The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving (2405.11299v2)

Abstract: We survey the LLM serving area to understand the intricate dynamics between cost-efficiency and accuracy, which is magnified by the growing need for longer contextual understanding when deploying models at a massive scale. Our findings reveal that works in this space optimize along three distinct but conflicting goals: improving serving context length (C), improving serving accuracy (A), and improving serving performance (P). Drawing inspiration from the CAP theorem in databases, we propose a CAP principle for LLM serving, which suggests that any optimization can improve at most two of these three goals simultaneously. Our survey categorizes existing works within this framework. We find the definition and continuity of user-perceived measurement metrics are crucial in determining whether a goal has been met, akin to prior CAP databases in the wild. We recognize the CAP principle for LLM serving as a guiding principle, rather than a formal theorem, to inform designers of the inherent and dynamic trade-offs in serving models. As serving accuracy and performance have been extensively studied, this survey focuses on works that extend serving context length and address the resulting challenges.

• The paper introduces the CAP principle for LLM serving, revealing that only two out of three goals—context length, accuracy, and performance—can be optimized simultaneously.
• It employs an extensive literature survey to assess techniques like dynamic memory, positional embeddings, and sparse attention for improving LLM deployment.
• Findings highlight practical trade-offs where strategies such as prompt compression and distributed inference enhance scalability while balancing accuracy and computational speed.

The CAP Principle for LLM Serving

The paper entitled "The CAP Principle for LLM Serving," authored by Pai Zeng et al., proposes an analogous framework to the CAP theorem in database systems but tailored for LLMs. This framework aims to address critical trade-offs in the deployment and serving of LLMs, namely between context length, accuracy, and performance.

Introduction

The prominence of LLMs built upon transformer architectures has significantly influenced the field of artificial intelligence. Applications founded upon LLMs have not only proliferated but also begun to exceed human capabilities in areas like image classification and visual reasoning. As we move towards artificial general intelligence (AGI), the deployment and serving of these models on a massive scale in an efficient and accurate manner becomes paramount. The inherent trade-off between serving performance (e.g., tokens per second) and accuracy has historically been a challenge, further complicated by the growing demand for longer contextual understanding.

Key Observations and the CAP Principle

To explore the intricate dynamics within this space, the authors conducted an extensive survey of existing literature and proposed the CAP principle for LLM serving. Inspired by the original CAP theorem in databases, the CAP principle postulates that any optimization directed at LLM serving can enhance at most two out of three goals: context length (C), accuracy (A), and performance (P). This principle serves as a guiding rather than a formal theorem to elucidate the inherent trade-offs in serving models.

Observations

1. Expanded Scope of Serving Systems: The serving system comprises a model serving layer and an agent serving layer. The former optimizes model structure, caching, and scheduling. The latter, emerging from complex real-world applications, leverages LLM-driven workflows to refine a model's accuracy and efficiency.
2. Distinct Goals of Optimization: Three conflicting goals are identified: improving context length (C), improving accuracy (A), and enhancing performance (P). The survey categorizes existing works based on which of these goals they prioritize.
3. The Trilemma: Progress in one direction (e.g., using positional embedding for context extension) does not enhance the others. For instance, techniques like quantization improve performance but may degrade accuracy.

Improving Context (C)

Model Memory

Model memory augments the transformer architecture by adding a dynamic and compressive memory system, enabling the model to capture long-range dependencies effectively. Notable works include Transformer-XL, Compressive Transformer, Memformer, and the latest, Infini-Attention, which seamlessly integrates compressive and dynamic memory with attention mechanisms.

Positional Embedding

Positional embedding techniques extend the model's context handling capability. Strategies such as ALiBi, XPOS, and CLEX are designed to handle longer contexts by either extrapolating existing positional encodings or interpolating them within the model's attention mechanism.

Improving Accuracy (A)

Addressing accuracy in the presence of long contexts poses unique challenges. Techniques like Attention Sorting, Attention Bucket, and Found-in-the-middle have been proposed, each with varying degrees of success. Found-in-the-middle demonstrates that preserving information from the pre-training phase can mitigate the long-term decay effect and improve the accuracy of long-context serving.

Improving Performance (P)

Sparse Attention

Sparse attention techniques, categorized into static and dynamic sparsity, reduce computational and memory overhead by selectively focusing on subsets of input data. Methods like Sparse Transformer, Longformer, and StreamingLLM redefine attention mechanisms to enhance computational performance while selectively sacrificing accuracy.

Linear Attention

Linear attention approximates the attention calculation, reducing the complexity from quadratic to linear. Works such as Linear Transformer and Performer utilize kernel methods to achieve this transformation, offering improved performance with manageable accuracy degradation.

Distributed Acceleration

The paper highlights advancements in sequence parallelism (SP) for distributed inference of LLMs. Noteworthy efforts include Blockwise Parallel Transformer, Ring Attention, and Elastic Sequence Parallelism, which distribute the workload across computational resources efficiently, thus optimizing performance for long-context inference tasks.

Improving Context and Performance (CP)

Prompt compression techniques like LLMLingua and Gist tokens enhance both context length and performance by condensing input sequences without significant loss of information. This line of work demonstrates that efficient prompt management can improve both scalability and responsiveness of LLM serving systems.

Improving Context and Accuracy (CA)

Agent memory systems extend serving context implicitly by dynamically managing memory and prompts within agents. Methods like MemWalker and ChatDev employ online memory management and offline reflection, creating an illusion of infinite context and enhancing task-specific accuracy over time.

Conclusion

The CAP principle for LLM serving encapsulates the fundamental trade-offs inherent in the deployment of large-scale AI models. By illustrating the intricate balance between context length, accuracy, and performance, this survey offers a structured approach to understanding and navigating the challenges of LLM serving. As both models and hardware evolve, future innovations are likely to achieve a true CAP through synergistic developments in model architectures and computational platforms.