Learn · 1

Tensors

A tensor is an array with a shape, living in GPU memory. Everything below runs in this page — edit any block and press Run, or ⌘/Ctrl + Enter.

Acquiring the device

await init() comes before anything else. Getting a WebGPU adapter is asynchronous and there is no way around it — call it once, then forget it. If it fails, borch stops there rather than falling back to something slower.

Making one

Tensor.from(data, shape) takes a flat array and the shape you want. The factory names are torch's: zeros, ones, arange, randn, eye, linspace.

Reading values is awaited

This is the second place borch differs from torch. The numbers live in GPU memory, and getting them back to JavaScript is a copy — so item() and toArray() return promises. Forward and backward passes are synchronous; only reading crosses back. Reductions that torch splits in two are split here too: amax() gives the value, max(dim) gives { values, indices }.

Operations

Elementwise operations broadcast the way torch's do. Note that operators take tensors, not numbers — JavaScript has no operator overloading, so a scalar is written Tensor.full([], 5), and powScalar(k) exists for the common case.

Shapes and views

reshape, transpose, slice and friends give you a view onto the same storage where torch would — the conformance suite checks that 13 of these share storage exactly as torch does, because a copy where torch shares is a bug you only notice much later.