Diving Deep into PyTorch Internals: Tensors, Autograd, and Kernel Writing

This blog post provides a detailed exploration of PyTorch's internals, covering tensor data structures, automatic differentiation (Autograd), and kernel writing. It begins by explaining the underlying implementation of tensors, including the concept of strides and how to use them to create tensor views. Next, it delves into the workings of Autograd, showing how gradients are computed via backpropagation. Finally, the post offers a practical guide to writing PyTorch kernels, including how to leverage PyTorch's tools for error checking, dtype dispatch, and parallelization. This is an excellent tutorial for developers with some PyTorch experience who want to understand its internals or contribute code.