vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention (2405.04437v3)

Abstract: PagedAttention is a popular approach for dynamic memory allocation in LLM serving systems. It enables on-demand allocation of GPU memory to mitigate KV cache fragmentation -- a phenomenon that crippled the batch size (and consequently throughput) in prior systems. However, in trying to allocate physical memory at runtime, PagedAttention ends up changing the virtual memory layout of the KV cache from contiguous to non-contiguous. Such a design leads to non-trivial programming and performance overheads. We present vAttention -- an approach that mitigates fragmentation in physical memory while retaining the contiguity of KV cache in virtual memory. We achieve this by decoupling the allocation of virtual and physical memory using CUDA virtual memory management APIs. We also introduce various LLM-specific optimizations to address the limitations of CUDA virtual memory support. Overall, vAttention is a simpler, portable, and performant alternative to PagedAttention: it supports various attention kernels out-of-the-box and improves LLM serving throughput by up to 1.23x compared to the use of PagedAttention-based kernels of FlashAttention and FlashInfer.

• The paper introduces vAttention, a dynamic memory management technique that maintains contiguous virtual memory allocation for LLM inference.
• It eliminates the need for rewriting attention kernels, reducing software complexity and maintenance overhead.
• Benchmark results show up to 1.97x faster processing than PagedAttention, significantly enhancing LLM performance.

Exploring vAttention: Efficient Memory Management for LLMs

Overview of vAttention

Recent advancements in AI and machine learning have underscored the critical role of efficient memory management in serving LLMs. The new system described in the research, known as vAttention, addresses inefficiencies in previous LLM memory management systems, notably those using the PagedAttention method. This paper presents vAttention as a technique that dynamically manages memory while maintaining contiguous virtual memory allocation, simplifying the overall system complexity and improving execution speed.

The Drawbacks of PagedAttention

PagedAttention has been a popular approach for dynamically allocating memory in LLM inference tasks. It divides memory into blocks allocated only as needed. Despite its clear benefits in reducing memory waste, the paper highlights several pitfalls:

• Software complexity: PagedAttention necessitates changes in both attention kernels and the memory management in the serving framework, adding layers of complexity.
• Rewriting of attention kernels: A non-contiguous virtual memory layout requires significant modifications to the original, contiguous memory-based kernels.
• Performance overhead: This memory management method introduces additional computation steps in attention operations, potentially slowing down the whole process.

By maintaining the concept of virtual contiguity, vAttention attempts to streamline these operations, thus avoiding the complexity and performance hits associated with PagedAttention.

How vAttention Works

vAttention optimizes GPU memory usage through on-demand physical memory allocation without prior reservation, leveraging existing system functionalities more effectively than PagedAttention. Here’s how vAttention operates:

• Dynamic Physical Allocation: It allocates virtual memory for the whole potential batch size from the start but assigns physical memory dynamically as data flows in, thus avoiding upfront physical memory reservation.
• Low-level System Utilization: The system uses low-level CUDA operations to separate the allocation of virtual and physical memory, which preserves contiguous memory access patterns and eliminates the need for extensive changes in attention kernels.

Practical Implications and Performance

The shift to vAttention has tangible benefits:

• Simpler integration and maintenance: Developers can use existing GPU kernels without modification, reducing the need for specialized knowledge and maintenance resources.
• Reduced latency and higher throughput: Benchmarks demonstrated that vAttention could process requests significantly faster—up to 1.97 times quicker than systems using the older PagedAttention approach.

The results reflect substantial potential for both improving LLM inference performance and simplifying the underlying software architecture.

Future Directions

While vAttention provides a robust framework for managing LLM memory efficiently, its integration with even lower-level system operations or exploring its adaptability across diverse hardware architectures could yield further improvements. Additionally, the community might explore the automatic tuning of page size based on model requirements and workload characteristics to optimize performance further.

Conclusion

vAttention redefines dynamic memory management in LLM deployment, addressing the critical limitations of previous systems like PagedAttention. By effectively leveraging built-in system capabilities to manage memory demand dynamically, it significantly simplifies the LLM serving pipeline and boosts operational efficiency. This innovation not only enhances current LLM applications but also sets a foundational approach that can influence future developments in machine learning infrastructure.