SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference (2502.18137v5)

Abstract: An efficient attention implementation is essential for large models due to its quadratic time complexity. Fortunately, attention commonly exhibits sparsity, i.e., many values in the attention map are near zero, allowing for the omission of corresponding computations. Many studies have utilized the sparse pattern to accelerate attention. However, most existing works focus on optimizing attention within specific models by exploiting certain sparse patterns of the attention map. A universal sparse attention that guarantees both the speedup and end-to-end performance of diverse models remains elusive. In this paper, we propose SpargeAttn, a universal sparse and quantized attention for any model. Our method uses a two-stage online filter: in the first stage, we rapidly and accurately predict the attention map, enabling the skip of some matrix multiplications in attention. In the second stage, we design an online softmax-aware filter that incurs no extra overhead and further skips some matrix multiplications. Experiments show that our method significantly accelerates diverse models, including language, image, and video generation, without sacrificing end-to-end metrics. The codes are available at https://github.com/thu-ml/SpargeAttn.

SpargeAttn: Universal Sparse Attention for Efficient Model Inference

The development and deployment of large-scale models across natural language processing, computer vision, and various AI applications are increasingly constrained by the quadratic time complexity of the attention mechanism. The paper "SpargeAttn: Accurate Sparse Attention Accelerating Any Model Inference" presents an innovative solution to this challenge by introducing SpargeAttn, a universal sparse and quantized attention method that accelerates model inference without any degradation in model performance.

Key Contributions

1. Sparse Attention with Universality: Unlike existing sparse attention mechanisms that are typically tailored to specific models and task patterns, SpargeAttn is designed for universal applicability across different types of models, including those in language, image, and video domains. The method achieves this universality by employing a novel two-stage online filter approach to handle attention map sparsity dynamically during inference.
2. Two-stage Online Filter Mechanism:
   • Stage 1: The method rapidly predicts the sparse regions of the attention map, allowing for the skipping of matrix multiplications where entries contribute negligibly. This process is facilitated using a selective token compression technique, which compresses the inner blocks of the query (Q) and key (K) based on their self-similarity, thus rapidly computing a pattern-free sparse mask.
   • Stage 2: Additionally, the online softmax-aware filter identifies and skips negligible multiplications, further optimizing the computational efficiency.
3. Quantization Integration: The SpargeAttn framework incorporates quantization, specifically within the SageAttention architecture, to enhance the speed of attention operations without compromising error metrics.

Experimental Results and Implications

Extensive experiments validate the efficacy of SpargeAttn across a spectrum of generative tasks, outperforming traditional dense and other sparse attention methods on both speed and computational efficiency while maintaining model accuracy. Key findings include:

• Speed Improvements: SpargeAttn delivers significant speedups ranging from 2.5x to 5x compared to both dense attention mechanisms and existing sparse attention baselines across all tested models.
• Minimal Overhead: The innovative dynamic approach to handling sparsity introduces minimal computational overhead, with the prediction phase of sparse blocks comprising a small fraction of overall attention operation latency.
• Error Metrics: There is a consistent retention of model quality across end-to-end performance metrics, indicating the robustness of the method in practical deployment scenarios.

The results demonstrate that the proposed attention framework is poised to have substantial practical implications for accelerating inference across diverse computational tasks without necessitating model retraining or architecture-specific modifications. The universal application and plug-and-play compatibility with existing model architectures present opportunities for its adoption in large models with extended sequence lengths, such as those used in NLP and computer vision tasks.

Future Directions

The theoretical and practical advancements introduced by SpargeAttn open the door for several avenues of future research:

• Adaptability: Investigating the adaptability of SpargeAttn within more specialized forms of attention, such as those oriented towards hierarchical models or real-time systems.
• Hardware Optimization: Deepening the integration of SpargeAttn with emerging AI hardware accelerators to further enhance computational efficiency.
• Cross-Compatibility: Assessing SpargeAttn's performance in concert with other optimization techniques, including mixed-precision training and distributed computing paradigms, to harness complementary efficiencies elsewhere within the model training and inference pipeline.

In conclusion, SpargeAttn presents a promising shift towards universally applicable, sparse, and efficient attention mechanisms, offering an enhanced framework independent of the model's specific domain requirements and providing a scalable solution to growing inference demands in contemporary large-scale AI deployments.