9 citations · 11 across the 4 of their papers we have counts for
4 papers
HexiSeq: Accommodating Long Context Training of LLMs over Heterogeneous Hardware
Yan Liang, Youhe Jiang, Ran Yan +3
Long-context training of large language models (LLMs) is commonly distributed with Context Parallelism (CP) and Head Parallelism (HP), but existing training systems largely assume…
DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
Ye Tian, Zhen Jia, Ziyue Luo +2
Diffusion models have emerged as dominant performers for image generation. To support training large diffusion models, this paper studies pipeline parallel training of diffusion mo…
Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
Chenyu Jiang, Ye Tian, Zhen Jia +3
The Mixture-of-Expert (MoE) technique plays a crucial role in expanding the size of DNN model parameters. However, it faces the challenge of extended all-to-all communication laten…
DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
Chenyu Jiang, Zhen Jia, Shuai Zheng +2
Multi-task model training has been adopted to enable a single deep neural network model (often a large language model) to handle multiple tasks (e.g., question answering and text s…