Memory-Efficient Adaptive Optimization
This addresses a critical bottleneck for researchers and practitioners training large-scale models by enabling larger models and batches with reduced memory usage.
The paper tackles the memory overhead of adaptive gradient optimizers like Adam, which limits model and batch sizes, by proposing a memory-efficient method that retains per-parameter adaptivity, achieving up to 2-fold speedups in training large translation and language models.
Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling. However, these methods maintain second-order statistics for each parameter, thus introducing significant memory overheads that restrict the size of the model being used as well as the number of examples in a mini-batch. We describe an effective and flexible adaptive optimization method with greatly reduced memory overhead. Our method retains the benefits of per-parameter adaptivity while allowing significantly larger models and batch sizes. We give convergence guarantees for our method, and demonstrate its effectiveness in training very large translation and language models with up to 2-fold speedups compared to the state-of-the-art.