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

2025-02-21

AI Summary

Existing attention optimization frameworks require extensive manual tuning for heterogeneous hardware and struggle to accommodate novel attention variants or hardware configurations. To address this, we propose a modular, programmable attention computation framework. Our approach introduces: (1) a decomposable attention operator design, enabling flexible composition of arbitrary attention variants; and (2) a unified intermediate representation (IR) coupled with a multi-backend auto-scheduler based on programmable kernel templates, facilitating algorithm-hardware co-optimization. The framework automatically adapts attention implementations across diverse model architectures and hardware platforms without manual re-tuning. Experimental evaluation demonstrates up to 10× speedup over state-of-the-art solutions on non-mainstream hardware configurations, including specialized accelerators and emerging processor architectures. The framework achieves both generality and efficiency while significantly reducing deployment overhead. Open-source implementation is publicly available.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Optimizes attention mechanisms across diverse hardware platforms
• Automates kernel optimization with programmable templates
• Enables scalable deployment with minimal manual tuning

Innovation

• Modular attention computation components
• Automated kernel optimization templates
• Cross-platform scheduling strategy