6 papers · 1 filter
DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention
Yuxiang Huang, Nuno M. T. Gonçalves, Federico Alvetreti +5
Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and subsequently apply fine-grain…
AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code
Shangzhan Li, Xinyu Yin, Xuanyu Jin +8
Vectorization via Single Instruction, Multiple Data (SIMD) architectures is a cornerstone of high-performance computing. To fully exploit hardware potential, developers often resor…
Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs
Yuzhuang Xu, Xu Han, Yuxuan Li +2
Large language models (LLMs) achieve strong performance but incur high deployment costs, motivating extremely low-bit but lossy quantization. Existing quantization algorithms mainl…
CAMERA: Multi-Matrix Joint Compression for MoE Models via Micro-Expert Redundancy Analysis
Yuzhuang Xu, Xu Han, Yuanchi Zhang +5
Large Language Models (LLMs) with Mixture-of-Experts (MoE) architectures are distinguished by their strong performance scaling with increasing parameters across a wide range of tas…
InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation
Weilin Zhao, Zihan Zhou, Zhou Su +10
Long-sequence processing is a critical capability for modern large language models. However, the self-attention mechanism in the standard Transformer architecture faces severe comp…
Speculative Decoding Meets Quantization: Compatibility Evaluation and Hierarchical Framework Design
Yudi Zhang, Weilin Zhao, Xu Han +4
Speculative decoding and quantization effectively accelerate memory-bound inference of large language models. Speculative decoding mitigates the memory bandwidth bottleneck by veri…