[Deprecate] Deprecate pooling multi task support. (#37956)

Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Signed-off-by: wang.yuqi <noooop@126.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

docs/models/pooling_models/README.md

 # Pooling Models
 
 !!! note
-    We currently support pooling models primarily for convenience. This is not guaranteed to provide any performance improvements over using Hugging Face Transformers or Sentence Transformers directly.
+    We currently support pooling models primarily for convenience. This is not guaranteed to provide any performance
+improvements over using Hugging Face Transformers or Sentence Transformers directly.
 
     We plan to optimize pooling models in vLLM. Please comment on <https://github.com/vllm-project/vllm/issues/21796> if you have any suggestions!
 
@@ -12,22 +13,38 @@ Natural Language Processing (NLP) can be primarily divided into the following tw
 - Natural Language Understanding (NLU)
 - Natural Language Generation (NLG)
 
-The generative models supported by vLLM cover a variety of task types, such as the large language models (LLMs) we are familiar with, multimodal models (VLM) that handle multimodal inputs like images, videos, and audio, speech-to-text transcription models, and real-time models that support streaming input. Their common feature is the ability to generate text. Taking it a step further, vLLM-Omni supports the generation of multimodal content, including images, videos, and audio.
+The generative models supported by vLLM cover a variety of task types, such as the large language models (LLMs) we are
+familiar with, multimodal models (VLM) that handle multimodal inputs like images, videos, and audio, speech-to-text
+transcription models, and real-time models that support streaming input. Their common feature is the ability to generate
+text. Taking it a step further, vLLM-Omni supports the generation of multimodal content, including images, videos, and audio.
 
-As the capabilities of generative models continue to improve, the boundaries of these models are also constantly expanding. However, certain application scenarios still require specialized small language models to efficiently complete specific tasks. These models typically have the following characteristics:
+As the capabilities of generative models continue to improve, the boundaries of these models are also constantly expanding.
+However, certain application scenarios still require specialized small language models to efficiently complete specific tasks.
+These models typically have the following characteristics:
 
 - They do not require content generation.
 - They only need to perform very limited functions, without requiring strong generalization, creativity, or high intelligence.
 - They demand extremely low latency and may operate on cost-constrained hardware.
 - Text-only models typically have fewer than 1 billion parameters, while multimodal models generally have fewer than 10 billion parameters.
 
-Although these models are relatively small in scale, they are still based on the Transformer architecture, similar or even identical to the most advanced large language models today. Many recently released pooling models are also fine-tuned from large language models, allowing them to benefit from the continuous improvements in large models. This architecture similarity enables them to reuse much of vLLM’s infrastructure. If compatible, we would be happy to help them leverage the latest features of vLLM as well.
+Although these models are relatively small in scale, they are still based on the Transformer architecture, similar or
+even identical to the most advanced large language models today. Many recently released pooling models are also fine-tuned
+from large language models, allowing them to benefit from the continuous improvements in large models. This architecture
+similarity enables them to reuse much of vLLM’s infrastructure. If compatible, we would be happy to help them leverage
+the latest features of vLLM as well.
 
 ### Sequence-wise Task and Token-wise Task
 
-The key distinction between sequence-wise task and token-wise task lies in their output granularity: sequence-wise task produces a single result for an entire input sequence, whereas token-wise task yields a result for each individual token within the sequence.
+The key distinction between sequence-wise task and token-wise task lies in their output granularity: sequence-wise task
+produces a single result for an entire input sequence, whereas token-wise task yields a result for each individual token
+within the sequence.
 
-Of course, we also have "plugin" tasks that allow users to customize input and output processors. For more information, please refer to [IO Processor Plugins](../../design/io_processor_plugins.md).
+Many Pooling models support both (sequence) task and token task. When the default pooling task (e.g. a sequence-wise task)
+is not what you want, you need to manually specify (e.g. a token-wise task) via `PoolerConfig(task=<task>)` offline or
+`--pooler-config.task <task>` online.
+
+Of course, we also have "plugin" tasks that allow users to customize input and output processors. For more information,
+please refer to [IO Processor Plugins](../../design/io_processor_plugins.md).
 
