feat: OpenAI 兼容的 chat completion API（流式+非流式+usage）
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parent
4e324d8f26
commit
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@ -1,15 +1,14 @@
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"""
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Inference Server with Continuous Batching Support
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FastAPI server for inference with continuous batching.
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Provides OpenAI-compatible chat completion endpoints.
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OpenAI-compatible chat completion server backed by continuous-batching inference.
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"""
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import json
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import logging
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import time
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import uuid
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from contextlib import asynccontextmanager
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List, Optional, Union
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import torch
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import uvicorn
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@ -27,17 +26,8 @@ _project_root = Path(__file__).parent.parent.parent
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class ServerState:
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"""Encapsulates all server runtime state.
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Attributes:
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engine: The inference engine instance.
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model_param: The loaded model.
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config: Server configuration dict.
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"""
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def __init__(self):
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self.engine: Optional[InferenceEngine] = None
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self.model_param: Optional[Any] = None
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self.config: Dict[str, Any] = {
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"device": "cuda",
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"dtype": torch.bfloat16,
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@ -49,6 +39,28 @@ class ServerState:
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_state = ServerState()
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class ChatMessage(BaseModel):
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role: str
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content: str
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class ChatCompletionRequest(BaseModel):
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"""OpenAI Chat Completion API request body."""
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model: str = "astrai"
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messages: List[ChatMessage]
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temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0)
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top_p: Optional[float] = Field(default=1.0, ge=0.0, le=1.0)
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stream: Optional[bool] = False
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stop: Optional[Union[str, List[str]]] = None
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max_tokens: Optional[int] = Field(default=2048, ge=1)
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n: Optional[int] = Field(default=1, ge=1)
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presence_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
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frequency_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
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logit_bias: Optional[Dict[int, float]] = None
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user: Optional[str] = None
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def configure_server(
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device: str = "cuda",
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dtype: torch.dtype = torch.bfloat16,
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@ -96,12 +108,12 @@ def load_model(
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raise FileNotFoundError(f"Parameter directory not found: {param_path}")
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tokenizer = AutoTokenizer.from_pretrained(param_path)
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_state.model_param = AutoModel.from_pretrained(param_path)
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_state.model_param.to(device=device, dtype=dtype)
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model = AutoModel.from_pretrained(param_path)
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model.to(device=device, dtype=dtype)
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logger.info(f"Model loaded on {device} with dtype {dtype}")
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_state.engine = InferenceEngine(
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model=_state.model_param,
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model=model,
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tokenizer=tokenizer,
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max_batch_size=max_batch_size,
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)
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@ -114,35 +126,37 @@ def _get_engine() -> InferenceEngine:
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return _state.engine
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class ChatMessage(BaseModel):
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role: str
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content: str
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class ChatCompletionRequest(BaseModel):
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messages: List[ChatMessage]
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temperature: float = Field(0.8, ge=0.0, le=2.0)
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top_p: float = Field(0.95, ge=0.0, le=1.0)
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top_k: int = Field(50, ge=0)
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max_tokens: int = Field(2048, ge=1)
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stream: bool = False
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system_prompt: Optional[str] = None
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class CompletionResponse(BaseModel):
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id: str = "chatcmpl-default"
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object: str = "chat.completion"
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created: int = 0
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model: str = "astrai"
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choices: List[Dict[str, Any]]
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def _make_chunk(
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delta: Dict[str, str],
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finish_reason: Optional[str] = None,
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*,
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resp_id: str,
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created: int,
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model: str,
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index: int = 0,
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) -> str:
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"""Build a single SSE ``data:`` chunk matching OpenAI streaming format."""
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data = {
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"id": resp_id,
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"object": "chat.completion.chunk",
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"created": created,
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"model": model,
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"choices": [
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{
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"index": index,
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"delta": delta,
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"finish_reason": finish_reason,
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}
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],
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}
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return f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
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@app.get("/health")
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async def health():
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return {
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"status": "ok",
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"model_loaded": _state.model_param is not None,
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"engine_ready": _state.engine is not None,
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"model_loaded": _state.engine is not None,
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}
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@ -151,14 +165,19 @@ async def get_stats():
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return _get_engine().get_stats()
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@app.post("/v1/chat/completions", response_model=CompletionResponse)
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@app.post("/v1/chat/completions")
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async def chat_completion(request: ChatCompletionRequest):
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"""OpenAI-compatible chat completion endpoint (streaming + non-streaming)."""
