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Define @jit and related decorators.
import sys
import warnings
import inspect
import logging
from numba.core.errors import DeprecationError, NumbaDeprecationWarning
from numba.stencils.stencil import stencil
from numba.core import config, extending, sigutils, registry
_logger = logging.getLogger(__name__)
# -----------------------------------------------------------------------------
# Decorators
_msg_deprecated_signature_arg = ("Deprecated keyword argument `{0}`. "
"Signatures should be passed as the first "
"positional argument.")
def jit(signature_or_function=None, locals={}, cache=False,
pipeline_class=None, boundscheck=None, **options):
This decorator is used to compile a Python function into native code.
The (optional) signature or list of signatures to be compiled.
If not passed, required signatures will be compiled when the
decorated function is called, depending on the argument values.
As a convenience, you can directly pass the function to be compiled
locals: dict
Mapping of local variable names to Numba types. Used to override the
types deduced by Numba's type inference engine.
target (deprecated): str
Specifies the target platform to compile for. Valid targets are cpu,
gpu, npyufunc, and cuda. Defaults to cpu.
pipeline_class: type numba.compiler.CompilerBase
The compiler pipeline type for customizing the compilation stages.
For a cpu target, valid options are:
nopython: bool
Set to True to disable the use of PyObjects and Python API
calls. The default behavior is to allow the use of PyObjects
and Python API. Default value is False.
forceobj: bool
Set to True to force the use of PyObjects for every value.
Default value is False.
looplift: bool
Set to True to enable jitting loops in nopython mode while
leaving surrounding code in object mode. This allows functions
to allocate NumPy arrays and use Python objects, while the
tight loops in the function can still be compiled in nopython
mode. Any arrays that the tight loop uses should be created
before the loop is entered. Default value is True.
error_model: str
The error-model affects divide-by-zero behavior.
Valid values are 'python' and 'numpy'. The 'python' model
raises exception. The 'numpy' model sets the result to
*+/-inf* or *nan*. Default value is 'python'.
inline: str or callable
The inline option will determine whether a function is inlined
at into its caller if called. String options are 'never'
(default) which will never inline, and 'always', which will
always inline. If a callable is provided it will be called with
the call expression node that is requesting inlining, the
caller's IR and callee's IR as arguments, it is expected to
return Truthy as to whether to inline.
NOTE: This inlining is performed at the Numba IR level and is in
no way related to LLVM inlining.
boundscheck: bool or None
Set to True to enable bounds checking for array indices. Out
of bounds accesses will raise IndexError. The default is to
not do bounds checking. If False, bounds checking is disabled,
out of bounds accesses can produce garbage results or segfaults.
However, enabling bounds checking will slow down typical
functions, so it is recommended to only use this flag for
debugging. You can also set the NUMBA_BOUNDSCHECK environment
variable to 0 or 1 to globally override this flag. The default
value is None, which under normal execution equates to False,
but if debug is set to True then bounds checking will be
A callable usable as a compiled function. Actual compiling will be
done lazily if no explicit signatures are passed.
The function can be used in the following ways:
1) jit(signatures, target='cpu', **targetoptions) -> jit(function)
Equivalent to:
d = dispatcher(function, targetoptions)
for signature in signatures:
Create a dispatcher object for a python function. Then, compile
the function with the given signature(s).
@jit("int32(int32, int32)")
def foo(x, y):
return x + y
@jit(["int32(int32, int32)", "float32(float32, float32)"])
def bar(x, y):
return x + y
2) jit(function, target='cpu', **targetoptions) -> dispatcher
Create a dispatcher function object that specializes at call site.
def foo(x, y):
return x + y
@jit(target='cpu', nopython=True)
def bar(x, y):
return x + y
if 'argtypes' in options:
raise DeprecationError(_msg_deprecated_signature_arg.format('argtypes'))
if 'restype' in options:
raise DeprecationError(_msg_deprecated_signature_arg.format('restype'))
if options.get('nopython', False) and options.get('forceobj', False):
raise ValueError("Only one of 'nopython' or 'forceobj' can be True.")
