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VisualRWKV: Visual Language model based on RWKV

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📖 Paper | 🤗 Model | 🐰 Demo

VisualRWKV is a visual language model based on RWKV language model, enabling RWKV to handle various visual tasks.

VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models [Paper]

Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence [Paper]

News and Updates

  • 2024.06.25 🔥 VisualRWKV-6.0 checkpoints are released! [weights]
  • 2024.05.11 🔥 VisualRWKV-6.0 code is released! [code]
  • 2024.03.25 🔥 VisualRWKV-5.0 is released!

Architecture

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Model Zoo

VisualRWKV weights, checkpoints, and related results are shown in the following markdown[Model Zoo].

Install

  1. Clone this repository and navigate to VisualRWKV folder, VisualRWKV-v6.0 is the stable version.
git clone https://github.com/howard-hou/VisualRWKV.git
cd VisualRWKV-v6/v6.0
  1. Install Package
conda create -n llava python=3.10 -y
conda activate visualrwkv
pip install --upgrade pip  # enable PEP 660 support

#reference:
pip install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
pip install pytorch-lightning==1.9.5 deepspeed==0.7.0 wandb ninja

best performance:
pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121
pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade

Pre-training and Fine-tuning

Lastest stable verion is VisualRWKV-v6/v6.0, please cd to the dir VisualRWKV-v6/v6.0 for running the code.

VisualRWKV training consists of two stages:

  • (1) Pre-training stage: use pretrain dataset to train a projection layer from frozen pretrained vision encoder to the frozen RWKV;
  • (2) Fine-tuning stage: use visual instruction data, to teach the model to follow visual instructions.

Pre-training

Download LLaVA-Pretrain dataset

You can download the LLaVA-Pretrain.

Download RWKV checkpoints for Pre-training

If you want to pretrain by yourself. You can download the RWKV checkpoints from the following links in the table.

VisualRWKV Version RWKV 1B6 RWKV 3B RWKV 7B
VisualRWKV-v6 RWKV-x060-World-1B6 RWKV-x060-World-3B RWKV-x060-World-7B

Pre-training command

You can refer to the following command to pretrain the VisualRWKV-v6.0 model. Also see scripts in the scripts/train directory.

# here is an example to use 4 GPUs to pretrain a 1B5 RWKV model
export CUDA_VISIBLE_DEVICES=0,1,2,3
python train.py --load_model /path/to/rwkv/checkpoint \
    --wandb "" --proj_dir path/to/output/ \
    --data_file /path/to/LLaVA-Pretrain/blip_laion_cc_sbu_558k.json \
    --data_type "json" --vocab_size 65536 \
    --ctx_len 1024 --epoch_steps 1000 --epoch_count 9 --epoch_begin 0 --epoch_save 0 \
    --micro_bsz 16 --accumulate_grad_batches 2 --n_layer 24 --n_embd 2048 --pre_ffn 0 \
    --lr_init 1e-3 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.99 --adam_eps 1e-8 \
    --accelerator gpu --devices 4 --precision bf16 --strategy deepspeed_stage_1 --grad_cp 0 \
    --image_folder /path/to/LLaVA-Pretrain/images/ \
    --vision_tower_name /path/to/openai/clip-vit-large-patch14-336 \
    --freeze_rwkv 24 --detail low --grid_size -1 --image_position first \
    --enable_progress_bar True

Visual Instruction Tuning

Prepare data

Please refer to the LLaVA project for visual instruction data.

Fine-tuning command

You can refer to the following command to fine-tune the VisualRWKV-v6.0 model. Also see scripts in the scripts/train directory.

# here is an example to use 8 GPUs to fine-tune a 1B5 RWKV model
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python train.py --model_path path/to/pretrained-visualrwkv \
    --wandb "" --proj_dir out/rwkv1b5-v060_mix665k \
    --data_file /path/to/LLaVA-Instruct-150K/shuffled_llava_v1_5_mix665k.json \
    --data_type "json" --vocab_size 65536 \
    --ctx_len 2048 --epoch_steps 1000 --epoch_count 20 --epoch_begin 0 --epoch_save 5 \
    --micro_bsz 8 --accumulate_grad_batches 2 --n_layer 24 --n_embd 2048 --pre_ffn 0 \
    --lr_init 2e-5 --lr_final 2e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.99 --adam_eps 1e-8 \
    --accelerator gpu --devices 8 --precision bf16 --strategy deepspeed_stage_1 --grad_cp 0 \
    --image_folder /path/to/LLaVA-Instruct-150K/images/ \
    --vision_tower_name /path/to/openai/clip-vit-large-patch14-336 \
    --freeze_rwkv 0 --freeze_proj 0 --detail low --grid_size -1 --image_position middle \
    --enable_progress_bar True