SCBench: A KV Cache-Centric Analysis of Long-Context Methods (2412.10319v2)

Abstract: Long-context LLMs have enabled numerous downstream applications but also introduced significant challenges related to computational and memory efficiency. To address these challenges, optimizations for long-context inference have been developed, centered around the KV cache. However, existing benchmarks often evaluate in single-request, neglecting the full lifecycle of the KV cache in real-world use. This oversight is particularly critical, as KV cache reuse has become widely adopted in LLMs inference frameworks, such as vLLM and SGLang, as well as by LLM providers, including OpenAI, Microsoft, Google, and Anthropic. To address this gap, we introduce SCBench(SharedContextBench), a comprehensive benchmark for evaluating long-context methods from a KV cachecentric perspective: 1) KV cache generation, 2) KV cache compression, 3) KV cache retrieval, 4) KV cache loading. Specifically, SCBench uses test examples with shared context, ranging 12 tasks with two shared context modes, covering four categories of long-context capabilities: string retrieval, semantic retrieval, global information, and multi-task. With it, we provide an extensive KV cache-centric analysis of eight categories long-context solutions, including Gated Linear RNNs, Mamba-Attention hybrids, and efficient methods such as sparse attention, KV cache dropping, quantization, retrieval, loading, and prompt compression. The evaluation is conducted on 8 long-context LLMs. Our findings show that sub-O(n) memory methods suffer in multi-turn scenarios, while sparse encoding with O(n) memory and sub-O(n^2) pre-filling computation perform robustly. Dynamic sparsity yields more expressive KV caches than static patterns, and layer-level sparsity in hybrid architectures reduces memory usage with strong performance. Additionally, we identify attention distribution shift issues in long-generation scenarios. https://aka.ms/SCBench.

• The paper introduces SCBench, a benchmark that evaluates long-context LLMs by analyzing the complete KV cache lifecycle across four critical stages.
• It reveals that sub-O(n) methods underperform in multi-turn scenarios while dynamic sparse attention offers improved performance.
• The study underscores the need for adaptive KV cache strategies to optimize memory usage and retrieval accuracy in real-world applications.

Comprehensive Analysis of Long-Context Methods from a KV Cache-Centric Perspective

The paper details the development and evaluation of SCBench, a benchmark explicitly designed to holistically evaluate long-context methods in LLMs from a KV cache-centric perspective. It addresses a significant omission in existing benchmarks that typically overlook the full lifecycle of the KV cache, particularly its reuse across multiple requests or turns in real-world applications. As current methods generally fall short in managing the full extent of the KV cache lifecycle, SCBench proposes a rigorous framework to assess these methods by dissecting their performance across four critical KV cache stages: generation, compression, retrieval, and loading.

The benchmark encompasses 12 tasks across four categories of long-context capabilities, namely string retrieval, semantic retrieval, global information processing, and multi-tasking. Several shared context modes further enrich the evaluation, specifically multi-turn and multi-request modes, reflecting common usage patterns in applications such as multi-turn dialogues and repository-level tasks. The analysis covers eight categories of long-context solutions, including Gated Linear RNNs and Mamba-Attention hybrids, and efficient methodologies like sparse attention, KV cache dropping, and quantization.

Key findings from the experiments demonstrate that sub-$O(n)$ memory methods, which often excel in single-turn scenarios, tend to underperform in multi-turn applications due to inadequate KV cache handling. The paper identifies that dynamic sparsity methods offer more expressive potential compared to static approaches, and sparse encoding, when paired with dense decoding, can effectively boost performance across scenarios.

Moreover, the analysis highlights several compelling insights regarding the practical performance and theoretical implications of these methods:

1. Sparse attention methods exhibit notable performance gains over sparse decoding methods as the number of query rounds increases, signaling a promising direction for future optimization efforts.
2. The inherent complexity of tasks influences the efficiency of various methods, where dynamic sparse attention effectively approximates full attention configurations, especially in scenarios where retrieval tasks demand precise and comprehensive memory utilization.
3. Distribution shifts, particularly in long-generation tasks, pose challenges to KV cache design, indicating the necessity for adaptive methods that can cope with changing patterns in data retrieval and handling across multiple requests.
4. The critical role of maintaining $O(n)$ memory in applications requiring consistent and reliable performance across repeated interactions, which sub-$O(n)$ methods cannot consistently guarantee in multi-request contexts.

These findings underscore the need for a nuanced understanding of KV cache dynamics in long-context LLMs. While current methodologies manage certain aspects of the KV cache lifecycle well, challenges remain, particularly regarding efficient memory usage and accurate long-term information retrieval. Dynamic approaches, notably those employing sophisticated sparse patterns, appear promising for balancing computational efficiency against the expansive memory demands of long contexts.

The implications of this paper suggest significant future avenues of research, including the potential redesign of long-context models with integrated KV cache management systems that adjust dynamically to both user input and model requirements, thereby optimizing performance while minimizing resource demands.

SCBench serves as a vital step towards comprehensive benchmarking practices for long-context models. It illuminates overlooked facets of KV cache lifecycle management and provides a robust framework for future innovations. This benchmark empowers researchers to drive improvements in both existing methodologies and emerging solutions, ultimately enhancing the efficiency and reliability of long-context LLM applications in increasingly complex and memory-intensive environments.