An Evolved Universal Transformer Memory (2410.13166v3)

Published 17 Oct 2024 in cs.LG, cs.AI, and cs.CL

Abstract: Prior methods propose to offset the escalating costs of modern foundation models by dropping specific parts of their contexts with hand-designed rules, while attempting to preserve their original performance. We overcome this trade-off with Neural Attention Memory Models (NAMMs), introducing a learned network for memory management that improves both the performance and efficiency of transformers. We evolve NAMMs atop pre-trained transformers to provide different latent contexts focusing on the most relevant information for individual layers and attention heads. NAMMs are universally applicable to any model using self-attention as they condition exclusively on the values in the produced attention matrices. Learning NAMMs on a small set of problems, we achieve substantial performance improvements across multiple long-context benchmarks while cutting the model's input contexts up to a fraction of the original sizes. We show the generality of our conditioning enables zero-shot transfer of NAMMs trained only on language to entirely new transformer architectures even across input modalities, with their benefits carrying over to vision and reinforcement learning.

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Authors (4)

Edoardo Cetin (15 papers)
Qi Sun (114 papers)
Tianyu Zhao (73 papers)
Yujin Tang (31 papers)

Summary

An Evolved Universal Transformer Memory

Recent advancements in transformer models have resulted in substantial improvements in machine learning tasks, but they continue to be hindered by significant computational and memory costs, especially when dealing with extensive contexts. The paper "An Evolved Universal Transformer Memory" explores a novel approach to memory management in transformer models, aiming to address this issue by evolving a neural attention-based framework that optimizes the storage and retrieval of contextual information in transformers.

Overview of Neural Attention Memory Models (NAMMs)

The authors introduce Neural Attention Memory Models (NAMMs), a novel class of networks designed to enhance the memory handling capabilities of transformers. NAMMs evolve on top of existing pre-trained transformers to manage the transformers’ Key-Value (KV) cache memory effectively. Unlike previous hand-crafted strategies which involve heuristic rules for token retention, NAMMs employ evolutionary strategies to learn which tokens should be maintained or discarded, enabling the transformers to use a smaller portion of their original context size without sacrificing, and often enhancing, performance across long-context tasks.

NAMMs leverage the evolution of neural networks to bypass the inherent limitations of non-differentiable memory management, managing binary selection (preserve/discard token) in a manner analogous to how natural evolution shapes memory in biological systems.

Key Contributions and Results

The main contribution of the paper is the introduction of NAMMs, which provide an innovative approach to optimizing the memory strategies of transformer models. The NAMMs were evaluated through multiple stages of incremental evolution on three tasks from the LongBench benchmark, showcasing considerable improvements over both the full-context transformer models and previous KV cache management strategies designed by hand.

Performance Improvements: The results demonstrated notable performance gains in LLMing tasks, evidenced by improvements across the LongBench, InfiniteBench, and ChouBun benchmarks. A significant finding is NAMMs' ability to improve memory efficiency and performance, reducing context sizes while maintaining and even exceeding baseline performance.
Zero-Shot Transferability: A particularly striking feature is NAMMs' ability to zero-shot transfer across diverse architectures and domains, including larger LLMs, vision-language tasks, and reinforcement learning settings. They outperformed established baselines like H2O and L2 in these settings, often enhancing performance while maintaining lower memory footprints.

Theoretical and Practical Implications

Theoretically, NAMMs present a new paradigm in memory management for transformer models, shifting the focus from hand-tuned heuristics to evolutionary strategies that automate optimal memory handling. This aligns with a broader trend toward meta-learning and automated machine learning techniques.

Practically, the implications are significant for deploying LMs and other transformer-based models in resource-constrained environments. By extending the effective context window and improving computational efficiency, NAMMs could reduce infrastructure costs and energy consumption, which are crucial factors as models scale up in size.

Future Directions and Speculative Developments

The paper opens several avenues for future research. One potential direction involves further exploring the parameter space of NAMMs, including their architectural variations and learning strategies. Integrating NAMMs with attention mechanism improvements, such as linear attention or more efficient tokenization strategies, could enhance their applicability.

Another area is the exploration of NAMMs' interaction with other forms of memory compression and caching strategies. Additionally, given the zero-shot transfer capabilities demonstrated, future work could evaluate combinations of modalities and tasks not explored in the current paper, such as audio processing or more diverse multilingual benchmarks.

Overall, the concept and implementation of NAMMs articulate a compelling case for evolving memory strategies as a core component of transformer optimization, potentially marking a significant step toward more efficient and versatile machine learning models.