COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression
arXiv:2602.15200v1 Announce Type: new Abstract: Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from iterative dictionary and coefficient updates. We propose COMPOT (Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers), a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs orthogonal dictionaries that enable closed-form Procrustes updates for the dictionary and analytical single-step sparse coding for the coefficients, eliminating iterative optimization. To handle heterogeneous layer sensitivity under a global compression budget, COMPOT further introduces a one-shot dynamic allocation strategy that adapti
arXiv:2602.15200v1 Announce Type: new Abstract: Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from iterative dictionary and coefficient updates. We propose COMPOT (Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers), a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs orthogonal dictionaries that enable closed-form Procrustes updates for the dictionary and analytical single-step sparse coding for the coefficients, eliminating iterative optimization. To handle heterogeneous layer sensitivity under a global compression budget, COMPOT further introduces a one-shot dynamic allocation strategy that adaptively redistributes layer-wise compression rates. Extensive experiments across diverse architectures and tasks show that COMPOT consistently delivers a superior quality-compression trade-off over strong low-rank and sparse baselines, while remaining fully compatible with post-training quantization for extreme compression. Code is available $\href{https://github.com/mts-ai/COMPOT}{here}$.
Executive Summary
The article 'COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression' introduces a novel, training-free framework for compressing Transformer models. The authors address the limitations of traditional truncated singular value decomposition (SVD) and iterative sparse dictionary learning methods by proposing a method that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT leverages orthogonal dictionaries and Procrustes updates, enabling closed-form solutions for dictionary and coefficient updates, thus eliminating the need for iterative optimization. The framework also includes a dynamic allocation strategy to adaptively redistribute compression rates across layers, ensuring optimal performance under a global compression budget. The study demonstrates that COMPOT outperforms existing low-rank and sparse baselines across various architectures and tasks, while remaining compatible with post-training quantization for extreme compression.
Key Points
- ▸ COMPOT is a training-free compression framework for Transformer models.
- ▸ It uses a small calibration dataset to estimate sparse weight factorization.
- ▸ Orthogonal dictionaries and Procrustes updates enable closed-form solutions, eliminating iterative optimization.
- ▸ A dynamic allocation strategy adaptively redistributes layer-wise compression rates.
- ▸ COMPOT outperforms existing baselines and is compatible with post-training quantization.
Merits
Innovative Approach
COMPOT introduces a novel method for compressing Transformer models that avoids the limitations of traditional SVD and iterative sparse dictionary learning. The use of orthogonal dictionaries and Procrustes updates is a significant advancement in the field.
Efficiency
By eliminating iterative optimization, COMPOT significantly reduces the computational overhead associated with model compression. This makes it a more efficient solution for large-scale applications.
Adaptability
The dynamic allocation strategy allows COMPOT to adaptively redistribute compression rates across layers, ensuring optimal performance under a global compression budget. This flexibility is crucial for handling heterogeneous layer sensitivity.
Demerits
Dependency on Calibration Dataset
The effectiveness of COMPOT relies heavily on the quality and representativeness of the calibration dataset. A poorly chosen dataset could lead to suboptimal compression results.
Complexity in Implementation
While the method is training-free, the implementation of orthogonal dictionaries and Procrustes updates may require specialized knowledge and computational resources, potentially limiting its accessibility.
Generalizability
The study demonstrates superior performance across diverse architectures and tasks, but further validation is needed to ensure its generalizability to all types of Transformer models and applications.
Expert Commentary
The article presents a significant advancement in the field of Transformer model compression. The authors' innovative use of orthogonal dictionaries and Procrustes updates addresses key limitations of traditional methods, offering a more efficient and adaptable solution. The dynamic allocation strategy is particularly noteworthy, as it allows for optimal performance under varying compression budgets. However, the dependency on a calibration dataset and the potential complexity in implementation are important considerations. The study's extensive experiments across diverse architectures and tasks provide strong evidence of COMPOT's effectiveness, but further research is needed to validate its generalizability. Overall, COMPOT represents a promising direction for future research in model compression and efficient machine learning.
Recommendations
- ✓ Further validation of COMPOT across a broader range of Transformer models and tasks to ensure its generalizability.
- ✓ Exploration of methods to reduce the dependency on calibration datasets, potentially through automated or semi-supervised approaches.
- ✓ Development of user-friendly tools and libraries to facilitate the implementation of COMPOT, making it more accessible to researchers and practitioners.
