Deconstructing What Makes a Good Optimizer for Language Models

• URL: http://arxiv.org/abs/2407.07972v1
• Date: Wed, 10 Jul 2024 18:11:40 GMT
• Title: Deconstructing What Makes a Good Optimizer for Language Models
• Authors: Rosie Zhao, Depen Morwani, David Brandfonbrener, Nikhil Vyas, Sham Kakade,
• Abstract summary: We compare several optimization algorithms, including SGD, Adafactor, Adam, and Lion, in the context of autoregressive language modeling. Our findings indicate that, except for SGD, these algorithms all perform comparably both in their optimal performance.
• Score: 7.9224468703944115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training language models becomes increasingly expensive with scale, prompting numerous attempts to improve optimization efficiency. Despite these efforts, the Adam optimizer remains the most widely used, due to a prevailing view that it is the most effective approach. We aim to compare several optimization algorithms, including SGD, Adafactor, Adam, and Lion, in the context of autoregressive language modeling across a range of model sizes, hyperparameters, and architecture variants. Our findings indicate that, except for SGD, these algorithms all perform comparably both in their optimal performance and also in terms of how they fare across a wide range of hyperparameter choices. Our results suggest to practitioners that the choice of optimizer can be guided by practical considerations like memory constraints and ease of implementation, as no single algorithm emerged as a clear winner in terms of performance or stability to hyperparameter misspecification. Given our findings, we further dissect these approaches, examining two simplified versions of Adam: a) signed momentum (Signum) which we see recovers both the performance and hyperparameter stability of Adam and b) Adalayer, a layerwise variant of Adam which we introduce to study Adam's preconditioning. Examining Adalayer leads us to the conclusion that the largest impact of Adam's preconditioning is restricted to the last layer and LayerNorm parameters, and, perhaps surprisingly, the remaining layers can be trained with SGD.

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