Mnemosyne: Learning to Train Transformers with Transformers
This addresses the need for efficient and automated optimizer tuning in machine learning, particularly for large-scale models, though it is incremental as it builds on existing learnable optimizer concepts.
The paper tackles the problem of automating optimizer design for neural networks by introducing Mnemosyne, a learnable optimizer based on spatio-temporal low-rank implicit attention Transformers, which outperforms LSTM optimizers, matches SOTA hand-designed optimizers in accuracy, and scales efficiently to large models like ViT-Hs and T5XXL.
In this work, we propose a new class of learnable optimizers, called \textit{Mnemosyne}. It is based on the novel spatio-temporal low-rank implicit attention Transformers that can learn to train entire neural network architectures, including other Transformers, without any task-specific optimizer tuning. We show that Mnemosyne: (a) outperforms popular LSTM optimizers (also with new feature engineering to mitigate catastrophic forgetting of LSTMs), (b) can successfully train Transformers while using simple meta-training strategies that require minimal computational resources, (c) matches accuracy-wise SOTA hand-designed optimizers with carefully tuned hyper-parameters (often producing top performing models). Furthermore, Mnemosyne provides space complexity comparable to that of its hand-designed first-order counterparts, which allows it to scale to training larger sets of parameters. We conduct an extensive empirical evaluation of Mnemosyne on: (a) fine-tuning a wide range of Vision Transformers (ViTs) from medium-size architectures to massive ViT-Hs (36 layers, 16 heads), (b) pre-training BERT models and (c) soft prompt-tuning large 11B+ T5XXL models. We complement our results with a comprehensive theoretical analysis of the compact associative memory used by Mnemosyne which we believe was never done before.