Distributed Sigmoid Loss

Unofficial PyTorch implementation for the loss function from Sigmoid Loss for Language Image Pre-Training. This code hasn't been reviewed yet.

TL;DR

This is a distributed implementation for Sigmoid Loss. This distributed implementation supports training on multi-GPUs.

Sigmoid Loss Overview

Zhai et al. proposed Sigmoid loss for Language Image Pre-training. This new loss is supposed to be a better alternative for InfoNCE (Clip) loss. Concretely, Sigmoid loss promises better convergence especially with smaller batch-sizes (Fig. 2) and noisier datasets (Fig. 7). Zhai et al. presented a pseudo implementation for Sigmoid Loss as follows

While helpful, this pseudo implementation assumes a single GPU. Yet, Vision-Language models are always trained on multiple GPUs. So, this repos delivers a distributed sigmoid loss implementation using PyTorch to run on multiple-GPUs.

Usage Notes

• This code works with DistributedDataParallel (DDP) only and not DataPrallel (DP).
• Please make sure to pass the loss parameters (t_prime, bias) to your optimizer (e.g., Adam optimizer). These parameters ought to be updated during training.

MISC Notes

• I am currently looking for people to review this code.
• Credit is due for KeremTurgutlu as I am borrowing some of his code
• Ross Wightman released his distributed implementation for Sigmoid loss which makes this repos absolute :)
• There are differences between Wightman's implementation and mine in terms of (1) communication between GPUs, (2) where the loss parameters (temperature and bias) are stored.
• I plan to both maintain this repos and publish tests to compare Wightman's implementation and mine.

Release History

• 1.0.0
  • 23 Sep 2023: First code commit
  • 25 Sep 2023: Fix t_prime initialize, i.e., set t` = log(10); Clean Code a bit (e.g., L2 Normalize outside the loss function)
  • 26 Sep 2023: Add Ross Wightman implementation to this repos for verification purpose.