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engine = _get_engine()
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resp_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
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created = int(time.time())
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model = request.model
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prompt = engine.tokenizer.apply_chat_template(
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[{"role": m.role, "content": m.content} for m in request.messages],
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tokenize=False,
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)
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prompt_tokens = len(engine.tokenizer.encode(prompt))
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if request.stream:
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agen = engine.generate_async(
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@ -166,12 +185,43 @@ async def chat_completion(request: ChatCompletionRequest):
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max_tokens=request.max_tokens,
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temperature=request.temperature,
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top_p=request.top_p,
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top_k=request.top_k,
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top_k=50,
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)
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async def event_stream():
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yield _make_chunk(
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{"role": "assistant"},
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finish_reason=None,
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resp_id=resp_id,
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created=created,
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model=model,
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)
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completion_tokens = 0
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async for token in agen:
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yield f"data: {json.dumps({'choices': [{'delta': {'content': token}}]})}\n\n"
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yield _make_chunk(
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{"content": token},
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finish_reason=None,
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resp_id=resp_id,
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created=created,
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model=model,
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)
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completion_tokens += 1
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yield _make_chunk(
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{},
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finish_reason="stop",
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resp_id=resp_id,
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created=created,
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model=model,
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)
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usage = {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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}
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yield f"data: {json.dumps(usage, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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return StreamingResponse(
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@ -179,30 +229,39 @@ async def chat_completion(request: ChatCompletionRequest):
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
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)
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else:
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result = engine.generate(
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prompt=prompt,
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stream=False,
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max_tokens=request.max_tokens,
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temperature=request.temperature,
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top_p=request.top_p,
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top_k=request.top_k,
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)
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import time
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completion_tokens = 0
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chunks: List[str] = []
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agen = engine.generate_async(
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prompt=prompt,
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max_tokens=request.max_tokens,
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temperature=request.temperature,
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top_p=request.top_p,
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top_k=50,
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)
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async for token in agen:
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chunks.append(token)
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completion_tokens += 1
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content = "".join(chunks)
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resp = CompletionResponse(
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id=f"chatcmpl-{int(time.time())}",
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created=int(time.time()),
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choices=[
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{
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"index": 0,
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"message": {"role": "assistant", "content": result},
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"finish_reason": "stop",
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}
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],
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)
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return resp
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return {
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"id": resp_id,
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"object": "chat.completion",
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"created": created,
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"model": model,
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"choices": [
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{
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"index": 0,
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"message": {"role": "assistant", "content": content},
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"finish_reason": "stop",
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}
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],
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"usage": {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": prompt_tokens + completion_tokens,
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},
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}
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@app.post("/generate")
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@ -215,6 +274,7 @@ async def generate(
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max_len: int = 2048,
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stream: bool = False,
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):
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"""Legacy non-OpenAI generation endpoint (kept for backward compat)."""
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engine = _get_engine()
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messages = []
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@ -242,15 +302,17 @@ async def generate(
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return StreamingResponse(text_stream(), media_type="text/plain")
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else:
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result = engine.generate(
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chunks = []
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for token in engine.generate(
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prompt=prompt,
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stream=False,
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stream=True,
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max_tokens=max_len,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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)
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return {"response": result}
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):
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chunks.append(token)
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return {"response": "".join(chunks)}
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def run_server(
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@ -14,21 +14,6 @@ def client():
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return TestClient(app)
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@pytest.fixture
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def mock_model_param():
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"""Create a mock ModelParameter."""
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mock_param = MagicMock()
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mock_param.model = MagicMock()
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mock_param.tokenizer = MagicMock()
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mock_param.config = MagicMock()
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mock_param.config.max_len = 100
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mock_param.tokenizer.encode = MagicMock(return_value=[1, 2, 3])
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mock_param.tokenizer.decode = MagicMock(return_value="mock response")
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mock_param.tokenizer.stop_ids = []
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mock_param.tokenizer.pad_id = 0
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return mock_param
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@pytest.fixture
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def mock_engine():
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"""Create a mock InferenceEngine."""
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@ -47,11 +32,14 @@ def mock_engine():
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"active_tasks": 0,
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"waiting_queue": 0,
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}
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mock.tokenizer.encode.return_value = [1, 2, 3]
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mock.tokenizer.decode.return_value = "mock response"
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mock.tokenizer.apply_chat_template.return_value = "mock prompt"
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return mock
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@pytest.fixture
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def loaded_model(mock_model_param, monkeypatch):
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"""Simulate that the model is loaded."""
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monkeypatch.setattr("astrai.inference.server._state.model_param", mock_model_param)
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return mock_model_param
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def loaded_model(mock_engine, monkeypatch):
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"""Simulate that the engine is loaded."""
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monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
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return mock_engine
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@ -1,34 +1,31 @@
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"""Unit tests for the inference HTTP server."""
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from unittest.mock import MagicMock
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import pytest
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def test_health_no_model(client, monkeypatch):
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"""GET /health should return 200 even when model not loaded."""
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monkeypatch.setattr("astrai.inference.server._state.model_param", None)
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"""GET /health should return 200 even when engine not loaded."""
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monkeypatch.setattr("astrai.inference.server._state.engine", None)
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response = client.get("/health")
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assert response.status_code == 200
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data = response.json()
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assert data["status"] == "ok"
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assert not data["model_loaded"]
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assert not data["engine_ready"]
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def test_health_with_model(client, loaded_model, mock_engine, monkeypatch):
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"""GET /health should return 200 when model is loaded."""
