AttentionEngine: A Versatile Framework for Efficient Attention Mechanisms on Diverse Hardware Platforms

TL;DR

AttentionEngine is a flexible, automated framework that optimizes attention mechanisms across various hardware platforms, significantly improving performance and reducing manual tuning efforts in large language models.

Contribution

It introduces a modular, cross-platform framework for efficient attention computation, enabling adaptable and automated optimization for diverse hardware and model configurations.

Findings

Achieves up to 10x performance improvement over existing methods.

Provides a scalable, flexible platform for attention mechanism deployment.

Reduces manual tuning through automated kernel optimization.

Abstract

Transformers and large language models (LLMs) have revolutionized machine learning, with attention mechanisms at the core of their success. As the landscape of attention variants expands, so too do the challenges of optimizing their performance, particularly across different hardware platforms. Current optimization strategies are often narrowly focused, requiring extensive manual intervention to accommodate changes in model configurations or hardware environments. In this paper, we introduce AttentionEngine, a comprehensive framework designed to streamline the optimization of attention mechanisms across heterogeneous hardware backends. By decomposing attention computation into modular operations with customizable components, AttentionEngine enables flexible adaptation to diverse algorithmic requirements. The framework further automates kernel optimization through a combination of programmable templates and a robust cross-platform scheduling strategy. Empirical results reveal performance gains of up to 10x on configurations beyond the reach of existing methods. AttentionEngine offers a scalable, efficient foundation for developing and deploying attention mechanisms with minimal manual tuning. Our code has been open-sourced and is available at https://github.com/microsoft/AttentionEngine.