ola/serve_ola/model_worker.py

"""
A model worker executes the model.
"""
import os, sys
os.environ['LOWRES_RESIZE'] = '384x32'
os.environ['HIGHRES_BASE'] = '0x32'
os.environ['VIDEO_RESIZE'] = "0x64"
os.environ['VIDEO_MAXRES'] = "480"
os.environ['VIDEO_MINRES'] = "288"
os.environ['MAXRES'] = '1536'
os.environ['MINRES'] = '0'
os.environ['REGIONAL_POOL'] = '2x'
os.environ['FORCE_NO_DOWNSAMPLE'] = '1'
os.environ['LOAD_VISION_EARLY'] = '1'
os.environ['SKIP_LOAD_VIT'] = '1'

sys.path.append('/mnt/lzy/Ola')

import argparse
import asyncio
import json
import time
import threading
import uuid

from fastapi import FastAPI, Request, BackgroundTasks
from fastapi.responses import StreamingResponse
import requests
import torch
import uvicorn
from functools import partial

from ola.constants import WORKER_HEART_BEAT_INTERVAL
from ola.utils import (build_logger, server_error_msg,
    pretty_print_semaphore)
from ola.model.builder import load_pretrained_model
from ola.mm_utils import process_anyres_highres_image_genli, load_image_from_base64, tokenizer_image_token, KeywordsStoppingCriteria
from ola.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from transformers import TextIteratorStreamer
from threading import Thread


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_base, model_name,
                 load_8bit, load_4bit):
        self.controller_addr = controller_addr
        self.worker_addr = worker_addr
        self.worker_id = worker_id
        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 model {self.model_name} on worker {worker_id} ...")
        self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(
            model_path, None, self.model_name, load_8bit, load_4bit, device_map='cuda:0')
        self.model = self.model.eval()
        self.model = self.model.bfloat16()
        self.is_multimodal = 'ola' in self.model_name.lower()

        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, f"Failed to register to controller: {r.text}"

    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}")

        print('skip heart beat')
        return

        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(),
        }

    @torch.inference_mode()
    def generate_stream(self, params):
        tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor

        prompt = params["prompt"]
        ori_prompt = prompt
        images = params.get("images", None)
        num_image_tokens = 0
        if images is not None and len(images) > 0 and self.is_multimodal:
            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]
                image_sizes = [image.size for image in images]
                logger.info(f"image_sizes: {image_sizes}")
                image_tensor, image_highres_tensor = process_anyres_highres_image_genli(images, image_processor, model.config)

                if type(image_tensor) is list:
                    image_tensor = [image_.to(self.model.device, dtype=torch.bfloat16) for image_ in image_tensor]
                else:
                    image_tensor = image_tensor.to(self.model.device, dtype=torch.bfloat16)

                if type(image_highres_tensor) is list:
                    image_highres_tensor = [image_.to(self.model.device, dtype=torch.bfloat16) for image_ in image_highres_tensor]
                else:
                    image_highres_tensor = image_highres_tensor.to(self.model.device, dtype=torch.bfloat16)

                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)

                # num_image_tokens = prompt.count(replace_token) * model.get_vision_tower().num_patches
            else:
                images = None
                image_sizes = None
            image_args = {"images": images, "images_highres": image_highres_tensor, "image_sizes": image_sizes}
        else:
            images = None
            image_args = {}

        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 = '<|im_end|>' if stop_str is None else stop_str
        do_sample = True if temperature > 0.001 else False

        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
        keywords = [stop_str]
        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15)

        # max_new_tokens = 1024 # min(max_new_tokens, max_context_length - input_ids.shape[-1] - 576)

        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

        thread = Thread(target=model.generate, kwargs=dict(
            inputs=input_ids,
            do_sample=do_sample,
            temperature=temperature,
            top_p=top_p,
            max_new_tokens=max_new_tokens,
            streamer=streamer,
            # stopping_criteria=[stopping_criteria],
            use_cache=True,
            modalities=['image']
            **image_args
        ))
        thread.start()

        start_time = time.time()
        generated_text = ori_prompt
        for new_text in streamer:
            generated_text += new_text
            if generated_text.endswith(stop_str):
                generated_text = generated_text[:-len(stop_str)]
            yield json.dumps({"text": generated_text, "error_code": 0}).encode() + b"\0"

        end_time = time.time()

        new_generated = generated_text[len(ori_prompt):]
        new_generated_tokens = tokenizer(new_generated).input_ids
        token_per_second = len(new_generated_tokens) / (end_time - start_time)
        print(f"token_per_second: {token_per_second}")

    def generate_stream_gate(self, params):
        # try:
            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 torch.cuda.CudaError as e:
        #     print("Caught torch.cuda.CudaError:", 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="0.0.0.0")
    parser.add_argument("--port", type=int, default=21002)
    parser.add_argument("--worker-address", type=str,
        default="http://0.0.0.0:21002")
    parser.add_argument("--controller-address", type=str,
        default="http://0.0.0.0:12345")
    parser.add_argument("--model-path", type=str, default="/mnt/lzy/ola-model/ola-7b")
    parser.add_argument("--model-base", type=str, default=None)
    parser.add_argument("--model-name", type=str)
    parser.add_argument("--multi-modal", action="store_true", help="Multimodal mode is automatically detected with model name, please make sure `llava` is included in the model path.")
    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")
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--load-4bit", action="store_true")
    args = parser.parse_args()
    logger.info(f"args: {args}")

    if args.multi_modal:
        logger.warning("Multimodal mode is automatically detected with model name, please make sure `llava` is included in the model path.")

    worker = ModelWorker(args.controller_address,
                         args.worker_address,
                         worker_id,
                         args.no_register,
                         args.model_path,
                         args.model_base,
                         args.model_name,
                         args.load_8bit,
                         args.load_4bit)
    uvicorn.run(app, host=args.host, port=args.port, log_level="info")