 ### Pooling Tasks
 
@@ -39,11 +56,13 @@ Of course, we also have "plugin" tasks that allow users to customize input and o
 | `token_embed`         | Token-wise    | vector representations for each token           |
 
 !!! note
-    Within classification tasks, there is a specialized subcategory: Cross-encoder (aka reranker) models. These models are a subset of classification models that accept two prompts as input and output num_labels equal to 1.
+    Within classification tasks, there is a specialized subcategory: Cross-encoder (aka reranker) models. These models
+are a subset of classification models that accept two prompts as input and output num_labels equal to 1.
 
 ### Score Types
 
-The scoring models is designed to compute similarity scores between two input prompts. It supports three model types (aka `score_type`): `cross-encoder`, `late-interaction`, and `bi-encoder`.
+The scoring models is designed to compute similarity scores between two input prompts. It supports three model types
+(aka `score_type`): `cross-encoder`, `late-interaction`, and `bi-encoder`.
 
 | Pooling Tasks         | Granularity   | Outputs                                      | Score Types        | scoring function         |
 |-----------------------|---------------|----------------------------------------------|--------------------|--------------------------|
@@ -250,11 +269,17 @@ We have split the `encode` task into two more specific token-wise tasks: `token_
 - `token_embed` is the same as `embed`, using normalization as the activation.
 - `token_classify` is the same as `classify`, by default using softmax as the activation.
 
-Pooling models now default support all pooling, you can use it without any settings.
+Pooling models now support token-wise task.
 
 - Extracting hidden states prefers using `token_embed` task.
 - Named Entity Recognition (NER) and reward models prefers using `token_classify` task.
 
 ### Score task
 
-`score` task is deprecated and will be removed in v0.20. Please use `classify` instead. Only when a classification model outputs num_labels equal to 1 can it be used as a scoring model and have its scoring API enabled.
+`score` task is deprecated and will be removed in v0.20. Please use `classify` instead. Only when a
+classification model outputs num_labels equal to 1 can it be used as a scoring model and have its scoring API enabled.
+
+### Pooling multitask support
+
+Pooling multitask support is deprecated and will be removed in v0.20. When the default pooling task is not what you want,
+you need to manually specify it via `PoolerConfig(task=<task>)` offline or `--pooler-config.task <task>` online.

docs/models/pooling_models/token_classify.md

@@ -13,6 +13,12 @@ The key distinction between (sequence) classification and token classification l
 
 Many classification models support both (sequence) classification and token classification. For further details on (sequence) classification, please refer to [this page](classify.md).
 
+!!! note
+
+    Pooling multitask support is deprecated and will be removed in v0.20. When the default pooling task (classify) is not 
+    what you want, you need to manually specify it via `PoolerConfig(task="token_classify")` offline or
+    `--pooler-config.task token_classify` online.
+
 ## Typical Use Cases
 
 ### Named Entity Recognition (NER)

docs/models/pooling_models/token_embed.md

@@ -13,6 +13,12 @@ The difference between the (sequence) embedding task and the token embedding tas
 
 Many embedding models support both (sequence) embedding and token embedding. For further details on (sequence) embedding, please refer to [this page](embed.md).
 
+!!! note
+
+    Pooling multitask support is deprecated and will be removed in v0.20. When the default pooling task (embed) is not 
+    what you want, you need to manually specify it via via `PoolerConfig(task="token_embed")` offline or
+    `--pooler-config.task token_embed` online.
+
 ## Typical Use Cases
 
 ### Multi-Vector Retrieval

tests/entrypoints/pooling/classify/test_offline.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
+import logging
 import weakref
 
 import pytest
@@ -67,8 +67,11 @@ def test_list_prompts(llm: LLM):
 
 
 @pytest.mark.skip_global_cleanup
-def test_token_classify(llm: LLM):
+def test_token_classify(llm: LLM, caplog_vllm):
+    with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
         outputs = llm.encode(prompt, pooling_task="token_classify", use_tqdm=False)
+        assert "deprecated" in caplog_vllm.text
+
     assert len(outputs) == 1
     assert isinstance(outputs[0], PoolingRequestOutput)
     assert outputs[0].prompt_token_ids == prompt_token_ids
@@ -107,8 +110,8 @@ def test_score_api(llm: LLM):
         llm.score("ping", "pong", use_tqdm=False)
 
