Hymba: Een hybride-hoofdarchitectuur voor kleine taalmodellen

Hymba: A Hybrid-head Architecture for Small Language Models

November 20, 2024
Auteurs: Xin Dong, Yonggan Fu, Shizhe Diao, Wonmin Byeon, Zijia Chen, Ameya Sunil Mahabaleshwarkar, Shih-Yang Liu, Matthijs Van Keirsbilck, Min-Hung Chen, Yoshi Suhara, Yingyan Lin, Jan Kautz, Pavlo Molchanov
cs.AI

Samenvatting

Wij stellen Hymba voor, een familie van kleine taalmodellen met een hybride-hoofd parallelle architectuur die transformer aandachtsmechanismen integreert met toestandsruimtemodellen (SSM's) voor verbeterde efficiëntie. Aandachtsmechanismen bieden een hoge-resolutie herinnering, terwijl SSM-hoofden efficiënte contextsamenvatting mogelijk maken. Daarnaast introduceren we leerzame meta-tokens die aan prompts worden toegevoegd, waarin cruciale informatie wordt opgeslagen en de last van "gedwongen aandacht" die gepaard gaat met aandachtsmechanismen verlichten. Dit model is verder geoptimaliseerd door het opnemen van cross-layer key-value (KV) delen en gedeeltelijke schuifraam-aandacht, resulterend in een compacte cache-grootte. Tijdens de ontwikkeling hebben we een gecontroleerde studie uitgevoerd waarin verschillende architecturen onder identieke omstandigheden werden vergeleken en significante voordelen van onze voorgestelde architectuur werden waargenomen. Opmerkelijk genoeg behaalt Hymba state-of-the-art resultaten voor kleine taalmodellen: Ons Hymba-1.5B-Base model overtreft alle sub-2B openbare modellen in prestaties en presteert zelfs beter dan Llama-3.2-3B met 1.32% hogere gemiddelde nauwkeurigheid, een 11.67x cache-groottevermindering en 3.49x doorvoer.
English
We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall, while SSM heads enable efficient context summarization. Additionally, we introduce learnable meta tokens that are prepended to prompts, storing critical information and alleviating the "forced-to-attend" burden associated with attention mechanisms. This model is further optimized by incorporating cross-layer key-value (KV) sharing and partial sliding window attention, resulting in a compact cache size. During development, we conducted a controlled study comparing various architectures under identical settings and observed significant advantages of our proposed architecture. Notably, Hymba achieves state-of-the-art results for small LMs: Our Hymba-1.5B-Base model surpasses all sub-2B public models in performance and even outperforms Llama-3.2-3B with 1.32% higher average accuracy, an 11.67x cache size reduction, and 3.49x throughput.

Summary

AI-Generated Summary

Paper Overview

The paper introduces Hymba, a family of small language models with a hybrid-head parallel architecture that combines transformer attention mechanisms with state space models. Hymba outperforms other models, achieving state-of-the-art results for small LMs, with the Hymba-1.5B-Base model surpassing sub-2B models in performance.

Core Contribution

  • Introduction of learnable meta tokens to reduce the burden on attention mechanisms.
  • Incorporation of cross-layer key-value sharing and partial sliding window attention to enhance efficiency.
  • Development of a hybrid-head architecture integrating attention and SSM heads for parallel processing.
  • Implementation of KV cache optimization strategies to improve cache efficiency and throughput.
  • Demonstration of the effectiveness of Hymba models in achieving superior performance compared to competitive baselines.

Research Context

The research addresses the need for efficient small language models by combining attention mechanisms with state space models to enhance recall capabilities and context summarization while maintaining high performance.

Keywords

Hybrid-head architecture, Meta tokens, Key-value sharing, State space models, Cache optimization, Attention mechanisms

Background

The research background involves the development of language models to improve efficiency and performance. The study aims to fill gaps in existing literature by introducing a hybrid-head architecture that combines attention and SSM heads for enhanced reasoning and accuracy.

Research Gap

Existing literature lacks efficient small language models that balance performance, cache efficiency, and throughput effectively.

Technical Challenges

Challenges include optimizing cache size, improving recall accuracy, and enhancing commonsense reasoning capabilities while maintaining high performance.

Prior Approaches

Previous solutions have focused on attention mechanisms or state space models individually, lacking a comprehensive hybrid approach like the one proposed in Hymba.

Methodology

The research methodology involves a hybrid-head architecture that combines attention and SSM heads for parallel processing, incorporating meta tokens, KV cache optimization, and efficient scaling strategies.

Theoretical Foundation

The architecture is based on a hybrid model combining transformer attention mechanisms with state space models for improved efficiency and performance.

Technical Architecture

The system design includes the integration of attention and SSM heads in the same layer, along with meta tokens and KV cache optimization strategies.

Implementation Details

Specific algorithms, methods, and tools are used for KV cache optimization, attention mechanisms, and the development of Hymba models of different sizes.

Innovation Points

The key technical advantages include the fusion of attention and SSM processing, the introduction of meta tokens, and the optimization of KV cache for improved recall accuracy and efficiency.

Experimental Validation

Experimental validation involves setup configurations, precise metrics, results analysis, and comparative evaluations with baseline models to demonstrate the effectiveness of Hymba models.

Setup

Exact configurations, parameters, datasets, and training techniques are specified for the experiments conducted on Hymba models.

Metrics

Evaluation criteria include accuracy, cache efficiency, throughput, recall capabilities, and commonsense reasoning accuracy.

Results

Quantitative and qualitative findings show the superior performance of Hymba models compared to competitive baselines across various tasks.

Comparative Analysis

Detailed comparisons with other model architectures on different downstream tasks highlight the advantages of Hymba in terms of accuracy, cache optimization, and throughput.

Impact and Implications

The impact and implications of the research are significant for the development of efficient small language models, with specific contributions, limitations, future research directions, and practical applications.

Key Findings

The key contributions include the introduction of hybrid-head architecture, meta tokens, and KV cache optimization strategies for improved performance.

Limitations

An honest assessment of limitations such as the trade-off between accuracy, cache size, and throughput in Hymba models.

Future Directions

Concrete research opportunities include further exploration of hybrid-head architectures, meta tokens, and KV cache optimization for enhanced efficiency.

Practical Significance

The practical applications of Hymba models in real-world tasks and the potential for efficient small language models in various domains are highlighted.

Uitgelichte Papers

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