Fixing Faulty Gradient Accumulation: Understanding the Issue and Its Resolution

Years of suboptimal model training?

When fine-tuning large language models (LLMs) locally, using large batch sizes is often impractical due to their substantial GPU memory consumption. To overcome this limitation, a technique called gradient accumulation is commonly used to simulate larger batch sizes. Instead of updating the model weights after processing each batch, gradient accumulation involves summing the gradients over several smaller mini-batches. The model weights are updated only after a predetermined number of these mini-batches have been processed. This method effectively mimics training with a larger batch size without the memory overhead typically associated with it.

For instance, setting a mini-batch size of 1 and accumulating gradients over 32 mini-batches should be equivalent to training with a full batch size of 32. However, I discovered that gradient accumulation often results in significantly degraded performance compared to training with larger actual batch sizes with popular deep-learning frameworks like Transformers.

After sharing this issue on X and Reddit, Daniel Han from Unsloth AI replicated the problem. He found that it was affecting not only gradient accumulation but also multi-GPU setups. In such…