Transformer^-1: Input-Adaptive Computation for Resource-Constrained Deployment

• URL: http://arxiv.org/abs/2501.16394v1
• Date: Sun, 26 Jan 2025 15:31:45 GMT
• Title: Transformer^-1: Input-Adaptive Computation for Resource-Constrained Deployment
• Authors: Lumen AI, Tengzhou No. 1 Middle School, Shihao Ji, Zihui Song, Fucheng Zhong, Jisen Jia, Zhaobo Wu, Zheyi Cao, Xu Tianhao,
• Abstract summary: This paper proposes a Transformer$-1$ architecture to address the resource waste caused by fixed computation paradigms in deep learning models under dynamic scenarios. In a benchmark test, our method reduces FLOPs by 42.7% and peak memory usage by 3% compared to the standard Transformer. We also conducted experiments on several natural language processing tasks and achieved significant improvements in resource efficiency.
• Score: 3.6219999155937113
• License:
• Abstract: Addressing the resource waste caused by fixed computation paradigms in deep learning models under dynamic scenarios, this paper proposes a Transformer$^{-1}$ architecture based on the principle of deep adaptivity. This architecture achieves dynamic matching between input features and computational resources by establishing a joint optimization model for complexity and computation. Our core contributions include: (1) designing a two-layer control mechanism, composed of a complexity predictor and a reinforcement learning policy network, enabling end-to-end optimization of computation paths; (2) deriving a lower bound theory for dynamic computation, proving the system's theoretical reach to optimal efficiency; and (3) proposing a layer folding technique and a CUDA Graph pre-compilation scheme, overcoming the engineering bottlenecks of dynamic architectures. In the ImageNet-1K benchmark test, our method reduces FLOPs by 42.7\% and peak memory usage by 34.1\% compared to the standard Transformer, while maintaining comparable accuracy ($\pm$0.3\%). Furthermore, we conducted practical deployment on the Jetson AGX Xavier platform, verifying the effectiveness and practical value of this method in resource-constrained environments. To further validate the generality of the method, we also conducted experiments on several natural language processing tasks and achieved significant improvements in resource efficiency.

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