The Solution for the AIGC Inference Performance Optimization Competition

TL;DR

This paper presents an optimized inference solution for Ernie models that significantly accelerates GPU-based processing using techniques like model pruning, FP16 precision, and multi-process data handling, achieving nearly ninefold speed improvements.

Contribution

The paper introduces a comprehensive optimization approach for Ernie model inference, combining model pruning, mixed-precision computation, and parallel data processing to enhance speed on GPU hardware.

Findings

Achieved up to 8.96x inference speedup

Maintained competitive model performance

Demonstrated effectiveness of combined optimization techniques

Abstract

In recent years, the rapid advancement of large-scale pre-trained language models based on transformer architectures has revolutionized natural language processing tasks. Among these, ChatGPT has gained widespread popularity, demonstrating human-level conversational abilities and attracting over 100 million monthly users by late 2022. Concurrently, Baidu's commercial deployment of the Ernie Wenxin model has significantly enhanced marketing effectiveness through AI-driven technologies. This paper focuses on optimizing high-performance inference for Ernie models, emphasizing GPU acceleration and leveraging the Paddle inference framework. We employ techniques such as Faster Transformer for efficient model processing, embedding layer pruning to reduce computational overhead, and FP16 half-precision inference for enhanced computational efficiency. Additionally, our approach integrates efficient data handling strategies using multi-process parallel processing to minimize latency. Experimental results demonstrate that our optimized solution achieves up to an 8.96x improvement in inference speed compared to standard methods, while maintaining competitive performance.