functorch is JAX-like composable function transforms for PyTorch.
We’ve integrated functorch into PyTorch. As the final step of the integration, the functorch APIs are deprecated as of PyTorch 2.0. Please use the torch.func APIs instead and see the migration guide and docs for more details.
What are composable function transforms?¶
A “function transform” is a higher-order function that accepts a numerical function and returns a new function that computes a different quantity.
functorch has auto-differentiation transforms (
grad(f)returns a function that computes the gradient of
f), a vectorization/batching transform (
vmap(f)returns a function that computes
fover batches of inputs), and others.
These function transforms can compose with each other arbitrarily. For example, composing
vmap(grad(f))computes a quantity called per-sample-gradients that stock PyTorch cannot efficiently compute today.
Why composable function transforms?¶
There are a number of use cases that are tricky to do in PyTorch today:
computing per-sample-gradients (or other per-sample quantities)
running ensembles of models on a single machine
efficiently batching together tasks in the inner-loop of MAML
efficiently computing Jacobians and Hessians
efficiently computing batched Jacobians and Hessians
vjp() transforms allows us to express the above without designing a separate subsystem for each.
This idea of composable function transforms comes from the JAX framework.
Check out our whirlwind tour or some of our tutorials mentioned below.