[Misc] Split up pooling tasks (#10820)

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

docs/source/index.rst

@@ -94,6 +94,8 @@ Documentation
    :caption: Models
 
    models/supported_models
+   models/generative_models
+   models/pooling_models
    models/adding_model
    models/enabling_multimodal_inputs
 

docs/source/models/generative_models.rst

+.. _generative_models:
+
+Generative Models
+=================
+
+vLLM provides first-class support for generative models, which covers most of LLMs.
+
+In vLLM, generative models implement the :class:`~vllm.model_executor.models.VllmModelForTextGeneration` interface.
+Based on the final hidden states of the input, these models output log probabilities of the tokens to generate,
+which are then passed through :class:`~vllm.model_executor.layers.Sampler` to obtain the final text.
+
+Offline Inference
+-----------------
+
+The :class:`~vllm.LLM` class provides various methods for offline inference.
+See :ref:`Engine Arguments <engine_args>` for a list of options when initializing the model.
+
+For generative models, the only supported :code:`task` option is :code:`"generate"`.
+Usually, this is automatically inferred so you don't have to specify it.
+
+``LLM.generate``
+^^^^^^^^^^^^^^^^
+
+The :class:`~vllm.LLM.generate` method is available to all generative models in vLLM.
+It is similar to `its counterpart in HF Transformers <https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate>`__,
+except that tokenization and detokenization are also performed automatically.
+
+.. code-block:: python
+
+    llm = LLM(model="facebook/opt-125m")
+    outputs = llm.generate("Hello, my name is")
+
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+You can optionally control the language generation by passing :class:`~vllm.SamplingParams`.
+For example, you can use greedy sampling by setting :code:`temperature=0`:
+
+.. code-block:: python
+
+    llm = LLM(model="facebook/opt-125m")
+    params = SamplingParams(temperature=0)
+    outputs = llm.generate("Hello, my name is", params)
+
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+A code example can be found in `examples/offline_inference.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference.py>`_.
+
+``LLM.beam_search``
+^^^^^^^^^^^^^^^^^^^
+
+The :class:`~vllm.LLM.beam_search` method implements `beam search <https://huggingface.co/docs/transformers/en/generation_strategies#beam-search-decoding>`__ on top of :class:`~vllm.LLM.generate`.
+For example, to search using 5 beams and output at most 50 tokens:
+
+.. code-block:: python
+
+    llm = LLM(model="facebook/opt-125m")
+    params = BeamSearchParams(beam_width=5, max_tokens=50)
+    outputs = llm.generate("Hello, my name is", params)
+
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+``LLM.chat``
+^^^^^^^^^^^^
+
+The :class:`~vllm.LLM.chat` method implements chat functionality on top of :class:`~vllm.LLM.generate`.
+In particular, it accepts input similar to `OpenAI Chat Completions API <https://platform.openai.com/docs/api-reference/chat>`__
+and automatically applies the model's `chat template <https://huggingface.co/docs/transformers/en/chat_templating>`__ to format the prompt.
+
+.. important::
+
+    In general, only instruction-tuned models have a chat template.
+    Base models may perform poorly as they are not trained to respond to the chat conversation.
+
+.. code-block:: python
+
+    llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
+    conversation = [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant"
+        },
+        {
+            "role": "user",
+            "content": "Hello"
+        },
+        {
+            "role": "assistant",
+            "content": "Hello! How can I assist you today?"
+        },
+        {
+            "role": "user",
+            "content": "Write an essay about the importance of higher education.",
+        },
+    ]
+    outputs = llm.chat(conversation)
+
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+A code example can be found in `examples/offline_inference_chat.py <https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_chat.py>`_.
+
+If the model doesn't have a chat template or you want to specify another one,
+you can explicitly pass a chat template:
+
+.. code-block:: python
+
+    from vllm.entrypoints.chat_utils import load_chat_template
+
+    # You can find a list of existing chat templates under `examples/`
+    custom_template = load_chat_template(chat_template="<path_to_template>")
+    print("Loaded chat template:", custom_template)
+
+    outputs = llm.chat(conversation, chat_template=custom_template)
+
+Online Inference
+----------------
+
+Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference.
+Please click on the above link for more details on how to launch the server.
+
+Completions API
+^^^^^^^^^^^^^^^
+
+Our Completions API is similar to ``LLM.generate`` but only accepts text.
+It is compatible with `OpenAI Completions API <https://platform.openai.com/docs/api-reference/completions>`__
+so that you can use OpenAI client to interact with it.
+A code example can be found in `examples/openai_completion_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_completion_client.py>`_.
+
+Chat API
+^^^^^^^^
+
+Our Chat API is similar to ``LLM.chat``, accepting both text and :ref:`multi-modal inputs <multimodal_inputs>`.
+It is compatible with `OpenAI Chat Completions API <https://platform.openai.com/docs/api-reference/chat>`__
+so that you can use OpenAI client to interact with it.
+A code example can be found in `examples/openai_chat_completion_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_completion_client.py>`_.

