CAME: Confidence-guided Adaptive Memory Efficient Optimization (2307.02047v2)

Abstract: Adaptive gradient methods, such as Adam and LAMB, have demonstrated excellent performance in the training of LLMs. Nevertheless, the need for adaptivity requires maintaining second-moment estimates of the per-parameter gradients, which entails a high cost of extra memory overheads. To solve this problem, several memory-efficient optimizers (e.g., Adafactor) have been proposed to obtain a drastic reduction in auxiliary memory usage, but with a performance penalty. In this paper, we first study a confidence-guided strategy to reduce the instability of existing memory efficient optimizers. Based on this strategy, we propose CAME to simultaneously achieve two goals: fast convergence as in traditional adaptive methods, and low memory usage as in memory-efficient methods. Extensive experiments demonstrate the training stability and superior performance of CAME across various NLP tasks such as BERT and GPT-2 training. Notably, for BERT pre-training on the large batch size of 32,768, our proposed optimizer attains faster convergence and higher accuracy compared with the Adam optimizer. The implementation of CAME is publicly available.

• The paper introduces a novel optimizer that leverages a confidence-guided strategy to balance memory efficiency with adaptive learning.
• Extensive experiments reveal that CAME reduces memory usage by about 15% in BERT pre-training while achieving convergence comparable to Adam and LAMB.
• The approach enables efficient large language model training on hardware with restricted resources, advancing scalable NLP research.

Expert Overview of "CAME: Confidence-guided Adaptive Memory Efficient Optimization"

Introduction

The paper "CAME: Confidence-guided Adaptive Memory Efficient Optimization" introduces a novel optimization framework aimed at improving the efficiency of training LLMs. The authors contend with the core challenge posed by existing adaptive gradient optimization methods, like Adam and LAMB, which demand substantial memory due to their reliance on second-moment gradient estimates. With the growing scale of LLMs, memory constraints have emerged as a critical bottleneck. While memory-efficient optimizers such as Adafactor have addressed memory issues, they tend to suffer from performance instability and degradation.

Methodology

The authors propose the Confidence-guided Adaptive Memory Efficient (CAME) optimizer, which uniquely integrates memory efficiency with adaptive learning techniques. The central innovation of CAME is a confidence-guided strategy that aims to mitigate the instability intrinsic to memory-efficient optimizers like Adafactor. This strategy evaluates the confidence in parameter updates by assessing the deviation between the exponential moving average (EMA) and the current gradient update. By dynamically calculating this deviation, CAME adjusts updates more precisely, offering a balance between convergence speed and memory usage.

Experimental Results

Extensive experiments validate CAME's performance across various natural language processing tasks, including BERT and GPT-2 training. Key findings are as follows:

• BERT Pre-training with Large Batch Sizes: CAME demonstrated superior performance in comparison to Adam, achieving faster convergence and higher accuracy during BERT pre-training with a batch size of 32,768. It attained comparable validation accuracy with LAMB while reducing memory usage by approximately 15%.
• GPT-2 and T5 Model Training: In both these cases, CAME mirrored the convergence speed of Adam, effectively maintaining performance without an increase in resource demand.

Implications

The introduction of CAME has significant practical implications for the NLP community. By effectively reducing the memory burden while maintaining robust convergence properties, CAME allows for the deployment of large-scale LLM training on more constrained hardware settings. Theoretically, the approach outlines a path for developing adaptive methods that do not compromise on either adaptivity or efficiency. Furthermore, the confidence-driven update mechanism could inspire similar frameworks in other domains of machine learning that exhibit high-dimensional optimization challenges.

Future Directions

Future research could build on CAME by optimizing the computational efficiency of its confidence-driven adjustments, potentially integrating it with other memory-efficient techniques. Expanding CAME's application to fields beyond NLP, such as computer vision and reinforcement learning, would further establish its versatility and robustness. Finally, rigorous theoretical analysis of CAME's inner workings could augment understanding and potential refinement of this approach.

Conclusion

CAME emerges as a promising contribution to efficient LLM optimization, addressing existing trade-offs between memory consumption and adaptive performance. Strategic computational innovations such as those proposed in CAME are crucial for advancing scalable machine learning solutions in environments constrained by hardware limitations. The successful implementation and deployment of such methodologies would likely catalyze further exploration in the intersection of adaptivity and efficiency within the broader AI landscape.