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PyTorch Internals: Ezyang's Blog
internals This post is a long form essay version of a talk about PyTorch internals, that I gave at the PyTorch NYC meetup on May 14, 2019. Hi everyone! Today I want to talk about the internals of PyTorch.
The purpose of this talk is to put a map in your hands: to tell you about the basic conceptual structure of a "tensor library that supports automatic differentiation", and give you some tools and tricks for finding your way around the codebase. In more descriptive terms, it implements the binding code that translates between the Python and C++ universe, and also some pretty important pieces of PyTorch, like the autograd engine and the JIT compiler. You can download and run the Docker images locally The CONTRIBUTING guide explains how to setup ccache; this is highly recommended, because sometimes it will help you get lucky and avoid a massive recompile when you edit a header.
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