A acumulação de gradiente é um método usado no treinamento aprendizado profundo models to effectively increase the tamanho do lote without requiring more memory. In traditional training, models are updated after processing a batch of data. However, when the batch size is too large for the available memory (especially in high-dimensional data like images or large modelos de linguagem), it can lead to out-of-memory errors. Gradient accumulation provides a solution to this problem.
The process works by dividing the total desired batch size into smaller mini-batches. Instead of updating the model weights after each mini-batch, the gradients calculated from each mini-batch are accumulated over a specified number of iterations. Only after processing enough mini-batches to reach the target batch size does the model perform an update. This means that the model can simulate training with a larger batch size while only using a fraction of the memory at any given time.
For example, if the desired batch size is 32, but memory constraints only allow for a mini-batch size of 8, the model can process four mini-batches in succession—accumulating gradients—before performing a single weight update. This technique can lead to more stable training and can help in achieving better convergence properties in some scenarios.
A acumulação de gradiente é particularmente útil em cenários como o treinamento de grandes redes neurais or when working with limited hardware resources. While it can introduce a slight increase in training time due to the accumulated iterations before an update, it allows practitioners to leverage larger effective batch sizes that can improve the performance of the model.