[Frontend] Add /classify endpoint

docs/source/models/pooling_models.md

@@ -140,6 +140,7 @@ Our [OpenAI-Compatible Server](#openai-compatible-server) provides endpoints tha
 
 - [Pooling API](#pooling-api) is similar to `LLM.encode`, being applicable to all types of pooling models.
 - [Embeddings API](#embeddings-api) is similar to `LLM.embed`, accepting both text and [multi-modal inputs](#multimodal-inputs) for embedding models.
+- [Classification API](#classification-api) is similar to `LLM.classify` and is applicable to sequence classification models.
 - [Score API](#score-api) is similar to `LLM.score` for cross-encoder models.
 
 ## Matryoshka Embeddings

docs/source/serving/openai_compatible_server.md

@@ -61,6 +61,8 @@ In addition, we have the following custom APIs:
   - Applicable to any model with a tokenizer.
 - [Pooling API](#pooling-api) (`/pooling`)
   - Applicable to all [pooling models](../models/pooling_models.md).
+- [Classification API](#classification-api) (`/classify`)
+  - Only applicable to [classification models](../models/pooling_models.md) (`--task classify`).
 - [Score API](#score-api) (`/score`)
   - Applicable to embedding models and [cross-encoder models](../models/pooling_models.md) (`--task score`).
 - [Re-rank API](#rerank-api) (`/rerank`, `/v1/rerank`, `/v2/rerank`)
@@ -443,6 +445,130 @@ The input format is the same as [Embeddings API](#embeddings-api), but the outpu
 
 Code example: <gh-file:examples/online_serving/openai_pooling_client.py>
 
+(classification-api)=
+
+### Classification API
+
+Our Classification API directly supports Hugging Face sequence-classification models such as [ai21labs/Jamba-tiny-reward-dev](https://huggingface.co/ai21labs/Jamba-tiny-reward-dev) and [jason9693/Qwen2.5-1.5B-apeach](https://huggingface.co/jason9693/Qwen2.5-1.5B-apeach).
+
+We automatically wrap any other transformer via `as_classification_model()`, which pools on the last token, attaches a `RowParallelLinear` head, and applies a softmax to produce per-class probabilities.
+
+Code example: <gh-file:examples/online_serving/openai_classification_client.py>
+
+#### Example Requests
+
+You can classify multiple texts by passing an array of strings:
+
+Request:
+
+```bash
+curl -v "http://127.0.0.1:8000/classify" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "jason9693/Qwen2.5-1.5B-apeach",
+    "input": [
+      "Loved the new café—coffee was great.",
+      "This update broke everything. Frustrating."
+    ]
+  }'
+```
+
+Response:
+
+```bash
+{
+  "id": "classify-7c87cac407b749a6935d8c7ce2a8fba2",
+  "object": "list",
+  "created": 1745383065,
+  "model": "jason9693/Qwen2.5-1.5B-apeach",
+  "data": [
+    {
+      "index": 0,
+      "label": "Default",
+      "probs": [
+        0.565970778465271,
+        0.4340292513370514
+      ],
+      "num_classes": 2
+    },
+    {
+      "index": 1,
+      "label": "Spoiled",
+      "probs": [
+        0.26448777318000793,
+        0.7355121970176697
+      ],
+      "num_classes": 2
+    }
+  ],
+  "usage": {
+    "prompt_tokens": 20,
+    "total_tokens": 20,
+    "completion_tokens": 0,
+    "prompt_tokens_details": null
+  }
+}
+```
+
+You can also pass a string directly to the `input` field:
+
+Request:
+
+```bash
+curl -v "http://127.0.0.1:8000/classify" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "jason9693/Qwen2.5-1.5B-apeach",
+    "input": "Loved the new café—coffee was great."
+  }'
+```
+
+Response:
+
+```bash
+{
+  "id": "classify-9bf17f2847b046c7b2d5495f4b4f9682",
+  "object": "list",
+  "created": 1745383213,
+  "model": "jason9693/Qwen2.5-1.5B-apeach",
+  "data": [
+    {
+      "index": 0,
+      "label": "Default",
+      "probs": [
+        0.565970778465271,
+        0.4340292513370514
+      ],
+      "num_classes": 2
+    }
+  ],
+  "usage": {
+    "prompt_tokens": 10,
+    "total_tokens": 10,
+    "completion_tokens": 0,
+    "prompt_tokens_details": null
+  }
+}
+```
+
+#### Extra parameters
+
+The following [pooling parameters](#pooling-params) are supported.
+
+:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
+:language: python
+:start-after: begin-classification-pooling-params
+:end-before: end-classification-pooling-params
+:::
+
+The following extra parameters are supported:
+
+:::{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
+:language: python
+:start-after: begin-classification-extra-params
+:end-before: end-classification-extra-params
+:::
+
 (score-api)=
 
