ai-content-maker/.venv/Lib/site-packages/numba/tests/test_cffi.py

190 lines
6.5 KiB
Python

import array
import numpy as np
from numba import jit, njit
import numba.core.typing.cffi_utils as cffi_support
from numba.core import types, errors
from numba.tests.support import TestCase, skip_unless_cffi
import numba.tests.cffi_usecases as mod
import unittest
enable_pyobj_flags = {'forceobj': True}
no_pyobj_flags = {'nopython': True}
@skip_unless_cffi
class TestCFFI(TestCase):
# Need to run the tests serially because of race conditions in
# cffi's OOL mode.
_numba_parallel_test_ = False
def setUp(self):
mod.init()
mod.init_ool()
def test_type_map(self):
signature = cffi_support.map_type(mod.ffi.typeof(mod.cffi_sin))
self.assertEqual(len(signature.args), 1)
self.assertEqual(signature.args[0], types.double)
def _test_function(self, pyfunc, flags=enable_pyobj_flags):
cfunc = jit((types.double,), **flags)(pyfunc)
for x in [-1.2, -1, 0, 0.1, 3.14]:
self.assertPreciseEqual(pyfunc(x), cfunc(x))
def test_sin_function(self):
self._test_function(mod.use_cffi_sin)
def test_bool_function_ool(self):
pyfunc = mod.use_cffi_boolean_true
cfunc = njit((),)(pyfunc)
self.assertEqual(pyfunc(), True)
self.assertEqual(cfunc(), True)
def test_sin_function_npm(self):
self._test_function(mod.use_cffi_sin, flags=no_pyobj_flags)
def test_sin_function_ool(self, flags=enable_pyobj_flags):
self._test_function(mod.use_cffi_sin_ool)
def test_sin_function_npm_ool(self):
self._test_function(mod.use_cffi_sin_ool, flags=no_pyobj_flags)
def test_two_funcs(self):
# Check that two constant functions don't get mixed up.
self._test_function(mod.use_two_funcs)
def test_two_funcs_ool(self):
self._test_function(mod.use_two_funcs_ool)
def test_function_pointer(self):
pyfunc = mod.use_func_pointer
cfunc = jit(nopython=True)(pyfunc)
for (fa, fb, x) in [
(mod.cffi_sin, mod.cffi_cos, 1.0),
(mod.cffi_sin, mod.cffi_cos, -1.0),
(mod.cffi_cos, mod.cffi_sin, 1.0),
(mod.cffi_cos, mod.cffi_sin, -1.0),
(mod.cffi_sin_ool, mod.cffi_cos_ool, 1.0),
(mod.cffi_sin_ool, mod.cffi_cos_ool, -1.0),
(mod.cffi_cos_ool, mod.cffi_sin_ool, 1.0),
(mod.cffi_cos_ool, mod.cffi_sin_ool, -1.0),
(mod.cffi_sin, mod.cffi_cos_ool, 1.0),
(mod.cffi_sin, mod.cffi_cos_ool, -1.0),
(mod.cffi_cos, mod.cffi_sin_ool, 1.0),
(mod.cffi_cos, mod.cffi_sin_ool, -1.0)]:
expected = pyfunc(fa, fb, x)
got = cfunc(fa, fb, x)
self.assertEqual(got, expected)
# A single specialization was compiled for all calls
self.assertEqual(len(cfunc.overloads), 1, cfunc.overloads)
def test_user_defined_symbols(self):
pyfunc = mod.use_user_defined_symbols
cfunc = jit(nopython=True)(pyfunc)
self.assertEqual(pyfunc(), cfunc())
def check_vector_sin(self, cfunc, x, y):
cfunc(x, y)
np.testing.assert_allclose(y, np.sin(x))
def _test_from_buffer_numpy_array(self, pyfunc, dtype):
x = np.arange(10).astype(dtype)
y = np.zeros_like(x)
cfunc = jit(nopython=True)(pyfunc)
self.check_vector_sin(cfunc, x, y)
def test_from_buffer_float32(self):
self._test_from_buffer_numpy_array(mod.vector_sin_float32, np.float32)
def test_from_buffer_float64(self):
self._test_from_buffer_numpy_array(mod.vector_sin_float64, np.float64)
def test_from_buffer_struct(self):
n = 10
x = np.arange(n) + np.arange(n * 2, n * 3) * 1j
y = np.zeros(n)
real_cfunc = jit(nopython=True)(mod.vector_extract_real)
real_cfunc(x, y)
np.testing.assert_equal(x.real, y)
imag_cfunc = jit(nopython=True)(mod.vector_extract_imag)
imag_cfunc(x, y)
np.testing.assert_equal(x.imag, y)
def test_from_buffer_pyarray(self):
pyfunc = mod.vector_sin_float32
cfunc = jit(nopython=True)(pyfunc)
x = array.array("f", range(10))
y = array.array("f", [0] * len(x))
self.check_vector_sin(cfunc, x, y)
def test_from_buffer_error(self):
pyfunc = mod.vector_sin_float32
cfunc = jit(nopython=True)(pyfunc)
# Non-contiguous array
x = np.arange(10).astype(np.float32)[::2]
y = np.zeros_like(x)
with self.assertRaises(errors.TypingError) as raises:
cfunc(x, y)
self.assertIn("from_buffer() unsupported on non-contiguous buffers",
str(raises.exception))
def test_from_buffer_numpy_multi_array(self):
c1 = np.array([1, 2], order='C', dtype=np.float32)
c1_zeros = np.zeros_like(c1)
c2 = np.array([[1, 2], [3, 4]], order='C', dtype=np.float32)
c2_zeros = np.zeros_like(c2)
f1 = np.array([1, 2], order='F', dtype=np.float32)
f1_zeros = np.zeros_like(f1)
f2 = np.array([[1, 2], [3, 4]], order='F', dtype=np.float32)
f2_zeros = np.zeros_like(f2)
f2_copy = f2.copy('K')
pyfunc = mod.vector_sin_float32
cfunc = jit(nopython=True)(pyfunc)
# No exception because of C layout and single dimension
self.check_vector_sin(cfunc, c1, c1_zeros)
# No exception because of C layout
cfunc(c2, c2_zeros)
sin_c2 = np.sin(c2)
sin_c2[1] = [0, 0] # Reset to zero, since cfunc only processes one row
np.testing.assert_allclose(c2_zeros, sin_c2)
# No exception because of single dimension
self.check_vector_sin(cfunc, f1, f1_zeros)
# Exception because multi-dimensional with F layout
with self.assertRaises(errors.TypingError) as raises:
cfunc(f2, f2_zeros)
np.testing.assert_allclose(f2, f2_copy)
self.assertIn("from_buffer() only supports multidimensional arrays with C layout",
str(raises.exception))
def test_indirect_multiple_use(self):
"""
Issue #2263
Linkage error due to multiple definition of global tracking symbol.
"""
my_sin = mod.cffi_sin
# Use two jit functions that references `my_sin` to ensure multiple
# modules
@jit(nopython=True)
def inner(x):
return my_sin(x)
@jit(nopython=True)
def foo(x):
return inner(x) + my_sin(x + 1)
# Error occurs when foo is being compiled
x = 1.123
self.assertEqual(foo(x), my_sin(x) + my_sin(x + 1))
if __name__ == '__main__':
unittest.main()