10 citations · 26 across the 10 of their papers we have counts for
4 papers · 1 filter
AutoSP: Unlocking Long-Context LLM Training Via Compiler-Based Sequence Parallelism
Ahan Gupta, Zhihao Wang, Neel Dani +3
Large-language-models (LLMs) demonstrate enormous utility in long-context tasks which require processing prompts that consist of tens to hundreds of thousands of tokens. However, e…
MAC-Attention: a Match-Amend-Complete Scheme for Fast and Accurate Attention Computation
Jinghan Yao, Sam Adé Jacobs, Walid Krichene +2
Long-context decoding in LLMs is IO-bound: each token re-reads an ever-growing KV cache. Prior accelerations cut bytes via compression, which lowers fidelity, or selection/eviction…
SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips
Xinyu Lian, Masahiro Tanaka, Olatunji Ruwase +1
The emergence of Superchips represents a significant advancement in next-generation AI hardware. These Superchips employ a tightly coupled heterogeneous architecture that integrate…
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…