 ### Score API

examples/online_serving/openai_classification_client.py

@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import pprint
+
+import requests
+
+
+def post_http_request(payload: dict, api_url: str) -> requests.Response:
+    headers = {"User-Agent": "Test Client"}
+    response = requests.post(api_url, headers=headers, json=payload)
+    return response
+
+
+def parse_args():
+    parse = argparse.ArgumentParser()
+    parse.add_argument("--host", type=str, default="localhost")
+    parse.add_argument("--port", type=int, default=8000)
+    parse.add_argument("--model",
+                       type=str,
+                       default="jason9693/Qwen2.5-1.5B-apeach")
+    return parse.parse_args()
+
+
+def main(args):
+    host = args.host
+    port = args.port
+    model_name = args.model
+
+    api_url = f"http://{host}:{port}/classify"
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    payload = {
+        "model": model_name,
+        "input": prompts,
+    }
+
+    classify_response = post_http_request(payload=payload, api_url=api_url)
+    pprint.pprint(classify_response.json())
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

tests/entrypoints/openai/test_classification.py

@@ -0,0 +1,156 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import requests
+
+from vllm.entrypoints.openai.protocol import ClassificationResponse
+
+from ...utils import RemoteOpenAIServer
+
+MODEL_NAME = "jason9693/Qwen2.5-1.5B-apeach"
+DTYPE = "float32"  # Use float32 to avoid NaN issue
+
+
+@pytest.fixture(scope="module")
+def server():
+    args = [
+        "--enforce-eager",
+        "--max-model-len",
+        "512",
+        "--dtype",
+        DTYPE,
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_single_input_classification(server: RemoteOpenAIServer,
+                                     model_name: str):
+    input_text = "This product was excellent and exceeded my expectations"
+
+    classification_response = requests.post(
+        server.url_for("classify"),
+        json={
+            "model": model_name,
+            "input": input_text
+        },
+    )
+
+    classification_response.raise_for_status()
+    output = ClassificationResponse.model_validate(
+        classification_response.json())
+
+    assert output.object == "list"
+    assert output.model == MODEL_NAME
+    assert len(output.data) == 1
+    assert hasattr(output.data[0], "label")
+    assert hasattr(output.data[0], "probs")
+
+
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_multiple_inputs_classification(server: RemoteOpenAIServer,
+                                        model_name: str):
+    input_texts = [
+        "The product arrived on time and works perfectly",
+        "I'm very satisfied with my purchase, would buy again",
+        "The customer service was helpful and resolved my issue quickly",
+        "This product broke after one week, terrible quality",
+        "I'm very disappointed with this purchase, complete waste of money",
+        "The customer service was rude and unhelpful",
+    ]
+
+    classification_response = requests.post(
+        server.url_for("classify"),
+        json={
+            "model": model_name,
+            "input": input_texts
+        },
+    )
+    output = ClassificationResponse.model_validate(
+        classification_response.json())
+
+    assert len(output.data) == len(input_texts)
+    for i, item in enumerate(output.data):
+        assert item.index == i
+        assert hasattr(item, "label")
+        assert hasattr(item, "probs")
+        assert len(item.probs) == item.num_classes
+        assert item.label in ["Default", "Spoiled"]
+
+
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_truncate_prompt_tokens(server: RemoteOpenAIServer, model_name: str):
+    long_text = "hello " * 600
+
+    classification_response = requests.post(
+        server.url_for("classify"),
+        json={
+            "model": model_name,
+            "input": long_text,
+            "truncate_prompt_tokens": 5
+        },
+    )
+
+    classification_response.raise_for_status()
+    output = ClassificationResponse.model_validate(
+        classification_response.json())
+
+    assert len(output.data) == 1
+    assert output.data[0].index == 0
+    assert hasattr(output.data[0], "probs")
+    assert output.usage.prompt_tokens == 5
+    assert output.usage.total_tokens == 5
+
+
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_invalid_truncate_prompt_tokens_error(server: RemoteOpenAIServer,
+                                              model_name: str):
+    classification_response = requests.post(
+        server.url_for("classify"),
+        json={
+            "model": model_name,
+            "input": "test",
+            "truncate_prompt_tokens": 513
+        },
+    )
+
+    error = classification_response.json()
+    assert classification_response.status_code == 400
+    assert error["object"] == "error"
+    assert "truncate_prompt_tokens" in error["message"]
+
+
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_empty_input_error(server: RemoteOpenAIServer, model_name: str):
+    classification_response = requests.post(
+        server.url_for("classify"),
+        json={
+            "model": model_name,
+            "input": ""
+        },
+    )
+
+    error = classification_response.json()
+    assert classification_response.status_code == 400
+    assert error["object"] == "error"
+
+
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+def test_batch_classification_empty_list(server: RemoteOpenAIServer,
+                                         model_name: str):
+    classification_response = requests.post(
+        server.url_for("classify"),
+        json={
+            "model": model_name,
+            "input": []
+        },
+    )
+    classification_response.raise_for_status()
+    output = ClassificationResponse.model_validate(
+        classification_response.json())
+
+    assert output.object == "list"
+    assert isinstance(output.data, list)
+    assert len(output.data) == 0