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""" |
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A model worker executes the model. |
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""" |
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import sys, os |
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sys.path.append(os.path.join(os.path.dirname(__file__), "..")) |
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|
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import argparse |
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import asyncio |
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import dataclasses |
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import logging |
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import json |
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import os |
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import sys |
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import time |
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from typing import List, Tuple, Union |
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import threading |
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import uuid |
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|
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from io import BytesIO |
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import base64 |
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|
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from fastapi import FastAPI, Request, BackgroundTasks |
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from fastapi.responses import StreamingResponse, JSONResponse |
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import numpy as np |
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import requests |
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from PIL import Image |
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|
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from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler |
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|
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try: |
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from transformers import ( |
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AutoTokenizer, |
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AutoModelForCausalLM, |
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LlamaTokenizer, |
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AutoModel, |
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) |
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except ImportError: |
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from transformers import ( |
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AutoTokenizer, |
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AutoModelForCausalLM, |
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LLaMATokenizer, |
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AutoModel, |
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) |
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from transformers import AutoProcessor, Blip2ForConditionalGeneration |
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|
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import torch |
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import torch.nn.functional as F |
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import uvicorn |
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|
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from serve.constants import WORKER_HEART_BEAT_INTERVAL, ErrorCode, SERVER_ERROR_MSG |
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from serve.utils import build_logger, pretty_print_semaphore |
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|
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GB = 1 << 30 |
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|
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now_file_name = os.__file__ |
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logdir = "logs/workers/" |
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os.makedirs(logdir, exist_ok=True) |
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logfile = os.path.join(logdir, f"{now_file_name}.log") |
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|
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worker_id = str(uuid.uuid4())[:6] |
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logger = build_logger(now_file_name, logfile) |
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global_counter = 0 |
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|
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model_semaphore = None |
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|
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def encode(image: Image): |
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buffered = BytesIO() |
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image.save(buffered, format="JPEG") |
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img_b64_str = base64.b64encode(buffered.getvalue()).decode() |
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return img_b64_str |
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|
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def heart_beat_worker(controller): |
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while True: |
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time.sleep(WORKER_HEART_BEAT_INTERVAL) |
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controller.send_heart_beat() |
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|
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class ModelWorker: |
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def __init__( |
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self, |
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controller_addr, |
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worker_addr, |
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worker_id, |
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no_register, |
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model_path, |
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model_names, |
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device, |
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): |
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self.controller_addr = controller_addr |
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self.worker_addr = worker_addr |
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self.worker_id = worker_id |
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self.model_names = model_names |
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self.device = device |
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model_id = "runwayml/stable-diffusion-v1-5" |
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) |
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pipe.to("cuda") |
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) |
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|
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self.pipe = pipe |
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|
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if not no_register: |
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self.register_to_controller() |
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self.heart_beat_thread = threading.Thread( |
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target=heart_beat_worker, args=(self,) |
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) |
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self.heart_beat_thread.start() |
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|
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def resize(self, image:Image, resize_w:int, resize_h:int): |
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image = image.resize((resize_w, resize_h)) |
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return image |
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|
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def register_to_controller(self): |
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logger.info("Register to controller") |
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|
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url = self.controller_addr + "/register_worker" |
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data = { |
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"worker_name": self.worker_addr, |
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"check_heart_beat": True, |
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"worker_status": self.get_status(), |
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} |
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r = requests.post(url, json=data) |
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assert r.status_code == 200 |
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|
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def send_heart_beat(self): |
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logger.info( |
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f"Send heart beat. Models: {self.model_names}. " |
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f"Semaphore: {pretty_print_semaphore(model_semaphore)}. " |
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f"global_counter: {global_counter}. " |
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f"worker_id: {worker_id}. " |
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) |
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|
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url = self.controller_addr + "/receive_heart_beat" |
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|
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while True: |
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try: |
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ret = requests.post( |
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url, |
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json={ |
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"worker_name": self.worker_addr, |
