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Running
on
Zero
Running
on
Zero
""" | |
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_llama3.constants import WORKER_HEART_BEAT_INTERVAL | |
from llava_llama3.utils import (build_logger, server_error_msg, | |
pretty_print_semaphore) | |
from llava_llama3.mm_utils import process_images, load_image_from_base64, tokenizer_image_token, expand2square | |
from llava_llama3.constants import DEFAULT_IMAGE_TOKEN | |
import sglang as sgl | |
from sglang.backend.runtime_endpoint import RuntimeEndpoint | |
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() | |
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,), daemon=True) | |
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 <image> tokens in prompt") | |
images = [load_image_from_base64(image) 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 | |
print({'prompt': prompt, 'max_new_tokens': max_new_tokens, 'temperature': temperature, 'top_p': top_p}) | |
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() | |
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) | |
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") | |