U.Pavan Kumar

Fixed a bug which caused all training losses to diverge for large gradient accumulation sizes. 1. First reported by @bnjmn_marie, GA is supposed to be mathematically equivalent to full batch training, but losses did not match. 2. We reproed the issue, and further investigation showed the L2 Norm betw bsz=16 and ga=16 was 10x larger. 3. The culprit was the cross entropy loss normalizer. 4. We ran training runs with denormalized CE Loss, and all training losses match. 5. We then re-normalized CE Loss with the correct denominator across all gradient accumulation steps, and verified all training loss curves match now. 6. We've already updated @UnslothAI with the fix, and wrote up more details in our blog post here: unsloth.ai/blog/gradient This issue impacts all libraries which use GA, and simple averaging of GA does not work for varying sequence lengths. This also impacts DDP and multi GPU training which accumulates gradients. Please update Unsloth via pip install --upgrade --no-cache-dir unsloth and use from unsloth import unsloth_train We have a Colab notebook using our fixed GA: colab.research.google.com/dr… and a Kaggle notebook: kaggle.com/code/danielhanche…