226 lines
7.3 KiB
Python
226 lines
7.3 KiB
Python
import logging
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import torch
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import uvicorn
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel, Field
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from astrai.config.param_config import ModelParameter
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from astrai.inference.generator import GenerationRequest, GeneratorFactory
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logger = logging.getLogger(__name__)
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# Global model parameter (loaded once)
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_model_param: Optional[ModelParameter] = None
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_project_root = Path(__file__).parent.parent.parent
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app = FastAPI(title="AstrAI Inference Server", version="0.1.0")
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def load_model(
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param_path: Optional[Path] = None,
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device: str = "cuda",
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dtype: torch.dtype = torch.bfloat16,
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):
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"""Load model parameters into global variable."""
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global _model_param
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if param_path is None:
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param_path = _project_root / "params"
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if not param_path.exists():
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raise FileNotFoundError(f"Parameter directory not found: {param_path}")
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_model_param = ModelParameter.load(param_path, disable_init=True)
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_model_param.to(device=device, dtype=dtype)
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logger.info(f"Model loaded on {device} with dtype {dtype}")
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# Pydantic models for API request/response
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class ChatMessage(BaseModel):
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role: str # "user", "assistant", "system"
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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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class StreamCompletionResponse(BaseModel):
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id: str = "chatcmpl-default"
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object: str = "chat.completion.chunk"
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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 convert_messages_to_history(
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messages: List[ChatMessage],
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) -> tuple[Optional[str], Optional[List[Tuple[str, str]]]]:
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"""Convert OpenAI-style messages to system_prompt and history."""
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system_prompt = None
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history: List[Tuple[str, str]] = []
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user_buffer = []
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assistant_buffer = []
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for msg in messages:
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if msg.role == "system":
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system_prompt = msg.content
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elif msg.role == "user":
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if assistant_buffer:
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# Flush previous pair
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history.append(("".join(user_buffer), "".join(assistant_buffer)))
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user_buffer = []
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assistant_buffer = []
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user_buffer.append(msg.content)
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elif msg.role == "assistant":
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assistant_buffer.append(msg.content)
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else:
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logger.warning(f"Unknown role {msg.role}")
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# If there is a pending user message without assistant, treat as current query
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# We'll handle this later
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return system_prompt, history if history else None
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@app.on_event("startup")
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async def startup_event():
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"""Load model on server startup."""
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try:
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load_model()
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except Exception as e:
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logger.error(f"Failed to load model: {e}")
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raise
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@app.get("/health")
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async def health():
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return {"status": "ok", "model_loaded": _model_param is not None}
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@app.post("/v1/chat/completions", response_model=CompletionResponse)
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async def chat_completion(request: ChatCompletionRequest):
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"""OpenAI‑compatible chat completion endpoint.
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Supports both streaming and non‑streaming modes.
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"""
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if _model_param is None:
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raise HTTPException(status_code=503, detail="Model not loaded")
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# Convert messages to query/history
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# For simplicity, assume the last user message is the query, previous messages are history
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system_prompt, history = convert_messages_to_history(request.messages)
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# Extract last user message as query
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user_messages = [m.content for m in request.messages if m.role == "user"]
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if not user_messages:
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raise HTTPException(status_code=400, detail="No user message found")
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query = user_messages[-1]
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# If there are multiple user messages, we could merge them, but for demo we keep simple
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gen_request = GenerationRequest(
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query=query,
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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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max_len=request.max_tokens,
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history=history,
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system_prompt=system_prompt,
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stream=request.stream,
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)
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if request.stream:
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# Return streaming response
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def generate_stream():
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generator = GeneratorFactory.create(_model_param, gen_request)
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for chunk in generator.generate(gen_request):
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# chunk is the cumulative response string
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# For OpenAI compatibility, we send incremental delta
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# For simplicity, we send the whole chunk each time
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yield f"data: {chunk}\n\n"
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yield "data: [DONE]\n\n"
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return StreamingResponse(
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generate_stream(),
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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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# Non‑streaming
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generator = GeneratorFactory.create(_model_param, gen_request)
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if gen_request.stream:
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# Should not happen because we set stream=False
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pass
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response_text = generator.generate(gen_request)
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# Build OpenAI‑style response
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import time
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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": response_text},
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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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@app.post("/generate")
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async def generate(
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query: str,
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history: Optional[List[List[str]]] = None,
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temperature: float = 0.8,
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top_p: float = 0.95,
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top_k: int = 50,
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max_len: int = 2048,
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stream: bool = False,
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):
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"""Simple generation endpoint compatible with existing GenerationRequest."""
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if _model_param is None:
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raise HTTPException(status_code=503, detail="Model not loaded")
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# Convert history format
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hist: Optional[List[Tuple[str, str]]] = None
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if history:
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hist = [
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(h[0], h[1]) for h in history
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] # assuming each item is [user, assistant]
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gen_request = GenerationRequest(
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query=query,
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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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max_len=max_len,
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history=hist,
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stream=stream,
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)
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if stream:
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def stream_generator():
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generator = GeneratorFactory.create(_model_param, gen_request)
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for chunk in generator.generate(gen_request):
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yield chunk + "\n"
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return StreamingResponse(stream_generator(), media_type="text/plain")
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else:
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generator = GeneratorFactory.create(_model_param, gen_request)
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result = generator.generate(gen_request)
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return {"response": result}
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def run_server(host: str = "0.0.0.0", port: int = 8000, reload: bool = False):
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"""Run the FastAPI server with uvicorn."""
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uvicorn.run("astrai.inference.server:app", host=host, port=port, reload=reload)
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