Star Attention: Efficient LLM Inference over Long Sequences

• URL: http://arxiv.org/abs/2411.17116v1
• Date: Tue, 26 Nov 2024 05:10:04 GMT
• Title: Star Attention: Efficient LLM Inference over Long Sequences
• Authors: Shantanu Acharya, Fei Jia, Boris Ginsburg,
• Abstract summary: We introduce Star Attention, a two-phase block-sparse approximation that improves computational efficiency by sharding attention across multiple hosts. Star Attention integrates seamlessly with most Transformer-based Large Language Models trained with global attention, reducing memory requirements and inference time by up to 11x while preserving 95-100% of accuracy.
• Score: 17.401430615714
• License:
• Abstract: Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism. We introduce Star Attention, a two-phase block-sparse approximation that improves computational efficiency by sharding attention across multiple hosts while minimizing communication overhead. In the first phase, the context is processed using blockwise-local attention across hosts, in parallel. In the second phase, query and response tokens attend to all prior cached tokens through sequence-global attention. Star Attention integrates seamlessly with most Transformer-based LLMs trained with global attention, reducing memory requirements and inference time by up to 11x while preserving 95-100% of accuracy.

Related papers

• Squeezed Attention: Accelerating Long Context Length LLM Inference [64.11145320159126]
  We propose Squeezed Attention as a mechanism to accelerate LLM applications where a large portion of the input prompt is fixed. We use K-means clustering offline to group the keys for the fixed context based on semantic similarity and represent each cluster with a single centroid value. We then compute exact attention using only these important keys from the fixed context, thereby reducing bandwidth and computational costs.
  arXiv  Detail & Related papers  (2024-11-14T18:54:19Z)
• Recycled Attention: Efficient inference for long-context language models [54.00118604124301]
  We propose Recycled Attention, an inference-time method which alternates between full context attention and attention over a subset of input tokens. When performing partial attention, we recycle the attention pattern of a previous token that has performed full attention and attend only to the top K most attended tokens. Compared to previously proposed inference-time acceleration method which attends only to local context or tokens with high accumulative attention scores, our approach flexibly chooses tokens that are relevant to the current decoding step.
  arXiv  Detail & Related papers  (2024-11-08T18:57:07Z)
• Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers [58.5711048151424]
  We introduce SPARSEK Attention, a novel sparse attention mechanism designed to overcome computational and memory obstacles. Our approach integrates a scoring network and a differentiable top-k mask operator, SPARSEK, to select a constant number of KV pairs for each query. Experimental results reveal that SPARSEK Attention outperforms previous sparse attention methods.
  arXiv  Detail & Related papers  (2024-06-24T15:55:59Z)
• Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences [60.489682735061415]
  We propose CHELA, which replaces state space models with short-long convolutions and implements linear attention in a divide-and-conquer manner. Our experiments on the Long Range Arena benchmark and language modeling tasks demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-06-12T12:12:38Z)
• SinkLoRA: Enhanced Efficiency and Chat Capabilities for Long-Context Large Language Models [4.497551890206997]
  Self-attention mechanism scales quadratically with sequence length. LongLoRA proposed shifted sparse attention (S(2)-Attn), effectively enabling context extension. SinkLoRA is still not as efficient as vanilla attention, reaching only 39% of the perplexity improvement compared to full attention.
  arXiv  Detail & Related papers  (2024-06-09T07:23:34Z)
• Lean Attention: Hardware-Aware Scalable Attention Mechanism for the Decode-Phase of Transformers [4.674454841332859]
  Transformer-based models have emerged as one of the most widely used architectures for natural language processing. These huge models are memory hungry and incur significant inference latency even on cutting edge AI-accelerators. We propose LeanAttention, a scalable technique of computing self-attention for the token-generation phase.
  arXiv  Detail & Related papers  (2024-05-17T00:52:39Z)
• Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention [6.713196608291278]
  This work introduces an efficient method to scale Transformer-based Large Language Models to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention.
  arXiv  Detail & Related papers  (2024-04-10T16:18:42Z)
• BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences [96.74779792715819]
  We propose a distributed attention framework named BurstAttention'' to optimize memory access and communication operations. The experimental results under different length settings demonstrate that BurstAttention offers significant advantages for processing long sequences.
  arXiv  Detail & Related papers  (2024-03-14T12:51:58Z)
• Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMs [39.16152482491236]
  Bifurcated attention is a method designed to enhance language model inference in shared-context batch decoding scenarios. Our approach addresses the challenge of redundant memory IO costs, a critical factor contributing to latency in high batch sizes and extended context lengths.
  arXiv  Detail & Related papers  (2024-03-13T16:30:57Z)
• Combiner: Full Attention Transformer with Sparse Computation Cost [142.10203598824964]
  We propose Combiner, which provides full attention capability in each attention head while maintaining low computation complexity. We show that most sparse attention patterns used in existing sparse transformers are able to inspire the design of such factorization for full attention. An experimental evaluation on both autoregressive and bidirectional sequence tasks demonstrates the effectiveness of this approach.
  arXiv  Detail & Related papers  (2021-07-12T22:43:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.