[Refactor] Relocate chat completion and anthropic tests (#36919)
Signed-off-by: sfeng33 <4florafeng@gmail.com>
This commit is contained in:
Flora Feng
398
tests/entrypoints/openai/chat_completion/test_chat_error.py
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398
tests/entrypoints/openai/chat_completion/test_chat_error.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass, field
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from typing import Any
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from vllm.config.multimodal import MultiModalConfig
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from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
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from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
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from vllm.entrypoints.openai.engine.protocol import GenerationError
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from vllm.entrypoints.openai.models.protocol import BaseModelPath
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from vllm.entrypoints.openai.models.serving import OpenAIServingModels
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from vllm.entrypoints.serve.render.serving import OpenAIServingRender
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from vllm.outputs import CompletionOutput, RequestOutput
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from vllm.renderers.hf import HfRenderer
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from vllm.tokenizers.registry import tokenizer_args_from_config
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from vllm.v1.engine.async_llm import AsyncLLM
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MODEL_NAME = "openai-community/gpt2"
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MODEL_NAME_SHORT = "gpt2"
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BASE_MODEL_PATHS = [
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BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME),
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BaseModelPath(name=MODEL_NAME_SHORT, model_path=MODEL_NAME_SHORT),
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]
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@dataclass
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class MockHFConfig:
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model_type: str = "any"
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@dataclass
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class MockModelConfig:
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task = "generate"
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runner_type = "generate"
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model = MODEL_NAME
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tokenizer = MODEL_NAME
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trust_remote_code = False
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tokenizer_mode = "auto"
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max_model_len = 100
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tokenizer_revision = None
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multimodal_config = MultiModalConfig()
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hf_config = MockHFConfig()
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hf_text_config = MockHFConfig()
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logits_processors: list[str] | None = None
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diff_sampling_param: dict | None = None
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allowed_local_media_path: str = ""
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allowed_media_domains: list[str] | None = None
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encoder_config = None
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generation_config: str = "auto"
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media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
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skip_tokenizer_init = False
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is_encoder_decoder: bool = False
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is_multimodal_model: bool = False
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def get_diff_sampling_param(self):
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return self.diff_sampling_param or {}
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@dataclass
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class MockParallelConfig:
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_api_process_rank: int = 0
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@dataclass
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class MockVllmConfig:
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model_config: MockModelConfig
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parallel_config: MockParallelConfig
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def _build_renderer(model_config: MockModelConfig):
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_, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
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return HfRenderer.from_config(
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MockVllmConfig(model_config, parallel_config=MockParallelConfig()),
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tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
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)
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def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
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models = OpenAIServingModels(
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engine_client=engine,
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base_model_paths=BASE_MODEL_PATHS,
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)
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serving_render = OpenAIServingRender(
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model_config=engine.model_config,
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renderer=engine.renderer,
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io_processor=engine.io_processor,
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model_registry=models.registry,
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request_logger=None,
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chat_template=None,
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chat_template_content_format="auto",
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)
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serving_chat = OpenAIServingChat(
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engine,
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models,
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response_role="assistant",
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openai_serving_render=serving_render,
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request_logger=None,
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chat_template=None,
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chat_template_content_format="auto",
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)
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async def _fake_preprocess_chat(*args, **kwargs):
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# return conversation, engine_prompts
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return (
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[{"role": "user", "content": "Test"}],
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[{"prompt_token_ids": [1, 2, 3]}],
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)
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serving_chat.openai_serving_render._preprocess_chat = AsyncMock(
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side_effect=_fake_preprocess_chat
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)
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return serving_chat
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@pytest.mark.asyncio
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async def test_chat_error_non_stream():
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"""test finish_reason='error' returns 500 InternalServerError (non-streaming)"""
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mock_engine = MagicMock(spec=AsyncLLM)
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mock_engine.errored = False
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mock_engine.model_config = MockModelConfig()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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mock_engine.renderer = _build_renderer(mock_engine.model_config)
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serving_chat = _build_serving_chat(mock_engine)
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completion_output = CompletionOutput(
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index=0,
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text="",
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token_ids=[],
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cumulative_logprob=None,
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logprobs=None,
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finish_reason="error",
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)
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request_output = RequestOutput(
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request_id="test-id",
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prompt="Test prompt",
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prompt_token_ids=[1, 2, 3],
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prompt_logprobs=None,
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outputs=[completion_output],
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finished=True,
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metrics=None,
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lora_request=None,
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encoder_prompt=None,
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encoder_prompt_token_ids=None,
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)
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async def mock_generate(*args, **kwargs):
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yield request_output
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mock_engine.generate = MagicMock(side_effect=mock_generate)
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request = ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[{"role": "user", "content": "Test prompt"}],
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max_tokens=10,
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stream=False,
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)
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with pytest.raises(GenerationError):
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await serving_chat.create_chat_completion(request)
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@pytest.mark.asyncio
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async def test_chat_error_stream():
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"""test finish_reason='error' returns 500 InternalServerError (streaming)"""
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mock_engine = MagicMock(spec=AsyncLLM)
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mock_engine.errored = False
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mock_engine.model_config = MockModelConfig()
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mock_engine.input_processor = MagicMock()
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mock_engine.io_processor = MagicMock()
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mock_engine.renderer = _build_renderer(mock_engine.model_config)
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serving_chat = _build_serving_chat(mock_engine)
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completion_output_1 = CompletionOutput(
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index=0,
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text="Hello",
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token_ids=[100],
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cumulative_logprob=None,
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logprobs=None,
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finish_reason=None,
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)
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request_output_1 = RequestOutput(
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request_id="test-id",
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prompt="Test prompt",
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prompt_token_ids=[1, 2, 3],
