def _get_function_globals_for_reduction(func):
"""
Analyse *func* and return a dictionary of global values suitable for
reduction.
"""
from numba.core import bytecode # needed here to avoid cyclic import
func_id = bytecode.FunctionIdentity.from_function(func)
bc = bytecode.ByteCode(func_id)
globs = bc.get_used_globals()
# Remember the module name so that the function gets a proper __module__
# when rebuilding. This is used to recreate the environment.
globs['__name__'] = func.__module__
return globs
def _get_function_globals_for_reduction(func):
"""
Analyse *func* and return a dictionary of global values suitable for
reduction.
"""
func_id = bytecode.FunctionIdentity.from_function(func)
bc = bytecode.ByteCode(func_id)
globs = bc.get_used_globals()
for k, v in globs.items():
# Make modules picklable by name
if isinstance(v, ModuleType):
globs[k] = _ModuleRef(v.__name__)
# Remember the module name so that the function gets a proper __module__
# when rebuilding. This is used to recreate the environment.
globs['__name__'] = func.__module__
return globs
def run_frontend(func, inline_closures=False):
"""
Run the compiler frontend over the given Python function, and return
the function's canonical Numba IR.
If inline_closures is Truthy then closure inlining will be run
"""
# XXX make this a dedicated Pipeline?
func_id = bytecode.FunctionIdentity.from_function(func)
interp = interpreter.Interpreter(func_id)
bc = bytecode.ByteCode(func_id=func_id)
func_ir = interp.interpret(bc)
if inline_closures:
inline_pass = InlineClosureCallPass(func_ir,
cpu.ParallelOptions(False), {},
False)
inline_pass.run()
post_proc = postproc.PostProcessor(func_ir)
post_proc.run()
return func_ir
