serve_openai_api.py

import argparse
import json
import re
import os
import sys

__package__ = "scripts"
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import time
import torch
import warnings
import uvicorn

from threading import Thread
from queue import Queue
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
from model.model_lora import apply_lora, load_lora

warnings.filterwarnings('ignore')

app = FastAPI()


def init_model(args):
    tokenizer = AutoTokenizer.from_pretrained(args.load_from)
    if 'model' in args.load_from:
        moe_suffix = '_moe' if args.use_moe else ''
        ckp = f'../{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth'
        model = MiniMindForCausalLM(MiniMindConfig(
            hidden_size=args.hidden_size,
            num_hidden_layers=args.num_hidden_layers,
            max_seq_len=args.max_seq_len,
            use_moe=bool(args.use_moe),
            inference_rope_scaling=args.inference_rope_scaling
        ))
        model.load_state_dict(torch.load(ckp, map_location=device), strict=True)
        if args.lora_weight != 'None':
            apply_lora(model)
            load_lora(model, f'../{args.save_dir}/lora/{args.lora_weight}_{args.hidden_size}.pth')
    else:
        model = AutoModelForCausalLM.from_pretrained(args.load_from, trust_remote_code=True)
    print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters()) / 1e6:.2f} M(illion)')
    return model.half().eval().to(device), tokenizer


class ChatRequest(BaseModel):
    model: str
    messages: list
    temperature: float = 0.7
    top_p: float = 0.92
    max_tokens: int = 8192
    stream: bool = True
    tools: list = []
    open_thinking: bool = False
    chat_template_kwargs: dict = None
    
    def get_open_thinking(self) -> bool:
        """兼容多种方式开启 thinking"""
        if self.open_thinking:
            return True
        if self.chat_template_kwargs:
            return self.chat_template_kwargs.get('open_thinking', False) or \
                   self.chat_template_kwargs.get('enable_thinking', False)
        return False


class CustomStreamer(TextStreamer):
    def __init__(self, tokenizer, queue):
        super().__init__(tokenizer, skip_prompt=True, skip_special_tokens=True)
        self.queue = queue
        self.tokenizer = tokenizer

    def on_finalized_text(self, text: str, stream_end: bool = False):
        self.queue.put(text)
        if stream_end:
            self.queue.put(None)


def parse_response(text):
    reasoning_content = None
    think_match = re.search(r'<think>(.*?)</think>', text, re.DOTALL)
    if think_match:
        reasoning_content = think_match.group(1).strip()
        text = re.sub(r'<think>.*?</think>\s*', '', text, flags=re.DOTALL)
    elif '</think>' in text:
        parts = text.split('</think>', 1)
        reasoning_content = parts[0].strip()
        text = parts[1].strip() if len(parts) > 1 else ''
    tool_calls = []
    for i, m in enumerate(re.findall(r'<tool_call>(.*?)</tool_call>', text, re.DOTALL)):
        try:
            call = json.loads(m.strip())
            tool_calls.append({"id": f"call_{int(time.time())}_{i}", "type": "function", "function": {"name": call.get("name", ""), "arguments": json.dumps(call.get("arguments", {}), ensure_ascii=False)}})
        except Exception:
            pass
    if tool_calls:
        text = re.sub(r'<tool_call>.*?</tool_call>', '', text, flags=re.DOTALL)
    return text.strip(), reasoning_content, tool_calls or None


def generate_stream_response(messages, temperature, top_p, max_tokens, tools=None, open_thinking=False):
    try:
        new_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, tools=tools or None, open_thinking=open_thinking)[-max_tokens:]
        inputs = tokenizer(new_prompt, return_tensors="pt", truncation=True).to(device)

        queue = Queue()
        streamer = CustomStreamer(tokenizer, queue)

        def _generate():
            model.generate(
                inputs.input_ids,
                max_new_tokens=max_tokens,
                do_sample=True,
                temperature=temperature,
                top_p=top_p,
                attention_mask=inputs.attention_mask,
                pad_token_id=tokenizer.pad_token_id,
                eos_token_id=tokenizer.eos_token_id,
                streamer=streamer
            )

