Efficient Heterogeneous Large Language Model Decoding with Model-Attention Disaggregation

• URL: http://arxiv.org/abs/2405.01814v2
• Date: Thu, 10 Apr 2025 14:56:01 GMT
• Title: Efficient Heterogeneous Large Language Model Decoding with Model-Attention Disaggregation
• Authors: Shaoyuan Chen, Wencong Xiao, Yutong Lin, Mingxing Zhang, Yingdi Shan, Jinlei Jiang, Kang Chen, Yongwei Wu,
• Abstract summary: Transformer-based large language models (LLMs) exhibit impressive performance in generative tasks but also introduce significant challenges in real-world serving.<n>We introduce model-attention disaggregation to enhance the efficiency of LLM decoding.<n>We develop and deploy Lamina, an LLM inference system that incorporates model-attention disaggregation in a distributed heterogeneous cluster.
• Score: 15.35494431928751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based large language models (LLMs) exhibit impressive performance in generative tasks but also introduce significant challenges in real-world serving due to inefficient use of the expensive, computation-optimized accelerators. Although disaggregated serving architectures have been proposed to split different phases of LLM inference, the efficiency of decoding phase is still low. This is caused by the varying resource demands of different operators in the transformer-based LLMs. Specifically, the attention operator is memory-intensive, exhibiting a memory access pattern that clashes with the strengths of modern accelerators, especially for long context requests. To enhance the efficiency of LLM decoding, we introduce model-attention disaggregation. This approach leverages a collection of cheap, memory-optimized devices for the attention operator while still utilizing high-end accelerators for other parts of the model. This heterogeneous setup ensures that each component is tailored to its specific workload, maximizing overall performance and cost efficiency. Our comprehensive analysis and experiments confirm the viability of splitting the attention computation over multiple devices. Also, the communication bandwidth required between heterogeneous devices proves to be manageable with prevalent networking technologies. To further validate our theory, we develop and deploy Lamina, an LLM inference system that incorporates model-attention disaggregation in a distributed heterogeneous cluster. Experimental results indicate that Lamina can provide 16.1 ~ 90.1% higher estimated throughput than existing solutions with similar costs.

Related papers

• Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning [39.73152182572741]
  This paper proposes a novel framework, named Quantized Split Federated Fine-Tuning Large AI Model (SFLAM) By partitioning the training load between edge devices and servers, SFLAM can facilitate the operation of large models on devices. SFLAM incorporates quantization management, power control, and bandwidth allocation strategies to enhance training efficiency.
  arXiv  Detail & Related papers  (2025-04-12T07:55:11Z)
• LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding [4.759109475818876]
  Implicit Neural Representations (INRs) are proving to be a powerful paradigm in unifying task modeling across diverse data domains. We introduce LIFT, a novel, high-performance framework that captures multiscale information through meta-learning. We also introduce ReLIFT, an enhanced variant of LIFT that incorporates residual connections and expressive frequency encodings.
  arXiv  Detail & Related papers  (2025-03-19T17:00:58Z)
• COSMOS: A Hybrid Adaptive Optimizer for Memory-Efficient Training of LLMs [81.01082659623552]
  Large Language Models (LLMs) have demonstrated remarkable success across various domains. Their optimization remains a significant challenge due to the complex and high-dimensional loss landscapes they inhabit.
  arXiv  Detail & Related papers  (2025-02-24T18:42:19Z)
• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge. Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments [1.0558515062670693]
  Large language models (LLMs) in real-world scenarios remains a critical challenge. These challenges often lead to inefficiencies in memory utilization, latency, and throughput. We develop a framework to address these issues, achieving prediction errors between 9.9% and 42.3% for key metrics such as batch latency, TTFT, and decode throughput.
  arXiv  Detail & Related papers  (2024-12-06T05:46:43Z)
• MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution [14.265237560766268]
  A flexible integration of attention across diverse spatial extents can yield significant performance enhancements. We introduce Multi-Range Attention Transformer (MAT) tailored for Super Resolution (SR) tasks. MAT adeptly capture dependencies across various spatial ranges, improving the diversity and efficacy of its feature representations.
  arXiv  Detail & Related papers  (2024-11-26T08:30:31Z)
• CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction [77.8576094863446]
  We propose a new detextbfCoupled dutextbfAl-interactive lineatextbfR atttextbfEntion (CARE) mechanism. We first propose an asymmetrical feature decoupling strategy that asymmetrically decouples the learning process for local inductive bias and long-range dependencies. By adopting a decoupled learning way and fully exploiting complementarity across features, our method can achieve both high efficiency and accuracy.
  arXiv  Detail & Related papers  (2024-11-25T07:56:13Z)
• Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design [59.00758127310582]
  We propose a novel framework Read-ME that transforms pre-trained dense LLMs into smaller MoE models. Our approach employs activation sparsity to extract experts. Read-ME outperforms other popular open-source dense models of similar scales.
  arXiv  Detail & Related papers  (2024-10-24T19:48:51Z)
• FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
  We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications. FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
  arXiv  Detail & Related papers  (2024-08-15T16:45:16Z)
• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• 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)
• Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization [0.6445087473595953]
  Large language models (LLMs) demonstrate outstanding performance in various tasks in machine learning. deploying LLM inference poses challenges due to the high compute and memory requirements. We present Tender, an algorithm-hardware co-design solution that enables efficient deployment of LLM inference at low precision.
  arXiv  Detail & Related papers  (2024-06-16T09:51:55Z)
• MELTing point: Mobile Evaluation of Language Transformers [8.238355633015068]
  We explore the current state of mobile execution of Large Language Models (LLMs) We have created our own automation infrastructure, MELT, which supports the headless execution and benchmarking of LLMs on device. We evaluate popular instruction fine-tuned LLMs and leverage different frameworks to measure their end-to-end and granular performance.
  arXiv  Detail & Related papers  (2024-03-19T15:51:21Z)
• FLatten Transformer: Vision Transformer using Focused Linear Attention [80.61335173752146]
  Linear attention offers a much more efficient alternative with its linear complexity. Current linear attention approaches either suffer from significant performance degradation or introduce additional computation overhead. We propose a novel Focused Linear Attention module to achieve both high efficiency and expressiveness.
  arXiv  Detail & Related papers  (2023-08-01T10:37:12Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)

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.