 
-@pytest.mark.parametrize("task", ["embed", "token_embed", "plugin"])
+@pytest.mark.parametrize("task", ["embed", "token_embed"])
 def test_unsupported_tasks(llm: LLM, task: PoolingTask):
-    err_msg = f"Unsupported task: '{task}' Supported tasks.+"
+    err_msg = "Embedding API is not supported by this model.+"
     with pytest.raises(ValueError, match=err_msg):
         llm.encode(prompt, pooling_task=task, use_tqdm=False)

tests/entrypoints/pooling/embed/test_offline.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
+import logging
 import weakref
 
 import pytest
 import torch
 import torch.nn.functional as F
 
-from vllm import LLM, PoolingParams
+from vllm import LLM, EmbeddingRequestOutput, PoolingParams
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.platforms import current_platform
+from vllm.tasks import PoolingTask
 
 MODEL_NAME = "intfloat/multilingual-e5-small"
 
-prompts = ["The chef prepared a delicious meal."]
+prompt = "The chef prepared a delicious meal."
+prompt_token_ids = [0, 581, 21861, 133888, 10, 8, 150, 60744, 109911, 5, 2]
+embedding_size = 384
 
 
 @pytest.fixture(scope="module")
@@ -44,16 +47,48 @@ def llm():
 
 
 @pytest.mark.skip_global_cleanup
-def test_token_embed(llm: LLM):
-    outputs = llm.encode(prompts, pooling_task="token_embed", use_tqdm=False)
+def test_str_prompts(llm: LLM):
+    outputs = llm.embed(prompt, use_tqdm=False)
+    assert len(outputs) == 1
+    assert isinstance(outputs[0], EmbeddingRequestOutput)
+    assert outputs[0].prompt_token_ids == prompt_token_ids
+    assert len(outputs[0].outputs.embedding) == embedding_size
+
+
+@pytest.mark.skip_global_cleanup
+def test_token_ids_prompts(llm: LLM):
+    outputs = llm.embed([prompt_token_ids], use_tqdm=False)
+    assert len(outputs) == 1
+    assert isinstance(outputs[0], EmbeddingRequestOutput)
+    assert outputs[0].prompt_token_ids == prompt_token_ids
+    assert len(outputs[0].outputs.embedding) == embedding_size
+
+
+@pytest.mark.skip_global_cleanup
+def test_list_prompts(llm: LLM):
+    outputs = llm.embed([prompt, prompt_token_ids], use_tqdm=False)
+    assert len(outputs) == 2
+    for i in range(len(outputs)):
+        assert isinstance(outputs[i], EmbeddingRequestOutput)
+        assert outputs[i].prompt_token_ids == prompt_token_ids
+        assert len(outputs[i].outputs.embedding) == embedding_size
+
+
+@pytest.mark.skip_global_cleanup
+def test_token_embed(llm: LLM, caplog_vllm):
+    with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
+        outputs = llm.encode(prompt, pooling_task="token_embed", use_tqdm=False)
+        assert "deprecated" in caplog_vllm.text
+
     multi_vector = outputs[0].outputs.data
     assert multi_vector.shape == (11, 384)
 
 
+@pytest.mark.skip_global_cleanup
 def test_pooling_params(llm: LLM):
     def get_outputs(normalize):
         outputs = llm.embed(
-            prompts,
+            [prompt],
             pooling_params=PoolingParams(use_activation=normalize),
             use_tqdm=False,
         )
@@ -70,3 +105,10 @@ def test_pooling_params(llm: LLM):
     assert torch.allclose(w_normal, F.normalize(wo_normal, p=2, dim=-1), atol=1e-2), (
         "w_normal should be close to normal(wo_normal)."
     )
+
+
+@pytest.mark.parametrize("task", ["token_classify", "classify"])
+def test_unsupported_tasks(llm: LLM, task: PoolingTask):
+    err_msg = "Classification API is not supported by this model.+"
+    with pytest.raises(ValueError, match=err_msg):
+        llm.encode(prompt, pooling_task=task, use_tqdm=False)

tests/entrypoints/pooling/score/test_online_rerank.py

@@ -206,7 +206,12 @@ async def test_pooling_classify(server: RemoteOpenAIServer, model_name: str):
 async def test_pooling_token_classify(server: RemoteOpenAIServer, model_name: str):
     response = requests.post(
         server.url_for("pooling"),
-        json={"model": model_name, "input": input_text, "encoding_format": "float"},
+        json={
+            "model": model_name,
+            "task": "token_classify",
+            "input": input_text,
+            "encoding_format": "float",
+        },
     )
 
     poolings = PoolingResponse.model_validate(response.json())

