MoE++ - A hybrid expert architecture jointly launched by Kunlun Tech and Peking University
MoE++ is a novel Mixture-of-Experts architecture jointly developed by Kunlun Tech 2050 Research Institute and Yuan Li's team at Peking University. It is based on the introduction of zero-computational-cost experts, namely zero experts, replication experts, and constant-cost experts...
What is MoE++?
MoE++ is a novel Mixture-of-Experts architecture jointly developed by Kunlun Tech 2050 Research Institute and the team led by Yuan Li at Peking University. It reduces computational costs and improves model performance by introducing zero-computational-cost experts, zero experts, replicated experts, and constant experts. MoE++ allows each token to dynamically interact with different numbers of feedforward network experts, even skipping certain layers to optimize computational resource allocation. MoE++ uses gated residuals to help tokens consider the routing path of the previous layer when selecting experts, achieving more stable routing. Experiments show that MoE++ outperforms traditional MoE models at the same model size, increasing expert throughput by 1.1 to 2.1 times, and is easy to deploy.
Main functions of MoE++
- Reduce computing costsMoE++ introduces zero-computation experts, enabling each token in the model to dynamically interact with different numbers of feedforward network (FFN) experts, and even skip certain layers, reducing unnecessary computation.
- Improve model performanceBy reducing the number of FFN experts required for simple tokens, MoE++ frees up more expert resources to focus on processing complex tokens, thus improving the overall performance of the model.
- Optimize resource allocationMoE++ improves computational efficiency by allowing the model to concentrate computational resources on the tokens that are more needed through flexible computational allocation.
- Stable routingMoE++ uses a gating residuals mechanism to allow tokens to refer to the routing path of the previous layer when selecting experts, thus achieving more stable expert selection.
- Easy to deployBecause the parameters of zero-computation experts are extremely small, MoE++ deploys all such experts on the same GPU, avoiding the communication overhead and uneven load issues caused by distributed FFN expert deployment.
Technical Principles of MoE++
- Zero-computational-consumption expertMoE++ introduces three types of zero-computational-cost experts, including zero experts (outputting empty vectors), copy experts (directly using input as output), and constant experts (replacing input with trainable vectors).
- Dynamic Expert SelectionUnlike traditional MoE methods, MoE++ supports dynamically selecting a different number of FFN experts to process each token based on its complexity.
- Gating residualMoE++ incorporates gating residuals into the expert selection process, allowing the token to consider the routing path of the previous layer when selecting an expert at the current layer, thereby enhancing the flow of information between different layers of the model.
- Heterogeneous expert structureMoE++'s expert structure is heterogeneous, with different types of experts (FFN experts and zero-computation experts) working together in the same model, improving the model's adaptability and flexibility.
- Load balancingMoE++ is based on the introduction of load balancing loss and expert capacity allocation strategy to ensure that the load of experts is balanced during the model training process, avoiding the problem of some experts being overloaded while others are idle.
MoE++ project address
- GitHub repository:https://github.com/SkyworkAI/MoE-plus-plus
- HuggingFace model library:https://huggingface.co/Chat-UniVi/MoE-Plus-Plus-7B
- arXiv technical paper:https://arxiv.org/pdf/2410.07348
Application Scenarios of MoE++
- Natural Language Processing (NLP) researchersResearchers are building and training more efficient large-scale language models to conduct research on language understanding, text generation, machine translation, question answering systems, and other areas.
- Enterprise developersEnterprise developers can create high-performance NLP applications, such as intelligent customer service, content recommendation systems, automatic summarization, and sentiment analysis, to improve the intelligence level of their products.
- Cloud computing and AI service providersThe provider integrates the MoE++ architecture to offer customers more efficient and lower-cost AI services, especially in scenarios that require processing large amounts of language data.
- academic institutionsAcademic institutions conduct teaching and research on various NLP tasks, helping students and researchers understand advanced deep learning models and algorithms.