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SampleAttention: Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention

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arxiv 2406.15486 v3 pith:HWHR52P5 submitted 2024-06-17 cs.CL cs.AIcs.LG

Qianchao Zhu , Jiangfei Duan , Chang Chen , Siran Liu , Guanyu Feng , Xin Lv , Xiao Chuanfu , Dahua Lin
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Chao Yang
This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords attentionsparsepatternssampleattentionlongnear-losslessaccuracyadaptive
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Large language models (LLMs) now support extremely long context windows, but the quadratic complexity of vanilla attention results in significantly long Time-to-First-Token (TTFT) latency. Existing approaches to address this complexity require additional pretraining or finetuning, and often sacrifice model accuracy. In this paper, we first provide both theoretical and empirical foundations for near-lossless sparse attention. We find dynamically capturing head-specific sparse patterns at runtime with low overhead is crucial. To address this, we propose SampleAttention, an adaptive structured and near-lossless sparse attention. Leveraging observed significant sparse patterns, SampleAttention attends to a fixed percentage of adjacent tokens to capture local window patterns, and employs a two-stage query-guided key-value filtering approach, which adaptively select a minimum set of key-values with low overhead, to capture column stripe patterns. Comprehensive evaluations show that SampleAttention can seamlessly replace vanilla attention in off-the-shelf LLMs with nearly no accuracy loss, and reduces TTFT by up to $2.42\times$ compared with FlashAttention.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. vAttention: Verified Sparse Attention

    cs.LG 2025-10 conditional novelty 6.0 of 10

    vAttention is a sparse attention method that mixes heavy-hitter tokens with a statistically sized random sample to provide (ε, δ)-guaranteed approximation of full attention.

  2. SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling

    cs.LG 2025-05 conditional novelty 6.0 of 10

    SALE is a training-free sparse attention method that uses 4-bit quantized query-key estimates and a relative attention score to skip unimportant blocks, achieving over 3.36x prefill speedup on 64K+ token contexts with...

  3. Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis

    cs.CL 2025-07 conditional novelty 5.0 of 10

    For Flamingo-style models, increasing the number of in-context examples improves language coherence but degrades visual-text alignment, and similarity-based image retrieval inflates CIDEr scores by encouraging caption...

  4. Breaking the Boundaries of Long-Context LLM Inference: Adaptive KV Management on a Single Commodity GPU

    cs.OS 2025-06 conditional novelty 5.0 of 10

    LeoAM reports a 3.46x average latency speedup for long-context LLM inference on one commodity GPU by adaptively chunking KV data and loading compact key abstracts from disk instead of full KV values.

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