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monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
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def test_health_with_model(client, loaded_model):
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"""GET /health should return 200 when engine is loaded."""
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response = client.get("/health")
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assert response.status_code == 200
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data = response.json()
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assert data["status"] == "ok"
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assert data["model_loaded"] is True
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assert data["engine_ready"] is True
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def test_generate_non_stream(client, loaded_model, mock_engine, monkeypatch):
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def test_generate_non_stream(client, loaded_model, monkeypatch):
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"""POST /generate with stream=false should return JSON response."""
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monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
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response = client.post(
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"/generate",
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params={
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@ -42,18 +39,18 @@ def test_generate_non_stream(client, loaded_model, mock_engine, monkeypatch):
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)
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assert response.status_code == 200
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data = response.json()
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assert data["response"] == "mock response"
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assert "response" in data
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def test_generate_stream(client, loaded_model, mock_engine, monkeypatch):
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def test_generate_stream(client, loaded_model, monkeypatch):
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"""POST /generate with stream=true should return plain text stream."""
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# Create a streaming mock
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def stream_gen():
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async def async_gen():
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yield "chunk1"
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yield "chunk2"
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mock_engine.generate.return_value = stream_gen()
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mock_engine = loaded_model
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mock_engine.generate_async.return_value = async_gen()
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monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
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response = client.post(
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"/generate",
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@ -68,24 +65,25 @@ def test_generate_stream(client, loaded_model, mock_engine, monkeypatch):
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headers={"Accept": "text/plain"},
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)
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assert response.status_code == 200
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assert response.headers["content-type"] == "text/plain; charset=utf-8"
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# The stream yields lines ending with newline
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content = response.content.decode("utf-8")
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assert "chunk1" in content
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assert "chunk2" in content
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def test_chat_completions_non_stream(client, loaded_model, mock_engine, monkeypatch):
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"""POST /v1/chat/completions with stream=false returns OpenAI‑style JSON."""
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mock_engine.generate.return_value = "Assistant reply"
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def test_chat_completions_non_stream(client, loaded_model, monkeypatch):
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"""POST /v1/chat/completions with stream=false returns OpenAI-style JSON."""
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async def async_gen():
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yield "Assistant reply"
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mock_engine = loaded_model
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mock_engine.generate_async.return_value = async_gen()
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monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
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response = client.post(
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"/v1/chat/completions",
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json={
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"messages": [{"role": "user", "content": "Hello"}],
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"temperature": 0.8,
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"top_p": 0.95,
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"top_k": 50,
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"max_tokens": 100,
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"stream": False,
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},
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@ -94,17 +92,18 @@ def test_chat_completions_non_stream(client, loaded_model, mock_engine, monkeypa
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data = response.json()
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assert data["object"] == "chat.completion"
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assert len(data["choices"]) == 1
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assert data["choices"][0]["message"]["content"] == "Assistant reply"
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assert "usage" in data
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assert "prompt_tokens" in data["usage"]
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def test_chat_completions_stream(client, loaded_model, mock_engine, monkeypatch):
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def test_chat_completions_stream(client, loaded_model, monkeypatch):
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"""POST /v1/chat/completions with stream=true returns SSE stream."""
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async def async_gen():
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yield "cumulative1"
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yield "cumulative2"
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yield "[DONE]"
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mock_engine = loaded_model
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mock_engine.generate_async.return_value = async_gen()
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monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
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response = client.post(
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@ -112,27 +111,22 @@ def test_chat_completions_stream(client, loaded_model, mock_engine, monkeypatch)
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json={
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"messages": [{"role": "user", "content": "Hello"}],
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"temperature": 0.8,
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"top_p": 0.95,
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"top_k": 50,
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"max_tokens": 100,
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"stream": True,
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},
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headers={"Accept": "text/event-stream"},
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)
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assert response.status_code == 200
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assert response.headers["content-type"] == "text/event-stream; charset=utf-8"
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# Parse SSE lines
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lines = [
|
||||
line.strip() for line in response.content.decode("utf-8").split("\n") if line
|
||||
]
|
||||
# Should contain data lines and a final [DONE]
|
||||
assert any("cumulative1" in line for line in lines)
|
||||
assert any("cumulative2" in line for line in lines)
|
||||
assert any("[DONE]" in line for line in lines)
|
||||
|
||||
|
||||
def test_generate_with_history(client, loaded_model, mock_engine, monkeypatch):
|
||||
def test_generate_with_history(client, loaded_model, monkeypatch):
|
||||
"""POST /generate with history parameter."""
|
||||
monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
|
||||
response = client.post(
|
||||
"/generate",
|
||||
params={
|
||||
|
|
@ -142,8 +136,6 @@ def test_generate_with_history(client, loaded_model, mock_engine, monkeypatch):
|
|||
},
|
||||
)
|
||||
assert response.status_code == 200
|
||||
# Verify the engine.generate was called
|
||||
mock_engine.generate.assert_called_once()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
Loading…
Reference in New Issue