test_serving_chat.py

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

import asyncio
from contextlib import suppress
from dataclasses import dataclass, field
from typing import Any, Optional
from unittest.mock import MagicMock

import pytest

from vllm.config import MultiModalConfig
from vllm.engine.multiprocessing.client import MQLLMEngineClient
from vllm.entrypoints.openai.protocol import ChatCompletionRequest
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
from vllm.entrypoints.openai.serving_models import (BaseModelPath,
                                                    OpenAIServingModels)
from vllm.transformers_utils.tokenizer import get_tokenizer

MODEL_NAME = "openai-community/gpt2"
CHAT_TEMPLATE = "Dummy chat template for testing {}"
BASE_MODEL_PATHS = [BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME)]


@dataclass
class MockHFConfig:
    model_type: str = "any"


@dataclass
class MockModelConfig:
    task = "generate"
    tokenizer = MODEL_NAME
    trust_remote_code = False
    tokenizer_mode = "auto"
    max_model_len = 100
    tokenizer_revision = None
    multimodal_config = MultiModalConfig()
    hf_config = MockHFConfig()
    logits_processor_pattern = None
    diff_sampling_param: Optional[dict] = None
    allowed_local_media_path: str = ""
    encoder_config = None
    generation_config: str = "auto"
    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)

    def get_diff_sampling_param(self):
        return self.diff_sampling_param or {}


@dataclass
class MockEngine:

    async def get_model_config(self):
        return MockModelConfig()


async def _async_serving_chat_init():
    engine = MockEngine()
    model_config = await engine.get_model_config()

    models = OpenAIServingModels(engine, model_config, BASE_MODEL_PATHS)
    serving_completion = OpenAIServingChat(engine,
                                           model_config,
                                           models,
                                           response_role="assistant",
                                           chat_template=CHAT_TEMPLATE,
                                           chat_template_content_format="auto",
                                           request_logger=None)
    return serving_completion


def test_async_serving_chat_init():
    serving_completion = asyncio.run(_async_serving_chat_init())
    assert serving_completion.chat_template == CHAT_TEMPLATE


@pytest.mark.asyncio
async def test_serving_chat_should_set_correct_max_tokens():
    mock_engine = MagicMock(spec=MQLLMEngineClient)
    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
    mock_engine.errored = False

    models = OpenAIServingModels(engine_client=mock_engine,
                                 base_model_paths=BASE_MODEL_PATHS,
                                 model_config=MockModelConfig())
    serving_chat = OpenAIServingChat(mock_engine,
                                     MockModelConfig(),
                                     models,
                                     response_role="assistant",
                                     chat_template=CHAT_TEMPLATE,
                                     chat_template_content_format="auto",
                                     request_logger=None)

    req = ChatCompletionRequest(
        model=MODEL_NAME,
        messages=[{
            "role": "user",
            "content": "what is 1+1?"
        }],
        guided_decoding_backend="outlines",
    )

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].max_tokens == 93

    req.max_tokens = 10
    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].max_tokens == 10

    # Setting server's max_tokens in the generation_config.json
    # lower than context_window - prompt_tokens
    mock_model_config = MockModelConfig()
    mock_model_config.diff_sampling_param = {
        "max_tokens": 10  # Setting server-side max_tokens limit
    }

    # Reinitialize the engine with new settings
    mock_engine = MagicMock(spec=MQLLMEngineClient)
    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
    mock_engine.errored = False

    # Initialize the serving chat
    models = OpenAIServingModels(engine_client=mock_engine,
                                 base_model_paths=BASE_MODEL_PATHS,
                                 model_config=mock_model_config)
    serving_chat = OpenAIServingChat(mock_engine,
                                     mock_model_config,
                                     models,
                                     response_role="assistant",
                                     chat_template=CHAT_TEMPLATE,
                                     chat_template_content_format="auto",
                                     request_logger=None)

    # Test Case 1: No max_tokens specified in request
    req = ChatCompletionRequest(
        model=MODEL_NAME,
        messages=[{
            "role": "user",
            "content": "what is 1+1?"
        }],
        guided_decoding_backend="outlines",
    )

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].max_tokens == 10

    # Test Case 2: Request's max_tokens set higher than server accepts
    req.max_tokens = 15

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].max_tokens == 10

    # Test Case 3: Request's max_tokens set lower than server accepts
    req.max_tokens = 5

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].max_tokens == 5

    # Setting server's max_tokens in the generation_config.json
    # higher than context_window - prompt_tokens
    mock_model_config = MockModelConfig()
    mock_model_config.diff_sampling_param = {
        "max_tokens": 200  # Setting server-side max_tokens limit
    }

