Transformer Attention With Soft Pruning for Efficient Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Transformer architectures face high computational complexity due to attention mechanisms, which are costly with complexity of O(N2d) for N nodes and d channels, limiting practicality and expressivity, especially in applications like image recognition and natural language processing.
Innovation Solution
Implement multi-neighborhood attention with soft pruning techniques, where different attention heads have varying neighborhood sizes and channel numbers, and apply soft masking to balance expressivity and efficiency by using a fixed training budget.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If attention mechanisms are used to focus on salient portions of input, then the model can process information more effectively, but the computational complexity increases to O(N^2d) which limits practicality
Solution Approach 1:
The input sequence is divided into multiple neighborhoods, and different attention heads are assigned to different neighborhoods. This segmentation allows the model to process local regions independently, reducing the overall computational complexity from O(N^2d) to O(Nkd) where k is the average neighborhood size, while still capturing important local patterns in the data
Solution Approach 2:
Different attention heads are configured with different neighborhood sizes and channel numbers to specialize in processing different types of local patterns. This local quality approach allows each attention head to be optimized for specific tasks, improving information processing effectiveness for particular patterns while keeping the overall computational cost manageable through selective application
2Adaptability or versatility
If the number of channels and neighborhood sizes are increased to improve model expressivity, then the model can capture more complex patterns, but the computational cost increases significantly
Solution Approach 1:
The model dynamically adjusts the number of channels and neighborhood sizes for different attention heads based on the specific task and input characteristics. During training, the architecture parameters are optimized to find the right balance between expressivity and computational cost, allowing the model to adapt its complexity to the requirements of each specific application rather than using a fixed high-cost configuration
Solution Approach 2:
The invention treats architecture parameters such as neighborhood size and channel number as learnable parameters that can be optimized during training. By changing these parameters dynamically based on task requirements and using soft pruning techniques with annealing, the model can achieve high expressivity when needed while reducing computational cost for simpler tasks, resolving the contradiction between adaptability and energy consumption
Data Source
AI summary
A processor-implemented method includes configuring a transformer model having multiple attention heads. Each attention head has a set of architecture parameters and weight parameters. The set of architecture parameters are determined for each attention head based on using a soft pruning technique according to a fixed training budget. In turn, the transformer model generates an inference based on the set architecture parameters and an input.