model/fastchat/serve/model_worker.py

"""
A model worker executes the model.
"""
import argparse
import asyncio
import dataclasses
import logging
import json
import os
import time
from typing import List, Union
import threading
import uuid

from fastapi import FastAPI, Request, BackgroundTasks
from fastapi.responses import StreamingResponse
import requests

try:
    from transformers import (
        AutoTokenizer,
        AutoModelForCausalLM,
        LlamaTokenizer,
        AutoModel,
    )
except ImportError:
    from transformers import (
        AutoTokenizer,
        AutoModelForCausalLM,
        LLaMATokenizer,
        AutoModel,
    )
import torch
import uvicorn

from fastchat.constants import WORKER_HEART_BEAT_INTERVAL
from fastchat.serve.inference import load_model, generate_stream
from fastchat.serve.serve_chatglm import chatglm_generate_stream
from fastchat.utils import build_logger, server_error_msg, pretty_print_semaphore

GB = 1 << 30

worker_id = str(uuid.uuid4())[:6]
logger = build_logger("model_worker", f"model_worker_{worker_id}.log")
global_counter = 0

model_semaphore = None


def heart_beat_worker(controller):
    while True:
        time.sleep(WORKER_HEART_BEAT_INTERVAL)
        controller.send_heart_beat()


class ModelWorker:
    def __init__(
        self,
        controller_addr,
        worker_addr,
        worker_id,
        no_register,
        model_path,
        model_name,
        device,
        num_gpus,
        max_gpu_memory,
        load_8bit=False,
    ):
        self.controller_addr = controller_addr
        self.worker_addr = worker_addr
        self.worker_id = worker_id
        if model_path.endswith("/"):
            model_path = model_path[:-1]
        self.model_name = model_name or model_path.split("/")[-1]
        self.device = device

        logger.info(f"Loading the model {self.model_name} on worker {worker_id} ...")
        self.model, self.tokenizer = load_model(
            model_path, device, num_gpus, max_gpu_memory, load_8bit
        )

        if hasattr(self.model.config, "max_sequence_length"):
            self.context_len = self.model.config.max_sequence_length
        elif hasattr(self.model.config, "max_position_embeddings"):
            self.context_len = self.model.config.max_position_embeddings
        else:
            self.context_len = 2048

        is_chatglm = "chatglm" in str(type(self.model)).lower()
        if is_chatglm:
            self.generate_stream_func = chatglm_generate_stream
        else:
            self.generate_stream_func = generate_stream

        if not no_register:
            self.register_to_controller()
            self.heart_beat_thread = threading.Thread(
                target=heart_beat_worker, args=(self,)
            )
            self.heart_beat_thread.start()

    def register_to_controller(self):
        logger.info("Register to controller")

        url = self.controller_addr + "/register_worker"
        data = {
            "worker_name": self.worker_addr,
            "check_heart_beat": True,
            "worker_status": self.get_status(),
        }
        r = requests.post(url, json=data)
        assert r.status_code == 200

    def send_heart_beat(self):
        logger.info(
            f"Send heart beat. Models: {[self.model_name]}. "
            f"Semaphore: {pretty_print_semaphore(model_semaphore)}. "
            f"global_counter: {global_counter}"
        )

        url = self.controller_addr + "/receive_heart_beat"

        while True:
            try:
                ret = requests.post(
                    url,
                    json={
                        "worker_name": self.worker_addr,
                        "queue_length": self.get_queue_length(),
                    },
                    timeout=5,
                )
                exist = ret.json()["exist"]
                break
            except requests.exceptions.RequestException as e:
                logger.error(f"heart beat error: {e}")
            time.sleep(5)

        if not exist:
            self.register_to_controller()

    def get_queue_length(self):
        if (
            model_semaphore is None
            or model_semaphore._value is None
            or model_semaphore._waiters is None
        ):
            return 0
        else:
            return (
                args.limit_model_concurrency
                - model_semaphore._value
                + len(model_semaphore._waiters)
            )

    def get_status(self):
        return {
            "model_names": [self.model_name],
            "speed": 1,
            "queue_length": self.get_queue_length(),
        }

    def generate_stream_gate(self, params):
        try:
            for output in self.generate_stream_func(
                self.model,
                self.tokenizer,
                params,
                self.device,
                self.context_len,
                args.stream_interval,
            ):
                ret = {
                    "text": output,
                    "error_code": 0,
                }
                yield json.dumps(ret).encode() + b"\0"
        except torch.cuda.OutOfMemoryError:
            ret = {
                "text": server_error_msg,
                "error_code": 1,
            }
            yield json.dumps(ret).encode() + b"\0"


app = FastAPI()


def release_model_semaphore():
    model_semaphore.release()


@app.post("/worker_generate_stream")
async def api_generate_stream(request: Request):
    global model_semaphore, global_counter
    global_counter += 1
    params = await request.json()

    if model_semaphore is None:
        model_semaphore = asyncio.Semaphore(args.limit_model_concurrency)
    await model_semaphore.acquire()
    generator = worker.generate_stream_gate(params)
    background_tasks = BackgroundTasks()
    background_tasks.add_task(release_model_semaphore)
    return StreamingResponse(generator, background=background_tasks)


@app.post("/worker_get_status")
async def api_get_status(request: Request):
    return worker.get_status()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--port", type=int, default=21002)
    parser.add_argument("--worker-address", type=str, default="http://localhost:21002")
    parser.add_argument(
        "--controller-address", type=str, default="http://localhost:21001"
    )
    parser.add_argument(
        "--model-path",
        type=str,
        default="facebook/opt-350m",
        help="The path to the weights",
    )
    parser.add_argument("--model-name", type=str, help="Optional name")
    parser.add_argument(
        "--device", type=str, choices=["cpu", "cuda", "mps"], default="cuda"
    )
    parser.add_argument("--num-gpus", type=int, default=1)
    parser.add_argument(
        "--gpus",
        type=str,
        default=None,
        help="A single GPU like 1 or multiple GPUs like 0,2"
    )
    parser.add_argument(
        "--max-gpu-memory",
        type=str,
        help="The maximum memory per gpu. Use a string like '13Gib'",
    )
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--limit-model-concurrency", type=int, default=5)
    parser.add_argument("--stream-interval", type=int, default=2)
    parser.add_argument("--no-register", action="store_true")
    args = parser.parse_args()
    logger.info(f"args: {args}")

    if args.gpus:
        if args.num_gpus and len(args.gpus.split(",")) < int(args.num_gpus):
            raise ValueError(f"Larger --num-gpus ({args.num_gpus}) than --gpus {args.gpus}!")
        os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
    
    worker = ModelWorker(
        args.controller_address,
        args.worker_address,
        worker_id,
        args.no_register,
        args.model_path,
        args.model_name,
        args.device,
        args.num_gpus,
        args.max_gpu_memory,
        args.load_8bit,
    )
    uvicorn.run(app, host=args.host, port=args.port, log_level="info")