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

173 lines
3.5 KiB
Python

"""
This file will be copied to a temporary directory in order to
exercise caching compiled Numba functions.
See test_dispatcher.py.
"""
import sys
import numpy as np
from numba import jit, prange
from numba.core import types
from numba.tests.ctypes_usecases import c_sin
from numba.tests.support import TestCase, captured_stderr
@jit(cache=True, nopython=True)
def simple_usecase(x):
return x
def simple_usecase_caller(x):
return simple_usecase(x)
@jit(cache=True, nopython=True)
def add_usecase(x, y):
return x + y + Z
@jit(cache=True, forceobj=True)
def add_objmode_usecase(x, y):
object()
return x + y + Z
@jit(nopython=True)
def add_nocache_usecase(x, y):
return x + y + Z
@jit(cache=True, nopython=True)
def inner(x, y):
return x + y + Z
@jit(cache=True, nopython=True)
def outer(x, y):
return inner(-y, x)
@jit(cache=False, nopython=True)
def outer_uncached(x, y):
return inner(-y, x)
@jit(cache=True, forceobj=True)
def looplifted(n):
object()
res = 0
for i in range(n):
res = res + i
return res
@jit(cache=True, nopython=True)
def use_c_sin(x):
return c_sin(x)
@jit(cache=True, nopython=True)
def use_c_sin_nest1(x):
return use_c_sin(x)
@jit(cache=True, nopython=True)
def use_c_sin_nest2(x):
return use_c_sin_nest1(x)
@jit(cache=True, nopython=True)
def ambiguous_function(x):
return x + 2
renamed_function1 = ambiguous_function
@jit(cache=True, nopython=True)
def ambiguous_function(x):
return x + 6
renamed_function2 = ambiguous_function
def make_closure(x):
@jit(cache=True, nopython=True)
def closure(y):
return x + y
return closure
closure1 = make_closure(3)
closure2 = make_closure(5)
closure3 = make_closure(7)
closure4 = make_closure(9)
biggie = np.arange(10**6)
@jit(cache=True, nopython=True)
def use_big_array():
return biggie
Z = 1
# Exercise returning a record instance. This used to hardcode the dtype
# pointer's value in the bitcode.
packed_record_type = np.dtype([('a', np.int8), ('b', np.float64)])
aligned_record_type = np.dtype([('a', np.int8), ('b', np.float64)], align=True)
packed_arr = np.empty(2, dtype=packed_record_type)
for i in range(packed_arr.size):
packed_arr[i]['a'] = i + 1
packed_arr[i]['b'] = i + 42.5
aligned_arr = np.array(packed_arr, dtype=aligned_record_type)
@jit(cache=True, nopython=True)
def record_return(ary, i):
return ary[i]
class _TestModule(TestCase):
"""
Tests for functionality of this module's functions.
Note this does not define any "test_*" method, instead check_module()
should be called by hand.
"""
def check_module(self, mod):
self.assertPreciseEqual(mod.add_usecase(2, 3), 6)
self.assertPreciseEqual(mod.add_objmode_usecase(2, 3), 6)
self.assertPreciseEqual(mod.outer_uncached(3, 2), 2)
self.assertPreciseEqual(mod.outer(3, 2), 2)
packed_rec = mod.record_return(mod.packed_arr, 1)
self.assertPreciseEqual(tuple(packed_rec), (2, 43.5))
aligned_rec = mod.record_return(mod.aligned_arr, 1)
self.assertPreciseEqual(tuple(aligned_rec), (2, 43.5))
@jit(cache=True)
def first_class_function_mul(x):
return x * x
@jit(cache=True)
def first_class_function_add(x):
return x + x
@jit(cache=True)
def first_class_function_usecase(f, x):
return f(x)
def self_test():
mod = sys.modules[__name__]
_TestModule().check_module(mod)
@jit(parallel=True, cache=True, nopython=True)
def parfor_usecase(ary):
return ary * ary + ary