When locally tuning large language models (LLMs), using large batch sizes is often impractical due to substantial GPU memory consumption. To overcome this limitation, a technique called gradient accumulation It is commonly used to simulate larger batch sizes. Instead of updating model weights after processing each batch, gradient accumulation involves summing the gradients over several smaller mini-batches. 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 example, setting a mini-batch size of 1 and accumulating gradients in 32 mini-batches should be equivalent to training with a full batch size of 32. However, I have found that accumulating gradients often results in significant performance degradation on compared to training with larger sizes. real batch sizes with popular deep learning frameworks like Transformers.
After sharing this problem on x.com/bnjmn_marie/status/1842202652672671964″ rel=”noopener ugc nofollow” target=”_blank”>unknown and redditDaniel Han ai/” rel=”noopener ugc nofollow” target=”_blank”>Non-lazy ai replied the problem. He found that it was affecting not only gradient buildup but also multi-GPU configurations. In such…
