class jax.tree_util.Partial(func, *args, **kw)[source]#

A version of functools.partial that works in pytrees.

Use it for partial function evaluation in a way that is compatible with JAX’s transformations, e.g., Partial(func, *args, **kwargs).

(You need to explicitly opt-in to this behavior because we didn’t want to give functools.partial different semantics than normal function closures.)

For example, here is a basic usage of Partial in a manner similar to functools.partial:

>>> import jax.numpy as jnp
>>> add_one = Partial(jnp.add, 1)
>>> add_one(2)
Array(3, dtype=int32, weak_type=True)

Pytree compatibility means that the resulting partial function can be passed as an argument within transformed JAX functions, which is not possible with a standard functools.partial function:

>>> from jax import jit
>>> @jit
... def call_func(f, *args):
...   return f(*args)
>>> call_func(add_one, 2)
Array(3, dtype=int32, weak_type=True)

Passing zero arguments to Partial effectively wraps the original function, making it a valid argument in JAX transformed functions:

>>> call_func(Partial(jnp.add), 1, 2)
Array(3, dtype=int32, weak_type=True)

Had we passed jnp.add to call_func directly, it would have resulted in a TypeError.

Note that if the result of Partial is used in the context where the value is traced, it results in all bound arguments being traced when passed to the partially-evaluated function:

>>> print_zero = Partial(print, 0)
>>> print_zero()
>>> call_func(print_zero)
Traced<ShapedArray(int32[], weak_type=True)>with<DynamicJaxprTrace(level=0/1)>





tuple of arguments to future partial calls


function object to use in future partial calls


dictionary of keyword arguments to future partial calls