Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction

Core Concepts

A speculative shortest-job-first (SSJF) scheduler that uses a lightweight proxy model to predict LLM output sequence lengths can reduce average job completion times by 30.5–39.6% and increase throughput by 2.2–3.6× compared to first-come-first-serve schedulers.

Abstract

The paper presents a speculative request scheduler called SSJF that addresses the non-deterministic nature of generative large language models (LLMs) to enable efficient interactive LLM serving. Key highlights: LLMs have unpredictable execution times due to their autoregressive nature, posing challenges for efficient serving in interactive AI applications. Existing LLM serving systems use first-come-first-serve (FCFS) scheduling, which suffers from head-of-line blocking issues. SSJF uses a lightweight proxy model (fine-tuned BERT-base) to predict the LLM output sequence lengths and schedules requests with shorter predicted lengths first. SSJF supports various batching modes (no batching, dynamic batching, continuous batching) without requiring changes to memory management or batching strategies. Evaluations on real-world datasets and production workload traces show that SSJF reduces average job completion times by 30.5–39.6% and increases throughput by 2.2–3.6× compared to FCFS schedulers. The proxy model introduces negligible overhead (7.6 ms on average) compared to the LLM execution time (9.8 s on average). The paper also discusses potential use cases of proxy models in LLM serving beyond request scheduling, such as memory management and caching.

Stats

The average LLM execution time is 9.8 s. The average proxy model inference latency is 7.6 ms, which is 0.02% of the total latency. The maximum proxy model inference latency is 20.2 ms, which is less than the minimum LLM execution time of 120 ms.

Quotes

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Key Insights Distilled From

Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction

by Haor... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08509.pdf

Deeper Inquiries

How can the proxy model-based prediction accuracy be further improved, especially for longer output sequences?

To enhance the accuracy of the proxy model-based prediction for longer output sequences, several strategies can be implemented: Dataset Augmentation: Increasing the diversity and size of the training dataset can help the proxy model learn more effectively. By including a wider range of conversation lengths and complexities, the model can better generalize to unseen data. Model Architecture: Experimenting with more complex architectures or ensembling multiple models can capture intricate patterns in the data, especially for longer sequences. Architectural modifications like adding attention mechanisms or transformer layers can improve the model's ability to predict accurately. Fine-tuning: Continuously fine-tuning the proxy model on new data and adjusting hyperparameters can refine its predictions over time. Fine-tuning on specific subsets of data that represent longer sequences can help the model focus on improving accuracy for such cases. Regularization Techniques: Implementing regularization methods like dropout or weight decay can prevent overfitting and improve the model's generalization ability, particularly for longer sequences where the complexity is higher. Transfer Learning: Leveraging pre-trained models or knowledge from related tasks can provide a head start for the proxy model, enabling it to learn faster and more accurately, especially for longer output sequences.

What are the potential trade-offs and challenges in integrating SSJF with speculative decoding techniques for LLM inference?

Integrating SSJF with speculative decoding techniques for LLM inference presents several trade-offs and challenges: Computational Overhead: Speculative decoding techniques often require additional computational resources to generate multiple potential outputs, which can increase the overall inference time and resource utilization. Complexity: Managing the interaction between SSJF scheduling decisions and speculative decoding can introduce complexity to the system, requiring careful coordination to ensure efficient and accurate inference. Accuracy vs. Speed: Speculative decoding aims to accelerate inference by making early predictions, but this may come at the cost of accuracy. Balancing speed and accuracy in the context of SSJF scheduling can be challenging. Resource Allocation: Speculative decoding may lead to resource contention, especially in multi-tenant environments where multiple models are being served concurrently. Allocating resources effectively to balance speculative decoding and SSJF scheduling can be a challenge. Dynamic Workloads: Adapting speculative decoding to varying workloads and input sequences can be complex. SSJF may need to dynamically adjust its scheduling decisions based on the outcomes of speculative decoding, adding another layer of complexity.

How can the SSJF scheduler be extended to handle starvation and support preemption in a multi-tenant LLM serving environment?

Extending the SSJF scheduler to address starvation and incorporate preemption in a multi-tenant LLM serving environment involves the following considerations: Aging Mechanism: Implementing an aging mechanism in the scheduler can prevent starvation by prioritizing older jobs over newer ones. Jobs that have been waiting in the queue for an extended period can be given higher priority for execution. Fairness Policies: Introducing fairness policies that consider the waiting time of jobs and the resource utilization of different tenants can help prevent starvation and ensure equitable resource allocation. Preemption Support: Enabling preemption in the scheduler allows for the interruption of lower-priority jobs to make way for higher-priority tasks. This feature can prevent resource hogging and ensure timely execution of critical jobs. Resource Reservation: Implementing resource reservation mechanisms can guarantee a minimum level of resources for each tenant, reducing the likelihood of starvation and ensuring consistent performance across tenants. Dynamic Prioritization: Incorporating dynamic prioritization based on job characteristics, tenant agreements, or service-level agreements can help the scheduler make informed decisions about preemption and resource allocation. By integrating these mechanisms into the SSJF scheduler, it can effectively handle starvation, support preemption, and optimize resource utilization in a multi-tenant LLM serving environment.