Official PyTorch implementation of the paper "DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training".
git clone https://github.com/IDEA-Research/DisCo-CLIP.git
cd DisCo-CLIP
pip install -e .
We implemented our method using disco.Gather , which is easy to use. more detail about disco.Gather in gather.py
import disco
...
all_image_feature = disco.Gather(image_feature)
all_text_feature = disco.Gather(text_feature)
# bs is batch size per gpu
# rank is global rank
logits_per_image = 100.0 * all_image_feature[bs*rank:bs*(rank+1)] @ all_text_feature.t()
logits_per_text = 100.0 * all_text_feature[bs*rank:bs*(rank+1)] @ all_image_feature.t()
label = torch.arange(logits_per_image.shape[0]).long().to(device) + rank * bs
loss1 = criterion_img(logits_per_image, label)
loss2 = criterion_text(logits_per_text, label)
loss = loss1 + loss2
loss.backward()
If you find this repository helpful, please consider citing:
@Article{chen2023discoclip,
author = {Yihao Chen and Xianbiao Qi and Jianan Wang and Lei Zhang},
title = {DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2023},
}
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