Efficient Attention Reuse for Cost-effective Multi-turn Conversation Inference in Large Language Models

Core Concepts

AttentionStore, a new attention mechanism, enables the reuse of key-value caches across multi-turn conversations, significantly reducing the repetitive computation overheads and improving the inference performance and cost-efficiency of large language models.

Abstract

The paper proposes AttentionStore, a new attention mechanism that enables the reuse of key-value (KV) caches across multi-turn conversations, rather than discarding them as in conventional attention mechanisms. Key highlights: Existing LLM serving engines are inefficient for multi-turn conversations due to the need to repeatedly compute the KV caches of historical tokens, incurring high serving costs. AttentionStore maintains a hierarchical KV caching system that leverages cost-effective memory/storage mediums to save KV caches for all requests. To reduce KV cache access overheads, AttentionStore employs layer-wise pre-loading and asynchronous saving schemes to overlap the KV cache access with the GPU computation. To ensure the KV caches to be accessed are placed in the fastest hierarchy, AttentionStore uses scheduler-aware fetching and eviction schemes. To avoid invalidation of saved KV caches due to context window overflow, AttentionStore decouples the positional encoding and effectively truncates the KV caches. Extensive experiments demonstrate that AttentionStore significantly decreases the time to the first token, improves the prompt prefilling throughput, and reduces the end-to-end inference cost for multi-turn conversations.

Stats

The prefilling time for 1K tokens accounts for 98% of the total prefilling time in multi-turn conversations. The KV cache generation speed is about 13.9GB/s, which can fully occupy the free HBM space within 14 seconds. 84% and 69% of conversation sessions have a context longer than 2K and 4K tokens, respectively.

Quotes

"Engaging in multi-turn conversations with humans is an essential capability of LLMs." "Executing multi-turn conversations in current LLM serving engines is highly inefficient, as it requires a large number of repetitive computations, incurring high serving costs." "Up to 98% of the prefilling cost comes from repetitive computation for the KV cache."

Key Insights Distilled From

AttentionStore

by Bin Gao,Zhuo... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19708.pdf

Deeper Inquiries

How can AttentionStore be extended to support other types of language models beyond autoregressive transformers?

AttentionStore can be extended to support other types of language models by adapting its mechanisms to suit the specific requirements of different models. For example: Support for non-autoregressive models: AttentionStore can be modified to handle non-autoregressive models by adjusting the caching and retrieval mechanisms to align with the parallel generation process of such models. Instead of focusing on historical tokens, the system can prioritize caching intermediate states or latent variables required for generation. Integration with transformer variants: AttentionStore can be tailored to work with transformer variants like BERT, RoBERTa, or XLNet by customizing the caching strategies to accommodate the unique architecture and attention mechanisms of these models. For instance, for models like BERT that do not generate text sequentially, the caching system can store and retrieve token representations or attention weights. Compatibility with hybrid models: AttentionStore can be designed to support hybrid models that combine elements of autoregressive and non-autoregressive architectures. The system can adapt to the specific requirements of these hybrid models, such as caching both token representations and latent variables for efficient inference. Scalability for large-scale models: AttentionStore can be optimized for scalability to handle large-scale language models with massive parameter sizes and complex architectures. This may involve implementing distributed caching systems and efficient data transfer protocols to support models with extensive memory and computation requirements. By customizing the caching, retrieval, and management strategies to suit the characteristics of different language models, AttentionStore can be extended to support a wide range of models beyond autoregressive transformers.

What are the potential challenges and trade-offs in applying AttentionStore to real-time interactive applications with strict latency requirements?

Latency trade-offs: One of the main challenges in applying AttentionStore to real-time interactive applications is balancing the trade-off between latency and caching efficiency. While caching KV caches can reduce computation time, the overhead of loading and managing the caches may introduce additional latency, especially in scenarios where real-time responses are crucial. Storage overhead: Real-time interactive applications with strict latency requirements may have limited storage resources for caching KV caches. AttentionStore's hierarchical caching system may require significant storage space, leading to potential challenges in managing storage overhead and ensuring efficient cache utilization. Dynamic workload: Real-time interactive applications often experience fluctuating workloads with varying levels of concurrency and request patterns. Adapting AttentionStore to dynamically adjust caching strategies based on workload changes while maintaining low latency can be a complex challenge. Concurrency and parallelism: Real-time applications may involve multiple concurrent requests that require efficient parallel processing. AttentionStore needs to handle concurrent access to the caching system effectively to ensure optimal performance without compromising latency requirements. Consistency and reliability: Maintaining the consistency and reliability of cached data in a real-time interactive environment is crucial. AttentionStore must implement robust mechanisms for cache invalidation, data consistency, and fault tolerance to ensure reliable operation under strict latency constraints. In real-time interactive applications, the successful implementation of AttentionStore requires careful consideration of these challenges and trade-offs to meet the stringent latency requirements while optimizing caching efficiency and resource utilization.

How can the KV cache management in AttentionStore be further optimized to reduce the storage overhead and improve the cache hit rate for long-running conversation sessions?

Dynamic cache sizing: Implement dynamic cache sizing mechanisms in AttentionStore to adjust the storage allocation based on the workload and session requirements. By dynamically allocating storage space for KV caches, the system can optimize resource utilization and reduce storage overhead. LRU-based eviction policies: Utilize LRU (Least Recently Used) or other efficient cache eviction policies to prioritize caching the most frequently accessed KV caches in the limited storage space. This can help improve the cache hit rate for long-running conversation sessions by retaining relevant data in the cache. Compression techniques: Implement data compression techniques to reduce the storage footprint of KV caches without compromising retrieval performance. By compressing cached data, AttentionStore can store more KV caches in the available storage space, leading to improved cache hit rates for long-running sessions. Tiered storage hierarchy: Introduce a tiered storage hierarchy in AttentionStore, with different levels of storage mediums (e.g., fast memory, SSDs, disks) based on the access frequency and importance of KV caches. By tiering the storage, the system can optimize cache hit rates and storage efficiency for long-running sessions. Adaptive caching strategies: Develop adaptive caching strategies that dynamically adjust the caching policies based on the access patterns and session characteristics. By intelligently managing the caching and eviction of KV caches, AttentionStore can optimize storage overhead and enhance cache hit rates for prolonged conversation sessions. By implementing these optimization techniques, AttentionStore can effectively reduce storage overhead, improve cache hit rates, and enhance the overall performance for long-running conversation sessions.