        Thread(target=_generate).start()

        full_text = ""
        emitted = 0
        thinking_ended = not bool(open_thinking)

        while True:
            text = queue.get()
            if text is None:
                break
            full_text += text

            if not thinking_ended:
                pos = full_text.find('</think>')
                if pos >= 0:
                    thinking_ended = True
                    new_r = full_text[emitted:pos]
                    if new_r:
                        yield json.dumps({"choices": [{"delta": {"reasoning_content": new_r}}]}, ensure_ascii=False)
                    emitted = pos + len('</think>')
                    after = full_text[emitted:].lstrip('\n')
                    emitted = len(full_text) - len(after)
                    if after:
                        yield json.dumps({"choices": [{"delta": {"content": after}}]}, ensure_ascii=False)
                        emitted = len(full_text)
                else:
                    new_r = full_text[emitted:]
                    if new_r:
                        yield json.dumps({"choices": [{"delta": {"reasoning_content": new_r}}]}, ensure_ascii=False)
                        emitted = len(full_text)
            else:
                new_c = full_text[emitted:]
                if new_c:
                    yield json.dumps({"choices": [{"delta": {"content": new_c}}]}, ensure_ascii=False)
                    emitted = len(full_text)

        _, _, tool_calls = parse_response(full_text)
        if tool_calls:
            yield json.dumps({"choices": [{"delta": {"tool_calls": tool_calls}}]}, ensure_ascii=False)
        yield json.dumps({"choices": [{"delta": {}, "finish_reason": "tool_calls" if tool_calls else "stop"}]}, ensure_ascii=False)

    except Exception as e:
        yield json.dumps({"error": str(e)})


@app.post("/v1/chat/completions")
async def chat_completions(request: ChatRequest):
    try:
        if request.stream:
            return StreamingResponse(
                (f"data: {chunk}\n\n" for chunk in generate_stream_response(
                    messages=request.messages,
                    temperature=request.temperature,
                    top_p=request.top_p,
                    max_tokens=request.max_tokens,
                    tools=request.tools,
                    open_thinking=request.get_open_thinking()
                )),
                media_type="text/event-stream"
            )
        else:
            new_prompt = tokenizer.apply_chat_template(
                request.messages,
                tokenize=False,
                add_generation_prompt=True,
                tools=request.tools or None,
                open_thinking=request.get_open_thinking()
            )[-request.max_tokens:]
            inputs = tokenizer(new_prompt, return_tensors="pt", truncation=True).to(device)
            with torch.no_grad():
                generated_ids = model.generate(
                    inputs["input_ids"],
                    max_length=inputs["input_ids"].shape[1] + request.max_tokens,
                    do_sample=True,
                    attention_mask=inputs["attention_mask"],
                    pad_token_id=tokenizer.pad_token_id,
                    eos_token_id=tokenizer.eos_token_id,
                    top_p=request.top_p,
                    temperature=request.temperature
                )
                answer = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
            content, reasoning_content, tool_calls = parse_response(answer)
            message = {"role": "assistant", "content": content}
            if reasoning_content:
                message["reasoning_content"] = reasoning_content
            if tool_calls:
                message["tool_calls"] = tool_calls
            return {
                "id": f"chatcmpl-{int(time.time())}",
                "object": "chat.completion",
                "created": int(time.time()),
                "model": "minimind",
                "choices": [
                    {
                        "index": 0,
                        "message": message,
                        "finish_reason": "tool_calls" if tool_calls else "stop"
                    }
                ]
            }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Server for MiniMind")
    parser.add_argument('--load_from', default='../model', type=str, help="模型加载路径（model=原生torch权重，其他路径=transformers格式）")
    parser.add_argument('--save_dir', default='out', type=str, help="模型权重目录")
    parser.add_argument('--weight', default='full_sft', type=str, help="权重名称前缀（pretrain, full_sft, dpo, reason, ppo_actor, grpo, spo）")
    parser.add_argument('--lora_weight', default='None', type=str, help="LoRA权重名称（None表示不使用，可选：lora_identity, lora_medical）")
    parser.add_argument('--hidden_size', default=768, type=int, help="隐藏层维度")
    parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
    parser.add_argument('--max_seq_len', default=8192, type=int, help="最大序列长度")
    parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）")
    parser.add_argument('--inference_rope_scaling', default=False, action='store_true', help="启用RoPE位置编码外推（4倍，仅解决位置编码问题）")
    parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help="运行设备")
    args = parser.parse_args()
    device = args.device
    model, tokenizer = init_model(args)
    uvicorn.run(app, host="0.0.0.0", port=8998)