jax.jit#

jax.jit(fun, in_shardings=UnspecifiedValue, out_shardings=UnspecifiedValue, static_argnums=None, static_argnames=None, donate_argnums=(), keep_unused=False, device=None, backend=None, inline=False, abstracted_axes=None)[source]#

Sets up fun for just-in-time compilation with XLA.

Parameters

  • fun (Callable) –

    Function to be jitted. fun should be a pure function, as side-effects may only be executed once.

    The arguments and return value of fun should be arrays, scalars, or (nested) standard Python containers (tuple/list/dict) thereof. Positional arguments indicated by static_argnums can be anything at all, provided they are hashable and have an equality operation defined. Static arguments are included as part of a compilation cache key, which is why hash and equality operators must be defined.

    JAX keeps a weak reference to fun for use as a compilation cache key, so the object fun must be weakly-referenceable. Most Callable objects will already satisfy this requirement.

  • in_shardings –

    Pytree of structure matching that of arguments to fun, with all actual arguments replaced by resource assignment specifications. It is also valid to specify a pytree prefix (e.g. one value in place of a whole subtree), in which case the leaves get broadcast to all values in that subtree.

    The in_shardings argument is optional. JAX will infer the shardings from the input jax.Array’s and defaults to replicating the input if the sharding cannot be inferred.

    The valid resource assignment specifications are:
    • XLACompatibleSharding, which will decide how the value

      will be partitioned. With this, using a mesh context manager is not required.

    The size of every dimension has to be a multiple of the total number of resources assigned to it. This is similar to pjit’s in_shardings.

  • out_shardings –

    Like in_shardings, but specifies resource assignment for function outputs. This is similar to pjit’s out_shardings.

    The out_shardings argument is optional. If not specified, jax.jit() will use GSPMD’s sharding propagation to figure out what the sharding of the output(s) should be.

  • static_argnums (Union[None, int, Sequence[int]]) –

    An optional int or collection of ints that specify which positional arguments to treat as static (compile-time constant). Operations that only depend on static arguments will be constant-folded in Python (during tracing), and so the corresponding argument values can be any Python object.

    Static arguments should be hashable, meaning both __hash__ and __eq__ are implemented, and immutable. Calling the jitted function with different values for these constants will trigger recompilation. Arguments that are not arrays or containers thereof must be marked as static.

    If neither static_argnums nor static_argnames is provided, no arguments are treated as static. If static_argnums is not provided but static_argnames is, or vice versa, JAX uses inspect.signature(fun) to find any positional arguments that correspond to static_argnames (or vice versa). If both static_argnums and static_argnames are provided, inspect.signature is not used, and only actual parameters listed in either static_argnums or static_argnames will be treated as static.

  • static_argnames (Union[str, Iterable[str], None]) – An optional string or collection of strings specifying which named arguments to treat as static (compile-time constant). See the comment on static_argnums for details. If not provided but static_argnums is set, the default is based on calling inspect.signature(fun) to find corresponding named arguments.

  • donate_argnums (Union[int, Sequence[int]]) –

    Specify which positional argument buffers are “donated” to the computation. It is safe to donate argument buffers if you no longer need them once the computation has finished. In some cases XLA can make use of donated buffers to reduce the amount of memory needed to perform a computation, for example recycling one of your input buffers to store a result. You should not reuse buffers that you donate to a computation, JAX will raise an error if you try to. By default, no argument buffers are donated. Note that donate_argnums only work for positional arguments, and keyword arguments will not be donated.

    For more details on buffer donation see the FAQ.

  • keep_unused (bool) – If False (the default), arguments that JAX determines to be unused by fun may be dropped from resulting compiled XLA executables. Such arguments will not be transferred to the device nor provided to the underlying executable. If True, unused arguments will not be pruned.

  • device (Optional[Device]) – This is an experimental feature and the API is likely to change. Optional, the Device the jitted function will run on. (Available devices can be retrieved via jax.devices().) The default is inherited from XLA’s DeviceAssignment logic and is usually to use jax.devices()[0].

  • backend (Optional[str]) – This is an experimental feature and the API is likely to change. Optional, a string representing the XLA backend: 'cpu', 'gpu', or 'tpu'.

  • inline (bool) – Specify whether this function should be inlined into enclosing jaxprs (rather than being represented as an application of the xla_call primitive with its own subjaxpr). Default False.

Return type

Wrapped

Returns

A wrapped version of fun, set up for just-in-time compilation.

Examples

In the following example, selu can be compiled into a single fused kernel by XLA:

>>> import jax
>>>
>>> @jax.jit
... def selu(x, alpha=1.67, lmbda=1.05):
...   return lmbda * jax.numpy.where(x > 0, x, alpha * jax.numpy.exp(x) - alpha)
>>>
>>> key = jax.random.PRNGKey(0)
>>> x = jax.random.normal(key, (10,))
>>> print(selu(x))  
[-0.54485  0.27744 -0.29255 -0.91421 -0.62452 -0.24748
-0.85743 -0.78232  0.76827  0.59566 ]

To pass arguments such as static_argnames when decorating a function, a common pattern is to use functools.partial():

>>> from functools import partial
>>>
>>> @partial(jax.jit, static_argnames=['n'])
... def g(x, n):
...   for i in range(n):
...     x = x ** 2
...   return x
>>>
>>> g(jnp.arange(4), 3)
Array([   0,    1,  256, 6561], dtype=int32)
Parameters

abstracted_axes (Optional[Any]) –