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prompt_logprobs=None,
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outputs=[completion_output_1],
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finished=False,
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metrics=None,
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lora_request=None,
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encoder_prompt=None,
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encoder_prompt_token_ids=None,
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)
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completion_output_2 = CompletionOutput(
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index=0,
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text="Hello",
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token_ids=[100],
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cumulative_logprob=None,
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logprobs=None,
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finish_reason="error",
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)
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request_output_2 = RequestOutput(
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request_id="test-id",
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prompt="Test prompt",
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prompt_token_ids=[1, 2, 3],
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prompt_logprobs=None,
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outputs=[completion_output_2],
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finished=True,
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metrics=None,
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lora_request=None,
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encoder_prompt=None,
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encoder_prompt_token_ids=None,
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)
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async def mock_generate(*args, **kwargs):
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yield request_output_1
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yield request_output_2
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mock_engine.generate = MagicMock(side_effect=mock_generate)
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request = ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[{"role": "user", "content": "Test prompt"}],
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max_tokens=10,
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stream=True,
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)
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response = await serving_chat.create_chat_completion(request)
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chunks = []
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async for chunk in response:
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chunks.append(chunk)
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assert len(chunks) >= 2
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assert any("Internal server error" in chunk for chunk in chunks), (
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f"Expected error message in chunks: {chunks}"
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)
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assert chunks[-1] == "data: [DONE]\n\n"
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@pytest.mark.parametrize(
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"image_content",
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[
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[{"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}],
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[{"image_url": {"url": "https://example.com/image.jpg"}}],
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],
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)
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def test_system_message_warns_on_image(image_content):
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"""Test that system messages with image content trigger a warning."""
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with patch(
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"vllm.entrypoints.openai.chat_completion.protocol.logger"
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) as mock_logger:
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ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[
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{
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"role": "system",
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"content": image_content,
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}
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],
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)
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mock_logger.warning_once.assert_called()
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call_args = str(mock_logger.warning_once.call_args)
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assert "System messages should only contain text" in call_args
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assert "image_url" in call_args
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def test_system_message_accepts_text():
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"""Test that system messages can contain text content."""
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# Should not raise an exception
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request = ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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],
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)
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assert request.messages[0]["role"] == "system"
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def test_system_message_accepts_text_array():
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"""Test that system messages can contain an array with text content."""
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# Should not raise an exception
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request = ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[
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{
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"role": "system",
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"content": [{"type": "text", "text": "You are a helpful assistant."}],
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},
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],
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)
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assert request.messages[0]["role"] == "system"
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def test_user_message_accepts_image():
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"""Test that user messages can still contain image content."""
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# Should not raise an exception
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request = ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What's in this image?"},
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{
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"type": "image_url",
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"image_url": {"url": "https://example.com/image.jpg"},
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},
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],
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},
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],
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)
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assert request.messages[0]["role"] == "user"
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@pytest.mark.parametrize(
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"audio_content",
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[
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[
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{
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"type": "input_audio",
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"input_audio": {"data": "base64data", "format": "wav"},
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}
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],
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[{"input_audio": {"data": "base64data", "format": "wav"}}],
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],
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)
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def test_system_message_warns_on_audio(audio_content):
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"""Test that system messages with audio content trigger a warning."""
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with patch(
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"vllm.entrypoints.openai.chat_completion.protocol.logger"
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) as mock_logger:
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ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[
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{
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"role": "system",
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"content": audio_content,
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}
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],
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)
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mock_logger.warning_once.assert_called()
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call_args = str(mock_logger.warning_once.call_args)
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assert "System messages should only contain text" in call_args
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assert "input_audio" in call_args
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@pytest.mark.parametrize(
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"video_content",
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[
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[{"type": "video_url", "video_url": {"url": "https://example.com/video.mp4"}}],
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[{"video_url": {"url": "https://example.com/video.mp4"}}],
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],
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)
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def test_system_message_warns_on_video(video_content):
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"""Test that system messages with video content trigger a warning."""
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with patch(
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"vllm.entrypoints.openai.chat_completion.protocol.logger"
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) as mock_logger:
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ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[
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{
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"role": "system",
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"content": video_content,
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}
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],
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)
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mock_logger.warning_once.assert_called()
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call_args = str(mock_logger.warning_once.call_args)
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assert "System messages should only contain text" in call_args
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assert "video_url" in call_args
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def test_json_schema_response_format_missing_schema():
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"""When response_format type is 'json_schema' but the json_schema field
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is not provided, request construction should raise a validation error
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so the API returns 400 instead of 500."""
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with pytest.raises(Exception, match="json_schema.*must be provided"):
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ChatCompletionRequest(
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model=MODEL_NAME,
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messages=[{"role": "user", "content": "hello"}],
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response_format={"type": "json_schema"},
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)
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