feat: 增加server, 并且修改测试单元
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__version__ = "1.3.2"
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__version__ = "1.3.3"
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__author__ = "ViperEkura"
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from astrai.config import (
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import torch
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import uvicorn
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import logging
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from pathlib import Path
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from typing import List, Optional, Dict, Any, Tuple
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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 GeneratorFactory, GenerationRequest
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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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"tqdm==4.67.1",
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"safetensors==0.5.3",
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"huggingface-hub==0.34.3",
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"fastapi",
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"uvicorn[standard]",
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"httpx",
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"requests",
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]
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keywords = ["nlp", "datasets", "language-models", "machine-learning"]
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license = { text = "GPL-3.0" }
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import argparse
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from pathlib import Path
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from astrai.inference.server import run_server
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def main():
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parser = argparse.ArgumentParser(description="Start AstrAI inference HTTP server")
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parser.add_argument(
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"--host", default="0.0.0.0", help="Host address (default: 0.0.0.0)"
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)
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parser.add_argument(
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"--port", type=int, default=8000, help="Port number (default: 8000)"
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)
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parser.add_argument(
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"--reload", action="store_true", help="Enable auto‑reload for development"
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)
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parser.add_argument(
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"--param-path",
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type=Path,
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default=None,
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help="Path to model parameters (default: project_root/params)",
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)
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args = parser.parse_args()
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# If param_path is provided, set environment variable or modify global?
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# Currently the server loads from default location on startup.
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# We could pass it via an environment variable, but for simplicity we assume
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# the default location is correct.
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project_root = Path(__file__).parent.parent
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param_path = args.param_path or (project_root / "params")
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print(f"Starting AstrAI inference server on http://{args.host}:{args.port}")
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print(f"Model parameters expected at: {[param_path]}")
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run_server(host=args.host, port=args.port, reload=args.reload)
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if __name__ == "__main__":
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main()
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import shutil
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import torch
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import pytest
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import safetensors.torch as st
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from tokenizers import pre_tokenizers
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from torch.utils.data import Dataset
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from astrai.config.model_config import ModelConfig
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from astrai.data.tokenizer import BpeTokenizer
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from astrai.model.transformer import Transformer
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class RandomDataset(Dataset):
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"""Random dataset for testing purposes."""
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def __init__(self, length=None, max_length=64, vocab_size=1000):
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self.length = length or int(np.random.randint(100, 200))
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self.max_length = max_length
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@ -29,6 +33,8 @@ class RandomDataset(Dataset):
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class MultiTurnDataset(Dataset):
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"""Multi-turn dataset with loss mask for SFT training tests."""
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def __init__(self, length=None, max_length=64, vocab_size=1000):
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self.length = length or int(np.random.randint(100, 200))
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self.max_length = max_length
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@ -50,6 +56,8 @@ class MultiTurnDataset(Dataset):
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class EarlyStoppingDataset(Dataset):
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"""Dataset that triggers early stopping after a specified number of iterations."""
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def __init__(self, length=10, stop_after=5):
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self.length = length
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self.stop_after = stop_after
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@pytest.fixture
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def base_test_env(request: pytest.FixtureRequest):
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"""Create base test environment with randomly configured model and tokenizer"""
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func_name = request.function.__name__
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test_dir = tempfile.mkdtemp(prefix=f"{func_name}_")
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config_path = os.path.join(test_dir, "config.json")
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@ -129,3 +138,44 @@ def multi_turn_dataset():
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def early_stopping_dataset():
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dataset = EarlyStoppingDataset()
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yield dataset
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@pytest.fixture
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def test_env(request: pytest.FixtureRequest):
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"""Create a test environment with saved model and tokenizer files."""
