Lean Attention: Hardware-Aware Scalable Attention Mechanism for the Decode-Phase of Transformers

TL;DR

This paper introduces LeanAttention, a hardware-aware scalable attention mechanism optimized for the decode-phase of transformers, significantly improving speed for long context lengths by parallelizing attention computation.

Contribution

The paper presents LeanAttention, a novel attention computation method that re-designs execution flow for the decode-phase, enabling efficient parallel processing of long contexts in transformer models.

Findings

Achieves 2.6x average speedup over FlashAttention-2.

Up to 8.33x speedup for 512k context lengths.

Effectively scales attention computation for long sequences.

Abstract

Transformer-based models have emerged as one of the most widely used architectures for natural language processing, natural language generation, and image generation. The size of the state-of-the-art models has increased steadily reaching billions of parameters. These huge models are memory hungry and incur significant inference latency even on cutting edge AI-accelerators, such as GPUs. Specifically, the time and memory complexity of the attention operation is quadratic in terms of the total context length, i.e., prompt and output tokens. Thus, several optimizations such as key-value tensor caching and FlashAttention computation have been proposed to deliver the low latency demands of applications relying on such large models. However, these techniques do not cater to the computationally distinct nature of different phases during inference. To that end, we propose LeanAttention, a scalable technique of computing self-attention for the token-generation phase (decode-phase) of decoder-only transformer models. LeanAttention enables scaling the attention mechanism implementation for the challenging case of long context lengths by re-designing the execution flow for the decode-phase. We identify that the associative property of online softmax can be treated as a reduction operation thus allowing us to parallelize the attention computation over these large context lengths. We extend the "stream-K" style reduction of tiled calculation to self-attention to enable parallel computation resulting in an average of 2.6x attention execution speedup over FlashAttention-2 and up to 8.33x speedup for 512k context lengths.