- jax.jit(fun, *, static_argnums=None, static_argnames=None, device=None, backend=None, donate_argnums=(), inline=False, keep_unused=False, abstracted_axes=None)#
funfor just-in-time compilation with XLA.
Function to be jitted.
funshould be a pure function, as side-effects may only be executed once.
The arguments and return value of
funshould be arrays, scalars, or (nested) standard Python containers (tuple/list/dict) thereof. Positional arguments indicated by
static_argnumscan 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
funfor use as a compilation cache key, so the object
funmust be weakly-referenceable. Most
Callableobjects will already satisfy this requirement.
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
__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.
static_argnamesis provided, no arguments are treated as static. If
static_argnumsis not provided but
static_argnamesis, or vice versa, JAX uses
inspect.signature(fun)to find any positional arguments that correspond to
static_argnames(or vice versa). If both
inspect.signatureis not used, and only actual parameters listed in either
static_argnameswill be treated as static.
None]) – An optional string or collection of strings specifying which named arguments to treat as static (compile-time constant). See the comment on
static_argnumsfor details. If not provided but
static_argnumsis set, the default is based on calling
inspect.signature(fun)to find corresponding named arguments.
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
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.
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.
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.
- Return type
A wrapped version of
fun, set up for just-in-time compilation.
In the following example,
selucan 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_argnameswhen decorating a function, a common pattern is to use
>>> 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)