RelayAttention for Efficient Large Language Model Serving with Long System Prompts

Core Concepts

Improving efficiency in large language model services with long system prompts through RelayAttention.

Abstract

この論文は、長いシステムプロンプトを使用する大規模言語モデルサービスの効率を向上させるために、RelayAttentionを導入しています。既存の注意力計算関数に最小限の適応を加えてRelayAttentionを構築できます。システムプロンプトの静的な長さに関わるシステムアテンションでは、FlashAttentionカーネルが使用されます。一方、成長するリクエスト固有コンテキストに対処するコンテキストアテンションでは、PagedAttentionカーネルが使用されます。リレー融合ステップでは、複数の要素ごとの操作が含まれる単一の統合カーネルがOpenAI Tritonで実装されます。

Stats

2.2: Llama-30B attention inference latency w.r.t. system prompt length (A40 GPU, batch size 32).
3.4: integrating RelayAttention into vLLM, observe up to 2.2× sustainable request rate and 2.0× throughput with the Llama2-7B model for a chatbot workload.

Quotes

"RelayAttention is a free lunch: it maintains the generation quality while requiring no model retraining."
"Our key observation is that handling these system prompts requires heavily redundant memory accesses in existing causal attention computation algorithms."

Key Insights Distilled From

RelayAttention for Efficient Large Language Model Serving with Long System Prompts

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

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

Deeper Inquiries

How can RelayAttention be adapted for different types of large language models

RelayAttention can be adapted for different types of large language models by integrating it into existing inference systems with minimal adaptations. The core idea of RelayAttention, which involves grouping multiple matrix-vector multiplications into single matrix-matrix multiplications to reduce redundant memory accesses, can be applied to various transformer-based LLMs. By modifying the attention computation function and making peripheral adaptations, such as prefilling system KV caches and adjusting position embeddings, RelayAttention can efficiently handle long system prompts in different model architectures.

What are the potential drawbacks or limitations of implementing RelayAttention in real-world applications

While RelayAttention offers significant efficiency gains in reducing redundant memory accesses for large language models, there are potential drawbacks or limitations to consider when implementing it in real-world applications. One limitation is that the efficiency gain diminishes when handling requests with long request-specific contexts compared to short shared system prompts. Additionally, RelayAttention is more suitable for batched inference scenarios rather than single-request processing due to its design focusing on optimizing throughput through batch processing.

How might the concept of reducing redundant memory accesses be applied to other areas of machine learning or artificial intelligence research

The concept of reducing redundant memory accesses implemented in RelayAttention can be applied to other areas of machine learning or artificial intelligence research beyond large language models. For example:

In computer vision tasks: Optimizing convolutional neural networks (CNNs) by reducing unnecessary data transfers between layers could improve the efficiency of image recognition systems.
In reinforcement learning algorithms: Minimizing redundant memory access during value function computations could enhance the training speed and performance of RL agents.
In natural language processing tasks: Streamlining attention mechanisms in sequence-to-sequence models could lead to faster translation or summarization processes by eliminating unnecessary memory reads.
By applying similar principles of reducing redundancy in memory access across various AI domains, researchers can optimize model performance and resource utilization effectively.