FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training

• URL: http://arxiv.org/abs/2411.07837v1
• Date: Tue, 12 Nov 2024 14:41:07 GMT
• Title: FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
• Authors: Philip Zmushko, Aleksandr Beznosikov, Martin Takáč, Samuel Horváth,
• Abstract summary: We introduce $textttFRUGAL$ ($textbfF$ull-$textbfR$ank $textbfU$pdates with $textbfG$r$textbfA$dient sp$textbfL$itting, a new memory-efficient optimization framework. Our framework can be integrated with various low-rank update selection techniques, including GaLore and BAdam.
• Score: 51.39495282347475
• License:
• Abstract: With the increase in the number of parameters in large language models, the process of pre-training and fine-tuning increasingly demands larger volumes of GPU memory. A significant portion of this memory is typically consumed by the optimizer state. To overcome this challenge, recent approaches such as low-rank adaptation (LoRA (Hu et al., 2021)), low-rank gradient projection (GaLore (Zhao et al., 2024)), and blockwise optimization (BAdam (Luo et al., 2024)) have been proposed. However, in all these algorithms, the $\textit{effective rank of the weight updates remains low-rank}$, which can lead to a substantial loss of information from the gradient. This loss can be critically important, especially during the pre-training stage. In this paper, we introduce $\texttt{FRUGAL}$ ($\textbf{F}$ull-$\textbf{R}$ank $\textbf{U}$pdates with $\textbf{G}$r$\textbf{A}$dient sp$\textbf{L}$itting), a new memory-efficient optimization framework. $\texttt{FRUGAL}$ leverages gradient splitting to perform low-dimensional updates using advanced algorithms (such as Adam), while updates along the remaining directions are executed via state-free methods like SGD or signSGD (Bernstein et al., 2018). Our framework can be integrated with various low-rank update selection techniques, including GaLore and BAdam. We provide theoretical convergence guarantees for our framework when using SGDM for low-dimensional updates and SGD for state-free updates. Additionally, our method consistently outperforms concurrent approaches across various fixed memory budgets, achieving state-of-the-art results in pre-training and fine-tuning tasks while balancing memory efficiency and performance metrics.

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