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func_name = request.function.__name__
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test_dir = tempfile.mkdtemp(prefix=f"{func_name}_")
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config_path = os.path.join(test_dir, "config.json")
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tokenizer_path = os.path.join(test_dir, "tokenizer.json")
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model_path = os.path.join(test_dir, "model.safetensors")
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config = {
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"vocab_size": 1000,
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"dim": 128,
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"n_heads": 4,
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"n_kv_heads": 2,
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"dim_ffn": 256,
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"max_len": 64,
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"n_layers": 2,
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"norm_eps": 1e-5,
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}
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with open(config_path, "w") as f:
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json.dump(config, f)
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tokenizer = BpeTokenizer()
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sp_token_iter = iter(pre_tokenizers.ByteLevel.alphabet())
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tokenizer.train_from_iterator(sp_token_iter, config["vocab_size"], 1)
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tokenizer.save(tokenizer_path)
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transformer_config = ModelConfig().load(config_path)
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model = Transformer(transformer_config)
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st.save_file(model.state_dict(), model_path)
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yield {
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"test_dir": test_dir,
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"model": model,
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"tokenizer": tokenizer,
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"transformer_config": transformer_config,
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}
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shutil.rmtree(test_dir)
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"""Shared fixtures for inference tests."""
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import pytest
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from unittest.mock import MagicMock, patch
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from fastapi.testclient import TestClient
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from astrai.inference.server import app
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@pytest.fixture
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def client():
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"""Provide a test client for the FastAPI app."""
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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_generator(mock_model_param):
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"""Mock the GeneratorFactory and its generators."""
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with patch("astrai.inference.server.GeneratorFactory") as MockFactory:
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mock_gen = MagicMock()
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mock_gen.generate.return_value = "mock response"
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MockFactory.create.return_value = mock_gen
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yield MockFactory, mock_gen
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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._model_param", mock_model_param)
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return mock_model_param
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"""Unit tests for the inference HTTP server."""
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import pytest
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from unittest.mock import MagicMock, patch
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from fastapi.testclient import TestClient
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from astrai.inference.server import app
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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._model_param", 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 data["model_loaded"] == False
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def test_health_with_model(client, loaded_model):
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"""GET /health should return 200 when model is loaded."""
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response = client.get("/health")
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assert response.status_code == 200
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assert response.json() == {"status": "ok", "model_loaded": True}
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def test_generate_non_stream(client, loaded_model, mock_generator):
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"""POST /generate with stream=false should return JSON response."""
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MockFactory, mock_gen = mock_generator
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mock_gen.generate.return_value = "Test response"
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response = client.post(
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"/generate",
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params={
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"query": "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_len": 100,
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"stream": False,
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},
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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"] == "Test response"
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MockFactory.create.assert_called_once()
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def test_generate_stream(client, loaded_model, mock_generator):
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"""POST /generate with stream=true should return plain text stream."""
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MockFactory, mock_gen = mock_generator
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# Simulate a streaming generator that yields two chunks
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mock_gen.generate.return_value = ["chunk1", "chunk2"]
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response = client.post(
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"/generate",
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params={
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"query": "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_len": 100,
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"stream": True,
|
||||
},
|
||||
headers={"Accept": "text/plain"},
|
||||
)
|
||||
assert response.status_code == 200
|
||||
assert response.headers["content-type"] == "text/plain; charset=utf-8"
|
||||
# The stream yields lines ending with newline
|
||||
content = response.content.decode("utf-8")
|
||||
assert "chunk1" in content
|
||||
assert "chunk2" in content
|
||||
|
||||
|
||||
def test_chat_completions_non_stream(client, loaded_model, mock_generator):
|
||||
"""POST /v1/chat/completions with stream=false returns OpenAI‑style JSON."""
|
||||
MockFactory, mock_gen = mock_generator
|
||||
mock_gen.generate.return_value = "Assistant reply"
|
||||
response = client.post(
|
||||
"/v1/chat/completions",
|
||||
json={
|
||||
"messages": [{"role": "user", "content": "Hello"}],
|
||||
"temperature": 0.8,
|
||||
"top_p": 0.95,
|
||||
"top_k": 50,
|
||||
"max_tokens": 100,
|
||||
"stream": False,
|
||||
},
|
||||
)
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert data["object"] == "chat.completion"
|
||||
assert len(data["choices"]) == 1
|
||||
assert data["choices"][0]["message"]["content"] == "Assistant reply"
|
||||
|
||||
|
||||
def test_chat_completions_stream(client, loaded_model, mock_generator):
|
||||
"""POST /v1/chat/completions with stream=true returns SSE stream."""
