15 citations · 73 across the 9 of their papers we have counts for
9 papers
DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference
Connor Holmes, Masahiro Tanaka, Michael Wyatt +8
The deployment and scaling of large language models (LLMs) have become critical as they permeate various applications, demanding high-throughput and low-latency serving systems. Ex…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model
Fengxiang Bie, Yibo Yang, Zhongzhu Zhou +10
Text-to-image generation (TTI) refers to the usage of models that could process text input and generate high fidelity images based on text descriptions. Text-to-image generation us…
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…
Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers
Zhewei Yao, Xiaoxia Wu, Conglong Li +4
Large-scale transformer models have become the de-facto architectures for various machine learning applications, e.g., CV and NLP. However, those large models also introduce prohib…
Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding
Connor Holmes, Minjia Zhang, Yuxiong He +1
In recent years, large pre-trained Transformer networks have demonstrated dramatic improvements in many natural language understanding tasks. However, the huge size of these models…
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM
Connor Holmes, Minjia Zhang, Yuxiong He +1
Natural Language Processing (NLP) has recently achieved success by using huge pre-trained Transformer networks. However, these models often contain hundreds of millions or even bil…
GraphZero: Breaking Symmetry for Efficient Graph Mining
Daniel Mawhirter, Sam Reinehr, Connor Holmes +2
Graph mining for structural patterns is a fundamental task in many applications. Compilation-based graph mining systems, represented by AutoMine, generate specialized algorithms fo…