Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads

• URL: http://arxiv.org/abs/2501.15113v1
• Date: Sat, 25 Jan 2025 07:28:13 GMT
• Title: Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads
• Authors: Xingyang He, Jie Liu, Shaowei Chen,
• Abstract summary: KV cache is a widely used technique for large language models (LLMs) inference. Previous studies have reduced the size of KV cache by either removing the same number of unimportant tokens for all attention heads or by allocating differentiated KV cache budgets for pre-identified attention heads. We propose Task-KV, a method that leverages the semantic differentiation of attention heads to allocate differentiated KV cache budgets across various tasks.
• Score: 4.797407445026818
• License:
• Abstract: KV cache is a widely used acceleration technique for large language models (LLMs) inference. However, its memory requirement grows rapidly with input length. Previous studies have reduced the size of KV cache by either removing the same number of unimportant tokens for all attention heads or by allocating differentiated KV cache budgets for pre-identified attention heads. However, due to the importance of attention heads varies across different tasks, the pre-identified attention heads fail to adapt effectively to various downstream tasks. To address this issue, we propose Task-KV, a method that leverages the semantic differentiation of attention heads to allocate differentiated KV cache budgets across various tasks. We demonstrate that attention heads far from the semantic center (called heterogeneous heads) make an significant contribution to task outputs and semantic understanding. In contrast, other attention heads play the role of aggregating important information and focusing reasoning. Task-KV allocates full KV cache budget to heterogeneous heads to preserve comprehensive semantic information, while reserving a small number of recent tokens and attention sinks for non-heterogeneous heads. Furthermore, we innovatively introduce middle activations to preserve key contextual information aggregated from non-heterogeneous heads. To dynamically perceive semantic differences among attention heads, we design a semantic separator to distinguish heterogeneous heads from non-heterogeneous ones based on their distances from the semantic center. Experimental results on multiple benchmarks and different model architectures demonstrate that Task-KV significantly outperforms existing baseline methods.

Related papers

• AttentionPredictor: Temporal Pattern Matters for Efficient LLM Inference [51.1972443343829]
  We propose AttentionPredictor, which is the first learning-based critical token identification approach. AttentionPredictor accurately predicts the attention score while consuming negligible memory. We also propose a cross-token critical cache prefetching framework that hides the token time overhead to accelerate the decoding stage.
  arXiv  Detail & Related papers  (2025-02-06T13:41:46Z)
• Cross-Self KV Cache Pruning for Efficient Vision-Language Inference [19.062950348441426]
  KV cache pruning has emerged as a promising technique for reducing memory and computation costs in long-context auto-regressive generation. We propose decomposing attention scores into intra-modality attention (within the same modality) and inter-modality attention (across modalities) Our final training-free method, textbfCross-textbfSelf textbfPruning (CSP), achieves competitive performance compared to models with full KV caches.
  arXiv  Detail & Related papers  (2024-12-05T22:47:17Z)
• PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation [65.36715026409873]
  Key-value (KV) cache, necessitated by the lengthy input and output sequences, notably contributes to the high inference cost. We present PrefixKV, which reframes the challenge of determining KV cache sizes for all layers into the task of searching for the optimal global prefix configuration. Our method achieves the state-of-the-art performance compared with others.
  arXiv  Detail & Related papers  (2024-12-04T15:48:59Z)
• Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning [19.942402563256962]
  Key-Value (KV) caching is a common technique to enhance the computational efficiency of Large Language Models (LLMs) We propose HeadKV, a head-level KV cache compression method, and Head KV-R2, which leverages a novel contextual reasoning ability estimation for compression. Our method retains just 1.5% of the KV cache while achieving 97% of the performance of the full KV cache on the contextual question answering benchmark.
  arXiv  Detail & Related papers  (2024-10-25T02:22:00Z)
• Efficient Inference of Vision Instruction-Following Models with Elastic Cache [76.44955111634545]
  We introduce Elastic Cache, a novel strategy for efficient deployment of instruction-following large vision-language models. We propose an importance-driven cache merging strategy to prune redundancy caches. For instruction encoding, we utilize the frequency to evaluate the importance of caches. Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation.
  arXiv  Detail & Related papers  (2024-07-25T15:29:05Z)
• RazorAttention: Efficient KV Cache Compression Through Retrieval Heads [11.708388082001074]
  We propose a novel compression technique for Key-Value cache that preserves all token information. RazorAttention achieves a reduction in KV cache size by over 70% without noticeable impacts on performance.
  arXiv  Detail & Related papers  (2024-07-22T01:12:23Z)
• CORM: Cache Optimization with Recent Message for Large Language Model Inference [57.109354287786154]
  We introduce an innovative method for optimizing the KV cache, which considerably minimizes its memory footprint. CORM, a KV cache eviction policy, dynamically retains essential key-value pairs for inference without the need for model fine-tuning. Our validation shows that CORM reduces the inference memory usage of KV cache by up to 70% with negligible performance degradation across six tasks in LongBench.
  arXiv  Detail & Related papers  (2024-04-24T16:11:54Z)
• Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs [82.08922896531618]
  We introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs) We conduct targeted profiling to discern the intrinsic structure of attention modules. Based on the recognized structure, we then construct the KV cache in an adaptive manner: evicting long-range contexts on attention heads emphasizing local contexts, discarding non-special tokens on attention heads centered on special tokens, and only employing the standard KV cache for attention heads that broadly attend to all tokens.
  arXiv  Detail & Related papers  (2023-10-03T05:17:08Z)
• Finding the Pillars of Strength for Multi-Head Attention [35.556186723898485]
  Recent studies have revealed some issues of Multi-Head Attention (MHA) We propose Grouped Head Attention, trained with a self-supervised group constraint that group attention heads. We additionally propose a Voting-to-Stay procedure to remove redundant heads, thus achieving a transformer with lighter weights.
  arXiv  Detail & Related papers  (2023-05-22T03:44:44Z)
• A Dynamic Head Importance Computation Mechanism for Neural Machine Translation [22.784419165117512]
  Multiple parallel attention mechanisms that use multiple attention heads facilitate greater performance of the Transformer model for various applications. In this work, we focus on designing a Dynamic Head Importance Computation Mechanism (DHICM) to dynamically calculate the importance of a head with respect to the input. We add an extra loss function to prevent the model from assigning same score to all heads, to identify more important heads and improvise performance.
  arXiv  Detail & Related papers  (2021-08-03T09:16:55Z)
• Low-Rank Bottleneck in Multi-head Attention Models [74.83235382203604]
  We argue that the scaling between the number of heads and the size of each head in the current architecture gives rise to a low-rank bottleneck in attention heads. We propose to set the head size of an attention unit to input sequence length, and independent of the number of heads, resulting in multi-head attention layers with provably more expressive power.
  arXiv  Detail & Related papers  (2020-02-17T16:16:40Z)

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.