Efficient Variance-Reduced Zeroth-Order Methods for Fine-Tuning Large Language Models

Core Concepts

Variance-reduced zeroth-order optimization methods can effectively fine-tune large language models while significantly reducing memory requirements compared to first-order methods.

Abstract

The paper introduces Memory-Efficient Zeroth-Order Stochastic Variance-Reduced Gradient (MeZO-SVRG), a variant of zeroth-order optimization that combines fullbatch and minibatch information to yield low-variance gradient estimators. Key highlights:

MeZO-SVRG outperforms the previous state-of-the-art MeZO method, achieving up to 20% higher test accuracies on various language model fine-tuning tasks, including masked and autoregressive models up to 7B parameters.
MeZO-SVRG consistently reduces the performance gap to first-order fine-tuning methods like SGD, while requiring 2x less GPU-hours to reach the same performance level as MeZO.
MeZO-SVRG significantly reduces the memory footprint compared to first-order methods, with at least 2x savings for autoregressive models. The memory savings progressively improve with larger batch sizes and context lengths.
The authors provide convergence guarantees for MeZO-SVRG and demonstrate its compatibility with non-differentiable objectives and parameter-efficient fine-tuning techniques.

Stats

Fine-tuning a 7B parameter language model requires 12x more memory than inference using first-order methods.
MeZO-SVRG achieves a 2x reduction in memory usage compared to first-order SGD for fine-tuning autoregressive models.
For RoBERTa-large fine-tuning, MeZO-SVRG provides up to 76% memory savings over first-order SGD as batch size and context length increase.

Quotes

"MeZO-SVRG consistently outperforms MeZO and significantly closes the gap to FO-SGD."
"MeZO-SVRG consumes more memory than MeZO due to its need to store copies of the parameters and fullbatch SPSA estimators, but compensates by delivering notable gains in test performance and computation time."

Key Insights Distilled From

Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

by Tanmay Gauta... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08080.pdf

Deeper Inquiries

How can MeZO-SVRG be extended to handle extremely large fine-tuning datasets where computing the fullbatch gradient is infeasible

To handle extremely large fine-tuning datasets where computing the fullbatch gradient is infeasible, MeZO-SVRG can be adapted by approximating the fullbatch estimator with a large-batch estimator. By using a large-batch estimator, which involves computing gradients over a larger subset of the dataset, MeZO-SVRG can still leverage more information than traditional minibatch estimators while avoiding the memory and computational constraints of fullbatch estimators. This approach allows MeZO-SVRG to scale effectively to handle massive datasets without compromising on performance or memory efficiency.

Can MeZO-SVRG be combined with parameter-efficient fine-tuning techniques like LoRA and prefix-tuning to further reduce the memory footprint

MeZO-SVRG can indeed be combined with parameter-efficient fine-tuning techniques like LoRA and prefix-tuning to further reduce the memory footprint. By integrating MeZO-SVRG with these techniques, the fine-tuning process can be optimized to focus on specific subsets of parameters that are crucial for the downstream task, thereby reducing the overall memory requirements. This combination allows for efficient adaptation of large language models to specialized tasks while maintaining a minimal memory footprint, making it ideal for resource-constrained environments.

What other variance reduction techniques could be explored to improve the stability and convergence of zeroth-order optimization methods for language model fine-tuning

In addition to the variance reduction techniques already explored in the context of zeroth-order optimization methods for language model fine-tuning, there are several other strategies that could be further investigated to improve stability and convergence. One potential approach is to incorporate techniques like control variates or control variate gradients, which can help reduce the variance of gradient estimators and enhance the overall optimization process. Additionally, exploring adaptive learning rate schemes or momentum-based methods within the framework of MeZO-SVRG could also contribute to better convergence properties and stability in large-scale fine-tuning tasks. By experimenting with a combination of these techniques, it is possible to enhance the performance and efficiency of zeroth-order optimization methods for language model fine-tuning.