Page Maintainers: @zou3519

• 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

• Read throughthe whirlwind tour
• Read through theadvanced autodiff tutorial
• Read through theper-sample-gradients tutorial
• Read through themodel ensembling tutorial

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 thatf accepts multiple Tensor arguments and returns a single Tensor argument.

Read through thisgdoc.

Back to theCore Frontend Onboarding