The Solution for the AIGC Inference Performance Optimization Competition
Sishun Pan, Haonan Xu, Zhonghua Wan, Yang Yang

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…
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Taxonomy
TopicsFault Detection and Control Systems
MethodsLinear Layer · Multi-Head Attention · Attention Is All You Need · Softmax · Byte Pair Encoding · Layer Normalization · Label Smoothing · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Adam