tests/entrypoints/pooling/token_classify/test_offline.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import logging
+import weakref
+
+import pytest
+
+from vllm import LLM, PoolingRequestOutput
+from vllm.config import PoolerConfig
+from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.tasks import PoolingTask
+
+MODEL_NAME = "jason9693/Qwen2.5-1.5B-apeach"
+
+prompt = "The chef prepared a delicious meal."
+prompt_token_ids = [785, 29706, 10030, 264, 17923, 15145, 13]
+num_labels = 2
+
+
+@pytest.fixture(scope="module")
+def llm():
+    # pytest caches the fixture so we use weakref.proxy to
+    # enable garbage collection
+    llm = LLM(
+        model=MODEL_NAME,
+        pooler_config=PoolerConfig(task="token_classify"),
+        max_num_batched_tokens=32768,
+        tensor_parallel_size=1,
+        gpu_memory_utilization=0.75,
+        enforce_eager=True,
+        seed=0,
+    )
+
+    yield weakref.proxy(llm)
+
+    del llm
+
+    cleanup_dist_env_and_memory()
+
+
+@pytest.mark.skip_global_cleanup
+def test_str_prompts(llm: LLM):
+    outputs = llm.encode(prompt, pooling_task="token_classify", use_tqdm=False)
+    assert len(outputs) == 1
+    assert isinstance(outputs[0], PoolingRequestOutput)
+    assert outputs[0].prompt_token_ids == prompt_token_ids
+    assert outputs[0].outputs.data.shape == (len(prompt_token_ids), num_labels)
+
+
+@pytest.mark.skip_global_cleanup
+def test_token_ids_prompts(llm: LLM):
+    outputs = llm.encode(
+        [prompt_token_ids], pooling_task="token_classify", use_tqdm=False
+    )
+    assert len(outputs) == 1
+    assert isinstance(outputs[0], PoolingRequestOutput)
+    assert outputs[0].prompt_token_ids == prompt_token_ids
+    assert outputs[0].outputs.data.shape == (len(prompt_token_ids), num_labels)
+
+
+@pytest.mark.skip_global_cleanup
+def test_score_api(llm: LLM):
+    err_msg = "Score API is only enabled for num_labels == 1."
+    with pytest.raises(ValueError, match=err_msg):
+        llm.score("ping", "pong", use_tqdm=False)
+
+
+@pytest.mark.parametrize("task", ["classify", "embed", "token_embed"])
+def test_unsupported_tasks(llm: LLM, task: PoolingTask, caplog_vllm):
+    if task == "classify":
+        with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
+            llm.encode(prompt, pooling_task=task, use_tqdm=False)
+        assert "deprecated" in caplog_vllm.text
+    else:
+        err_msg = "Embedding API is not supported by this model.+"
+
+        with pytest.raises(ValueError, match=err_msg):
+            llm.encode(prompt, pooling_task=task, use_tqdm=False)

tests/entrypoints/pooling/token_classify/test_online.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import requests
+
+from tests.utils import RemoteOpenAIServer
+from vllm.entrypoints.pooling.pooling.protocol import PoolingResponse
+
+MODEL_NAME = "jason9693/Qwen2.5-1.5B-apeach"
+DTYPE = "float32"  # Use float32 to avoid NaN issue
+input_text = "This product was excellent and exceeded my expectations"
+input_tokens = [1986, 1985, 572, 9073, 323, 33808, 847, 16665]
+
+
+@pytest.fixture(scope="module")
+def server():
+    args = [
+        "--enforce-eager",
+        "--max-model-len",
+        "512",
+        "--dtype",
+        DTYPE,
+        "--pooler-config.task",
+        "token_classify",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_pooling_token_classify(server: RemoteOpenAIServer, model_name: str):
+    task = "token_classify"
+    response = requests.post(
+        server.url_for("pooling"),
+        json={
+            "model": model_name,
+            "input": input_text,
+            "encoding_format": "float",
+            "task": task,
+        },
+    )
+    poolings = PoolingResponse.model_validate(response.json())
+    assert len(poolings.data) == 1
+    assert len(poolings.data[0].data) == 8
+    assert len(poolings.data[0].data[0]) == 2
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize("task", ["classify", "embed", "token_embed", "plugin"])
+async def test_pooling_not_supported(
+    server: RemoteOpenAIServer, model_name: str, task: str
+):
+    response = requests.post(
+        server.url_for("pooling"),
+        json={
+            "model": model_name,
+            "input": input_text,
+            "encoding_format": "float",
+            "task": task,
+        },
+    )
+
+    if task != "classify":
+        assert response.json()["error"]["type"] == "BadRequestError"
+        err_msg = f"Unsupported task: {task!r}"
+        assert response.json()["error"]["message"].startswith(err_msg)