docs/source/models/pooling_models.rst

+.. _pooling_models:
+
+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
+before returning them.
+
+.. note::
+
+    We currently support pooling models primarily as a matter of convenience.
+    As shown in the :ref:`Compatibility Matrix <compatibility_matrix>`, most vLLM features are not applicable to
+    pooling models as they only work on the generation or decode stage, so performance may not improve as much.
+
+Offline Inference
+-----------------
+
+The :class:`~vllm.LLM` class provides various methods for offline inference.
+See :ref:`Engine Arguments <engine_args>` for a list of options when initializing the model.
+
+For pooling models, we support the following :code:`task` options:
+
+- Embedding (:code:`"embed"` / :code:`"embedding"`)
+- Classification (:code:`"classify"`)
+- Sentence Pair Scoring (:code:`"score"`)
+- Reward Modeling (:code:`"reward"`)
+
+The selected task determines the default :class:`~vllm.model_executor.layers.Pooler` that is used:
+
+- Embedding: Extract only the hidden states corresponding to the last token, and apply normalization.
+- Classification: Extract only the hidden states corresponding to the last token, and apply softmax.
+- Sentence Pair Scoring: Extract only the hidden states corresponding to the last token, and apply softmax.
+- Reward Modeling: Extract all of the hidden states and return them directly.
+
+When loading `Sentence Transformers <https://huggingface.co/sentence-transformers>`__ models,
+we attempt to override the default pooler based on its Sentence Transformers configuration file (:code:`modules.json`).
+
+You can customize the model's pooling method via the :code:`override_pooler_config` option,
+which takes priority over both the model's and Sentence Transformers's defaults.
+
+``LLM.encode``
+^^^^^^^^^^^^^^
+
+The :class:`~vllm.LLM.encode` method is available to all pooling models in vLLM.
+It returns the aggregated hidden states directly.
+
+.. code-block:: python
+
+    llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed")
+    outputs = llm.encode("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}")
+
+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.score``
+^^^^^^^^^^^^^
+
+The :class:`~vllm.LLM.score` method outputs similarity scores between sentence pairs.
+It is primarily designed for `cross-encoder models <https://www.sbert.net/examples/applications/cross-encoder/README.html>`__.
+These types of models serve as rerankers between candidate query-document pairs in RAG systems.
+
+.. note::
+
+    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.
+
+Online Inference
+----------------
+
+Our `OpenAI Compatible Server <../serving/openai_compatible_server>`__ can be used for online inference.
+Please click on the above link for more details on how to launch the server.
+
+Embeddings API
+^^^^^^^^^^^^^^
+
+Our Embeddings API is similar to ``LLM.encode``, accepting both text and :ref:`multi-modal inputs <multimodal_inputs>`.
+
+The text-only API is compatible with `OpenAI Embeddings API <https://platform.openai.com/docs/api-reference/embeddings>`__
+so that you can use OpenAI client to interact with it.
+A code example can be found in `examples/openai_embedding_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_embedding_client.py>`_.
+
+The multi-modal API is an extension of the `OpenAI Embeddings API <https://platform.openai.com/docs/api-reference/embeddings>`__
+that incorporates `OpenAI Chat Completions API <https://platform.openai.com/docs/api-reference/chat>`__,
+so it is not part of the OpenAI standard. Please see :ref:`this page <multimodal_inputs>` for more details on how to use it.
+
+Score API
+^^^^^^^^^
+
+Our Score API is similar to ``LLM.score``.
+Please see `this page <../serving/openai_compatible_server.html#score-api-for-cross-encoder-models>`__ for more details on how to use it.

docs/source/models/supported_models.rst

@@ -3,11 +3,21 @@
 Supported Models
 ================
 
-vLLM supports a variety of generative and embedding models from `HuggingFace (HF) Transformers <https://huggingface.co/models>`_.
-This page lists the model architectures that are currently supported by vLLM.
+vLLM supports generative and pooling models across various tasks.
+If a model supports more than one task, you can set the task via the :code:`--task` argument.
+
+For each task, we list the model architectures that have been implemented in vLLM.
 Alongside each architecture, we include some popular models that use it.
 
