kosmos-2 0.1.0

kosmos-2 - Pytorch

Kosmos-2

My personal implementation of Kosmos-2, Kosmos-2: Grounding Multimodal Large Language Models to the World, much simpler codebase

Install

pip3 install qwen

---

Usage

import torch
from qwen.model import QwenVL

#usage
img = torch.randn(1, 3, 256, 256)
caption = torch.randint(0, 20000, (1, 1024))

model = QwenVL()
output = model(img, caption)
print(output.shape)

---

Inference

from qwen.inference import QwenVLChat


qwen_chat = QwenVLChat(model_name="Qwen/Qwen-VL-Chat", device_map="cuda")
response = qwen_chat.chat([
    {"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
    {"text": "这是什么?"}
])
print(response)

Training

• There is a file with a table of all the datasets used in the paper here

from qwen.train import Train


def train():
    os.environ['MASTER_ADDR'] #'localhost'
    os.environ['MASTER_PORT'] #= '9994'
    
    # # [CRITICAL] Pay attention to this when scaling to multiple GPUs and clusters
    os.environ['RANK']       #= str(0) # Number of nodes (servers)
    os.environ['WORLD_SIZE'] # = str(torch.cuda.device_count())

    dist.init_process_group(backend='nccl') #init_method="env://")
    
    Train()

if __name__ == '__main__':
    train()

1. Set the environment variables:

   • ENTITY_NAME: Your wandb project name
   • OUTPUT_DIR: Directory to save the weights (e.g., ./weights)
   • MASTER_ADDR: For distributed training
   • MASTER_PORT For master port distributed training
   • RANK- Number of nodes services
   • WORLD_SIZE Number of gpus

2. Configure the training:

   • Accelerate Config
   • Enable Deepspeed 3
   • Accelerate launch train_distributed_accelerate.py

For more information, refer to the Training SOP.

---

Todo

• Position aware vision language adapter, compresses image features. Singer layer cross attention module inited randomly => group of trainable embeddings as query vectors + image features from the visual encoder as keys for cross attention ops => OUTPUT: compresses visual feature sequence to a fixed lnegth of 256, 2d absolute positional encodings are integrated into the cross attentions mechanisms query key pairs => compressed feature sequence of length of 256 => fed into decoder llm

• Bounding Boxes, for any given accurate bounding box, a norm process is applied in the range [0, 1000] and transformed into a string format (Xtope, Ytople)(Xottomright, Ybottomright) -> the string is tokenized as text and does not require positional vocabulary. Detection strings and regular text strings, two special tokens and are added to the beginning and end of the bounding box string. + another sed of special tokens ( and ) is introduced.

Citations

Please use the following to cite this work:

@article{bai2023qwen,
  title={Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities},
  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
  journal={arXiv preprint arXiv:2308.12966},
  year={2023},
  url={https://doi.org/10.48550/arXiv.2308.12966}
}

For more details, please refer to the full paper.