Scatterbrain: Unifying Sparse and Low-rank Attention Approximation (2110.15343v1)

Abstract: Recent advances in efficient Transformers have exploited either the sparsity or low-rank properties of attention matrices to reduce the computational and memory bottlenecks of modeling long sequences. However, it is still challenging to balance the trade-off between model quality and efficiency to perform a one-size-fits-all approximation for different tasks. To better understand this trade-off, we observe that sparse and low-rank approximations excel in different regimes, determined by the softmax temperature in attention, and sparse + low-rank can outperform each individually. Inspired by the classical robust-PCA algorithm for sparse and low-rank decomposition, we propose Scatterbrain, a novel way to unify sparse (via locality sensitive hashing) and low-rank (via kernel feature map) attention for accurate and efficient approximation. The estimation is unbiased with provably low error. We empirically show that Scatterbrain can achieve 2.1x lower error than baselines when serving as a drop-in replacement in BigGAN image generation and pre-trained T2T-ViT. On a pre-trained T2T Vision transformer, even without fine-tuning, Scatterbrain can reduce 98% of attention memory at the cost of only 1% drop in accuracy. We demonstrate Scatterbrain for end-to-end training with up to 4 points better perplexity and 5 points better average accuracy than sparse or low-rank efficient transformers on LLMing and long-range-arena tasks.

Scatterbrain: Unifying Sparse and Low-Rank Attention Approximation

The paper "Scatterbrain: Unifying Sparse and Low-rank Attention Approximation" addresses the computational and memory challenges associated with modeling long sequences in efficient Transformer models. Recent endeavors have focused either on sparse or low-rank properties of attention matrices. Both approaches offer reductions in the computational overhead but are generally optimized within different operational regimes. To this end, the authors propose Scatterbrain, a novel method that unifies sparse and low-rank approximation strategies to optimize both computational efficiency and model performance.

Key Contributions

1. Unified Approximation Approach: The investigation reveals that sparse and low-rank approximations for attention matrices excel under different softmax temperature regimes in attention. Scatterbrain synthesizes these techniques using locality sensitive hashing for sparse approximations and kernel feature maps for low-rank approximations.
2. Error and Efficiency: Scatterbrain delivers an unbiased estimation with provably low error. Empirical results indicate that it achieves a $2.1 \times$ reduction in error compared to existing baselines when deployed in BigGAN image generation and pre-trained T2T-ViT models.
3. Performance in Pre-Trained Models: Remarkably, Scatterbrain can achieve a 98\% reduction in attention memory with only a 1\% drop in accuracy when implemented in a pre-trained T2T Vision Transformer, without requiring fine-tuning.
4. Enhanced Model Training: The framework demonstrates superior performance metrics with improvements of up to 4 points in perplexity and 5 points in average accuracy over existing sparse or low-rank transformers for LLMs and long-range tasks.

Implications

In practical terms, Scatterbrain promises significant reductions in computational resource requirements for models dealing with extensive sequences. This can potentially facilitate more scalable model deployment and can be instrumental in applications with limited hardware resources. Theoretically, this research bridges the gap between sparse and low-rank approximations by illustrating their complementary strengths and integrating them into a unified framework.

Future Directions

The paper sets an intriguing precedent for future work to explore adaptive mechanisms that dynamically select or balance sparse and low-rank components based on specific task requirements or sequence characteristics. Moreover, the adaptability of Scatterbrain across different domains such as natural language processing and computer vision underscores the potential to expand this framework for various types of attention mechanisms beyond Transformers.

In conclusion, Scatterbrain presents a valuable amalgamation of sparse and low-rank techniques, enhancing our ability to efficiently manage long-sequence modeling in Transformer architectures. This development opens avenues for further exploration in efficient approximation methods and their applications across diverse fields in artificial intelligence.