57 citations · 76 across the 11 of their papers we have counts for
11 papers
DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
Yujie Wang, Siwei Chen, Longzan Luo +4
Reinforcement Learning (RL) has become pivotal for improving model capabilities yet suffers from rollout efficiency bottlenecks due to the long-tail response length distribution. W…
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…
Training-free and Adaptive Sparse Attention for Efficient Long Video Generation
Yifei Xia, Suhan Ling, Fangcheng Fu +4
Generating high-fidelity long videos with Diffusion Transformers (DiTs) is often hindered by significant latency, primarily due to the computational demands of attention mechanisms…
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…
Improving Automatic Parallel Training via Balanced Memory Workload Optimization
Yujie Wang, Youhe Jiang, Xupeng Miao +5
Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-…
Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism
Xupeng Miao, Yujie Wang, Youhe Jiang +4
Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) mode…
SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene
Yujie Wang, Xuelin Chen, Baoquan Chen
We present a 3D generative model for general natural scenes. Lacking necessary volumes of 3D data characterizing the target scene, we propose to learn from a single scene. Our key…