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from numba import jit, typeof
from numba.core import types, serialize, sigutils
from numba.core.typing import npydecl
from numba.core.typing.templates import AbstractTemplate, signature
from import _internal
from numba.core.dispatcher import Dispatcher
from numba.parfors import array_analysis
from import ufuncbuilder
from import numpy_support
def make_dufunc_kernel(_dufunc):
from import npyimpl
class DUFuncKernel(npyimpl._Kernel):
npyimpl._Kernel subclass responsible for lowering a DUFunc kernel
(element-wise function) inside a broadcast loop (which is
generated by npyimpl.numpy_ufunc_kernel()).
dufunc = _dufunc
def __init__(self, context, builder, outer_sig):
super(DUFuncKernel, self).__init__(context, builder, outer_sig)
self.inner_sig, self.cres = self.dufunc.find_ewise_function(
def generate(self, *args):
isig = self.inner_sig
osig = self.outer_sig
cast_args = [self.cast(val, inty, outty)
for val, inty, outty in
zip(args, osig.args, isig.args)]
if self.cres.objectmode:
func_type = self.context.call_conv.get_function_type(
types.pyobject, [types.pyobject] * len(isig.args))
func_type = self.context.call_conv.get_function_type(
isig.return_type, isig.args)
module = self.builder.block.function.module
entry_point = module.get_or_insert_function(
func_type, name=self.cres.fndesc.llvm_func_name)
_, res = self.context.call_conv.call_function(
self.builder, entry_point, isig.return_type, isig.args,
return self.cast(res, isig.return_type, osig.return_type)
DUFuncKernel.__name__ += _dufunc.ufunc.__name__
return DUFuncKernel
class DUFuncLowerer(object):
'''Callable class responsible for lowering calls to a specific DUFunc.
def __init__(self, dufunc):
self.kernel = make_dufunc_kernel(dufunc)
self.libs = []
def __call__(self, context, builder, sig, args):
from import npyimpl
return npyimpl.numpy_ufunc_kernel(context, builder, sig, args,
class DUFunc(serialize.ReduceMixin, _internal._DUFunc):
Dynamic universal function (DUFunc) intended to act like a normal
Numpy ufunc, but capable of call-time (just-in-time) compilation
of fast loops specialized to inputs.
# NOTE: __base_kwargs must be kept in synch with the kwlist in
# _internal.c:dufunc_init()
__base_kwargs = set(('identity', '_keepalive', 'nin', 'nout'))
def __init__(self, py_func, identity=None, cache=False, targetoptions={}):
if isinstance(py_func, Dispatcher):
py_func = py_func.py_func
dispatcher = jit(_target='npyufunc',
self._initialize(dispatcher, identity)
def _initialize(self, dispatcher, identity):
identity = ufuncbuilder.parse_identity(identity)
super(DUFunc, self).__init__(dispatcher, identity=identity)
# Loop over a copy of the keys instead of the keys themselves,
# since we're changing the dictionary while looping.
self._lower_me = DUFuncLowerer(self)
self.__name__ = dispatcher.py_func.__name__
self.__doc__ = dispatcher.py_func.__doc__
def _reduce_states(self):
NOTE: part of ReduceMixin protocol
siglist = list(self._dispatcher.overloads.keys())
return dict(
def _rebuild(cls, dispatcher, identity, frozen, siglist):
NOTE: part of ReduceMixin protocol
self = _internal._DUFunc.__new__(cls)
self._initialize(dispatcher, identity)
# Re-add signatures
for sig in siglist:
if frozen:
return self
def build_ufunc(self):
For compatibility with the various *UFuncBuilder classes.
return self
def targetoptions(self):
return self._dispatcher.targetoptions
def nin(self):
return self.ufunc.nin
def nout(self):
return self.ufunc.nout
def nargs(self):
return self.ufunc.nargs
def ntypes(self):
return self.ufunc.ntypes
def types(self):
return self.ufunc.types
def identity(self):
return self.ufunc.identity
def disable_compile(self):
Disable the compilation of new signatures at call time.
