Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models (2501.17088v1)

Abstract: Large pre-trained models have achieved outstanding results in sequence modeling. The Transformer block and its attention mechanism have been the main drivers of the success of these models. Recently, alternative architectures, such as Selective Structured State Space Models (SSMs), have been proposed to address the inefficiencies of Transformers. This paper explores the compression of SSM-based models, particularly Mamba and its hybrids. We study the sensitivity of these models to the removal of selected components at different granularities to reduce the model size and computational overhead, thus improving their efficiency while maintaining accuracy. The proposed solutions, collectively referred to as Mamba-Shedder, achieve a speedup of up to 1.4x during inference, demonstrating that model efficiency can be improved by eliminating several redundancies with minimal impact on the overall model performance. The code is available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.

• The paper introduces Mamba-Shedder, a structured pruning technique that enhances SSM efficiency by selectively removing redundant Transformer components.
• It presents detailed empirical analyses showing up to 1.4x inference speedups in Mamba and hybrid models without sacrificing accuracy.
• The study offers practical guidance for optimizing model performance in resource-constrained environments while maintaining robust outcomes.

Insights on Post-Transformer Compression with Mamba-Shedder

The research paper "Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models" explores an innovative approach to enhancing the efficiency of Selective Structured State Space Models (SSMs) by addressing specific inefficiencies inherent in Transformer-based architectures. This work focuses specifically on the Mamba models and their hybrid variants, examining how structured pruning can be utilized to augment computational efficiency during inference times, while maintaining model accuracy.

Selective Structured State Space Models like the Mamba have emerged as promising alternatives to traditional Transformer architectures, primarily due to their linear scaling capabilities with sequence length during training, alongside constant state sizes during generative tasks. Despite their inherent efficiency, understanding their redundancy and compressing them further without performance sacrifice presents significant research opportunities.

Key Contributions and Methodologies

The paper makes several noteworthy contributions:

• Pruning Solution Development: The introduction of Mamba-Shedder as a pruning technique specifically designed for selective SSMs enhances both computational and memory efficiency. This algorithm examines the structural components of Mamba models, identifying and pruning the least impactful elements.
• Empirical Analysis: A comprehensive set of experiments analyses the sensitivity of SSM architectures, distinguishing how the interdependencies of SSM and Transformer blocks within hybrid models determine the scope for efficiency-accuracy trade-offs.
• Hybrid Model Insights: The paper extends its scope to hybrid models like Zamba, which blend Mamba and Transformer blocks, assessing how different granularities of pruning (down to channel-level) impact these systems.

Experiments with Mamba-Shedder revealed that the Mamba models' efficiency could be markedly improved. When applied to the Mamba-2.8B and its successors, it achieved a 1.4x speedup during inference phases. These results highlight the potential to remove redundancies intrinsic to Mamba architectures effectively.

Performance Analysis

Results presented in the paper showed noteworthy findings:

1. Pruning Impact on Mamba Blocks: Mamba-1 models (with S6 blocks) displayed higher tolerance to whole block removal without proportional decreases in accuracy, compared to Mamba-2 models (with SSD blocks), which exhibited more sensitivity to block removal but showed resilience in SSM module pruning.
2. Structured Pruning in Hybrids: In hybrid models like Zamba2-2.7B, pruning not only entire Mamba and Transformer blocks but also at levels such as MLP blocks and channel groups, maintains competitive performance with substantial reductions in computational burden.
3. Efficiency Gains: These pruning strategies led to demonstrable inference accelerations—the paper documents a 1.29x speedup in Mamba-1 models and up to 1.39x in Zamba hybrid architectures after comprehensive pruning strategies were applied and followed by fine-tuning.

Practical and Theoretical Implications

The implications of this paper are manifold. Practically, the demonstrated improvements in inference speed suggest that models utilizing SSMs and their hybrids could potentially be deployed in resource-constrained environments without significant computational overheads. Theoretically, this work extends the understanding of model redundancy and component interdependence within novel architectures, informing future innovations in AI model development.

In summary, the research outlined in "Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models" contributes to the ongoing evolution of efficient sequence models. It presents substantial evidence for the effectiveness of structured pruning techniques in reducing computational demands while preserving accuracy, underscoring the potential of selective structured state space models as viable, efficient alternatives in modern AI applications. Future work will likely build on these findings to further refine pruning strategies and explore new synergies between different model components and architectures.