
layout: blog_detail
title: 'The torch.linalg module: Accelerated Linear Algebra with Autograd in PyTorch'
author: Mike Ruberry, Ivan Yashchuk, Xiao Wang, Mario Lezcano and Natalia Gimelshein
featured-img: 'assets/images/cholesky-decomposition.png'

Linear algebra is essential to deep learning and scientific computing, and it’s always been a core part of PyTorch. PyTorch 1.9 extends PyTorch’s support for linear algebra operations with the torch.linalg module. This module, documented here, has 26 operators, including faster and easier to use versions of older PyTorch operators, every function from NumPy’s linear algebra module extended with accelerator and autograd support, and a few operators that are completely new. This makes the torch.linalg immediately familiar to NumPy users and an exciting update to PyTorch’s linear algebra support.