""" A model worker executes the model. """ import argparse import asyncio from concurrent.futures import ThreadPoolExecutor import json import time import threading import uuid from fastapi import FastAPI, Request, BackgroundTasks from fastapi.responses import StreamingResponse import requests import re import uvicorn from functools import partial from llava.constants import WORKER_HEART_BEAT_INTERVAL from llava.utils import build_logger, server_error_msg, pretty_print_semaphore from llava.model.builder import load_pretrained_model from llava.mm_utils import process_images, load_image_from_base64, tokenizer_image_token, expand2square from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from transformers import AutoTokenizer import sglang as sgl from sglang.test.test_utils import add_common_sglang_args_and_parse, select_sglang_backend from sglang.backend.runtime_endpoint import RuntimeEndpoint from sglang.utils import read_jsonl, dump_state_text from sglang.lang.interpreter import ProgramState 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() @sgl.function def pipeline(s, prompt, max_tokens): for p in prompt: if type(p) is str: s += p else: s += sgl.image(p) s += sgl.gen("response", max_tokens=max_tokens) class ModelWorker: def __init__(self, controller_addr, worker_addr, sgl_endpoint, worker_id, no_register, model_name): self.controller_addr = controller_addr self.worker_addr = worker_addr self.worker_id = worker_id # Select backend backend = RuntimeEndpoint(sgl_endpoint) sgl.set_default_backend(backend) model_path = backend.model_info["model_path"] if model_path.endswith("/"): model_path = model_path[:-1] if model_name is None: model_paths = model_path.split("/") if model_paths[-1].startswith("checkpoint-"): self.model_name = model_paths[-2] + "_" + model_paths[-1] else: self.model_name = model_paths[-1] else: self.model_name = model_name logger.info(f"Loading the SGLANG model {self.model_name} on worker {worker_id} ...") 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: return 0 else: return args.limit_model_concurrency - model_semaphore._value + (len(model_semaphore._waiters) if model_semaphore._waiters is not None else 0) def get_status(self): return { "model_names": [self.model_name], "speed": 1, "queue_length": self.get_queue_length(), } async def generate_stream(self, params): ori_prompt = prompt = params["prompt"] images = params.get("images", None) if images is not None and len(images) > 0: if len(images) > 0: if len(images) != prompt.count(DEFAULT_IMAGE_TOKEN): raise ValueError("Number of images does not match number of tokens in prompt") images = [load_image_from_base64(image) for image in images] # FIXME: hacky padding images = [expand2square(image, tuple(int(x * 255) for x in [0.48145466, 0.4578275, 0.40821073])) for image in images] # FIXME: for image-start/end token # replace_token = DEFAULT_IMAGE_TOKEN # if getattr(self.model.config, 'mm_use_im_start_end', False): # replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN # prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token) prompt = prompt.replace(" " + DEFAULT_IMAGE_TOKEN + "\n", DEFAULT_IMAGE_TOKEN) prompt_split = prompt.split(DEFAULT_IMAGE_TOKEN) prompt = [] for i in range(len(prompt_split)): prompt.append(prompt_split[i]) if i < len(images): prompt.append(images[i]) else: prompt = [prompt] temperature = float(params.get("temperature", 1.0)) top_p = float(params.get("top_p", 1.0)) # max_context_length = getattr(model.config, 'max_position_embeddings', 2048) max_new_tokens = min(int(params.get("max_new_tokens", 256)), 1024) stop_str = params.get("stop", None) stop_str = [stop_str] if stop_str is not None else None if max_new_tokens < 1: yield json.dumps({"text": ori_prompt + "Exceeds max token length. Please start a new conversation, thanks.", "error_code": 0}).encode() + b"\0" return # print(prompt) state = pipeline.run(prompt, max_new_tokens, temperature=temperature, top_p=top_p, stream=True) generated_text = ori_prompt async for text_outputs in state.text_async_iter(var_name="response"): generated_text += text_outputs yield json.dumps({"text": generated_text, "error_code": 0}).encode() + b"\0" async def generate_stream_gate(self, params): try: async for x in self.generate_stream(params): yield x except ValueError as e: print("Caught ValueError:", e) ret = { "text": server_error_msg, "error_code": 1, } yield json.dumps(ret).encode() + b"\0" except Exception as e: print("Caught Unknown Error", e) ret = { "text": server_error_msg, "error_code": 1, } yield json.dumps(ret).encode() + b"\0" app = FastAPI() def release_model_semaphore(fn=None): model_semaphore.release() if fn is not None: fn() @app.post("/worker_generate_stream") async def 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() worker.send_heart_beat() generator = worker.generate_stream_gate(params) background_tasks = BackgroundTasks() background_tasks.add_task(partial(release_model_semaphore, fn=worker.send_heart_beat)) return StreamingResponse(generator, background=background_tasks) @app.post("/worker_get_status") async def 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-name", type=str) parser.add_argument("--sgl-endpoint", type=str) parser.add_argument("--limit-model-concurrency", type=int, default=5) parser.add_argument("--stream-interval", type=int, default=1) parser.add_argument("--no-register", action="store_true") args = parser.parse_args() logger.info(f"args: {args}") worker = ModelWorker(args.controller_address, args.worker_address, args.sgl_endpoint, worker_id, args.no_register, args.model_name) uvicorn.run(app, host=args.host, port=args.port, log_level="info")