Efficient Orchestration of Language Model Inference through Prompt-Based Clustering and Expert Model Deployment

Conceptos Básicos

Expert Router is a system that efficiently orchestrates multiple specialized language models to enhance scalability and throughput for large language model inference.

Resumen

The content introduces Expert Router, a system designed to orchestrate multiple expert language models efficiently. The key highlights are:

Expert Router utilizes a central routing gateway that distributes incoming requests using a k-means clustering method. This approach partitions incoming requests among available language models, maximizing overall throughput.

The system is evaluated using Llama 2 models with varying configurations, including quantized and non-quantized versions. These models are benchmarked against a tensor-parallelized baseline model.

Extensive experiments are conducted with up to 1,000 concurrent users, providing insights into the system's performance from both user and infrastructure perspectives.

The results demonstrate that Expert Router's orchestration technique maintains throughput comparable to the tensor-parallelized baseline model and enhances the average system throughput in certain high-load scenarios.

The parameter count of the individual expert models does not significantly affect the architecture's performance, allowing for the deployment of larger quantized models without compromising system throughput.

Estadísticas

The system is evaluated on a DGX H100 system with eight H100 GPUs.
The baseline model is a Llama 2 70B model with tensor parallelization across all eight GPUs and FP16 weights.
The Expert Router configurations include:

70B Llama 2 models with INT8 quantized weights and KV-cache
13B Llama 2 models with FP16 weights and a larger KV-cache
13B Llama 2 models with FP8 weights and KV-cache

Citas

"Expert Router is a parallel inference system with a central routing gateway that distributes incoming requests using a clustering method."
"The results demonstrate Expert Router's effectiveness in handling high-load scenarios and achieving higher throughput rates, particularly under many concurrent users."

Ideas clave extraídas de

Expert Router: Orchestrating Efficient Language Model Inference through Prompt Classification

by Josef Pichlm... a las arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15153.pdf

Consultas más profundas

How can the Expert Router's performance be further improved by incorporating more advanced clustering algorithms or expanding the criteria beyond just the prompt domain?

Incorporating more advanced clustering algorithms or expanding the criteria beyond just the prompt domain can enhance the Expert Router's performance in several ways. One approach could involve utilizing hierarchical clustering techniques to group similar prompts or requests based on various features beyond just the text content. By considering additional factors such as user behavior, context, or metadata associated with the requests, the clustering algorithm can create more refined clusters that lead to better distribution of requests to the expert models.
Furthermore, incorporating reinforcement learning algorithms into the clustering process can enable the system to adapt and optimize the clustering strategy based on real-time feedback and performance metrics. By continuously learning and adjusting the clustering criteria, the Expert Router can dynamically improve its efficiency in routing requests to the most suitable expert models.
Additionally, integrating anomaly detection mechanisms within the clustering algorithm can help identify and handle outlier requests or unusual patterns effectively. By detecting and addressing anomalies in the request distribution, the system can maintain stability and performance even in challenging scenarios.
Expanding the criteria beyond just the prompt domain to include factors like user preferences, historical interactions, or contextual information can provide a more comprehensive understanding of the requests. This holistic approach can lead to more accurate clustering and routing decisions, ultimately improving the overall performance and scalability of the Expert Router.

What are the potential drawbacks or limitations of the Expert Router approach compared to other model orchestration techniques, such as integrated Mixture-of-Experts models?

While the Expert Router approach offers several advantages in orchestrating multiple expert models efficiently, it also has some potential drawbacks and limitations compared to other model orchestration techniques like integrated Mixture-of-Experts (MoE) models.
One limitation of the Expert Router approach is the potential overhead introduced by the routing gateway component. The need for routing requests to different expert models based on clustering results can add latency to the overall inference process, especially in scenarios with a large number of concurrent users. This overhead can impact the real-time responsiveness of the system and may require additional optimization to minimize delays.
Another drawback is the complexity of managing and coordinating multiple independent expert models within the system. Ensuring the synchronization and consistency of results across different models can be challenging, especially when dealing with diverse tasks and data domains. This complexity may require more sophisticated monitoring and control mechanisms to maintain system performance and reliability.
In contrast, integrated MoE models offer a more unified and streamlined approach to leveraging multiple experts within a single model architecture. By incorporating expert modules directly into the model structure, MoE models can potentially reduce the overhead associated with routing requests and improve overall inference efficiency. Additionally, MoE models can benefit from shared parameters and joint training, leading to better integration and collaboration among the experts.
Overall, while the Expert Router approach provides flexibility and scalability in orchestrating expert models, it may face challenges in terms of latency, coordination, and complexity compared to integrated MoE models.

Could the Expert Router architecture be extended to support dynamic scaling of the number of expert models per cluster based on real-time demand patterns?

Yes, the Expert Router architecture could be extended to support dynamic scaling of the number of expert models per cluster based on real-time demand patterns. By incorporating adaptive scaling mechanisms, the system can efficiently allocate resources and adjust the number of expert models in each cluster to meet changing workload requirements.
One approach to enable dynamic scaling is to implement auto-scaling algorithms that monitor system metrics such as request volume, response times, and resource utilization. Based on predefined thresholds or machine learning models, the system can automatically scale up or down the number of expert models in a cluster to optimize performance and resource utilization.
Additionally, incorporating load balancing techniques can help distribute incoming requests evenly across the available expert models, ensuring efficient utilization of resources and maintaining system stability during fluctuating demand periods. By dynamically adjusting the number of expert models per cluster, the system can adapt to varying workloads and optimize throughput and latency.
Furthermore, integrating predictive analytics and forecasting algorithms can enable the system to anticipate demand patterns and proactively scale the number of expert models to meet future requirements. By leveraging historical data and real-time insights, the Expert Router can make informed decisions on scaling operations to ensure optimal performance and responsiveness.
Overall, by extending the Expert Router architecture to support dynamic scaling of expert models based on real-time demand patterns, the system can enhance its agility, efficiency, and scalability in handling varying workloads and optimizing resource utilization.