def _fpext(tyctx, val):
def impl(cgctx, builder, signature, args):
val = args[0]
return builder.fpext(val, lc.Type.double())
sig = types.float64(types.float32)
return sig, impl
def test_common_field(self):
"""
Test that subtypes do not require new compilations
"""
njit_sig = njit(types.float64(typeof(self.a_rec1)))
functions = [
njit(self.func), # jitted function with open njit
njit_sig(self.func) # jitted fc with closed signature
]
for fc in functions:
fc(self.a_rec1)
fc.disable_compile()
y = fc(self.ab_rec1)
self.assertEqual(self.value, y)
def test_isinstance_numba_types(self):
# This makes use of type aliasing between python scalars and NumPy
# scalars, see also test_numba_types()
pyfunc = isinstance_usecase_numba_types
cfunc = jit(nopython=True)(pyfunc)
inputs = ((types.int32(1), 'int32'), (types.int64(2), 'int64'),
(types.float32(3.0), 'float32'), (types.float64(4.0),
'float64'),
(types.complex64(5j), 'no match'), (typed.List([1, 2]),
'typed list'),
(typed.Dict.empty(types.int64, types.int64), 'typed dict'))
for inpt, expected in inputs:
got = cfunc(inpt)
self.assertEqual(expected, got)
import warnings
from numba.core.imputils import Registry
from numba.core import types
from numba.core.itanium_mangler import mangle
from .hsaimpl import _declare_function
registry = Registry()
lower = registry.lower
# -----------------------------------------------------------------------------
_unary_b_f = types.int32(types.float32)
_unary_b_d = types.int32(types.float64)
_unary_f_f = types.float32(types.float32)
_unary_d_d = types.float64(types.float64)
_binary_f_ff = types.float32(types.float32, types.float32)
_binary_d_dd = types.float64(types.float64, types.float64)
function_descriptors = {
'isnan': (_unary_b_f, _unary_b_d),
'isinf': (_unary_b_f, _unary_b_d),
'ceil': (_unary_f_f, _unary_d_d),
'floor': (_unary_f_f, _unary_d_d),
'fabs': (_unary_f_f, _unary_d_d),
'sqrt': (_unary_f_f, _unary_d_d),
'exp': (_unary_f_f, _unary_d_d),
'expm1': (_unary_f_f, _unary_d_d),
'log': (_unary_f_f, _unary_d_d),
'log10': (_unary_f_f, _unary_d_d),
'log1p': (_unary_f_f, _unary_d_d),
def int_to_float(x):
return types.float64(x) / 2
