Basics

This section describes useful concepts across TorchOpt.

TorchOpt Types

`torchopt.base.GradientTransformation`(init, ...)	A pair of pure functions implementing a gradient transformation.
`torchopt.base.TransformInitFn`(args, *kwargs)	A callable type for the `init()` step of a `GradientTransformation`.
`torchopt.base.TransformUpdateFn`(args, *kwargs)	A callable type for the `update()` step of a `GradientTransformation`.

PyTrees

PyTrees is an essential concept in TorchOpt. They can be thought as a generalization of vectors. They are a way to structure parameters or weights using tuples and dictionaries. Many solvers in TorchOpt have native support for pytrees.

Floating-Point Precision

TorchOpt uses single (32-bit) floating precision (torch.float32) by default. However, for some algorithms, this may not be enough. Double (64-bit) floating precision (torch.float64) can be enabled by adding the following lines at the beginning of the file:

import torch

torch.set_default_dtype(torch.float64)