Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer

Pytorch implementation of our Q-ViT accepted by NeurIPS2022.

Tips

Any problem, please contact the first author (Email: yanjingli@buaa.edu.cn).

Our code is heavily borrowed from DeiT (https://github.com/facebookresearch/deit).

Dependencies

• Python 3.8
• Pytorch 1.7.1
• Torchvision 0.8.2
• timm 0.4.12

Training:

Train Q-ViT Deit-T 4bits:

We train the 2/3/4 bits Q-ViT Deit-T with 512 batchsize and 3e-4 lr. Please note that we use DistribuedSampler for Tiny models.

When training 2/3 bits Q-ViT Deit-T, please change the model into 'twobits_deit_tiny_patch16_224/threebits_deit_tiny_patch16_224'

python -m torch.distributed.launch --master_port=12345 --nproc_per_node=4 --use_env main.py --model fourbits_deit_tiny_patch16_224 --epochs 300 --warmup-epochs 0 --weight-decay 0. --batch-size 128 --data-path /mnt/lustre/share/images/ --lr 3e-4 --no-repeated-aug --output_dir ./dist_4bit_tiny_lamb_3e-4_300_512 --distillation-type hard --teacher-model vit_deit_tiny_distilled_patch16_224 --opt fusedlamb

Train Q-ViT Deit-S 2/3/4bits:

We train the 2/3/4 bits Q-ViT Deit-S with 512 batchsize and 3e-4 lr. Please note that we use RASampler for Small models.

When training 2/3 bits Q-ViT Deit-S, please change the model into 'twobits_deit_small_patch16_224/threebits_deit_small_patch16_224'

python -m torch.distributed.launch --master_port=12345 --nproc_per_node=4 --use_env main.py --model fourbits_deit_small_patch16_224 --epochs 300 --warmup-epochs 0 --weight-decay 0. --batch-size 128 --data-path /mnt/lustre/share/images/ --lr 3e-4 --repeated-aug --output_dir ./dist_4bit_small_lamb_3e-4_300_512 --distillation-type hard --teacher-model vit_deit_small_distilled_patch16_224 --opt fusedlamb

Evaluation:

Eval Q-ViT Deit-S 2bits: (72.0% Top-1 Acc.):

> python -m torch.distributed.launch --master_port=1234 --nproc_per_node=1 --use_env main.py --model twobits_deit_small_patch16_224 --weight-decay 0. --batch-size 64  --data-path /dataset/ImageNet --output_dir ./eval --resume ./best_checkpoint_2bit.pth --eval

Eval Q-ViT Deit-S 3bits: (79.1% Top-1 Acc.):

> python -m torch.distributed.launch --master_port=1234 --nproc_per_node=1 --use_env main.py --model threebits_deit_small_patch16_224 --weight-decay 0. --batch-size 64  --data-path /dataset/ImageNet --output_dir ./eval --resume ./best_checkpoint_3bit.pth --eval

Checkpoints:

Q-ViT Deit-T

Methods	Top-1 acc	Top-5 acc	Quantized model link
Q-Deit-T (4-bit)	74.3	91.6	Model

Q-ViT Deit-S

Methods	Top-1 acc	Top-5 acc	Quantized model link
Q-DeiT-S (3-bit)	79.1	94.3	Model
Q-Deit-S (2-bit)	72.0	90.3	Model

Training codes and other models will be open-sourced successively.