4 papers
Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers
Tim Tsz-Kit Lau, Weijie Su
A striking geometric disparity has long persisted in the practice of deep learning. While modern neural network architectures naturally exhibit rich symmetry and equivariance prope…
PolarGrad: A Class of Matrix-Gradient Optimizers from a Unifying Preconditioning Perspective
Tim Tsz-Kit Lau, Qi Long, Weijie Su
The ever-growing scale of deep learning models and training data underscores the critical importance of efficient optimization methods. While preconditioned gradient methods such a…
Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu +2
An appropriate choice of batch sizes in large-scale model training is crucial, yet it involves an intrinsic yet inevitable dilemma: large-batch training improves training efficienc…
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods
Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu +2
Modern deep neural networks often require distributed training with many workers due to their large size. As the number of workers increases, communication overheads become the mai…