tests/entrypoints/pooling/token_embed/test_offline.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import logging
+import weakref
+
+import pytest
+
+from vllm import LLM, PoolingRequestOutput
+from vllm.config import PoolerConfig
+from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.platforms import current_platform
+from vllm.tasks import PoolingTask
+
+MODEL_NAME = "intfloat/multilingual-e5-small"
+
+prompt = "The chef prepared a delicious meal."
+prompt_token_ids = [0, 581, 21861, 133888, 10, 8, 150, 60744, 109911, 5, 2]
+embedding_size = 384
+
+
+@pytest.fixture(scope="module")
+def llm():
+    # ROCm: Use FLEX_ATTENTION backend as it's the only attention backend
+    # that supports encoder-only models on ROCm.
+    attention_config = None
+    if current_platform.is_rocm():
+        attention_config = {"backend": "FLEX_ATTENTION"}
+
+    # pytest caches the fixture so we use weakref.proxy to
+    # enable garbage collection
+    llm = LLM(
+        model=MODEL_NAME,
+        pooler_config=PoolerConfig(task="token_embed"),
+        max_num_batched_tokens=32768,
+        tensor_parallel_size=1,
+        gpu_memory_utilization=0.75,
+        enforce_eager=True,
+        seed=0,
+        attention_config=attention_config,
+    )
+    assert embedding_size == llm.model_config.embedding_size
+
+    yield weakref.proxy(llm)
+
+    del llm
+    cleanup_dist_env_and_memory()
+
+
+@pytest.mark.skip_global_cleanup
+def test_str_prompts(llm: LLM):
+    outputs = llm.encode(prompt, pooling_task="token_embed", use_tqdm=False)
+    assert len(outputs) == 1
+    assert isinstance(outputs[0], PoolingRequestOutput)
+    assert outputs[0].outputs.data.shape == (11, 384)
+
+
+@pytest.mark.skip_global_cleanup
+def test_token_ids_prompts(llm: LLM):
+    outputs = llm.encode([prompt_token_ids], pooling_task="token_embed", use_tqdm=False)
+    assert len(outputs) == 1
+    assert isinstance(outputs[0], PoolingRequestOutput)
+    assert outputs[0].outputs.data.shape == (11, 384)
+
+
+@pytest.mark.parametrize("task", ["embed", "classify", "token_classify"])
+def test_unsupported_tasks(llm: LLM, task: PoolingTask, caplog_vllm):
+    if task == "embed":
+        with caplog_vllm.at_level(level=logging.WARNING, logger="vllm"):
+            llm.encode(prompt, pooling_task=task, use_tqdm=False)
+        assert "deprecated" in caplog_vllm.text
+    else:
+        err_msg = "Classification API is not supported by this model.+"
+
+        with pytest.raises(ValueError, match=err_msg):
+            llm.encode(prompt, pooling_task=task, use_tqdm=False)

tests/entrypoints/pooling/token_embed/test_online.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+import pytest
+import requests
+
+from tests.utils import RemoteOpenAIServer
+from vllm.entrypoints.pooling.pooling.protocol import PoolingResponse
+
+MODEL_NAME = "intfloat/multilingual-e5-small"
+DTYPE = "bfloat16"
+input_text = "The best thing about vLLM is that it supports many different models"
+input_tokens = [
+    0,
+    581,
+    2965,
+    13580,
+    1672,
+    81,
+    23708,
+    594,
+    83,
+    450,
+    442,
+    8060,
+    7,
+    5941,
+    12921,
+    115774,
+    2,
+]
+
+
+@pytest.fixture(scope="module")
+def server():
+    args = [
+        "--runner",
+        "pooling",
+        "--dtype",
+        DTYPE,
+        "--enforce-eager",
+        "--max-model-len",
+        "512",
+        "--pooler-config.task",
+        "token_embed",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_pooling_token_embed(server: RemoteOpenAIServer, model_name: str):
+    task = "token_embed"
+    response = requests.post(
+        server.url_for("pooling"),
+        json={
+            "model": model_name,
+            "input": input_text,
+            "encoding_format": "float",
+            "task": task,
+        },
+    )
+
+    poolings = PoolingResponse.model_validate(response.json())
+
+    assert len(poolings.data) == 1
+    assert len(poolings.data[0].data) == len(input_tokens)
+    assert len(poolings.data[0].data[0]) == 384
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize("task", ["embed", "classify", "token_classify", "plugin"])
+async def test_pooling_not_supported(
+    server: RemoteOpenAIServer, model_name: str, task: str
+):
+    response = requests.post(
+        server.url_for("pooling"),
+        json={
+            "model": model_name,
+            "input": "test",
+            "encoding_format": "float",
+            "task": task,
+        },
+    )
+
+    if task != "embed":
+        assert response.json()["error"]["type"] == "BadRequestError"
+        err_msg = f"Unsupported task: {task!r}"
+        assert response.json()["error"]["message"].startswith(err_msg)

