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"""
A model worker executes the model.
"""
import argparse
import asyncio
import json
import time
import threading
import uuid
import requests
import torch
from functools import partial
from mplug_owl2.constants import WORKER_HEART_BEAT_INTERVAL
from mplug_owl2.utils import (build_logger, server_error_msg,
pretty_print_semaphore)
from mplug_owl2.model.builder import load_pretrained_model
from mplug_owl2.mm_utils import process_images, load_image_from_base64, tokenizer_image_token, KeywordsStoppingCriteria
from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_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")
class ModelWorker:
def __init__(self, model_path, model_base, model_name, load_8bit, load_4bit, device):
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
self.device = device
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, model_base, self.model_name, load_8bit, load_4bit, device=self.device)
self.is_multimodal = True
@torch.inference_mode()
def predict_stream(self, params):
tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor
prompt = params["prompt"] + "The quality of the image is"
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]
images = process_images(images, image_processor, model.config)
if type(images) is list:
images = [image.to(self.model.device, dtype=torch.float16) for image in images]
else:
images = images.to(self.model.device, dtype=torch.float16)
replace_token = DEFAULT_IMAGE_TOKEN
prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token)
num_image_tokens = prompt.count(replace_token) * (model.get_model().visual_abstractor.config.num_learnable_queries + 1)
else:
images = None
image_args = {"images": images}
else:
images = None
image_args = {}
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
logits = model.forward(
input_ids=input_ids,
use_cache=True,
**image_args).logits[0,-1]
print(logits.shape)
softmax_logits = torch.softmax(logits[[1781,6588,6460]], 0)
print(tokenizer(["good", "average", "poor"]))
fake_streamer = []
for id_, word in enumerate(["good", "average", "poor"]):
stream_ = f"Probability of {word} quality: {softmax_logits[id_].item():.4f};\n"
fake_streamer.append(stream_)
quality_score = 0.5 * softmax_logits[1] + softmax_logits[0]
stream_ = f"Quality score: {quality_score:.4f} (range [0,1])."
fake_streamer.append(stream_)
generated_text = ori_prompt.replace("The quality of the image is", "")
for new_text in fake_streamer:
generated_text += new_text
yield json.dumps({"text": generated_text, "error_code": 0}).encode()
@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]
images = process_images(images, image_processor, model.config)
if type(images) is list:
images = [image.to(self.model.device, dtype=torch.float16) for image in images]
else:
images = images.to(self.model.device, dtype=torch.float16)
replace_token = DEFAULT_IMAGE_TOKEN
prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token)
num_image_tokens = prompt.count(replace_token) * (model.get_model().visual_abstractor.config.num_learnable_queries + 1)
else:
images = None
image_args = {"images": images}
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_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
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)
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,
**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()
def predict_stream_gate(self, params):
try:
for x in self.predict_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()
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() |