Attention in Low|Rank Space for KV Cache Compression
Attention in Low-Rank Space for KV Cache Compression - arXiv
We propose Eigen Attention, which performs the attention operation in a low-rank space, thereby reducing the KV cache memory overhead.
Attention in Low-Rank Space for KV Cache Compression
To address this, we propose Eigen Attention, which performs the attention operation in a low-rank space, thereby reducing the KV cache memory ...
Attention in Low-Rank Space for KV Cache Compression
Through extensive experiments over OPT, MPT, and Llama model families, we demonstrate that Eigen Attention results in up to 40% reduction in KV ...
Attention in Low-Rank Space for KV Cache Compression
This work proposes Eigen Attention, which performs the attention operation in a low-rank space, thereby reducing the KV cache memory overhead and results in ...
Eigen Attention: Attention in Low-Rank Space for KV Cache ...
In this paper, we propose a novel compression technique for KV cache that preserves all token information. Our investigation reveals that: i) Most attention ...
Attention in Low-Rank Space for KV Cache Compression - Synthical
Eigen Attention: Attention in Low-Rank Space for KV Cache Compression ... No PDF file specified. Loading full text... 10%.
Palu: KV-Cache Compression with Low-Rank Projection
Post-training KV-Cache compression methods typically either sample a subset of effectual tokens or quantize the data into lower numerical ...
Machine Learning on X: "Eigen Attention: Attention in Low-Rank ...
Eigen Attention: Attention in Low-Rank Space for KV Cache Compression. https://t.co/fQZZmMYypT.
Awesome-Efficient-LLM/kv_cache_compression.md at main - GitHub
KV Cache Compression ; Loki: Low-Rank Keys for Efficient Sparse Attention Prajwal Singhania, Siddharth Singh, Shwai He, Soheil Feizi, Abhinav Bhatele, image ...
PapersAnon on X: "Eigen Attention: Attention in Low-Rank Space for ...
Eigen Attention: Attention in Low-Rank Space for KV Cache Compression Approximates KQV inputs using eigenvectors. Uses a small calibration ...
Adaptive KV Cache Compression for LLMs - arxiv-sanity
Adaptive KV cache compression seeks to discern the saliency of tokens, preserving vital information while aggressively compressing those of less importance.
[R] Super simple KV Cache compression : r/LocalLLaMA - Reddit
TLDR, we noticed a strong correlation between the L2 norm of token key projections in the KV cache and the attention scores they receive; we use ...
Analysis of attention map - Research - Hugging Face Forums
there are many research about KV cache drops,it based on low information of some tokens,but when analyzing attention score, I feel that my ...
Making Workers AI faster and more efficient - The Cloudflare Blog
The most common approach to compressing the KV cache involves identifying vectors within it that are unlikely to be queried by future attention ...
(PDF) LoRC: Low-Rank Compression for LLMs KV Cache with a ...
applicable to pre-trained LLMs. ... However, these methods either ignore inter-layer dependencies or require attention pattern analysis,. and the ...
KV cache compression for high-throughput LLM inference - Reddit
Do I understand correctly that with the limited observation window you just need O(L) memory (so not squared for full KV. EDIT: just to clarify ...
Synthesizing Recurrence with KV Cache Compression for Efficient ...
Constant Low-rank Cache Size: Low-rank caches in LESS occupy constant memory with respect ... Scatterbrain: Unifying sparse and low-rank attention ...
LoRC: Low-Rank Compression for LLMs KV Cache with ... - CatalyzeX
... memory consumption scales linearly with sequence length and batch ... attention variants integrated in upcycling stages, which requires ...
Adaptive KV Cache Compression for LLMs - enlsp 2023
... compress the KV cache to minimize memory usage and boost generation speed. Yet, these methods often overlook the intricate attention structure in LLMs. As ...
A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache ...
Surprisingly, we find a clear correlation between the $L_2$ and the attention scores over cached KV pairs, where a low $L_2$ of a key embedding usually leads to ...