tests/plugins_tests/test_bge_m3_sparse_io_processor_plugins.py

@@ -102,7 +102,7 @@ async def test_bge_m3_sparse_plugin_online(
     """Test BGE-M3 sparse plugin in online mode via API."""
     request_payload = {
         "model": model_config["model_name"],
-        "task": "token_classify",
+        "task": "plugin",
         "data": {"input": model_config["test_input"], "return_tokens": return_tokens},
     }
 
@@ -166,7 +166,7 @@ def test_bge_m3_sparse_plugin_offline(vllm_runner, return_tokens: bool):
         default_torch_num_threads=1,
     ) as llm_runner:
         llm = llm_runner.get_llm()
-        pooler_output = llm.encode(prompt, pooling_task="token_classify")
+        pooler_output = llm.encode(prompt, pooling_task="plugin")
 
     outputs = pooler_output[0]
 
@@ -213,7 +213,7 @@ def test_bge_m3_sparse_plugin_offline_multiple_inputs(vllm_runner):
         default_torch_num_threads=1,
     ) as llm_runner:
         llm = llm_runner.get_llm()
-        pooler_output = llm.encode(prompts, pooling_task="token_classify")
+        pooler_output = llm.encode(prompts, pooling_task="plugin")
 
     outputs = pooler_output[0]
 

vllm/config/model.py

@@ -25,7 +25,7 @@ from vllm.config.scheduler import RunnerType
 from vllm.config.utils import config, getattr_iter
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.tasks import ScoreType
+from vllm.tasks import PoolingTask, ScoreType, SupportedTask
 from vllm.transformers_utils.config import (
     ConfigFormat,
     get_config,
@@ -1409,6 +1409,41 @@ class ModelConfig:  # type: ignore[misc]
 
         return diff_sampling_param
 
+    def get_pooling_task(
+        self, supported_tasks: tuple[SupportedTask, ...]
+    ) -> PoolingTask | None:
+        if self.pooler_config is None:
+            return None
+
+        pooling_task = self.pooler_config.task
+
+        if pooling_task is not None:
+            if self.pooler_config.task in supported_tasks:
+                return self.pooler_config.task
+            else:
+                raise RuntimeError(
+                    f"Unsupported task: {pooling_task!r} "
+                    f"Supported tasks: {supported_tasks}"
+                )
+
+        if "token_classify" in supported_tasks:
+            for architecture in self.architectures:
+                if "ForTokenClassification" in architecture:
+                    return "token_classify"
+
+        priority: list[PoolingTask] = [
+            "embed&token_classify",
+            "embed",
+            "classify",
+            "token_embed",
+            "token_classify",
+            "plugin",
+        ]
+        for task in priority:
+            if task in supported_tasks:
+                return task
+        return None
+
     @cached_property
     def is_encoder_decoder(self) -> bool:
         """Extract the HF encoder/decoder model flag."""

vllm/config/pooler.py

@@ -5,6 +5,7 @@ from typing import Any, Literal, get_args
 
 from vllm.config.utils import config
 from vllm.logger import init_logger
+from vllm.tasks import PoolingTask
 from vllm.utils.hashing import safe_hash
 
 logger = init_logger(__name__)
@@ -20,6 +21,11 @@ TOK_POOLING_TYPES: tuple[TokenPoolingType, ...] = get_args(TokenPoolingType)
 class PoolerConfig:
     """Controls the behavior of output pooling in pooling models."""
 
