57 citations · 76 across the 11 of their papers we have counts for
6 papers · 1 filter
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
Xinyi Liu, Yujie Wang, Fangcheng Fu +4
Expert parallelism is vital for effectively training Mixture-of-Experts (MoE) models, enabling different devices to host distinct experts, with each device processing different inp…
Concertina: Data-Centric Adaptive Pipeline Parallelism for Efficient Heterogeneous Long-Context LLM Training
Shiju Wang, Yujie Wang, Ao Sun +6
Long context training is crucial for extending LLM context windows. Existing schemes, such as sequence parallelism, incur substantial communication overhead. Pipeline parallelism (…
Galvatron: An Automatic Distributed System for Efficient Foundation Model Training
Xinyi Liu, Yujie Wang, Shenhan Zhu +4
Galvatron is a distributed system for efficiently training large-scale Foundation Models. It overcomes the complexities of selecting optimal parallelism strategies by automatically…
FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism
Yujie Wang, Shiju Wang, Shenhan Zhu +7
Extending the context length (i.e., the maximum supported sequence length) of LLMs is of paramount significance. To facilitate long context training of LLMs, sequence parallelism h…
Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization
Haoyang Li, Fangcheng Fu, Hao Ge +7
As the scale of models and training data continues to grow, there is an expanding reliance on more GPUs to train large-scale models, which inevitably increases the likelihood of en…
Spindle: Efficient Distributed Training of Multi-Task Large Models via Wavefront Scheduling
Yujie Wang, Shenhan Zhu, Fangcheng Fu +6
Recent foundation models are capable of handling multiple tasks and multiple data modalities with the unified base model structure and several specialized model components. However…