3 papers
cs.LG2026
HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention
Yufei Xu, Fanxu Meng, Fan Jiang +11
Token-level sparse attention mechanisms, exemplified by DeepSeek Sparse Attention (DSA), achieve fine-grained key selection by scoring every historical key for each query through a…
cs.LG2025
TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference
Xiaojuan Tang, Fanxu Meng, Pingzhi Tang +4
Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value states into a low-rank latent vector, caching only this vector to reduce memory. In tensor parall…
cs.LG2025
TransMLA: Multi-Head Latent Attention Is All You Need
Fanxu Meng, Pingzhi Tang, Xiaojuan Tang +3
In this paper, we present TransMLA, a framework that seamlessly converts any GQA-based pre-trained model into an MLA-based model. Our approach enables direct compatibility with Dee…