CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling

This is the official implementation of Diversified Multiplet Upcycling proposed in CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling

Set up

To get started, create and activate a Conda environment,

conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=<your cuda version>
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git
cd CLIP-MoE
pip install -e .

Evaluation

Here we provide the weights of the CLIP-MoE 4 experts with top-2 activation obtained from both ShareGPT4V and Recap-DataComp. The model weights are available on Hugging Face. Please download the weights. After that you can modify and use the evaluation scripts in CLIP-MoE/eval.

Training

Datasets

Please prepare ShareGPT4V or Recap-DataComp. Notice that our code currently only support regular image folder datasets. If you are using img2dataset to prepare the data, please set --output_format files instead of --output_format webdataset. For Recap-DataComp we only need a small subset (around 1-10M samples) to demonstrate the performance improvement.

Expert Extraction Using MCL

Multistage Contrastive Learning contains multiple iterations of training-clustering. To obtain a final CLIP-MoE with N experts, we need to perform N-1 stages of MCL.

Stage0

First, save the base model weights (e.g., OpenAI CLIP ViT-L-14):

srun --gres=gpu:8 python CLIP-MoE/train/train_mcl.py --epochs 0 --exp-name clip-mcl-s0

Which will save the base model weights to CLIP-MoE/train/checkpoints as clip-mcl-s0_weights.pt Second, get the image and text representations of the whole training dataset:

srun --gres=gpu:8 python CLIP-MoE/train/inference.py --epochs 0 --exp-name clip-mcl-s0 --checkpoint-dir CLIP-MoE/train/checkpoints/clip-mcl-s0_weights.pt

Which will generate the representations into CLIP-MoE/train/save_mcl_tmp/, splitting by the number of gpus used. Third, do clustering on the representations. Please set the parameters accordingly and run CLIP-MoE/train/cluster.ipynb. This will save the clustering results and pseudo_labels (the accumulated clustering results of all previous stages) for the next stage of MCL in CLIP-MoE/train/save_mcl_tmp/clip-mcl_0_pseudo_labels.pt

Stage1

Fine-tuning the base CLIP model according to the mcl pseudo labels, and freeze all parameters except the mlp layers:

srun --gres=gpu:8 python CLIP-MoE/train/train_mcl.py --epochs 1 --exp-name clip-mcl-s1 --MCL-label-path CLIP-MoE/train/save_mcl_tmp/clip-mcl_0_pseudo_labels.pt --lock-except-mlp

Then do the inference and clustering accordingly, and continue for N-1 stages.

Initialization of CLIP-MOE and Continuous Fine-tuning

After expert extraction, we can use the obtained series of CLIP models with different mlp layers to build an MoE. First set the checkpoint list in line 26 of CLIP-MoE/train/train_moe.py. For example:

checkpoints=['CLIP-MoE/train/checkpoints/clip-mcl-s0_weights.pt',
    'CLIP-MoE/train/checkpoints/clip-mcl-s1_weights.pt',
    'CLIP-MoE/train/checkpoints/clip-mcl-s2_weights.pt',
    'CLIP-MoE/train/checkpoints/clip-mcl-s3_weights.pt']

Then, fine-tune the routers to get an optimized routing strategy. By defult we freeze all parameters except the routers. If you want to do large-scale continuous training, you can try different settings.

srun --gres=gpu:8 python CLIP-MoE/train/train_moe.py --epochs 1 --top-k 2 --lock-except-gate

This will optimize the routers with both CLIP loss and router balance loss, and finally save a CLIP-MoE model.

Acknowledgement

• Long-CLIP The codebase we built upon.
• MCL Original implementation of Multistage Contrastive Learning.