This is my note for reading PyTorch’s JIT source. We begin by looking at torch.jit.script to find the frontend that compiles the Python code into PyTorch’s tree views, and the backend that compiles tree views to graph. We also read the structure of the internal representation of PyTorch’s graph. Finally we go to graph executor to look at how the computation graph is further compiled into instructions and how the action of these instructions are defined and executed.

This is my note for reading PyTorch’s JIT source. We begin by looking at torch.jit.script and torch.jit.script_method to find the frontend that compiles the Python code into PyTorch’s tree views, and the backend that compiles tree views to graph. We also read the structure of the internal representation of PyTorch’s graph. Finally we go to graph executor to look at how the computation graph is further compiled into instructions and how the action of these instructions are defined and executed.

这篇是阅读PyTorch源代码整理的笔记，方便以后翻阅。这里主要是想知道PyTorch的operators的定义都是怎么组织的，以及如果要添加新的operator的话，该怎么做。

计算学习理论中的一类很重要的问题是研究训练集训练出来的模型在训练集以外的表现，即泛化误差（generalization error）。Rademacher复杂度就是用来描述泛化误差的数学工具。