5 papers
EcoSpa: Efficient Transformer Training with Coupled Sparsity
Jinqi Xiao, Cheng Luo, Lingyi Huang +8
Transformers have become the backbone of modern AI, yet their high computational demands pose critical system challenges. While sparse training offers efficiency gains, existing me…
TensorGRaD: Tensor Gradient Robust Decomposition for Memory-Efficient Neural Operator Training
Sebastian Loeschcke, David Pitt, Robert Joseph George +5
Scientific problems require resolving multi-scale phenomena across different resolutions and learning solution operators in infinite-dimensional function spaces. Neural operators p…
MOM: Memory-Efficient Offloaded Mini-Sequence Inference for Long Context Language Models
Junyang Zhang, Tianyi Zhu, Cheng Luo +1
Long-context language models exhibit impressive performance but remain challenging to deploy due to high GPU memory demands during inference. We propose Memory-efficient Offloaded…
HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading
Cheng Luo, Zefan Cai, Hanshi Sun +7
Transformer-based large language models (LLMs) demonstrate impressive performance in long context generation. Extending the context length has disproportionately shifted the memory…
Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training
Cheng Luo, Jiawei Zhao, Zhuoming Chen +2
We introduce Mini-Sequence Transformer (MsT), a simple and effective methodology for highly efficient and accurate LLM training with extremely long sequences. MsT partitions input…