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function transforms (aka torch.func, functorch)

Manuel edited this page 2024-07-03 19:36:27 +02:00

Table of Contents

• Scope
• Learn about function transforms
• Exercise
• Understand how PyTorch implements composable function transforms
• Next

Page Maintainers: @zou3519

Scope

• understand what composable function transforms are and their most common use cases
• understand what DynamicLayerStack is and how it is used to implement composition of function transforms

Learn about function transforms

• Read through the whirlwind tour
• Read through the advanced autodiff tutorial
• Read through the per-sample-gradients tutorial
• Read through the model ensembling tutorial

Exercise

The advanced autodiff tutorial explains how to compute Jacobians via a composition of vmap and vjp.

1. Without looking at the source code for jacfwd or torch.autograd.functional.jacobian, write a function to compute the Jacobian using forward-mode AD and a for-loop. Note that forward-mode AD computes Jacobian-vector products while reverse-mode AD (vjp, grad) compute vector-Jacobian products.
2. Write a function to compute the Jacobian by composing vmap and jvp.

The APIs should have the following signature:

def jacobian(f, *args):
    pass

You can assume that f accepts multiple Tensor arguments and returns a single Tensor argument.

Understand how PyTorch implements composable function transforms

Read through this gdoc.

Next

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