More Efficient Inference in Mixture-of-Experts Models: Strategies against the Straggler Effect

Mixture-of-Experts (MoE) models have emerged as a promising architecture for large language models. By activating only a subset of the experts within the model, they offer an optimal balance between performance and efficiency. However, under expert parallelism, a common method for accelerating inference, MoE models suffer from inefficiencies due to an unbalanced allocation of tokens to experts. This results in some experts being overloaded while others remain underutilized. This imbalance, which leads to poor resource utilization and increased latency, is referred to as the "straggler effect." The name stems from the fact that the overall delay is determined by the most heavily loaded expert – similar to a long-distance race where the pace is set by the slowest runner.

To address this issue, various approaches have been developed. One promising approach is called "Capacity-Aware Inference." This method encompasses two key techniques: "Capacity-Aware Token Drop" and "Capacity-Aware Token Reroute."

Capacity-Aware Token Drop

In Capacity-Aware Token Drop, overloaded tokens are discarded to regulate the maximum latency of the MoE model. Instead of waiting for the overloaded expert to process all tokens, some tokens are specifically ignored. This allows for faster processing and reduces overall latency. The selection of tokens to be discarded is done strategically to minimize the impact on model performance.

Capacity-Aware Token Reroute

The second technique, Capacity-Aware Token Reroute, distributes overflowing tokens to underutilized experts. Instead of discarding tokens, they are dynamically rerouted to distribute the load more evenly. This leads to better utilization of all available resources and also reduces latency. The challenge here lies in the efficient and rapid rerouting of tokens without creating additional overhead.

By combining Capacity-Aware Token Drop and Capacity-Aware Token Reroute, the utilization of both heavily and lightly loaded experts is optimized. This leads to a more efficient MoE inference pipeline. Initial experiments show promising results and significant improvements in inference efficiency. For example, an average performance increase of 0.2% and a 1.94x speedup in inference speed were achieved with Mixtral-8x7B-Instruct. These results highlight the potential of Capacity-Aware Inference for the efficient use of large MoE models in real-world applications.

Research in the field of MoE models is dynamic and promising. Optimizing inference speed and efficiency is crucial for the widespread application of these models in various fields, from text generation and translation to language processing and image analysis. Capacity-Aware Inference represents an important step in this direction and contributes to realizing the full potential of MoE models.

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