutput.from_seq_group(seq_group)
-        else:
-            return RequestOutput.from_seq_group(seq_group, use_cache,
-                                                seq_id_to_seq_group)
+    def from_base(request_output: PoolingRequestOutput):
+        return ClassificationRequestOutput(
+            request_id=request_output.request_id,
+            outputs=ClassificationOutput.from_base(request_output.outputs),
+            prompt_token_ids=request_output.prompt_token_ids,
+            finished=request_output.finished,
+        )
 
 
-def __getattr__(name: str):
-    import warnings
+@dataclass
+class ScoringOutput:
+    """The output data of one scoring output of a request.
 
-    if name == "EmbeddingOutput":
-        msg = ("EmbeddingOutput has been renamed to PoolingOutput. "
-               "The original name will be removed in an upcoming version.")
+    Args:
+        score: The similarity score, which is a scalar value.
+    """
+    score: float
+
+    @staticmethod
+    def from_base(pooling_output: PoolingOutput):
+        pooled_data = pooling_output.data
+        if pooled_data.ndim != 0:
+            raise ValueError("pooled_data should be a scalar score")
 
-        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+        return ScoringOutput(pooled_data.item())
 
-        return PoolingOutput
+    def __repr__(self) -> str:
+        return f"ScoringOutput(score={self.score})"
 
-    if name == "EmbeddingRequestOutput":
-        msg = ("EmbeddingRequestOutput has been renamed to "
-               "PoolingRequestOutput. "
-               "The original name will be removed in an upcoming version.")
+    @property
+    def embedding(self) -> list[float]:
+        msg = ("`LLM.score()` now returns scalar scores. "
+               "Please access it via the `score` attribute. ")
+        warnings.warn(msg, DeprecationWarning, stacklevel=2)
 
-        warnings.warn(DeprecationWarning(msg), stacklevel=2)
+        return [self.score]
 
-        return PoolingRequestOutput
 
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+class ScoringRequestOutput(PoolingRequestOutput[ScoringOutput]):
+
+    @staticmethod
+    def from_base(request_output: PoolingRequestOutput):
+        return ScoringRequestOutput(
+            request_id=request_output.request_id,
+            outputs=ScoringOutput.from_base(request_output.outputs),
+            prompt_token_ids=request_output.prompt_token_ids,
+            finished=request_output.finished,
+        )