-For other models, you can check the :code:`config.json` file inside the model repository.
+Loading a Model
+^^^^^^^^^^^^^^^
+
+HuggingFace Hub
++++++++++++++++
+
+By default, vLLM loads models from `HuggingFace (HF) Hub <https://huggingface.co/models>`_.
+
+To determine whether a given model is supported, you can check the :code:`config.json` file inside the HF repository.
 If the :code:`"architectures"` field contains a model architecture listed below, then it should be supported in theory.
 
 .. tip::
@@ -17,38 +27,57 @@ If the :code:`"architectures"` field contains a model architecture listed below,
 
         from vllm import LLM
 
-        llm = LLM(model=...)  # Name or path of your model
+        # For generative models (task=generate) only
+        llm = LLM(model=..., task="generate")  # Name or path of your model
         output = llm.generate("Hello, my name is")
         print(output)
 
-    If vLLM successfully generates text, it indicates that your model is supported.
+        # For pooling models (task={embed,classify,reward}) only
+        llm = LLM(model=..., task="embed")  # Name or path of your model
+        output = llm.encode("Hello, my name is")
+        print(output)
+
+    If vLLM successfully returns text (for generative models) or hidden states (for pooling models), it indicates that your model is supported.
 
 Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` and :ref:`Enabling Multimodal Inputs <enabling_multimodal_inputs>` 
 for instructions on how to implement your model in vLLM.
 Alternatively, you can `open an issue on GitHub <https://github.com/vllm-project/vllm/issues/new/choose>`_ to request vLLM support.
 
-.. note::
-    To use models from `ModelScope <https://www.modelscope.cn>`_ instead of HuggingFace Hub, set an environment variable:
+ModelScope
+++++++++++
+
+To use models from `ModelScope <https://www.modelscope.cn>`_ instead of HuggingFace Hub, set an environment variable:
 
-    .. code-block:: shell
+.. code-block:: shell
 
     $ export VLLM_USE_MODELSCOPE=True
 
-    And use with :code:`trust_remote_code=True`.
+And use with :code:`trust_remote_code=True`.
 
-    .. code-block:: python
+.. code-block:: python
 
     from vllm import LLM
 
-        llm = LLM(model=..., revision=..., trust_remote_code=True)  # Name or path of your model
+    llm = LLM(model=..., revision=..., task=..., trust_remote_code=True)
+
+    # For generative models (task=generate) only
     output = llm.generate("Hello, my name is")
     print(output)
 
-Text-only Language Models
-^^^^^^^^^^^^^^^^^^^^^^^^^
+    # For pooling models (task={embed,classify,reward}) only
+    output = llm.encode("Hello, my name is")
+    print(output)
+
+List of Text-only Language Models
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Text Generation
----------------
+Generative Models
++++++++++++++++++
+
+See :ref:`this page <generative_models>` for more information on how to use generative models.
+
+Text Generation (``--task generate``)
+-------------------------------------
 
 .. list-table::
   :widths: 25 25 50 5 5
@@ -328,8 +357,24 @@ Text Generation
 .. note::
     Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096.
 
-Text Embedding
---------------
+Pooling Models
+++++++++++++++
+
+See :ref:`this page <pooling_models>` for more information on how to use pooling models.
+
+.. important::
+    Since some model architectures support both generative and pooling tasks,
+    you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode.
+
+Text Embedding (``--task embed``)
+---------------------------------
+
+Any text generation model can be converted into an embedding model by passing :code:`--task embed`.
+
+.. note::
+    To get the best results, you should use pooling models that are specifically trained as such.
+
+The following table lists those that are tested in vLLM.
 
 .. list-table::
   :widths: 25 25 50 5 5
@@ -371,13 +416,6 @@ Text Embedding
     - 
     - 
 
-.. important::
-  Some model architectures support both generation and embedding tasks.
-  In this case, you have to pass :code:`--task embedding` to run the model in embedding mode.
-
-.. tip::
-  You can override the model's pooling method by passing :code:`--override-pooler-config`.
-
 .. note::
   :code:`ssmits/Qwen2-7B-Instruct-embed-base` has an improperly defined Sentence Transformers config.
   You should manually set mean pooling by passing :code:`--override-pooler-config '{"pooling_type": "MEAN"}'`.
@@ -389,8 +427,8 @@ Text Embedding
   On the other hand, its 1.5B variant (:code:`Alibaba-NLP/gte-Qwen2-1.5B-instruct`) uses causal attention
   despite being described otherwise on its model card.
 
