Scaling Mixture-of-Expert Large Language Models: Balancing Performance and Inference Efficiency

Kernekoncepter

Mixture-of-Expert (MoE) language models can scale model size without increasing training cost, but face challenges in inference efficiency. This work studies the optimal training budget allocation for MoE models by incorporating both model performance and inference cost as key metrics.

Resumé

The paper investigates the scaling behavior of Mixture-of-Expert (MoE) large language models and proposes a novel approach to determine the optimal training budget allocation. Key highlights:

Scaling Law for MoE Models:

Extends the existing scaling law for dense Transformer models to incorporate the number of experts (E) as a key factor.
Observes a diminishing return when increasing the number of experts, similar to previous work.

Inference Efficiency Constraints:

Identifies that an excessive number of experts can significantly increase the inference cost due to memory constraints.
Proposes to jointly consider model performance (validation loss) and inference cost as dual metrics for budget allocation.

Optimal Budget Allocation:

Finds that MoE models with fewer experts (4 or 8) exhibit better inference efficiency but require much higher training budgets to reach the same performance as models with more experts.
Introduces the concept of "over-training" - training a smaller MoE model with more experts, which can achieve both better performance and lower inference cost compared to the loss-optimal model with fewer experts, under the same training budget.

The paper provides a comprehensive analysis on balancing the trade-offs between model quality, training cost, and inference efficiency when scaling MoE language models.

Statistik

"Increasing the number of experts in an MoE model (almost) does not raise the computational cost, enabling the model to incorporate more knowledge through extra parameters without inflating pre-training expenses."
"An excessive number of experts makes the model impractical for inference. The available memory to store KV caches is squeezed, leading to decreased batch size and increased cost per query."
"Under the same quality of a loss-optimal 4-expert MoE, an over-trained 8- or 16-expert MoE only needs 47.0% to 52.0% inference cost."
"With the same inference cost, an over-trained 16-expert MoE can save up to 68.4% training budget compared to the loss-optimal 4-expert MoE."

Citater

"Mixture-of-Expert (MoE) based large language models (LLMs), such as the recent Mixtral and DeepSeek-MoE, have shown great promise in scaling model size without suffering from the quadratic growth of training cost of dense transformers."
"Although our findings suggest a loss-optimal configuration with Emax experts, such a setup is not practical for actual deployment. The main reason is that an excessive number of experts makes the model impractical for inference."
"Under the same quality of a loss-optimal 4-expert MoE, an over-trained 8- or 16-expert MoE only needs 47.0% to 52.0% inference cost. With the same inference cost, an over-trained 16-expert MoE can save up to 68.4% training budget."

Vigtigste indsigter udtrukket fra

Toward Inference-optimal Mixture-of-Expert Large Language Models

by Longfei Yun,... kl. arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02852.pdf

Dybere Forespørgsler

How can the proposed over-training approach be extended to other types of large language models beyond MoE, such as dense Transformer models

The proposed over-training approach can be extended to other types of large language models, such as dense Transformer models, by considering the trade-off between model performance and inference efficiency. In the context of dense Transformers, the same principles can be applied to determine an optimal model size that balances performance and inference cost. By training a smaller model than the loss-optimal configuration, the inference cost can be significantly reduced while still maintaining a reasonable level of performance. This approach can be achieved by adjusting the model size, training dataset size, and other hyperparameters to find the optimal balance.

What are the potential challenges and limitations of the over-training approach, and how can they be addressed

One potential challenge of the over-training approach is the trade-off between model performance and inference efficiency. While over-training a smaller model can lead to lower inference costs, there may be a marginal drop in performance compared to the loss-optimal configuration. To address this challenge, it is essential to carefully analyze the impact of reducing the model size on performance and explore ways to mitigate any potential loss in quality. Additionally, optimizing the training process, hyperparameters, and dataset selection can help improve the overall efficiency of the over-trained model.

How can the insights from this work on balancing model performance and inference efficiency be applied to the broader field of efficient AI system design

The insights from this work on balancing model performance and inference efficiency can be applied to the broader field of efficient AI system design by guiding the development of large language models and other AI systems. By considering both model quality and inference cost during the design and training phases, researchers and practitioners can optimize the overall efficiency of AI systems. This approach can lead to the development of more cost-effective and scalable models that deliver high performance while minimizing inference costs. Additionally, the principles of budget allocation and trade-offs between different metrics can inform the design of AI systems across various domains, enabling the creation of more efficient and effective solutions.