299 citations · 813 across the 27 of their papers we have counts for
5 papers · 1 filter
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang +4
Computation in a typical Transformer-based large language model (LLM) can be characterized by batch size, hidden dimension, number of layers, and sequence length. Until now, system…
DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention
Zhewei Yao, Xiaoxia Wu, Conglong Li +6
Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial co…
DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales
Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase +16
ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance…
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Guanhua Wang, Heyang Qin, Sam Ade Jacobs +6
Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability. How…
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan +3
Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computat…