# If disabling compilation then there must be at least one signature
assert len(self._dispatcher.overloads) > 0
self._frozen = True
def add(self, sig):
Compile the DUFunc for the given signature.
args, return_type = sigutils.normalize_signature(sig)
return self._compile_for_argtys(args, return_type)
def _compile_for_args(self, *args, **kws):
nin = self.ufunc.nin
if kws:
if 'out' in kws:
out = kws.pop('out')
args += (out,)
if kws:
raise TypeError("unexpected keyword arguments to ufunc: %s"
% ", ".join(repr(k) for k in sorted(kws)))
args_len = len(args)
assert (args_len == nin) or (args_len == nin + self.ufunc.nout)
assert not kws
argtys = []
for arg in args[:nin]:
argty = typeof(arg)
if isinstance(argty, types.Array):
argty = argty.dtype
# To avoid a mismatch in how Numba types scalar values as
# opposed to Numpy, we need special logic for scalars.
# For example, on 64-bit systems, numba.typeof(3) => int32, but
# np.array(3).dtype => int64.
# Note: this will not handle numpy "duckarrays" correctly,
# including but not limited to those implementing `__array__`
# and `__array_ufunc__`.
argty = numpy_support.map_arrayscalar_type(arg)
return self._compile_for_argtys(tuple(argtys))
def _compile_for_argtys(self, argtys, return_type=None):
Given a tuple of argument types (these should be the array
dtypes, and not the array types themselves), compile the
element-wise function for those inputs, generate a UFunc loop
wrapper, and register the loop with the Numpy ufunc object for
this DUFunc.
if self._frozen:
raise RuntimeError("compilation disabled for %s" % (self,))
assert isinstance(argtys, tuple)
if return_type is None:
sig = argtys
sig = return_type(*argtys)
cres, argtys, return_type = ufuncbuilder._compile_element_wise_function(
self._dispatcher, self.targetoptions, sig)
actual_sig = ufuncbuilder._finalize_ufunc_signature(
cres, argtys, return_type)
dtypenums, ptr, env = ufuncbuilder._build_element_wise_ufunc_wrapper(
cres, actual_sig)
self._add_loop(int(ptr), dtypenums)
self._keepalive.append((ptr, cres.library, env))
return cres
def _install_type(self, typingctx=None):
"""Constructs and installs a typing class for a DUFunc object in the
input typing context. If no typing context is given, then
_install_type() installs into the typing context of the
dispatcher object (should be same default context used by
jit() and njit()).
if typingctx is None:
typingctx = self._dispatcher.targetdescr.typing_context
_ty_cls = type('DUFuncTyping_' + self.ufunc.__name__,
dict(key=self, generic=self._type_me))
typingctx.insert_user_function(self, _ty_cls)
def find_ewise_function(self, ewise_types):
Given a tuple of element-wise argument types, find a matching
signature in the dispatcher.
Return a 2-tuple containing the matching signature, and
compilation result. Will return two None's if no matching
signature was found.
if self._frozen:
# If we cannot compile, coerce to the best matching loop
loop = numpy_support.ufunc_find_matching_loop(self, ewise_types)
if loop is None:
return None, None
ewise_types = tuple(loop.inputs + loop.outputs)[:len(ewise_types)]
for sig, cres in self._dispatcher.overloads.items():
if sig.args == ewise_types:
return sig, cres
return None, None
def _type_me(self, argtys, kwtys):
Implement AbstractTemplate.generic() for the typing class
built by DUFunc._install_type().
Return the call-site signature after either validating the
element-wise signature or compiling for it.
assert not kwtys
ufunc = self.ufunc
_handle_inputs_result = npydecl.Numpy_rules_ufunc._handle_inputs(
ufunc, argtys, kwtys)
base_types, explicit_outputs, ndims, layout = _handle_inputs_result
explicit_output_count = len(explicit_outputs)
if explicit_output_count > 0:
ewise_types = tuple(base_types[:-len(explicit_outputs)])
ewise_types = tuple(base_types)
sig, cres = self.find_ewise_function(ewise_types)
if sig is None:
# Matching element-wise signature was not found; must
# compile.
if self._frozen:
raise TypeError("cannot call %s with types %s"
% (self, argtys))
sig, cres = self.find_ewise_function(ewise_types)
assert sig is not None
if explicit_output_count > 0:
outtys = list(explicit_outputs)
elif ufunc.nout == 1:
if ndims > 0:
outtys = [types.Array(sig.return_type, ndims, layout)]
outtys = [sig.return_type]
raise NotImplementedError("typing gufuncs (nout > 1)")
return signature(*outtys)
def _install_cg(self, targetctx=None):
Install an implementation function for a DUFunc object in the
given target context. If no target context is given, then
_install_cg() installs into the target context of the
dispatcher object (should be same default context used by
jit() and njit()).
if targetctx is None:
targetctx = self._dispatcher.targetdescr.target_context
_any = types.Any
_arr = types.Array
# Either all outputs are explicit or none of them are
sig0 = (_any,) * self.ufunc.nin + (_arr,) * self.ufunc.nout
sig1 = (_any,) * self.ufunc.nin
[(self._lower_me, self, sig) for sig in (sig0, sig1)])
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