no code implementations • 1 Nov 2023 • Ruihang Lai, Junru Shao, Siyuan Feng, Steven S. Lyubomirsky, Bohan Hou, Wuwei Lin, Zihao Ye, Hongyi Jin, Yuchen Jin, Jiawei Liu, Lesheng Jin, Yaxing Cai, Ziheng Jiang, Yong Wu, Sunghyun Park, Prakalp Srivastava, Jared G. Roesch, Todd C. Mowry, Tianqi Chen
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models.
3 code implementations • 8 Mar 2023 • Cody Hao Yu, Haozheng Fan, Guangtai Huang, Zhen Jia, Yizhi Liu, Jie Wang, Zach Zheng, Yuan Zhou, Haichen Shen, Junru Shao, Mu Li, Yida Wang
In this paper, we present RAF, a deep learning compiler for training.
2 code implementations • 11 Jul 2022 • Zihao Ye, Ruihang Lai, Junru Shao, Tianqi Chen, Luis Ceze
We propose SparseTIR, a sparse tensor compilation abstraction that offers composable formats and composable transformations for deep learning workloads.
2 code implementations • 9 Jul 2022 • Siyuan Feng, Bohan Hou, Hongyi Jin, Wuwei Lin, Junru Shao, Ruihang Lai, Zihao Ye, Lianmin Zheng, Cody Hao Yu, Yong Yu, Tianqi Chen
Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives.
no code implementations • 26 May 2022 • Junru Shao, Xiyou Zhou, Siyuan Feng, Bohan Hou, Ruihang Lai, Hongyi Jin, Wuwei Lin, Masahiro Masuda, Cody Hao Yu, Tianqi Chen
Experimental results show that MetaSchedule can cover the search space used in the state-of-the-art tensor program optimization frameworks in a modular way.
1 code implementation • 6 Jun 2018 • Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov
We show that one cause for such success is due to the fact that the multi-branch architecture is less non-convex in terms of duality gap.
10 code implementations • ACL 2017 • Xinya Du, Junru Shao, Claire Cardie
We study automatic question generation for sentences from text passages in reading comprehension.
no code implementations • 17 Sep 2015 • Shicong Liu, Junru Shao, Hongtao Lu
We propose a novel distance to calculate distance between high dimensional vector pairs, utilizing vector quantization generated encodings.
no code implementations • 17 Sep 2015 • Shicong Liu, Hongtao Lu, Junru Shao
In this paper, we propose an improved residual vector quantization (IRVQ) method, our IRVQ learns codebook with a hybrid method of subspace clustering and warm-started k-means on each stage to prevent performance gain from dropping, and uses a multi-path encoding scheme to encode a vector with lower distortion.
no code implementations • 17 Sep 2015 • Shicong Liu, Junru Shao, Hongtao Lu
Further, we propose Aggregating-Tree (A-Tree), a non-exhaustive search method using HCLAE to perform efficient ANN-Search.