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model_worker.py
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"""
A model worker executes the model.
"""
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 mplug_docowl.utils import (build_logger, server_error_msg,
pretty_print_semaphore)
from mplug_docowl.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN,WORKER_HEART_BEAT_INTERVAL
from mplug_docowl.conversation import conv_templates, SeparatorStyle
from mplug_docowl.model.builder import load_pretrained_model
from mplug_docowl.mm_utils import load_image_from_base64, process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from mplug_docowl.processor import DocProcessor
from transformers import TextIteratorStreamer
from threading import Thread
from icecream import ic
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,
model_path, model_base, model_name,
resolution, anchors, add_global_img,
load_8bit, load_4bit, device):
if model_path.endswith("/"):
model_path = model_path[:-1]
self.model_name = get_model_name_from_path(model_path)
self.device = device
logger.info(f"Loading the model {self.model_name} on worker {worker_id} ...")
self.tokenizer, self.model, _, self.context_len = load_pretrained_model(
model_path, model_base, self.model_name, load_8bit, load_4bit, device=self.device)
self.resolution=resolution
self.token_num_each_img = (self.resolution/14)*(self.resolution/14)/self.model.get_model().vision2text.conv_patch
self.doc_image_processor = DocProcessor(image_size=resolution, anchors=anchors, add_global_img=add_global_img, add_textual_crop_indicator=True)
self.is_multimodal = True
@torch.inference_mode()
def generate_stream(self, params):
tokenizer, model = self.tokenizer, self.model
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:
images = [load_image_from_base64(image) for image in images]
# docowl only support 1 image, so only keep the last image
image = images[-1]
assert prompt.count(DEFAULT_IMAGE_TOKEN) == 1
images, patch_positions, prompt = self.doc_image_processor(images=image, query=prompt)
images = images.to(self.model.device, dtype=torch.float16)
patch_positions = patch_positions.to(self.model.device)
replace_token = DEFAULT_IMAGE_TOKEN
prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token)
num_image_tokens = prompt.count(replace_token) * (self.token_num_each_img+1)
else:
images = None
patch_positions = None
image_args = {"images": images, "patch_positions":patch_positions}
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', 4096)
max_context_length = 4096
max_new_tokens = min(int(params.get("max_new_tokens", 256)), 1024)
stop_str = params.get("stop", None)
# do_sample = True if temperature > 0.001 else False
do_sample = False
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
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 = min(max_new_tokens, max_context_length - input_ids.shape[-1] - num_image_tokens)
ic(max_context_length, input_ids.shape[-1], num_image_tokens, max_new_tokens)
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()
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,
**image_args
))
thread.start()
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()
# replace < > to [ ] to avoide <doc>,<md>,<ocr>,<bbox> are removed by web code
yield json.dumps({"text": generated_text.replace('<','[').replace('>',']'), "error_code": 0}).encode()
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()
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()
except Exception as e:
print("Caught Unknown Error", e)
ret = {
"text": server_error_msg,
"error_code": 1,
}
yield json.dumps(ret).encode()