Faster AI Inference: New Framework Optimises Attention on NVIDIA GPUs.

FlashMLA-ETAP accelerates Multi-Head Latent Attention inference on H20 GPUs. By reconfiguring attention computation via the Efficient Transpose Attention Pipeline ETAP, the framework achieves a 2.78x speedup over FlashMLA at a 64K sequence length, surpassing FlashAttention-3 and FlashInfer in both speed and numerical stability.

The computational demands of large language models necessitate continual optimisation of inference processes, particularly when deploying models on commercially available hardware. Researchers are now focusing on refining the attention mechanism – a core component of these models – to reduce redundant calculations and improve speed. Pengcuo Dege from Tencent, alongside Qiuming Luo, Rui Mao from Shenzhen University, and Chang Kong from Shenzhen Polytechnic University, detail their work in ‘FlashMLA-ETAP: Efficient Transpose Attention Pipeline for Accelerating MLA Inference on NVIDIA H20 GPUs’, presenting a novel framework designed to accelerate Multi-Head Latent Attention (MLA) on NVIDIA H20 GPUs through a reconfiguration of attention computation via transposition.

Optimised Attention Mechanisms Enhance Large Language Model Inference

Large language models (LLMs) require increasingly efficient inference – the process of using a trained model to generate outputs – particularly when deployed on standard server hardware. Recent research introduces FlashMLA-ETAP, a framework designed to accelerate inference for Multi-Head Latent Attention (MLA) on NVIDIA H100 GPUs. The system addresses performance bottlenecks caused by limited memory bandwidth and computational redundancy within conventional attention mechanisms.

Attention mechanisms are a core component of LLMs, allowing the model to focus on relevant parts of the input sequence when generating text. Multi-Head Latent Attention (MLA) is a specific implementation of this, designed to improve performance and efficiency.

At the heart of FlashMLA-ETAP is the Efficient Transpose Attention Pipeline (ETAP). This reconfigures the attention computation process using a transposition strategy. Transposition involves rearranging the order of data elements, in this case, the key-value (KV) context length, to better align with Weighted General Matrix Multiplication (WGMMA) operations. WGMMA is a highly optimised matrix multiplication technique commonly used on GPUs. By optimising data layout, ETAP minimises redundant computations and maximises hardware utilisation.

Performance evaluations demonstrate a significant speedup. FlashMLA-ETAP achieves a 2.78x improvement over existing implementations when processing sequences of 64,000 tokens with a batch size of 16. Importantly, ETAP substantially outperforms established techniques: it delivers improvements of 5.24x over FlashAttention-3 and 4.94x over FlashInfer.

These gains are achieved without compromising numerical stability. The framework exhibits a 15.2x improvement in root mean squared error (RMSE) compared to FlashAttention-3. RMSE is a measure of the difference between predicted and actual values; a lower RMSE indicates greater accuracy. This improvement ensures reliable and accurate results, critical for LLM applications.

ETAP’s design facilitates integration with existing frameworks like FlashAttention-3 and FlashInfer. This interoperability, supported by a detailed theoretical analysis, broadens its applicability and eases adoption. This hardware-aware optimisation unlocks the potential of LLMs and accelerates the development of more efficient models.