Exploring Pretraining of Mixture-of-Experts (MoE) Models: Hands-on with the Mixtral Open Source Project

Optimize model performance and efficiency with MoE models, featuring multiple expert layers and lower computation costs. Learn more about MoE and Switch Transformers!

Compared to traditional Dense models, Mixture of Experts (MoE) models are optimized, especially in the linear projection layer.

MoE models replace a single fully connected layer with multiple expert layers (e.g., Mixtral uses 8 expert layers).

In the Switch Transformer paper, each token prediction selects two of these 8 expert layers for linear inference.

This aims to improve model performance and efficiency.

What's the advantage of this design?

Firstly, by introducing expert layers, only a part of the network is activated in each computation, reducing resource consumption.

Specifically, during inference, MoE models only compute the two selected expert layers instead of activating the entire network.

This significantly reduces computation, lowering inference costs.

Although MoE models have a larger total number of parameters, the actual parameters involved in each inference are much smaller.

This means that even if an MoE model has more parameters than a traditional Dense model, its actual computation cost is lower.

For example, in Mixtral 8X7B, each expert layer has 700 million parameters, but only two layers are used for each inference, so only 1.4 billion parameters are involved in the computation.

More importantly, MoE models can select the most suitable expert layers for the current task, showing stronger adaptability and generalization.

Each expert layer can focus on specific data or tasks, significantly enhancing overall model performance.

As a result, MoE models not only have lower inference costs but also often perform better than larger Dense models.

In Mixtral 8X7B, 8 independent expert layers each have 700 million parameters.

During inference, the model dynamically selects the two best expert layers, achieving efficient inference.

This design improves computational efficiency and enhances the ability to handle large-scale data and complex tasks.