Transformer Head Pruning for Low-Memory NLP Inference
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Solution Overview
Problem
Transformer-based natural language processing models face challenges such as high memory usage, high prediction latency, high power dissipation, poor inference performance on resource-constrained devices, poor ease of training/fine-tuning, and difficulty in deployment and maintenance due to their large size.
Innovation Solution
Pruning techniques are applied to reduce the size of transformer models by removing self-attention heads while maintaining performance levels, using methods like A* search to optimize the pruning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer models are used for natural language processing, then processing capability and accuracy are improved, but memory usage and computational resources increase
Solution Approach 1:
The patent extracts and removes redundant self-attention heads from the transformer model architecture. By identifying and eliminating unnecessary attention heads through pruning techniques, the model achieves reduced memory usage and computational resource requirements while maintaining core processing capabilities through the retained attention mechanisms.
2Measurement precision
If transformer models are used for natural language processing, then processing capability is improved, but training time increases
Solution Approach 1:
The patent applies extraction by removing redundant self-attention heads that contribute to extended training times. The pruning process eliminates unnecessary computational components, thereby reducing the time required for training while preserving the essential language processing capabilities through the remaining attention heads.
3Measurement precision
If transformer models are used for natural language processing, then processing capability is improved, but power dissipation increases
Solution Approach 1:
The patent removes redundant self-attention heads that cause excessive power dissipation. By extracting and eliminating these unnecessary components, the model maintains its language processing capabilities while significantly reducing energy consumption and heat generation during operation.
4Measurement precision
If transformer models are used for natural language processing, then processing capability is improved, but ease of deployment on resource-constrained devices deteriorates
Solution Approach 1:
The patent extracts and removes redundant self-attention heads to create a pruned model that is easier to deploy on resource-constrained devices. The pruning process reduces computational complexity and resource requirements, making the model suitable for deployment on devices with limited processing power while preserving essential language processing capabilities.
Data Source
AI summary
An example electronic computing device can include: a processor; and a system memory, the system memory including instructions which, when executed by the processor, cause the electronic computing device to: receive a model for natural language processing of data, the model including a plurality of self-attention heads; prune the model by removing one or more of the plurality of self-attention heads of the model to create a pruned model; and evaluate a classification accuracy of the pruned model to maintain a performance level.