Efficient Large Language Model Serving with RelayAttention for Long System Prompts

핵심 개념

The author proposes RelayAttention to address the efficiency issues caused by long system prompts in large language model serving, eliminating redundant memory accesses and improving throughput without the need for model retraining.

초록

The paper addresses the bottleneck of using long system prompts in large language model services, proposing RelayAttention as a solution. By reducing redundant memory accesses, the algorithm improves efficiency without requiring model retraining. Extensive experiments demonstrate significant improvements in throughput and processing time across different GPUs and models.
Key Points:

Long system prompts can hinder large language model serving efficiency.
RelayAttention eliminates redundant memory accesses for improved performance.
The algorithm maintains generation quality without requiring retraining.
Experiments show substantial improvements in throughput and processing time.

통계

"RelayAttention is a free lunch: it maintains the generation quality while requiring no model retraining."
"We still observe up to 2.2× sustainable request rate and 2.0× throughput with the Llama2-7B model for a chatbot workload."

인용구

"RelayAttention is a novel approach to compute exact causal attention."
"Our key contributions can be summarized as..."

핵심 통찰 요약

RelayAttention for Efficient Large Language Model Serving with Long System Prompts

by Lei Zhu,Xinj... 게시일 arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.14808.pdf

더 깊은 질문

How can RelayAttention impact other areas beyond large language model serving?

RelayAttention's efficiency improvements in handling long system prompts can have a significant impact on various AI applications beyond just large language model serving. For instance, in natural language processing tasks such as machine translation or text summarization, where the input sequences may be lengthy and require attention mechanisms, RelayAttention could enhance inference speed and reduce latency. Moreover, in computer vision applications like object detection or image captioning that utilize transformer architectures with attention mechanisms, RelayAttention could optimize memory access and computation for better performance.

What potential drawbacks or limitations might arise from implementing RelayAttention?

While RelayAttention offers advantages in optimizing memory access during attention computations for LLMs with long system prompts, there are some potential drawbacks to consider. One limitation is the dependency on batch processing; since the efficiency gains are more pronounced with larger batch sizes, real-time or single-request scenarios may not benefit as much from RelayAttention. Additionally, the implementation of separate system KV caches for offline preparation adds complexity to the system architecture and requires additional storage resources.

How could advancements in efficient attention algorithms like RelayAttention influence future developments in AI technology?

Advancements in efficient attention algorithms like RelayAttention have broader implications for future developments in AI technology. By addressing bottlenecks related to memory access and computation inefficiencies inherent in transformer-based models, these algorithms pave the way for faster inference speeds and lower latency across various AI applications. This optimization can lead to improved user experiences by enabling quicker responses from chatbots, enhancing real-time decision-making capabilities of autonomous systems, and facilitating more complex natural language understanding tasks efficiently. Furthermore, these advancements contribute to making deep learning models more scalable and cost-effective by maximizing hardware utilization while maintaining high-quality outputs.