Search Results for author: Lianmin Zheng

Found 19 papers, 13 papers with code

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

1 code implementation21 Sep 2023 Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric. P Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang

Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications.

Chatbot Instruction Following

Efficient Memory Management for Large Language Model Serving with PagedAttention

2 code implementations12 Sep 2023 Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, Ion Stoica

On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage.

Language Modelling Large Language Model +1

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

1 code implementation24 Jun 2023 Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, Zhangyang Wang, Beidi Chen

Based on these insights, we propose Heavy Hitter Oracle (H$_2$O), a KV cache eviction policy that dynamically retains a balance of recent and H$_2$ tokens.

Judging LLM-as-a-judge with MT-Bench and Chatbot Arena

2 code implementations9 Jun 2023 Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, Ion Stoica

Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences.

Chatbot Language Modelling +1

On Optimal Caching and Model Multiplexing for Large Model Inference

1 code implementation3 Jun 2023 Banghua Zhu, Ying Sheng, Lianmin Zheng, Clark Barrett, Michael I. Jordan, Jiantao Jiao

Theoretically, we provide an optimal algorithm for jointly optimizing both approaches to reduce the inference cost in both offline and online tabular settings.

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

1 code implementation13 Mar 2023 Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang

As a result, when running OPT-175B on a single 16GB GPU, FlexGen achieves significantly higher throughput compared to state-of-the-art offloading systems, reaching a generation throughput of 1 token/s for the first time with an effective batch size of 144.

Language Modelling Large Language Model

AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving

2 code implementations22 Feb 2023 Zhuohan Li, Lianmin Zheng, Yinmin Zhong, Vincent Liu, Ying Sheng, Xin Jin, Yanping Huang, Zhifeng Chen, Hao Zhang, Joseph E. Gonzalez, Ion Stoica

Model parallelism is conventionally viewed as a method to scale a single large deep learning model beyond the memory limits of a single device.

On Optimizing the Communication of Model Parallelism

no code implementations10 Nov 2022 Yonghao Zhuang, Hexu Zhao, Lianmin Zheng, Zhuohan Li, Eric P. Xing, Qirong Ho, Joseph E. Gonzalez, Ion Stoica, Hao Zhang

This pattern emerges when the two paradigms of model parallelism - intra-operator and inter-operator parallelism - are combined to support large models on large clusters.

TensorIR: An Abstraction for Automatic Tensorized Program Optimization

2 code implementations9 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.

BIG-bench Machine Learning

NumS: Scalable Array Programming for the Cloud

no code implementations28 Jun 2022 Melih Elibol, Vinamra Benara, Samyu Yagati, Lianmin Zheng, Alvin Cheung, Michael I. Jordan, Ion Stoica

LSHS is a local search method which optimizes operator placement by minimizing maximum memory and network load on any given node within a distributed system.

regression Scheduling

GACT: Activation Compressed Training for Generic Network Architectures

1 code implementation22 Jun 2022 Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han, Jianfei Chen, Zhiyuan Liu, Jie Tang, Joey Gonzalez, Michael Mahoney, Alvin Cheung

Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.

Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning

1 code implementation28 Jan 2022 Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica

Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations.

A Hardware-Software Blueprint for Flexible Deep Learning Specialization

no code implementations11 Jul 2018 Thierry Moreau, Tianqi Chen, Luis Vega, Jared Roesch, Eddie Yan, Lianmin Zheng, Josh Fromm, Ziheng Jiang, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.

Code Generation Style Transfer

Learning to Optimize Tensor Programs

no code implementations NeurIPS 2018 Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

1 code implementation12 Feb 2018 Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.

Size-to-depth: A New Perspective for Single Image Depth Estimation

no code implementations13 Jan 2018 Yiran Wu, Sihao Ying, Lianmin Zheng

To overcome these problems, we propose a new perspective for single monocular image depth estimation problem: size to depth.

Depth Estimation

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

3 code implementations2 Dec 2017 Lianmin Zheng, Jiacheng Yang, Han Cai, Wei-Nan Zhang, Jun Wang, Yong Yu

Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.

Multi-agent Reinforcement Learning reinforcement-learning +1

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