-Reward Modeling
----------------
+Reward Modeling (``--task reward``)
+-----------------------------------
 
 .. list-table::
   :widths: 25 25 50 5 5
@@ -416,11 +454,8 @@ Reward Modeling
   For process-supervised reward models such as :code:`peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly,
   e.g.: :code:`--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
 
-.. note::
-    As an interim measure, these models are supported in both offline and online inference via Embeddings API.
-
-Classification
----------------
+Classification (``--task classify``)
+------------------------------------
 
 .. list-table::
   :widths: 25 25 50 5 5
@@ -437,11 +472,8 @@ Classification
     - ✅︎
     - ✅︎
 
-.. note::
-    As an interim measure, these models are supported in both offline and online inference via Embeddings API.
-
-Sentence Pair Scoring
----------------------
+Sentence Pair Scoring (``--task score``)
+----------------------------------------
 
 .. list-table::
   :widths: 25 25 50 5 5
@@ -468,13 +500,10 @@ Sentence Pair Scoring
     - 
     - 
 
-.. note::
-    These models are supported in both offline and online inference via Score API.
-
 .. _supported_mm_models:
 
-Multimodal Language Models
-^^^^^^^^^^^^^^^^^^^^^^^^^^
+List of Multimodal Language Models
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 The following modalities are supported depending on the model:
 
@@ -491,8 +520,15 @@ On the other hand, modalities separated by :code:`/` are mutually exclusive.
 
 - e.g.: :code:`T / I` means that the model supports text-only and image-only inputs, but not text-with-image inputs.
 
-Text Generation
----------------
+See :ref:`this page <multimodal_inputs>` on how to pass multi-modal inputs to the model.
+
+Generative Models
++++++++++++++++++
+
+See :ref:`this page <generative_models>` for more information on how to use generative models.
+
+Text Generation (``--task generate``)
+-------------------------------------
 
 .. list-table::
   :widths: 25 25 15 20 5 5 5
@@ -696,8 +732,24 @@ Text Generation
   The official :code:`openbmb/MiniCPM-V-2` doesn't work yet, so we need to use a fork (:code:`HwwwH/MiniCPM-V-2`) for now.
   For more details, please see: https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630
 
-Multimodal Embedding
---------------------
+Pooling Models
+++++++++++++++
+
+See :ref:`this page <pooling_models>` for more information on how to use pooling models.
+
+.. important::
+    Since some model architectures support both generative and pooling tasks,
+    you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode.
+
+Text Embedding (``--task embed``)
+---------------------------------
+
+Any text generation model can be converted into an embedding model by passing :code:`--task embed`.
+
+.. note::
+    To get the best results, you should use pooling models that are specifically trained as such.
+
+The following table lists those that are tested in vLLM.
 
 .. list-table::
   :widths: 25 25 15 25 5 5
@@ -728,12 +780,7 @@ Multimodal Embedding
     - 
     - ✅︎
 
-.. important::
-  Some model architectures support both generation and embedding tasks.
-  In this case, you have to pass :code:`--task embedding` to run the model in embedding mode.
-
-.. tip::
-  You can override the model's pooling method by passing :code:`--override-pooler-config`.
+----
 
 Model Support Policy
 =====================

docs/source/usage/compatibility_matrix.rst

@@ -39,13 +39,13 @@ Feature x Feature
      - :abbr:`prmpt adptr (Prompt Adapter)`
      - :ref:`SD <spec_decode>`
      - CUDA graph
-     - :abbr:`emd (Embedding Models)`
+     - :abbr:`pooling (Pooling Models)`
      - :abbr:`enc-dec (Encoder-Decoder Models)`
      - :abbr:`logP (Logprobs)`
      - :abbr:`prmpt logP (Prompt Logprobs)`
      - :abbr:`async output (Async Output Processing)`
      - multi-step
-     - :abbr:`mm (Multimodal)`
+     - :abbr:`mm (Multimodal Inputs)`
      - best-of
      - beam-search
      - :abbr:`guided dec (Guided Decoding)`
@@ -151,7 +151,7 @@ Feature x Feature
      - 
      - 
      - 
-   * - :abbr:`emd (Embedding Models)`
+   * - :abbr:`pooling (Pooling Models)`
      - ✗
      - ✗
      - ✗ 
@@ -253,7 +253,7 @@ Feature x Feature
      - 
      - 
      - 
-   * - :abbr:`mm (Multimodal)`
+   * - :abbr:`mm (Multimodal Inputs)`
      - ✅
      -  `✗ <https://github.com/vllm-project/vllm/pull/8348>`__ 
      -  `✗ <https://github.com/vllm-project/vllm/pull/7199>`__ 
@@ -386,7 +386,7 @@ Feature x Hardware
      - ✅
      - ✗
      - ✅
-   * - :abbr:`emd (Embedding Models)`
+   * - :abbr:`pooling (Pooling Models)`
      - ✅
      - ✅
      - ✅
@@ -402,7 +402,7 @@ Feature x Hardware
      - ✅
      - ✅
      - ✗
-   * - :abbr:`mm (Multimodal)`
+   * - :abbr:`mm (Multimodal Inputs)`
      - ✅
      - ✅
      - ✅

examples/offline_inference_embedding.py

@@ -9,7 +9,12 @@ prompts = [
 ]
 
