This paper introduces Jittor, a fully just-in-time (JIT) compiled deep learning framework. With JIT compilation, we can achieve higher performance while making systems highly customizable. Jittor provides classes of Numpy-like operators, which we call meta-operators. A deep learning model built upon these meta-operators is compiled into high-performance CPU or GPU code in real-time. To manage metaoperators, Jittor uses a highly optimized way of executing computation graphs, which we call unified graph execution. This approach is as easy to use as dynamic graph execution yet has the efficiency of static graph execution. It also provides other improvements, including operator fusion, cross iteration fusion, and unified memory.

本文介绍了Jittor，这是一个完全实时（JIT）编译的深度学习框架。通过JIT编译，我们可以在使系统高度可定制的同时实现更高的性能。Jittor提供了类似Numpy运算符的类，我们称之为元运算符。基于这些元运算符的深度学习模型可实时编译为高性能CPU或GPU代码。为了管理元运算符，Jittor使用了一种高度优化的方式来执行计算图，我们称其为统一图执行。这种方法与动态图执行一样容易使用，但具有静态图执行的效率。它还提供了其他改进，包括运算符融合，交叉迭代融合和统一内存。