
layout: blog_detail
title: 'Tensor Comprehensions in PyTorch'
author: Priya Goyal (FAIR), Nicolas Vasilache (FAIR), Oleksandr Zinenko (Inria & DI ENS), Theodoros Theodoridis (ETH Zürich), Zachary DeVito (FAIR), William S. Moses (MIT CSAIL), Sven Verdoolaege (FAIR), Andrew Adams (FAIR), Albert Cohen (Inria & DI ENS & FAIR)
redirect_from: /2018/03/05/tensor-comprehensions.html

Tensor Comprehensions (TC) is a tool that lowers the barrier for writing high-performance code. It generates GPU code from a simple high-level language and autotunes the code for specific input sizes.
We highly recommend reading the Tensor Comprehensions blogpost first.
If you ran into any of the following scenarios, TC is a useful tool for you.

Your PyTorch layer is large and slow, and you contemplated writing a dedicated C++ or CUDA code for it. But you don't know how to program in CUDA or write low-level code.