+    task: PoolingTask | None = None
+    """
+    The task used for pooling.
+    """
+
     pooling_type: SequencePoolingType | TokenPoolingType | None = None
     """
     The pooling method used for pooling.

vllm/entrypoints/llm.py

@@ -382,16 +382,19 @@ class LLM:
         self.llm_engine = LLMEngine.from_engine_args(
             engine_args=engine_args, usage_context=UsageContext.LLM_CLASS
         )
+        self.model_config = self.llm_engine.model_config
         self.engine_class = type(self.llm_engine)
 
         self.request_counter = Counter()
         self.default_sampling_params: dict[str, Any] | None = None
 
         supported_tasks = self.llm_engine.get_supported_tasks()
-        logger.info("Supported tasks: %s", supported_tasks)
         self.supported_tasks = supported_tasks
+        self.pooling_task = self.model_config.get_pooling_task(supported_tasks)
+        if self.pooling_task is not None:
+            logger.info("Supported pooling task: %s", self.pooling_task)
 
-        self.model_config = self.llm_engine.model_config
+        self.runner_type = self.model_config.runner_type
         self.renderer = self.llm_engine.renderer
         self.chat_template = load_chat_template(chat_template)
         self.io_processor = self.llm_engine.io_processor
@@ -1072,31 +1075,7 @@ class LLM:
             pooled hidden states in the same order as the input prompts.
         """
 
-        if pooling_task is None:
-            raise ValueError(
-                "pooling_task required for `LLM.encode`\n"
-                "Please use one of the more specific methods or set the "
-                "pooling_task when using `LLM.encode`:\n"
-                "  - For embeddings, use `LLM.embed(...)` "
-                'or `pooling_task="embed"`.\n'
-                "  - For classification logits, use `LLM.classify(...)` "
-                'or `pooling_task="classify"`.\n'
-                "  - For similarity scores, use `LLM.score(...)`.\n"
-                "  - For rewards, use `LLM.reward(...)` "
-                'or `pooling_task="token_classify"`\n'
-                "  - For token classification, "
-                'use `pooling_task="token_classify"`\n'
-                '  - For multi-vector retrieval, use `pooling_task="token_embed"`'
-            )
-
-        model_config = self.model_config
-        runner_type = model_config.runner_type
-        if runner_type != "pooling":
-            raise ValueError(
-                "LLM.encode() is only supported for pooling models. "
-                "Try passing `--runner pooling` to use the model as a "
-                "pooling model."
-            )
+        self._verify_pooling_task(pooling_task)
 
         if isinstance(prompts, dict) and "data" in prompts:
             if self.io_processor is None:
@@ -1206,6 +1185,65 @@ class LLM:
                 )
         return outputs
 
+    def _verify_pooling_task(self, pooling_task: PoolingTask | None):
+        if self.runner_type != "pooling":
+            raise ValueError(
+                "LLM.encode() is only supported for pooling models. "
+                "Try passing `--runner pooling` to use the model as a "
+                "pooling model."
+            )
+
+        if pooling_task is None:
+            raise ValueError(
+                "pooling_task required for `LLM.encode`\n"
+                "Please use one of the more specific methods or set the "
+                "pooling_task when using `LLM.encode`:\n"
+                "  - For embeddings, use `LLM.embed(...)` "
+                'or `pooling_task="embed"`.\n'
+                "  - For classification logits, use `LLM.classify(...)` "
+                'or `pooling_task="classify"`.\n'
+                "  - For similarity scores, use `LLM.score(...)`.\n"
+                "  - For rewards, use `LLM.reward(...)` "
+                'or `pooling_task="token_classify"`\n'
+                "  - For token classification, "
+                'use `pooling_task="token_classify"`\n'
+                '  - For multi-vector retrieval, use `pooling_task="token_embed"`'
+            )
+
+        if (
+            pooling_task in ("embed", "token_embed")
+            and pooling_task not in self.supported_tasks
+        ):
+            raise ValueError(
+                "Embedding API is not supported by this model. "
+                "Try converting the model using `--convert embed`."
+            )
+
+        if (
+            pooling_task in ("classify", "token_classify")
+            and pooling_task not in self.supported_tasks
+        ):
+            raise ValueError(
+                "Classification API is not supported by this model. "
+                "Try converting the model using `--convert classify`."
+            )
+
+        # plugin task uses io_processor.parse_request to verify inputs
+        if pooling_task != "plugin" and pooling_task != self.pooling_task:
+            if pooling_task not in self.supported_tasks:
+                raise ValueError(
+                    f"Unsupported task: {pooling_task!r} "
+                    f"Supported tasks: {self.supported_tasks}"
+                )
+            else:
+                logger.warning_once(
+                    "Pooling multitask support is deprecated and will "
+                    "be removed in v0.20. When the default pooling task is "
+                    "not what you want, you need to manually specify it "
+                    'via PoolerConfig(task="%s"). ',
+                    pooling_task,
+                )
+
     def embed(
         self,
         prompts: PromptType | Sequence[PromptType],
@@ -1239,11 +1277,6 @@ class LLM:
             A list of `EmbeddingRequestOutput` objects containing the
             embedding vectors in the same order as the input prompts.
         """
-        if "embed" not in self.supported_tasks:
-            raise ValueError(
-                "Embedding API is not supported by this model. "
-                "Try converting the model using `--convert embed`."
-            )
 