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"queue_length": self.get_queue_length(), |
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}, |
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timeout=5, |
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) |
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exist = ret.json()["exist"] |
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break |
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except requests.exceptions.RequestException as e: |
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logger.error(f"heart beat error: {e}") |
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time.sleep(5) |
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|
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if not exist: |
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self.register_to_controller() |
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|
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def get_queue_length(self): |
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if ( |
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model_semaphore is None |
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or model_semaphore._value is None |
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or model_semaphore._waiters is None |
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): |
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return 0 |
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else: |
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return ( |
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args.limit_model_concurrency |
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- model_semaphore._value |
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+ len(model_semaphore._waiters) |
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) |
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|
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def get_status(self): |
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return { |
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"model_names": self.model_names, |
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"speed": 1, |
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"queue_length": self.get_queue_length(), |
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} |
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|
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def generate_stream_func(self, model, params, device): |
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prompt = params["prompt"] |
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images = self.pipe(prompt, num_inference_steps=20).images |
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image = images[0] |
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pred_dict = { |
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"edited_image": encode(image), |
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} |
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return pred_dict |
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|
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def generate_gate(self, params): |
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try: |
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|
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ret = {"text": "", "error_code": 0} |
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ret = self.generate_stream_func( |
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None, |
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params, |
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self.device, |
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) |
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except torch.cuda.OutOfMemoryError as e: |
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ret = { |
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"text": f"{SERVER_ERROR_MSG}\n\n({e})", |
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"error_code": ErrorCode.CUDA_OUT_OF_MEMORY, |
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} |
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except (ValueError, RuntimeError) as e: |
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ret = { |
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"text": f"{SERVER_ERROR_MSG}\n\n({e})", |
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"error_code": ErrorCode.INTERNAL_ERROR, |
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} |
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return ret |
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app = FastAPI() |
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|
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def release_model_semaphore(): |
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model_semaphore.release() |
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|
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def acquire_model_semaphore(): |
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global model_semaphore, global_counter |
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global_counter += 1 |
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if model_semaphore is None: |
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model_semaphore = asyncio.Semaphore(args.limit_model_concurrency) |
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return model_semaphore.acquire() |
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|
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def create_background_tasks(): |
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background_tasks = BackgroundTasks() |
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background_tasks.add_task(release_model_semaphore) |
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return background_tasks |
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|
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|
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@app.post("/worker_generate") |
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async def api_generate(request: Request): |
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params = await request.json() |
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await acquire_model_semaphore() |
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output = worker.generate_gate(params) |
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release_model_semaphore() |
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return JSONResponse(output) |
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|
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@app.post("/worker_get_status") |
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async def api_get_status(request: Request): |
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return worker.get_status() |
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|
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@app.post("/model_details") |
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async def model_details(request: Request): |
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return {"context_length": worker.context_len} |
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|
|
|
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--host", type=str, default="localhost") |
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parser.add_argument("--port", type=int, default=21307) |
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parser.add_argument("--worker-address", type=str, default="http://localhost:21307") |
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parser.add_argument( |
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"--controller-address", type=str, default="http://localhost:21001" |
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) |
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|
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parser.add_argument( |
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"--model-path", type=str, default="Salesforce/blip2-opt-2.7b" |
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) |
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|
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parser.add_argument( |
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"--model-names", |
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default="stable-diffusion,sd", |
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type=lambda s: s.split(","), |
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help="Optional display comma separated names", |
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) |
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parser.add_argument("--device", type=str, default="cuda") |
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parser.add_argument("--limit-model-concurrency", type=int, default=5) |
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parser.add_argument("--stream-interval", type=int, default=2) |
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parser.add_argument("--no-register", action="store_true") |
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args = parser.parse_args() |
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logger.info(f"args: {args}") |
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|
|
|
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worker = ModelWorker( |
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args.controller_address, |
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args.worker_address, |
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worker_id, |
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args.no_register, |
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args.model_path, |
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args.model_names, |
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args.device, |
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) |
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uvicorn.run(app, host=args.host, port=args.port, log_level="info") |
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|