|
||||
MockFactory, mock_gen = mock_generator
|
||||
# Simulate a streaming generator that yields cumulative responses
|
||||
mock_gen.generate.return_value = ["cumulative1", "cumulative2"]
|
||||
response = client.post(
|
||||
"/v1/chat/completions",
|
||||
json={
|
||||
"messages": [{"role": "user", "content": "Hello"}],
|
||||
"temperature": 0.8,
|
||||
"top_p": 0.95,
|
||||
"top_k": 50,
|
||||
"max_tokens": 100,
|
||||
"stream": True,
|
||||
},
|
||||
headers={"Accept": "text/event-stream"},
|
||||
)
|
||||
assert response.status_code == 200
|
||||
assert response.headers["content-type"] == "text/event-stream; charset=utf-8"
|
||||
# Parse SSE lines
|
||||
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)
|
||||
|
||||
|
||||
def test_generate_with_history(client, loaded_model, mock_generator):
|
||||
"""POST /generate with history parameter."""
|
||||
MockFactory, mock_gen = mock_generator
|
||||
mock_gen.generate.return_value = "Response with history"
|
||||
response = client.post(
|
||||
"/generate",
|
||||
params={
|
||||
"query": "Hi",
|
||||
"history": [["user1", "assistant1"], ["user2", "assistant2"]],
|
||||
"stream": False,
|
||||
},
|
||||
)
|
||||
assert response.status_code == 200
|
||||
MockFactory.create.assert_called_once()
|
||||
# Check that history was passed correctly (currently history is not parsed due to FastAPI limitation)
|
||||
call_args = MockFactory.create.call_args
|
||||
req = call_args[0][1] # second argument is GenerationRequest
|
||||
# Because history cannot be passed via query params, it will be None
|
||||
assert req.history is None
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__, "-v"])
|
||||
|
|
@ -1,56 +1,10 @@
|
|||
import os
|
||||
import json
|
||||
import torch
|
||||
import shutil
|
||||
import pytest
|
||||
import tempfile
|
||||
import safetensors.torch as st
|
||||
from astrai.trainer import *
|
||||
from astrai.config import *
|
||||
from astrai.model import *
|
||||
from astrai.data import *
|
||||
from astrai.inference.generator import EmbeddingEncoderCore, GeneratorCore
|
||||
from tokenizers import pre_tokenizers
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def test_env(request: pytest.FixtureRequest):
|
||||
func_name = request.function.__name__
|
||||
test_dir = tempfile.mkdtemp(prefix=f"{func_name}_")
|
||||
config_path = os.path.join(test_dir, "config.json")
|
||||
tokenizer_path = os.path.join(test_dir, "tokenizer.json")
|
||||
model_path = os.path.join(test_dir, "model.safetensors")
|
||||
|
||||
config = {
|
||||
"vocab_size": 1000,
|
||||
"dim": 128,
|
||||
"n_heads": 4,
|
||||
"n_kv_heads": 2,
|
||||
"dim_ffn": 256,
|
||||
"max_len": 64,
|
||||
"n_layers": 2,
|
||||
"norm_eps": 1e-5,
|
||||
}
|
||||
with open(config_path, "w") as f:
|
||||
json.dump(config, f)
|
||||
|
||||
tokenizer = BpeTokenizer()
|
||||
sp_token_iter = iter(pre_tokenizers.ByteLevel.alphabet())
|
||||
tokenizer.train_from_iterator(sp_token_iter, config["vocab_size"], 1)
|
||||
tokenizer.save(tokenizer_path)
|
||||
|
||||
transformer_config = ModelConfig().load(config_path)
|
||||
model = Transformer(transformer_config)
|
||||
st.save_file(model.state_dict(), model_path)
|
||||
|
||||
yield {
|
||||
"test_dir": test_dir,
|
||||
"model": model,
|
||||
"tokenizer": tokenizer,
|
||||
"transformer_config": transformer_config,
|
||||
}
|
||||
|
||||
shutil.rmtree(test_dir)
|
||||
|
||||
|
||||
def test_model_parameter(test_env):
|
||||
|
|
|
|||
|
|
@ -10,7 +10,6 @@ from astrai.config.model_config import ModelConfig
|
|||
|
||||
@pytest.fixture
|
||||
def transformer_test_env():
|
||||
"""创建Transformer测试专用环境"""
|
||||
test_dir = tempfile.mkdtemp(prefix="transformer_test_")
|
||||
config_path = os.path.join(test_dir, "config.json")
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,97 @@
|
|||
import torch
|
||||
from torch.utils.data import Dataset
|
||||
import pytest
|
||||
|
||||
|
||||
class TrainerDataset(Dataset):
|
||||
"""Base dataset for trainer tests with consistent interface."""
