HyperParallel-Mpipe: A Composable Algebra System for Optimizing MLLM Training over Supernode Clusters
Improves training efficiency of multimodal LLMs for practitioners using supernode clusters.
MLLM training suffers from low MFU; Mpipe uses a schedule algebra to derive efficient parallel schedules, achieving 2.70x speedup in small-scale and 1.21x in 512-card settings on Ascend 910C NPU clusters.
Modern AI applications have expanded beyond text-only interaction into a wide range of multimodal scenarios, making multimodal large language models (MLLMs) crucial for both research and industry. However, compared with traditional decoder-only LLM training, large-scale MLLM training often shows much lower MFU. We analyze the key pain points in MLLM training and introduce Mpipe, which uses a schedule algebra to derive concrete runtime behavior from a compact schedule specification. From this algebra, Mpipe derives transpose, a multimodal-aware heterogeneous parallel schedule that remaps modality-encoder computation into otherwise idle pipeline regions. On Ascend 910C NPU clusters, Mpipe achieves 2.70x speedup in a small-scale setting and 1.21x speedup in a 512-card large-scale setting.