 # Create an LLM.
-model = LLM(model="intfloat/e5-mistral-7b-instruct", enforce_eager=True)
+model = LLM(
+    model="intfloat/e5-mistral-7b-instruct",
+    task="embed",  # You should pass task="embed" for embedding models
+    enforce_eager=True,
+)
+
 # Generate embedding. The output is a list of PoolingRequestOutputs.
 outputs = model.encode(prompts)
 # Print the outputs.

examples/offline_inference_vision_language_embedding.py

@@ -59,7 +59,7 @@ def run_e5_v(query: Query):
 
     llm = LLM(
         model="royokong/e5-v",
-        task="embedding",
+        task="embed",
         max_model_len=4096,
     )
 
@@ -88,7 +88,7 @@ def run_vlm2vec(query: Query):
 
     llm = LLM(
         model="TIGER-Lab/VLM2Vec-Full",
-        task="embedding",
+        task="embed",
         trust_remote_code=True,
         mm_processor_kwargs={"num_crops": 4},
     )

tests/compile/test_basic_correctness.py

@@ -55,7 +55,7 @@ test_settings = [
     # embedding model
     TestSetting(
         model="BAAI/bge-multilingual-gemma2",
-        model_args=["--task", "embedding"],
+        model_args=["--task", "embed"],
         pp_size=1,
         tp_size=1,
         attn_backend="FLASHINFER",
@@ -65,7 +65,7 @@ test_settings = [
     # encoder-based embedding model (BERT)
     TestSetting(
         model="BAAI/bge-base-en-v1.5",
-        model_args=["--task", "embedding"],
+        model_args=["--task", "embed"],
         pp_size=1,
         tp_size=1,
         attn_backend="XFORMERS",

tests/core/test_scheduler_encoder_decoder.py

@@ -37,7 +37,7 @@ def test_scheduler_schedule_simple_encoder_decoder():
     num_seq_group = 4
     max_model_len = 16
     scheduler_config = SchedulerConfig(
-        task="generate",
+        "generate",
         max_num_batched_tokens=64,
         max_num_seqs=num_seq_group,
         max_model_len=max_model_len,

tests/entrypoints/openai/test_vision_embedding.py

@@ -27,7 +27,7 @@ TEST_IMAGE_URLS = [
 def server():
     args = [
         "--task",
-        "embedding",
+        "embed",
         "--dtype",
         "bfloat16",
         "--max-model-len",

tests/models/embedding/language/test_embedding.py

@@ -54,7 +54,7 @@ def test_models(
         hf_outputs = hf_model.encode(example_prompts)
 
     with vllm_runner(model,
-                     task="embedding",
+                     task="embed",
                      dtype=dtype,
                      max_model_len=None,
                      **vllm_extra_kwargs) as vllm_model:

tests/models/embedding/language/test_scoring.py

@@ -35,9 +35,7 @@ def test_llm_1_to_1(vllm_runner, hf_runner, model_name, dtype: str):
     with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
         hf_outputs = hf_model.predict([text_pair]).tolist()
 
-    with vllm_runner(model_name,
-                     task="embedding",
-                     dtype=dtype,
+    with vllm_runner(model_name, task="score", dtype=dtype,
                      max_model_len=None) as vllm_model:
         vllm_outputs = vllm_model.score(text_pair[0], text_pair[1])
 
@@ -58,9 +56,7 @@ def test_llm_1_to_N(vllm_runner, hf_runner, model_name, dtype: str):
     with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
         hf_outputs = hf_model.predict(text_pairs).tolist()
 
-    with vllm_runner(model_name,
-                     task="embedding",
-                     dtype=dtype,
+    with vllm_runner(model_name, task="score", dtype=dtype,
                      max_model_len=None) as vllm_model:
         vllm_outputs = vllm_model.score(TEXTS_1[0], TEXTS_2)
 
@@ -82,9 +78,7 @@ def test_llm_N_to_N(vllm_runner, hf_runner, model_name, dtype: str):
     with hf_runner(model_name, dtype=dtype, is_cross_encoder=True) as hf_model:
         hf_outputs = hf_model.predict(text_pairs).tolist()
 
