OTAS: An Elastic Transformer Serving System via Token Adaptation (2401.05031v1)

Abstract: Transformer model empowered architectures have become a pillar of cloud services that keeps reshaping our society. However, the dynamic query loads and heterogeneous user requirements severely challenge current transformer serving systems, which rely on pre-training multiple variants of a foundation model, i.e., with different sizes, to accommodate varying service demands. Unfortunately, such a mechanism is unsuitable for large transformer models due to the additional training costs and excessive I/O delay. In this paper, we introduce OTAS, the first elastic serving system specially tailored for transformer models by exploring lightweight token management. We develop a novel idea called token adaptation that adds prompting tokens to improve accuracy and removes redundant tokens to accelerate inference. To cope with fluctuating query loads and diverse user requests, we enhance OTAS with application-aware selective batching and online token adaptation. OTAS first batches incoming queries with similar service-level objectives to improve the ingress throughput. Then, to strike a tradeoff between the overhead of token increment and the potentials for accuracy improvement, OTAS adaptively adjusts the token execution strategy by solving an optimization problem. We implement and evaluate a prototype of OTAS with multiple datasets, which show that OTAS improves the system utility by at least 18.2%.

• The paper introduces token adaptation as a key innovation that dynamically adjusts token sequences to balance accuracy and latency.
• It employs application-aware selective batching and online optimization to achieve at least an 18.2% improvement in utility over traditional methods.
• The study offers a scalable, cost-effective serving solution for large-scale transformer models, paving the way for adaptive AI infrastructures.

An Overview of "OTAS: An Elastic Transformer Serving System via Token Adaptation"

The paper "OTAS: An Elastic Transformer Serving System via Token Adaptation" presents a novel approach to improving the efficiency of transformer model serving in cloud environments. Transformer models, widely recognized for their efficacy across various AI applications, introduce substantial computational burdens due to dynamic query loads and heterogeneous user requirements. Conventional approaches, such as model adaptation, which involve pre-training multiple model variants, incur prohibitive costs for large-scale transformer models, both in terms of training and I/O latency. This paper proposes an elastic serving system, OTAS, that leverages token adaptation to manage these challenges effectively.

Core Concepts and Methodology

The cornerstone of OTAS is token adaptation. The system introduces prompting tokens to enhance the model's accuracy and removes redundant tokens to expedite the inference process. This approach contrasts with model adaptation, which typically necessitates maintaining and switching between multiple pre-trained models. Token adaptation, however, permits fine-grained control over computational resources by modifying token sequences on-the-fly, providing an adaptive mechanism to balance accuracy and latency in response to varying service demands.

OTAS incorporates several critical elements to optimize serving efficiency:

1. Application-aware Selective Batching: Incoming queries are batched based on similar service-level objectives, streamlining token adaptation processes and improving throughput.
2. Online Token Adaptation: The solution dynamically adjusts token execution strategies through an optimization process that maximizes utility while meeting latency constraints.

The research demonstrates these strategies using a prototype implementation, which reveals significant improvements in system utility and throughput. The evaluation across multiple datasets showcases an at least 18.2% improvement in utility compared to existing methodologies.

Numerical Results and Implications

The experimental results indicate that OTAS not only enhances throughput but also aligns model serving processes with real-time query demands, ensuring high accuracy without the overhead of multiple model versions. Such a framework introduces a pragmatic solution to the prohibitive costs associated with training and deploying variants of large-scale transformer models, offering a scalable and efficient alternative.

The numerical results assert that OTAS outperforms existing frameworks by effectively negotiating the trade-offs between accuracy enhancement and latent computational resource utilization through flexible token adaptation. This adaptability ultimately serves both economic and operational goals by optimizing resource allocation to suit fluctuating workloads.

Practical and Theoretical Implications

From a practical standpoint, the introduction of OTAS paves the way for more efficient deployment of transformer models in cloud services. The system's ability to scale and adjust dynamically to user demands ensures that AI services remain economically viable and operationally efficient, aligning with the rising adoption of AI-driven applications worldwide. For service providers like those managing large-scale platforms (e.g., Facebook’s tens of trillions of queries daily), OTAS offers a valuable infrastructure tool that could lead to substantial reductions in operational costs while maintaining quality of service.

Theoretically, this work enriches the ongoing discussion on efficient model serving, suggesting that exploration into sub-component adaptation (such as token adaptation) can yield significant gains over traditional model-centric adaptations. It opens new avenues for research into adaptive AI infrastructures that can self-optimize based on real-time data flows and feedback mechanisms.

Future Directions

While OTAS presents compelling advantages over existing approaches, future work might explore expanding the framework to encompass broader types of neural networks, beyond transformers. Additionally, integrating deeper learning for adaptive balancing mechanisms could further refine token adaptation efficacy. This paper sets the groundwork for an innovative frontier in AI deployment strategies, one where flexibility and efficiency are paramount and achieved through the smart management of intrinsic model properties like tokens.

In conclusion, "OTAS: An Elastic Transformer Serving System via Token Adaptation" offers a compelling paradigm shift in transformer model serving, promising enhanced performance through a pragmatic approach to token management. The implications of this research are both profound and practical, providing a robust foundation for future investigations and applications in AI serving systems.