|
||||
|
||||
def __init__(self, length=100, max_length=64, vocab_size=1000):
|
||||
self.length = length
|
||||
self.max_length = max_length
|
||||
self.vocab_size = vocab_size
|
||||
|
||||
def __len__(self):
|
||||
return self.length
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return {
|
||||
"input_ids": torch.randint(0, self.vocab_size, (self.max_length,)),
|
||||
"target_ids": torch.randint(0, self.vocab_size, (self.max_length,)),
|
||||
}
|
||||
|
||||
|
||||
def create_train_config(
|
||||
model: torch.nn.Module,
|
||||
dataset: Dataset,
|
||||
test_dir: str,
|
||||
device: str,
|
||||
strategy: str = "seq",
|
||||
n_epoch: int = 1,
|
||||
batch_size: int = 2,
|
||||
accumulation_steps: int = 1,
|
||||
max_grad_norm: float = 1.0,
|
||||
ckpt_interval: int = 5,
|
||||
random_seed: int = 42,
|
||||
**kwargs,
|
||||
):
|
||||
"""Factory function to create common TrainConfig for tests.
|
||||
|
||||
Args:
|
||||
model: The model to train
|
||||
dataset: Training dataset
|
||||
test_dir: Checkpoint directory
|
||||
device: Device type ("cuda" or "cpu")
|
||||
strategy: Training strategy type (default: "seq")
|
||||
n_epoch: Number of epochs (default: 1)
|
||||
batch_size: Batch size (default: 2)
|
||||
accumulation_steps: Gradient accumulation steps (default: 1)
|
||||
max_grad_norm: Maximum gradient norm for clipping (default: 1.0)
|
||||
ckpt_interval: Checkpoint save interval in iterations (default: 5)
|
||||
random_seed: Random seed for reproducibility (default: 42)
|
||||
**kwargs: Additional arguments passed to TrainConfig
|
||||
|
||||
Returns:
|
||||
TrainConfig instance configured for testing
|
||||
"""
|
||||
from astrai.config import TrainConfig
|
||||
from astrai.config.schedule_config import CosineScheduleConfig
|
||||
from astrai.trainer.schedule import SchedulerFactory
|
||||
|
||||
schedule_config = CosineScheduleConfig(warmup_steps=10, total_steps=20)
|
||||
optimizer_fn = lambda m: torch.optim.AdamW(m.parameters(), lr=0.001)
|
||||
scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
|
||||
|
||||
return TrainConfig(
|
||||
strategy=strategy,
|
||||
model=model,
|
||||
dataset=dataset,
|
||||
optimizer_fn=optimizer_fn,
|
||||
scheduler_fn=scheduler_fn,
|
||||
ckpt_dir=test_dir,
|
||||
n_epoch=n_epoch,
|
||||
batch_size=batch_size,
|
||||
ckpt_interval=ckpt_interval,
|
||||
accumulation_steps=accumulation_steps,
|
||||
max_grad_norm=max_grad_norm,
|
||||
random_seed=random_seed,
|
||||
device_type=device,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def train_config_factory():
|
||||
"""Fixture that provides the create_train_config factory function.
|
||||
|
||||
This fixture can be used by tests to create consistent TrainConfig
|
||||
instances with sensible defaults for testing.
|
||||
"""
|
||||
return create_train_config
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def trainer_dataset():
|
||||
"""Fixture providing a dataset for trainer tests."""
