YuYan 11B — это масштабная языковая модель, созданная для эффективного внедрения ИИ в реальные бизнес-процессы. Благодаря интеграции с решением Easy and Efficient Transformer (EET), нейросеть обеспечивает высокую скорость генерации текста при минимальных затратах на инфраструктуру.
Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce inference costs. To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels. First, we design highly optimized kernels for long inputs and large hidden sizes. Second, we propose a flexible CUDA memory manager to reduce the memory footprint when deploying a large model. Compared with the state-of-the-art transformer inference library (Faster Transformer v4.0), EET can achieve an average of 1.40-4.20x speedup on the transformer decoder layer with an A100 GPU