vllm/tests/entrypoints/openai/test_serving_tokens.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import httpx
import pytest
import pytest_asyncio
from transformers import AutoTokenizer

from vllm.config import ModelConfig
from vllm.v1.engine.detokenizer import check_stop_strings

from ...utils import RemoteOpenAIServer

MODEL_NAME = "Qwen/Qwen3-0.6B"
GEN_ENDPOINT = "/inference/v1/generate"


def get_vocab_size(model_name):
    config = ModelConfig(
        model=model_name,
        seed=0,
        dtype="bfloat16",
    )
    return config.get_vocab_size()


@pytest.fixture(scope="module")
def tokenizer():
    return AutoTokenizer.from_pretrained(MODEL_NAME)


@pytest.fixture(scope="module")
def messages():
    return [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "How many countries are in the EU?"},
    ]


@pytest.fixture(scope="module")
def server(request):
    args = [
        "--dtype",
        "bfloat16",
        "--max-model-len",
        "1024",
        "--enforce-eager",
    ]

    extra_args = getattr(request, "param", None)
    if extra_args is not None:
        args = args + (
            list(extra_args)
            if isinstance(extra_args, (list, tuple))
            else [str(extra_args)]
        )

    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
        yield remote_server


@pytest_asyncio.fixture
async def client(server: RemoteOpenAIServer):
    transport = httpx.AsyncHTTPTransport(uds=server.uds) if server.uds else None
    headers = {"Authorization": f"Bearer {server.DUMMY_API_KEY}"}
    async with httpx.AsyncClient(
        transport=transport,
        base_url=server.url_root,
        timeout=600,
        headers=headers,
    ) as c:
        yield c


@pytest.mark.asyncio
async def test_generate_endpoint(client):
    payload = {
        "model": MODEL_NAME,
        "token_ids": [1, 2, 3],
        "sampling_params": {"max_tokens": 5},
        "stream": False,
    }
    resp = await client.post(GEN_ENDPOINT, json=payload)
    resp.raise_for_status()
    data = resp.json()
    assert "choices" in data


@pytest.mark.asyncio
async def test_same_response_as_chat_completions(client, tokenizer, messages):
    token_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        enable_thinking=False,  # default with Qwen3
    )
    for ignore_eos in [True, False]:
        payload = {
            "model": MODEL_NAME,
            "token_ids": token_ids,
            "sampling_params": {
                "max_tokens": 24,
                "temperature": 0.0,
                # NOTE coordinator will set this to skip detokenization
                "detokenize": False,
                "ignore_eos": ignore_eos,
            },
            "stream": False,
        }
        generate_resp = await client.post(GEN_ENDPOINT, json=payload)
        generate_data = generate_resp.json()
        generate_res = tokenizer.decode(
            generate_data["choices"][0]["token_ids"], skip_special_tokens=True
        )

        payload = {
            "model": MODEL_NAME,
            "messages": messages,
            "max_tokens": 24,
            "temperature": 0.0,
            "stream": False,
            "ignore_eos": ignore_eos,
            "chat_template_kwargs": dict(enable_thinking=False),
        }
        completions_resp = await client.post("/v1/chat/completions", json=payload)
        completions_data = completions_resp.json()
        completions_res = completions_data["choices"][0]["message"]["content"]

        assert generate_res == completions_res


@pytest.mark.asyncio
async def test_stop_string_workflow(client, tokenizer, messages):
    token_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        enable_thinking=False,  # default with Qwen3
    )
    payload = {
        "model": MODEL_NAME,
        "token_ids": token_ids,
        "sampling_params": {
            "max_tokens": 24,
            "temperature": 0.0,
            "detokenize": False,
            # stop strings are only supported when detokenize is True.
            "stop": ["27 member"],
        },
        # TODO stream test is much more interesting
        "stream": False,
    }
    with pytest.raises(httpx.HTTPStatusError):
        generate_resp = await client.post(GEN_ENDPOINT, json=payload)
        generate_resp.raise_for_status()

    payload["sampling_params"]["stop"] = None
    generate_resp = await client.post(
        GEN_ENDPOINT, json=payload, headers={"X-Request-Id": "42"}
    )
    generate_data = generate_resp.json()
    generate_res = tokenizer.decode(
        generate_data["choices"][0]["token_ids"], skip_special_tokens=True
    )

    # NOTE This is under the responsibility of the coordinator
    # stop_checker = StopChecker(
    #     max_model_len=1024, get_tokenizer_for_seq=lambda _: tokenizer
    # )
    stop_str, truncate_to = check_stop_strings(
        generate_res, len(generate_res), ["27 member"], False
    )
    assert stop_str == "27 member"
    # abort request that hit stop string (requires tokens-only mode)
    # res = await client.post("/abort_requests", json={"request_ids": ["generate-tokens-42"]}) # noqa: E501
    # res.raise_for_status()
    generate_res = generate_res[:truncate_to]

    # Get stop_str response from chat completions
    payload = {
        "model": MODEL_NAME,
        "messages": messages,
        "max_tokens": 24,
        "temperature": 0.0,
        "stream": False,
        "stop": ["27 member"],
        "chat_template_kwargs": dict(enable_thinking=False),
    }
    completions_resp = await client.post("/v1/chat/completions", json=payload)
    completions_data = completions_resp.json()
    completions_res = completions_data["choices"][0]["message"]["content"]
    assert generate_res == completions_res


@pytest.mark.asyncio
@pytest.mark.parametrize(
    "server",
    [
        [
            "--enable-lora",
            "--lora-modules",
            "Alice=charent/self_cognition_Alice",
            "Bob=charent/self_cognition_Bob",
            "--max-lora-rank",
            "64",
            "--max-cpu-loras",
            "2",
        ]
    ],
    indirect=True,
)
async def test_generate_with_lora_adapter(client, tokenizer, messages):
    # Verify adapters are listed
    models_resp = await client.get("/v1/models")
    models_resp.raise_for_status()
    models = {m["id"] for m in models_resp.json().get("data", [])}
    assert {"Alice", "Bob"}.issubset(models)

    # Generate using a LoRA adapter by specifying its name as the model
    payload = {
        "model": "Alice",
        "token_ids": [1, 2, 3],
        "sampling_params": {"max_tokens": 5},
        "stream": False,
    }
    resp = await client.post(GEN_ENDPOINT, json=payload)
    resp.raise_for_status()
    data = resp.json()
    assert "choices" in data

    token_ids = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        enable_thinking=False,  # default with Qwen3
    )
    payload = {
        "model": "Alice",
        "token_ids": token_ids,
        "sampling_params": {
            "max_tokens": 24,
            "temperature": 0.0,
            "detokenize": False,
        },
        "stream": False,
    }
    generate_resp = await client.post(GEN_ENDPOINT, json=payload)
    generate_data = generate_resp.json()
    generate_res = tokenizer.decode(
        generate_data["choices"][0]["token_ids"], skip_special_tokens=True
    )

    payload = {
        "model": "Alice",
        "messages": messages,
        "max_tokens": 24,
        "temperature": 0.0,
        "stream": False,
        "chat_template_kwargs": dict(enable_thinking=False),
    }
    completions_resp = await client.post("/v1/chat/completions", json=payload)
    completions_data = completions_resp.json()
    completions_res = completions_data["choices"][0]["message"]["content"]

    assert generate_res == completions_res