📄 中文摘要
该研究提出了一种通过CUDA流交错来隐藏主机与设备之间的同步，以优化PyTorch解码器模型中的令牌生成过程。通过有效管理CUDA流，可以减少GPU和CPU之间的等待时间，从而提高模型的推理速度和整体性能。此方法不仅提升了计算效率，还为大规模模型的实时应用提供了可能性，尤其是在自然语言处理和生成任务中，优化后的模型能够更快速地生成高质量的输出。研究结果表明，流的交错使用显著改善了令牌生成的延迟，推动了深度学习模型在实际应用中的可用性。
优化 PyTorch 解码器模型中的令牌生成
出处: Optimizing Token Generation in PyTorch Decoder Models
发布: 2026年2月24日
📄 English Summary
Optimizing Token Generation in PyTorch Decoder Models
This research presents a method to hide host-device synchronization in PyTorch decoder models through CUDA stream interleaving, optimizing the token generation process. By effectively managing CUDA streams, the waiting time between GPU and CPU can be reduced, enhancing the inference speed and overall performance of the model. This approach not only improves computational efficiency but also enables real-time applications of large-scale models, particularly in natural language processing and generation tasks, where the optimized model can generate high-quality outputs more rapidly. Results indicate that the interleaving of streams significantly reduces the latency in token generation, advancing the usability of deep learning models in practical applications.