Modern Mixture-of-Experts (MoE) models increasingly rely on large-scale AI accelerator clusters for efficient training. Ascend NPUs expose heterogeneous on-chip compute resources, including matrix-oriented AIC units and vector-oriented AIV units with explicit cross-queue synchronization support. However, existing training frameworks largely execute MoE operators in a serialized kernel-by-kernel manner, leaving substantial heterogeneous parallelism underutilized. This paper presents HyperParallel-MoE, a compilation and scheduling framework for MoE training on Ascend NPUs. HyperParallel-MoE transforms operator-level MoE execution into a statically scheduled tile-level heterogeneous taskflow spanning AIC and AIV resources. It introduces AIV-driven one-sided communication to eliminate host-side collective synchronization, dependency-preserving tile task generation to unify communication and computation under a common task abstraction, and event-driven static scheduling to coordinate cross-queue execution with low runtime overhead. HyperParallel-MoE further executes the compiled taskflow within a unified runtime that concurrently drives AIC and AIV workers inside a single kernel launch, enabling fine-grained overlap among communication, matrix computation, and vector computation while preserving existing optimized operators. We implement HyperParallel-MoE in the MindSpore and MindFormers stack and evaluate it using DeepSeek-style MoE models on Ascend A3 clusters. Across multiple expert-parallel configurations, HyperParallel-MoE reduces Dispatch-to-Combine MoE-FFN latency by up to 1.58x, demonstrating that tile-level heterogeneous scheduling can substantially improve MoE training efficiency on modern NPUs.

翻译：现代混合专家（MoE）模型日益依赖大规模AI加速器集群以实现高效训练。昇腾NPU在芯片上集成了异构计算资源，包括面向矩阵运算的AIC单元和支持显式跨队列同步的向量运算AIV单元。然而现有训练框架大多以串行的逐内核方式执行MoE算子，导致大量异构并行性未被充分利用。本文提出HyperParallel-MoE——面向昇腾NPU上MoE训练的编译与调度框架。该框架将算子级MoE执行转化为静态调度的分块级异构任务流，使其横跨AIC与AIV资源。它引入AIV驱动的单边通信以消除主机侧集合同步、保持依赖关系一致的分块任务生成机制以在统一任务抽象下融合通信与计算，以及事件驱动的静态调度以协调跨队列执行并降低运行时开销。HyperParallel-MoE进一步在统一运行时中执行编译生成的任务流，该运行时可在单次内核启动中并发驱动AIC与AIV工作单元，在保留现有优化算子的同时实现通信、矩阵计算与向量计算之间的细粒度重叠。我们在MindSpore与MindFormers技术栈中实现HyperParallel-MoE，并在昇腾A3集群上使用DeepSeek风格MoE模型进行评估。在多种专家并行配置下，HyperParallel-MoE可将从分发到合并的MoE前馈网络延迟降低至多1.58倍，表明分块级异构调度能显著提升现代NPU上的MoE训练效率。