         items = self.encode(
             prompts,
@@ -1289,11 +1322,6 @@ class LLM:
             A list of `ClassificationRequestOutput` objects containing the
             embedding vectors in the same order as the input prompts.
         """
-        if "classify" not in self.supported_tasks:
-            raise ValueError(
-                "Classification API is not supported by this model. "
-                "Try converting the model using `--convert classify`."
-            )
 
         items = self.encode(
             prompts,

vllm/entrypoints/pooling/__init__.py

@@ -45,6 +45,12 @@ def register_pooling_api_routers(
     supported_tasks: tuple["SupportedTask", ...],
     model_config: ModelConfig | None = None,
 ):
+    if model_config is None:
+        return
+
+    pooling_task = model_config.get_pooling_task(supported_tasks)
+
+    if pooling_task is not None:
         from vllm.entrypoints.pooling.pooling.api_router import router as pooling_router
 
         app.include_router(pooling_router)
@@ -91,6 +97,7 @@ def init_pooling_state(
                 engine_client,
                 state.openai_serving_models,
                 state.openai_serving_render,
+                supported_tasks=supported_tasks,
                 request_logger=request_logger,
                 chat_template=resolved_chat_template,
                 chat_template_content_format=args.chat_template_content_format,

vllm/entrypoints/pooling/pooling/serving.py

@@ -37,6 +37,7 @@ from vllm.inputs import ProcessorInputs
 from vllm.logger import init_logger
 from vllm.outputs import PoolingRequestOutput
 from vllm.renderers.inputs.preprocess import prompt_to_seq
+from vllm.tasks import SupportedTask
 from vllm.utils.async_utils import merge_async_iterators
 from vllm.utils.serial_utils import EmbedDType, EncodingFormat, Endianness
 
@@ -49,6 +50,7 @@ class OpenAIServingPooling(OpenAIServing):
         engine_client: EngineClient,
         models: OpenAIServingModels,
         openai_serving_render: OpenAIServingRender,
+        supported_tasks: tuple[SupportedTask, ...],
         *,
         request_logger: RequestLogger | None,
         chat_template: str | None,
@@ -60,7 +62,8 @@ class OpenAIServingPooling(OpenAIServing):
             models=models,
             request_logger=request_logger,
         )
-
+        self.supported_tasks = supported_tasks
+        self.pooling_task = self.model_config.get_pooling_task(supported_tasks)
         self.openai_serving_render = openai_serving_render
         self.chat_template = chat_template
         self.chat_template_content_format: Final = chat_template_content_format
@@ -86,9 +89,27 @@ class OpenAIServingPooling(OpenAIServing):
 
         lora_request = self._maybe_get_adapters(request)
 
+        if request.task is None:
+            request.task = self.pooling_task
+
         if getattr(request, "dimensions", None) is not None:
             return self.create_error_response("dimensions is currently not supported")
 
+        # plugin task uses io_processor.parse_request to verify inputs
+        if request.task != "plugin" and request.task != self.pooling_task:
+            if request.task not in self.supported_tasks:
+                raise ValueError(
+                    f"Unsupported task: {request.task!r} "
+                    f"Supported tasks: {self.supported_tasks}"
+                )
+            else:
+                logger.warning_once(
+                    "Pooling multitask support is deprecated and will be removed "
+                    "in v0.20. When the default pooling task is not what you want, you "
+                    'need to manually specify it via --pooler-config.task "%s". ',
+                    request.task,
+                )
+
         engine_prompts: Sequence[ProcessorInputs]
         if use_io_processor := isinstance(request, IOProcessorRequest):
             if self.io_processor is None: