12 citations · 33 across the 15 of their papers we have counts for
15 papers
Scaling Laws for Linear Complexity Language Models
Xuyang Shen, Dong Li, Ruitao Leng +3
The interest in linear complexity models for large language models is on the rise, although their scaling capacity remains uncertain. In this study, we present the scaling laws for…
Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention
Zhen Qin, Weigao Sun, Dong Li +3
We present Lightning Attention, the first linear attention implementation that maintains a constant training speed for various sequence lengths under fixed memory consumption. Due…
You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet
Zhen Qin, Yuxin Mao, Xuyang Shen +4
Linear attention mechanisms have gained prominence in causal language models due to their linear computational complexity and enhanced speed. However, the inherent decay mechanism…
Unlocking the Secrets of Linear Complexity Sequence Model from A Unified Perspective
Zhen Qin, Xuyang Shen, Dong Li +4
We present the Linear Complexity Sequence Model (LCSM), a comprehensive solution that unites various sequence modeling techniques with linear complexity, including linear attention…
HGRN2: Gated Linear RNNs with State Expansion
Zhen Qin, Songlin Yang, Weixuan Sun +4
Hierarchically gated linear RNN (HGRN, \citealt{HGRN}) has demonstrated competitive training speed and performance in language modeling while offering efficient inference. However,…
Linear Attention Sequence Parallelism
Weigao Sun, Zhen Qin, Dong Li +3
Sequence parallelism (SP) serves as a prevalent strategy to handle long sequences that exceed the memory limit of a single device. However, for linear sequence modeling methods lik…
CO2: Efficient Distributed Training with Full Communication-Computation Overlap
Weigao Sun, Zhen Qin, Weixuan Sun +5
The fundamental success of large language models hinges upon the efficacious implementation of large-scale distributed training techniques. Nevertheless, building a vast, high-perf…
Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models
Zhen Qin, Weigao Sun, Dong Li +3
Linear attention is an efficient attention mechanism that has recently emerged as a promising alternative to conventional softmax attention. With its ability to process tokens in l…
Linearized Relative Positional Encoding
Zhen Qin, Weixuan Sun, Kaiyue Lu +6
Relative positional encoding is widely used in vanilla and linear transformers to represent positional information. However, existing encoding methods of a vanilla transformer are…
TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer
Zhen Qin, Dong Li, Weigao Sun +8
We present TransNormerLLM, the first linear attention-based Large Language Model (LLM) that outperforms conventional softmax attention-based models in terms of both accuracy and ef…
Toeplitz Neural Network for Sequence Modeling
Zhen Qin, Xiaodong Han, Weixuan Sun +6
Sequence modeling has important applications in natural language processing and computer vision. Recently, the transformer-based models have shown strong performance on various seq…
Fine-grained Audible Video Description
Xuyang Shen, Dong Li, Jinxing Zhou +9
We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD). It aims to provide detailed textual descriptions for the given audibl…
AttMEMO : Accelerating Transformers with Memoization on Big Memory Systems
Yuan Feng, Hyeran Jeon, Filip Blagojevic +3
Transformer models gain popularity because of their superior inference accuracy and inference throughput. However, the transformer is computation-intensive, causing a long inferenc…
Linear Video Transformer with Feature Fixation
Kaiyue Lu, Zexiang Liu, Jianyuan Wang +8
Vision Transformers have achieved impressive performance in video classification, while suffering from the quadratic complexity caused by the Softmax attention mechanism. Some stud…
Neural Architecture Search on Efficient Transformers and Beyond
Zexiang Liu, Dong Li, Kaiyue Lu +4
Recently, numerous efficient Transformers have been proposed to reduce the quadratic computational complexity of standard Transformers caused by the Softmax attention. However, mos…