-    with vllm_runner(model_name,
-                     task="embedding",
-                     dtype=dtype,
+    with vllm_runner(model_name, task="score", dtype=dtype,
                      max_model_len=None) as vllm_model:
         vllm_outputs = vllm_model.score(TEXTS_1, TEXTS_2)
 

tests/models/embedding/vision_language/test_dse_qwen2_vl.py

@@ -93,7 +93,7 @@ def _run_test(
     # if we run HF first, the cuda initialization will be done and it
     # will hurt multiprocessing backend with fork method (the default method).
     with vllm_runner(model,
-                     task="embedding",
+                     task="embed",
                      dtype=dtype,
                      enforce_eager=True,
                      max_model_len=8192) as vllm_model:

tests/models/embedding/vision_language/test_llava_next.py

@@ -47,7 +47,7 @@ def _run_test(
     # if we run HF first, the cuda initialization will be done and it
     # will hurt multiprocessing backend with fork method (the default method).
     with vllm_runner(model,
-                     task="embedding",
+                     task="embed",
                      dtype=dtype,
                      max_model_len=4096,
                      enforce_eager=True) as vllm_model:

tests/models/embedding/vision_language/test_phi3v.py

@@ -39,7 +39,7 @@ def _run_test(
     # vLLM needs a fresh new process without cuda initialization.
     # if we run HF first, the cuda initialization will be done and it
     # will hurt multiprocessing backend with fork method (the default method).
-    with vllm_runner(model, task="embedding", dtype=dtype,
+    with vllm_runner(model, task="embed", dtype=dtype,
                      enforce_eager=True) as vllm_model:
         vllm_outputs = vllm_model.encode(input_texts, images=input_images)
 

tests/test_config.py

@@ -7,11 +7,17 @@ from vllm.model_executor.layers.pooler import PoolingType
 from vllm.platforms import current_platform
 
 
-@pytest.mark.parametrize(("model_id", "expected_task"), [
-    ("facebook/opt-125m", "generate"),
-    ("intfloat/e5-mistral-7b-instruct", "embedding"),
-])
-def test_auto_task(model_id, expected_task):
+@pytest.mark.parametrize(
+    ("model_id", "expected_runner_type", "expected_task"),
+    [
+        ("facebook/opt-125m", "generate", "generate"),
+        ("intfloat/e5-mistral-7b-instruct", "pooling", "embed"),
+        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify"),
+        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "score"),
+        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "reward"),
+    ],
+)
+def test_auto_task(model_id, expected_runner_type, expected_task):
     config = ModelConfig(
         model_id,
         task="auto",
@@ -22,6 +28,7 @@ def test_auto_task(model_id, expected_task):
         dtype="float16",
     )
 
+    assert config.runner_type == expected_runner_type
     assert config.task == expected_task
 
 

vllm/config.py

@@ -45,13 +45,27 @@ else:
 
 logger = init_logger(__name__)
 
-_EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
+_POOLING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
 _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120
 
-TaskOption = Literal["auto", "generate", "embedding"]
+TaskOption = Literal["auto", "generate", "embedding", "embed", "classify",
+                     "score", "reward"]
 
-# "draft" is only used internally for speculative decoding
-_Task = Literal["generate", "embedding", "draft"]
+_ResolvedTask = Literal["generate", "embed", "classify", "score", "reward",
+                        "draft"]
+
+RunnerType = Literal["generate", "pooling", "draft"]
+
+_RUNNER_TASKS: Dict[RunnerType, List[_ResolvedTask]] = {
+    "generate": ["generate"],
+    "pooling": ["embed", "classify", "score", "reward"],
+    "draft": ["draft"],
+}
+
+_TASK_RUNNER: Dict[_ResolvedTask, RunnerType] = {
+    task: runner
+    for runner, tasks in _RUNNER_TASKS.items() for task in tasks
+}
 
 HfOverrides = Union[Dict[str, Any], Callable[[PretrainedConfig],
                                              PretrainedConfig]]
@@ -144,7 +158,7 @@ class ModelConfig:
     def __init__(
             self,
             model: str,
-            task: Union[TaskOption, _Task],
+            task: Union[TaskOption, Literal["draft"]],
             tokenizer: str,
             tokenizer_mode: str,
             trust_remote_code: bool,
@@ -295,6 +309,7 @@ class ModelConfig:
         supported_tasks, task = self._resolve_task(task, self.hf_config)
         self.supported_tasks = supported_tasks
         self.task: Final = task
+
         self.pooler_config = self._init_pooler_config(override_pooler_config)
 
         self._verify_quantization()
@@ -323,7 +338,7 @@ class ModelConfig:
         override_pooler_config: Optional["PoolerConfig"],
     ) -> Optional["PoolerConfig"]:
 
