Trace a Token Through TP/CP/SP/EP/DP/PP in Moe Inference (charlesxu.io)

A recent deep dive into the mechanics of a Mixture-of-Experts (MoE) model has unveiled the intricate token routing process during prefill across 32 GPUs utilizing six distinct types of parallelism: data, context, sequence, tensor, expert, and pipeline. This exploration illustrates how a single token travels through multiple hops—from landing on GPU 11, to K/V swapping with adjacent GPUs, dispatching to two experts, and finally leaving key data structures across specific GPUs. Notably, the token's journey encompasses significant communication operations and showcases how each parallel type contributes to efficiency and output while highlighting the complexities of coordination among GPUs. The significance of this work lies in its ability to demystify the synergy between various parallel processing methods in MoE models, which are becoming increasingly vital in scaling AI workloads. Each parallelism contributes uniquely to throughput and latency, illustrating both the benefits and costs of communication between GPUs. For instance, data parallelism boosts throughput without direct communication costs, while expert parallelism enhances model capacity at the cost of complex communications. This analysis opens new doors for optimizing GPU usage in AI applications, improving model efficiency, and shaping future architectures in AI/ML research.