if 'target' in options:
target = options.pop('target')
warnings.warn("The 'target' keyword argument is deprecated.", NumbaDeprecationWarning)
target = options.pop('_target', 'cpu')
options['boundscheck'] = boundscheck
# Handle signature
if signature_or_function is None:
# No signature, no function
pyfunc = None
sigs = None
elif isinstance(signature_or_function, list):
# A list of signatures is passed
pyfunc = None
sigs = signature_or_function
elif sigutils.is_signature(signature_or_function):
# A single signature is passed
pyfunc = None
sigs = [signature_or_function]
# A function is passed
pyfunc = signature_or_function
sigs = None
dispatcher_args = {}
if pipeline_class is not None:
dispatcher_args['pipeline_class'] = pipeline_class
wrapper = _jit(sigs, locals=locals, target=target, cache=cache,
targetoptions=options, **dispatcher_args)
if pyfunc is not None:
return wrapper(pyfunc)
return wrapper
def _jit(sigs, locals, target, cache, targetoptions, **dispatcher_args):
dispatcher = registry.dispatcher_registry[target]
def wrapper(func):
if extending.is_jitted(func):
raise TypeError(
"A jit decorator was called on an already jitted function "
f"{func}. If trying to access the original python "
f"function, use the {func}.py_func attribute."
if not inspect.isfunction(func):
raise TypeError(
"The decorated object is not a function (got type "
if config.ENABLE_CUDASIM and target == 'cuda':
from numba import cuda
return cuda.jit(func)
if config.DISABLE_JIT and not target == 'npyufunc':
return func
disp = dispatcher(py_func=func, locals=locals,
if cache:
if sigs is not None:
# Register the Dispatcher to the type inference mechanism,
# even though the decorator hasn't returned yet.
from numba.core import typeinfer
with typeinfer.register_dispatcher(disp):
for sig in sigs:
return disp
return wrapper
def generated_jit(function=None, target='cpu', cache=False,
pipeline_class=None, **options):
This decorator allows flexible type-based compilation
of a jitted function. It works as `@jit`, except that the decorated
function is called at compile-time with the *types* of the arguments
and should return an implementation function for those types.
dispatcher_args = {}
if pipeline_class is not None:
dispatcher_args['pipeline_class'] = pipeline_class
wrapper = _jit(sigs=None, locals={}, target=target, cache=cache,
targetoptions=options, impl_kind='generated',
if function is not None:
return wrapper(function)
return wrapper
def njit(*args, **kws):
Equivalent to jit(nopython=True)
See documentation for jit function/decorator for full description.
if 'nopython' in kws:
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
if 'forceobj' in kws:
warnings.warn('forceobj is set for njit and is ignored', RuntimeWarning)
del kws['forceobj']
kws.update({'nopython': True})
return jit(*args, **kws)
def cfunc(sig, locals={}, cache=False, pipeline_class=None, **options):
This decorator is used to compile a Python function into a C callback
usable with foreign C libraries.
@cfunc("float64(float64, float64)", nopython=True, cache=True)
def add(a, b):
return a + b
sig = sigutils.normalize_signature(sig)
def wrapper(func):
from numba.core.ccallback import CFunc
additional_args = {}
if pipeline_class is not None:
additional_args['pipeline_class'] = pipeline_class
res = CFunc(func, sig, locals=locals, options=options, **additional_args)
if cache:
return res
return wrapper
def jit_module(**kwargs):
""" Automatically ``jit``-wraps functions defined in a Python module
Note that ``jit_module`` should only be called at the end of the module to
be jitted. In addition, only functions which are defined in the module
``jit_module`` is called from are considered for automatic jit-wrapping.
See the Numba documentation for more information about what can/cannot be
:param kwargs: Keyword arguments to pass to ``jit`` such as ``nopython``
or ``error_model``.
# Get the module jit_module is being called from
frame = inspect.stack()[1]
module = inspect.getmodule(frame[0])
# Replace functions in module with jit-wrapped versions
for name, obj in module.__dict__.items():
if inspect.isfunction(obj) and inspect.getmodule(obj) == module:
_logger.debug("Auto decorating function {} from module {} with jit "
"and options: {}".format(obj, module.__name__, kwargs))
module.__dict__[name] = jit(obj, **kwargs)
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