-        if self.task == "embedding":
+        if self.runner_type == "pooling":
             user_config = override_pooler_config or PoolerConfig()
 
             base_config = get_pooling_config(self.model, self.revision)
@@ -357,60 +372,90 @@ class ModelConfig:
                 "either 'auto', 'slow' or 'mistral'.")
         self.tokenizer_mode = tokenizer_mode
 
+    def _get_preferred_task(
+        self,
+        architectures: List[str],
+        supported_tasks: Set[_ResolvedTask],
+    ) -> Optional[_ResolvedTask]:
+        model_id = self.model
+        if get_pooling_config(model_id, self.revision):
+            return "embed"
+        if ModelRegistry.is_cross_encoder_model(architectures):
+            return "score"
+
+        suffix_to_preferred_task: List[Tuple[str, _ResolvedTask]] = [
+            # Other models follow this pattern
+            ("ForCausalLM", "generate"),
+            ("ForConditionalGeneration", "generate"),
+            ("ForSequenceClassification", "classify"),
+            ("ChatModel", "generate"),
+            ("LMHeadModel", "generate"),
+            ("EmbeddingModel", "embed"),
+            ("RewardModel", "reward"),
+        ]
+        _, arch = ModelRegistry.inspect_model_cls(architectures)
+
+        for suffix, pref_task in suffix_to_preferred_task:
+            if arch.endswith(suffix) and pref_task in supported_tasks:
+                return pref_task
+
+        return None
+
     def _resolve_task(
         self,
-        task_option: Union[TaskOption, _Task],
+        task_option: Union[TaskOption, Literal["draft"]],
         hf_config: PretrainedConfig,
-    ) -> Tuple[Set[_Task], _Task]:
+    ) -> Tuple[Set[_ResolvedTask], _ResolvedTask]:
         if task_option == "draft":
             return {"draft"}, "draft"
 
         architectures = getattr(hf_config, "architectures", [])
 
-        task_support: Dict[_Task, bool] = {
+        runner_support: Dict[RunnerType, bool] = {
             # NOTE: Listed from highest to lowest priority,
             # in case the model supports multiple of them
             "generate": ModelRegistry.is_text_generation_model(architectures),
-            "embedding": ModelRegistry.is_pooling_model(architectures),
+            "pooling": ModelRegistry.is_pooling_model(architectures),
         }
-        supported_tasks_lst: List[_Task] = [
-            task for task, is_supported in task_support.items() if is_supported
+        supported_runner_types_lst: List[RunnerType] = [
+            runner_type
+            for runner_type, is_supported in runner_support.items()
+            if is_supported
+        ]
+
+        supported_tasks_lst: List[_ResolvedTask] = [
+            task for runner_type in supported_runner_types_lst
+            for task in _RUNNER_TASKS[runner_type]
         ]
         supported_tasks = set(supported_tasks_lst)
 
         if task_option == "auto":
             selected_task = next(iter(supported_tasks_lst))
 
-            if len(supported_tasks) > 1:
-                suffix_to_preferred_task: List[Tuple[str, _Task]] = [
-                    # Hardcode the models that are exceptions
-                    ("AquilaModel", "generate"),
-                    ("ChatGLMModel", "generate"),
-                    # Other models follow this pattern
-                    ("ForCausalLM", "generate"),
-                    ("ForConditionalGeneration", "generate"),
-                    ("ChatModel", "generate"),
-                    ("LMHeadModel", "generate"),
-                    ("EmbeddingModel", "embedding"),
-                    ("RewardModel", "embedding"),
-                    ("ForSequenceClassification", "embedding"),
-                ]
-                info, arch = ModelRegistry.inspect_model_cls(architectures)
-
-                for suffix, pref_task in suffix_to_preferred_task:
-                    if arch.endswith(suffix) and pref_task in supported_tasks:
-                        selected_task = pref_task
-                        break
-                else:
-                    if (arch.endswith("Model")
-                            and info.architecture.endswith("ForCausalLM")
-                            and "embedding" in supported_tasks):
-                        selected_task = "embedding"
+            if len(supported_tasks_lst) > 1:
+                preferred_task = self._get_preferred_task(
+                    architectures, supported_tasks)
+                if preferred_task is not None:
+                    selected_task = preferred_task
 
                 logger.info(
                     "This model supports multiple tasks: %s. "
                     "Defaulting to '%s'.", supported_tasks, selected_task)
         else:
+            # Aliases
+            if task_option == "embedding":
+                preferred_task = self._get_preferred_task(
+                    architectures, supported_tasks)
+                if preferred_task != "embed":
+                    msg = ("The 'embedding' task will be restricted to "
+                           "embedding models in a future release. Please "
+                           "pass `--task classify`, `--task score`, or "
+                           "`--task reward` explicitly for other pooling "
+                           "models.")
+                    warnings.warn(msg, DeprecationWarning, stacklevel=2)
+
+                task_option = preferred_task or "embed"
+
             if task_option not in supported_tasks:
                 msg = (
                     f"This model does not support the '{task_option}' task. "
@@ -533,7 +578,7 @@ class ModelConfig:
 