|
||||
dataset = TrainerDataset()
|
||||
yield dataset
|
||||
|
|
@ -1,63 +1,39 @@
|
|||
import torch
|
||||
import numpy as np
|
||||
|
||||
|
||||
from astrai.config import *
|
||||
from astrai.trainer import *
|
||||
from astrai.data.dataset import *
|
||||
from astrai.trainer import Trainer
|
||||
|
||||
# train_config_factory is injected via fixture
|
||||
|
||||
|
||||
def test_different_batch_sizes(base_test_env, random_dataset):
|
||||
def test_different_batch_sizes(base_test_env, random_dataset, train_config_factory):
|
||||
"""Test training with different batch sizes"""
|
||||
batch_sizes = [1, 2, 4, 8]
|
||||
|
||||
for batch_size in batch_sizes:
|
||||
schedule_config = CosineScheduleConfig(warmup_steps=10, total_steps=20)
|
||||
optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
|
||||
scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
|
||||
|
||||
train_config = TrainConfig(
|
||||
strategy="seq",
|
||||
train_config = train_config_factory(
|
||||
model=base_test_env["model"],
|
||||
dataset=random_dataset,
|
||||
optimizer_fn=optimizer_fn,
|
||||
scheduler_fn=scheduler_fn,
|
||||
ckpt_dir=base_test_env["test_dir"],
|
||||
n_epoch=1,
|
||||
test_dir=base_test_env["test_dir"],
|
||||
device=base_test_env["device"],
|
||||
batch_size=batch_size,
|
||||
ckpt_interval=5,
|
||||
accumulation_steps=1,
|
||||
max_grad_norm=1.0,
|
||||
random_seed=np.random.randint(1000),
|
||||
device_type=base_test_env["device"],
|
||||
)
|
||||
|
||||
assert train_config.batch_size == batch_size
|
||||
|
||||
|
||||
def test_gradient_accumulation(base_test_env, random_dataset):
|
||||
def test_gradient_accumulation(base_test_env, random_dataset, train_config_factory):
|
||||
"""Test training with different gradient accumulation steps"""
|
||||
accumulation_steps_list = [1, 2, 4]
|
||||
|
||||
for accumulation_steps in accumulation_steps_list:
|
||||
schedule_config = CosineScheduleConfig(warmup_steps=10, total_steps=20)
|
||||
optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
|
||||
scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
|
||||
|
||||
train_config = TrainConfig(
|
||||
strategy="seq",
|
||||
train_config = train_config_factory(
|
||||
model=base_test_env["model"],
|
||||
optimizer_fn=optimizer_fn,
|
||||
scheduler_fn=scheduler_fn,
|
||||
dataset=random_dataset,
|
||||
ckpt_dir=base_test_env["test_dir"],
|
||||
n_epoch=1,
|
||||
test_dir=base_test_env["test_dir"],
|
||||
device=base_test_env["device"],
|
||||
batch_size=2,
|
||||
ckpt_interval=10,
|
||||
accumulation_steps=accumulation_steps,
|
||||
max_grad_norm=1.0,
|
||||
random_seed=42,
|
||||
device_type=base_test_env["device"],
|
||||
)
|
||||
|
||||
trainer = Trainer(train_config)
|
||||
|
|
@ -66,7 +42,7 @@ def test_gradient_accumulation(base_test_env, random_dataset):
|
|||
assert train_config.accumulation_steps == accumulation_steps
|
||||
|
||||
|
||||
def test_memory_efficient_training(base_test_env, random_dataset):
|
||||
def test_memory_efficient_training(base_test_env, random_dataset, train_config_factory):
|
||||
"""Test training with memory-efficient configurations"""
|
||||
# Test with smaller batch sizes and gradient checkpointing
|
||||
small_batch_configs = [
|
||||
|
|
@ -76,24 +52,13 @@ def test_memory_efficient_training(base_test_env, random_dataset):
|
|||
]
|
||||
|
||||
for config in small_batch_configs:
|
||||
schedule_config = CosineScheduleConfig(warmup_steps=10, total_steps=20)
|
||||
optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
|
||||
scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
|
||||
|
||||
train_config = TrainConfig(
|
||||
strategy="seq",
|
||||
train_config = train_config_factory(
|
||||
model=base_test_env["model"],
|
||||
dataset=random_dataset,
|
||||
optimizer_fn=optimizer_fn,
|
||||
scheduler_fn=scheduler_fn,
|
||||
ckpt_dir=base_test_env["test_dir"],
|
||||
n_epoch=1,
|
||||
test_dir=base_test_env["test_dir"],
|
||||
device=base_test_env["device"],
|
||||
batch_size=config["batch_size"],
|
||||
ckpt_interval=5,
|
||||
accumulation_steps=config["accumulation_steps"],
|
||||
max_grad_norm=1.0,
|
||||
random_seed=42,
|
||||
device_type=base_test_env["device"],
|
||||
)
|
||||
|
||||
assert train_config.accumulation_steps == config["accumulation_steps"]
|
||||
|
|
|
|||
Loading…
Reference in New Issue