    # Reinitialize the engine with new settings
    mock_engine = MagicMock(spec=MQLLMEngineClient)
    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
    mock_engine.errored = False

    # Initialize the serving chat
    models = OpenAIServingModels(engine_client=mock_engine,
                                 base_model_paths=BASE_MODEL_PATHS,
                                 model_config=mock_model_config)
    serving_chat = OpenAIServingChat(mock_engine,
                                     mock_model_config,
                                     models,
                                     response_role="assistant",
                                     chat_template=CHAT_TEMPLATE,
                                     chat_template_content_format="auto",
                                     request_logger=None)

    # Test case 1: No max_tokens specified, defaults to context_window
    req = ChatCompletionRequest(
        model=MODEL_NAME,
        messages=[{
            "role": "user",
            "content": "what is 1+1?"
        }],
        guided_decoding_backend="outlines",
    )

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].max_tokens == 93

    # Test Case 2: Request's max_tokens set higher than server accepts
    req.max_tokens = 100

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].max_tokens == 93

    # Test Case 3: Request's max_tokens set lower than server accepts
    req.max_tokens = 5

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].max_tokens == 5


@pytest.mark.asyncio
async def test_serving_chat_could_load_correct_generation_config():

    mock_model_config = MockModelConfig()
    mock_model_config.diff_sampling_param = {
        "temperature": 0.5,
        "repetition_penalty": 1.05
    }

    mock_engine = MagicMock(spec=MQLLMEngineClient)
    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
    mock_engine.errored = False

    # Initialize the serving chat
    models = OpenAIServingModels(engine_client=mock_engine,
                                 base_model_paths=BASE_MODEL_PATHS,
                                 model_config=mock_model_config)
    serving_chat = OpenAIServingChat(mock_engine,
                                     mock_model_config,
                                     models,
                                     response_role="assistant",
                                     chat_template=CHAT_TEMPLATE,
                                     chat_template_content_format="auto",
                                     request_logger=None)

    req = ChatCompletionRequest(
        model=MODEL_NAME,
        messages=[{
            "role": "user",
            "content": "what is 1+1?"
        }],
        guided_decoding_backend="outlines",
    )

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].temperature == 0.5
    assert mock_engine.generate.call_args.args[1].repetition_penalty == 1.05

    # Test the param when user set it
    req.temperature = 0.1

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].temperature == 0.1
    assert mock_engine.generate.call_args.args[1].repetition_penalty == 1.05

    # Test When temperature==0.0
    req.temperature = 0.0

    with suppress(Exception):
        await serving_chat.create_chat_completion(req)

    assert mock_engine.generate.call_args.args[1].temperature == 0.0
    assert mock_engine.generate.call_args.args[1].repetition_penalty == 1.05


@pytest.mark.parametrize("model_type", ["gpt_oss", "any"])
@pytest.mark.asyncio
async def test_serving_chat_did_set_correct_cache_salt(model_type):
    mock_model_config = MockModelConfig()
    mock_model_config.hf_config.model_type = model_type

    mock_engine = MagicMock(spec=MQLLMEngineClient)
    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
    mock_engine.errored = False

    # Initialize the serving chat
    models = OpenAIServingModels(engine_client=mock_engine,
                                 base_model_paths=BASE_MODEL_PATHS,
                                 model_config=mock_model_config)
    serving_chat = OpenAIServingChat(mock_engine,
                                     mock_model_config,
                                     models,
                                     response_role="assistant",
                                     chat_template=CHAT_TEMPLATE,
                                     chat_template_content_format="auto",
                                     request_logger=None)

    # Test cache_salt
    req = ChatCompletionRequest(
        model=MODEL_NAME,
        messages=[{
            "role": "user",
            "content": "what is 1+1?"
        }],
    )

    # By default, cache_salt in the engine prompt is not set
    with suppress(Exception):
        await serving_chat.create_chat_completion(req)
    assert "cache_salt" not in mock_engine.generate.call_args.args[0]

    # Test with certain cache_salt
    req.cache_salt = "test_salt"
    with suppress(Exception):
        await serving_chat.create_chat_completion(req)
    assert mock_engine.generate.call_args.args[0]["cache_salt"] == "test_salt"