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 "
f"{type(func)}).")
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,
targetoptions=targetoptions,
**dispatcher_args)
if cache:
disp.enable_caching()
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:
disp.compile(sig)
disp.disable_compile()
return disp
def _jit(func):
targetoptions = kws.copy()
targetoptions['debug'] = debug
targetoptions['link'] = link
targetoptions['opt'] = opt
targetoptions['fastmath'] = fastmath
targetoptions['device'] = device
targetoptions['extensions'] = extensions
disp = CUDADispatcher(func, targetoptions=targetoptions)
if cache:
disp.enable_caching()
if device:
from numba.core import typeinfer
with typeinfer.register_dispatcher(disp):
disp.compile_device(argtypes)
else:
disp.compile(argtypes)
disp._specialized = True
disp.disable_compile()
return disp
def wrapper(func):
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,
targetoptions=targetoptions,
**dispatcher_args)
if cache:
disp.enable_caching()
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:
disp.compile(sig)
disp.disable_compile()
return disp