         # Async postprocessor is not necessary with embedding mode
         # since there is no token generation
-        if self.task == "embedding":
+        if self.runner_type == "pooling":
             self.use_async_output_proc = False
 
         # Reminder: Please update docs/source/usage/compatibility_matrix.rst
@@ -750,6 +795,14 @@ class ModelConfig:
         architectures = getattr(self.hf_config, "architectures", [])
         return ModelRegistry.is_cross_encoder_model(architectures)
 
+    @property
+    def supported_runner_types(self) -> Set[RunnerType]:
+        return {_TASK_RUNNER[task] for task in self.supported_tasks}
+
+    @property
+    def runner_type(self) -> RunnerType:
+        return _TASK_RUNNER[self.task]
+
 
 class CacheConfig:
     """Configuration for the KV cache.
@@ -1096,7 +1149,7 @@ class ParallelConfig:
 class SchedulerConfig:
     """Scheduler configuration."""
 
-    task: str = "generate"  # The task to use the model for.
+    runner_type: str = "generate"  # The runner type to launch for the model.
 
     # Maximum number of tokens to be processed in a single iteration.
     max_num_batched_tokens: int = field(default=None)  # type: ignore
@@ -1164,11 +1217,11 @@ class SchedulerConfig:
                 # for higher throughput.
                 self.max_num_batched_tokens = max(self.max_model_len, 2048)
 
-            if self.task == "embedding":
-                # For embedding, choose specific value for higher throughput
+            if self.runner_type == "pooling":
+                # Choose specific value for higher throughput
                 self.max_num_batched_tokens = max(
                     self.max_num_batched_tokens,
-                    _EMBEDDING_MODEL_MAX_NUM_BATCHED_TOKENS,
+                    _POOLING_MODEL_MAX_NUM_BATCHED_TOKENS,
                 )
             if self.is_multimodal_model:
                 # The value needs to be at least the number of multimodal tokens

vllm/core/scheduler.py

@@ -337,7 +337,7 @@ class Scheduler:
         self.lora_config = lora_config
 
         version = "selfattn"
-        if (self.scheduler_config.task == "embedding"
+        if (self.scheduler_config.runner_type == "pooling"
                 or self.cache_config.is_attention_free):
             version = "placeholder"
 

vllm/engine/arg_utils.py

@@ -1066,7 +1066,7 @@ class EngineArgs:
                 if (is_gpu and not use_sliding_window and not use_spec_decode
                         and not self.enable_lora
                         and not self.enable_prompt_adapter
-                        and model_config.task != "embedding"):
+                        and model_config.runner_type != "pooling"):
                     self.enable_chunked_prefill = True
                     logger.warning(
                         "Chunked prefill is enabled by default for models with "
@@ -1083,7 +1083,8 @@ class EngineArgs:
                 "errors during the initial memory profiling phase, or result "
                 "in low performance due to small KV cache space. Consider "
                 "setting --max-model-len to a smaller value.", max_model_len)
-        elif self.enable_chunked_prefill and model_config.task == "embedding":
+        elif (self.enable_chunked_prefill
+              and model_config.runner_type == "pooling"):
             msg = "Chunked prefill is not supported for embedding models"
             raise ValueError(msg)
 
@@ -1144,7 +1145,7 @@ class EngineArgs:
                 " please file an issue with detailed information.")
 
         scheduler_config = SchedulerConfig(
-            task=model_config.task,
+            runner_type=model_config.runner_type,
             max_num_batched_tokens=self.max_num_batched_tokens,
             max_num_seqs=self.max_num_seqs,
             max_model_len=model_config.max_model_len,

vllm/engine/llm_engine.py

@@ -288,7 +288,7 @@ class LLMEngine:
 
         self.model_executor = executor_class(vllm_config=vllm_config, )
 
-        if self.model_config.task != "embedding":
+        if self.model_config.runner_type != "pooling":
             self._initialize_kv_caches()
 
         # If usage stat is enabled, collect relevant info.
@@ -1123,7 +1123,7 @@ class LLMEngine:
                             seq_group.metrics.model_execute_time = (
                                 o.model_execute_time)
 
-            if self.model_config.task == "embedding":
+            if self.model_config.runner_type == "pooling":
                 self._process_sequence_group_outputs(seq_group, output)
             else:
                 self.output_processor.process_prompt_logprob(seq_group, output)