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Large language models (LLMs) with long sequences begin to power more and more fundamentally new applications we use every day.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew W. Senior, Paul A. Tucker, Ke Yang, and Andrew Y. Ng · 2012
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Scaling distributed machine learning with the parameter server
Mu Li, David G Andersen, Jun Woo Park, Alexander J Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J Shekita, and Bor-Yiing Su · 2014
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Communication efficient distributed machine learning with the parameter server
Mu Li, David G. Andersen, Alexander J. Smola, and Kai Yu · 2014
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2017
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Beyond data and model parallelism for deep neural networks
Zhihao Jia, Matei Zaharia, and Alex Aiken · 2018
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Horovod: fast and easy distributed deep learning in tensorflow
Alexander Sergeev and Mike Del Balso · 2018
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Gpipe: efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, and Zhifeng Chen · 2019
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Pipedream: generalized pipeline parallelism for dnn training
Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, and Matei Zaharia · 2019
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A generic communication scheduler for distributed dnn training acceleration
Yanghua Peng, Yibo Zhu, Yangrui Chen, Yixin Bao, Bairen Yi, Chang Lan, Chuan Wu, and Chuanxiong Guo · 2019
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Supporting very large models using automatic dataflow graph partitioning
Minjie Wang, Chien-chin Huang, and Jinyang Li · 2019
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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Span-based localizing network for natural language video localization
Hao Zhang, Aixin Sun, Wei Jing, and Joey Tianyi Zhou · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Zero: memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
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Fast, flexible allocation for nvidia cuda with rapids memory manage
NVIDIA Developers · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
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Automatic cross-replica sharding of weight update in data-parallel training
Yuanzhong Xu, HyoukJoong Lee, Dehao Chen, Hongjun Choi, Blake Hechtman, and Shibo Wang · 2020
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Pytorch distributed: Experiences on accelerating data parallel training
Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, and Soumith Chintala · 2020
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Document-level relation extraction with adaptive thresholding and localized context pooling
Wenxuan Zhou, Kevin Huang, Tengyu Ma, and Jing Huang · 2021
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Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 2021
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Zi-Hang Jiang, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
Recent advances in deep learning based dialogue systems: A systematic survey
Jinjie Ni, Tom Young, Vlad Pandelea, Fuzhao Xue, and Erik Cambria · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Accurate medium-range global weather forecasting with 3d neural networks
Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian · 2023
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The impact of large language models on scientific discovery: a preliminary study using gpt-4
Microsoft Azure Quantum Microsoft Research AI4Science · 2023
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Transformer-based deep learning for predicting protein properties in the life sciences
Abel Chandra, Laura Tünnermann, Tommy Löfstedt, and Regina Gratz · 2023
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Cited alongside, same era.
Efficient large-scale language model training on gpu clusters using megatron-lm
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, and Matei Zaharia · 2021
Cited alongside, same era.
Dapple: a pipelined data parallel approach for training large models
Shiqing Fan, Yi Rong, Chen Meng, Zongyan Cao, Siyu Wang, Zhen Zheng, Chuan Wu, Guoping Long, Jun Yang, Lixue Xia, Lansong Diao, Xiaoyong Liu, and Wei Lin · 2021
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Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning
Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, and Yuxiong He · 2021
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Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Piper: Multidimensional planner for dnn parallelization
Jakub M. Tarnawski, Deepak Narayanan, and Amar Phanishayee · 2021
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Varuna: scalable, low-cost training of massive deep learning models
Sanjith Athlur, Nitika Saran, Muthian Sivathanu, Ramachandran Ramjee, and Nipun Kwatra · 2022
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Alpa: Automating inter- and intra-operator parallelism for distributed deep learning
Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P. Xing, Joseph E. Gonzalez, and Ion Stoica · 2022
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Merak: An efficient distributed dnn training framework with automated 3d parallelism for giant foundation models
Zhiquan Lai, Shengwei Li, Xudong Tang, Keshi Ge, Weijie Liu, Yabo Duan, Linbo Qiao, and Dongsheng Li · 2023
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Reducing activation recomputation in large transformer models
Vijay Anand Korthikanti, Jared Casper, Sangkug Lym, Lawrence McAfee, Michael Andersch, Mohammad Shoeybi, and Bryan Catanzaro · 2023
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Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Shuaiwen Leon Song, Samyam Rajbhandari, and Yuxiong He · 2023
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Ring attention with blockwise transformers for near-infinite context
Hao Liu, Matei Zaharia, and Pieter Abbeel · 2023
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Lightseq: Sequence level parallelism for distributed training of long context transformers
Dacheng Li, Rulin Shao, Anze Xie, Eric P Xing, Joseph E Gonzalez, Ion Stoica, Xuezhe Ma, and Hao Zhang · 2023
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Accelerating large language models with accelerated transformers
PyTorch · 2023
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Optimizing your llm in production
Patrick von Platen · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Zero++: Extremely efficient collective communication for giant model training
Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Connor Holmes, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, and Yuxiong He · 2023
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On optimizing the communication of model parallelism
Yonghao Zhuang, Hexu Zhao, Lianmin Zheng, Zhuohan Li, Eric Xing, Qirong Ho, Joseph Gonzalez, Ion Stoica, and Hao Zhang · 2023
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Nccl user guide: Allreduce
NVIDIA Developers · 2023
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2023
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Blockwise parallel transformer for large context models
Hao Liu and Pieter Abbeel · 2023
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Torchddp
torch · 2023
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Amsp: Super-scaling llm training via advanced model states partitioning
Qiaoling Chen, Qinghao Hu, Zhisheng Ye, Guoteng Wang, Peng Sun, Yonggang Wen, and Tianwei Zhang · 2023
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Rethinking memory and communication cost for efficient large language model training
Chan Wu, Hanxiao Zhang, Lin Ju, Jinjing Huang, Youshao Xiao, Zhaoxin Huan, Siyuan Li, Fanzhuang Meng, Lei Liang, Xiaolu Zhang, and Jun Zhou · 2023
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