FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving (2501.01005v2)

Abstract: Transformers, driven by attention mechanisms, form the foundation of LLMs. As these models scale up, efficient GPU attention kernels become essential for high-throughput and low-latency inference. Diverse LLM applications demand flexible and high-performance attention solutions. We present FlashInfer: a customizable and efficient attention engine for LLM serving. FlashInfer tackles KV-cache storage heterogeneity using block-sparse format and composable formats to optimize memory access and reduce redundancy. It also offers a customizable attention template, enabling adaptation to various settings through Just-In-Time (JIT) compilation. Additionally, FlashInfer's load-balanced scheduling algorithm adjusts to dynamism of user requests while maintaining compatibility with CUDAGraph which requires static configuration. FlashInfer have been integrated into leading LLM serving frameworks like SGLang, vLLM and MLC-Engine. Comprehensive kernel-level and end-to-end evaluations demonstrate FlashInfer's ability to significantly boost kernel performance across diverse inference scenarios: compared to state-of-the-art LLM serving solutions, FlashInfer achieve 29-69% inter-token-latency reduction compared to compiler backends for LLM serving benchmark, 28-30% latency reduction for long-context inference, and 13-17% speedup for LLM serving with parallel generation.

• The paper demonstrates that FlashInfer improves LLM inference efficiency by leveraging a unified block-sparse format that reduces inter-token latency by up to 69%.
• It introduces innovative composable memory formats and a JIT compiler to generate specialized CUDA templates for various attention mechanisms.
• The study details a dynamic load-balanced scheduling framework that minimizes SM idle time, enabling faster token generation and overall performance gains.

FlashInfer: A Customizable and Efficient Attention Engine for LLM Serving

The paper presents FlashInfer, an advanced attention mechanism for optimizing transformer-based LLMs in serving scenarios. At its core, FlashInfer leverages efficient GPU attention kernels to address the increasing demands of scalable and responsive model inference. Given the foundational role of attention mechanisms in transformer architectures, FlashInfer aims to alleviate key challenges in memory management and computational efficiency associated with LLM scaling.

FlashInfer introduces innovative techniques to enhance kernel performance across diverse inference environments. The primary contributions are as follows:

1. Unified Block-Sparse Format: The research addresses the variability in key-value (KV) cache storage through a unified block-sparse format. This format, which accommodates arbitrary block sizes, optimizes memory access patterns and enhances the efficiency of KV cache management. By supporting fine-grained sparsity, such as vector-level sparsity, the system maximizes memory throughput while maintaining structural adaptability.
2. Composable Formats for Memory Efficiency: Drawing inspiration from frameworks like SparseTIR, FlashInfer employs composable formats that allow for more efficient handling of shared prefixes in attention computation. This approach reduces memory fragmentation and improves memory access speed by strategically decomposing the KV cache into optimally formatted blocks based on prior knowledge of shared structures.
3. JIT Compilation for Customization: FlashInfer incorporates a Just-In-Time (JIT) compiler to generate specialized CUDA/CUTLASS templates for various attention variants. This feature enables the system to rapidly adapt to new attention mechanisms and configurations, ensuring high-performance execution tailored to specific hardware architectures.
4. Load-Balanced Scheduling: To manage diverse workload patterns and input dynamics, FlashInfer implements a dynamic scheduling framework that balances the computational load across streaming multiprocessors (SMs). This approach minimizes SM idle time, efficiently distributing workloads within constraints of variable sequence lengths while maintaining compatibility with CUDAGraphs' static configuration requirements.
5. High-End Performance Metrics: Comprehensive evaluations demonstrate significant performance gains. FlashInfer achieves 29-69% reductions in inter-token latency compared to leading LLM serving solutions such as Triton, with additional improvements observed in long-context inference scenarios. The system also facilitates a 13-17% speedup in parallel token generation processes, underscoring its utility in latency-sensitive applications.

The implications of FlashInfer are multifaceted, offering both practical and theoretical advancements for AI deployment. Practically, the system contributes to more efficient and cost-effective deployment of transformer models in real-world applications by reducing resource consumption and enhancing throughput. Theoretically, FlashInfer's flexible architecture paves the way for exploring even more complex attention models and integration of sparse formats without sacrificing performance.

Future directions in AI could see the integration of FlashInfer with higher-level domain-specific languages (DSLs) and broadening its support to additional hardware architectures. This adaptability positions FlashInfer as a vital tool in optimizing LLM performance, particularly as models and datasets continue to increase in size and complexity. The work exemplifies a step towards sustainable AI, balancing the need for expansive model capabilities with operational efficiency.