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

import ctypes
import ctypes.util
import os
import sys
import threading
import warnings

import numpy as np

import unittest
from numba import jit
from numba.core import errors
from numba.tests.support import TestCase, tag


# This CPython API function is a portable way to get the current thread id.
PyThread_get_thread_ident = ctypes.pythonapi.PyThread_get_thread_ident
PyThread_get_thread_ident.restype = ctypes.c_long
PyThread_get_thread_ident.argtypes = []

# A way of sleeping from nopython code
if os.name == 'nt':
    sleep = ctypes.windll.kernel32.Sleep
    sleep.argtypes = [ctypes.c_uint]
    sleep.restype = None
    sleep_factor = 1  # milliseconds
else:
    sleep = ctypes.CDLL(ctypes.util.find_library("c")).usleep
    sleep.argtypes = [ctypes.c_uint]
    sleep.restype = ctypes.c_int
    sleep_factor = 1000  # microseconds


def f(a, indices):
    # If run from one thread at a time, the function will always fill the
    # array with identical values.
    # If run from several threads at a time, the function will probably
    # fill the array with differing values.
    for idx in indices:
        # Let another thread run
        sleep(10 * sleep_factor)
        a[idx] = PyThread_get_thread_ident()

f_sig = "void(int64[:], intp[:])"

def lifted_f(a, indices):
    """
    Same as f(), but inside a lifted loop
    """
    object()   # Force object mode
    for idx in indices:
        # Let another thread run
        sleep(10 * sleep_factor)
        a[idx] = PyThread_get_thread_ident()

def object_f(a, indices):
    """
    Same as f(), but in object mode
    """
    for idx in indices:
        # Let another thread run
        sleep(10 * sleep_factor)
        object()   # Force object mode
        a[idx] = PyThread_get_thread_ident()


class TestGILRelease(TestCase):

    def make_test_array(self, n_members):
        return np.arange(n_members, dtype=np.int64)

    def run_in_threads(self, func, n_threads):
        # Run the function in parallel over an array and collect results.
        threads = []
        # Warm up compilation, since we don't want that to interfere with
        # the test proper.
        func(self.make_test_array(1), np.arange(1, dtype=np.intp))
        arr = self.make_test_array(50)
        for i in range(n_threads):
            # Ensure different threads write into the array in different
            # orders.
            indices = np.arange(arr.size, dtype=np.intp)
            np.random.shuffle(indices)
            t = threading.Thread(target=func, args=(arr, indices))
            threads.append(t)
        for t in threads:
            t.start()
        for t in threads:
            t.join()
        return arr

    def check_gil_held(self, func):
        arr = self.run_in_threads(func, n_threads=4)
        distinct = set(arr)
        self.assertEqual(len(distinct), 1, distinct)

    def check_gil_released(self, func):
        for n_threads in (4, 12, 32):
            # Try harder each time. On an empty machine 4 threads seems
            # sufficient, but in some contexts (e.g. Travis CI) we need more.
            arr = self.run_in_threads(func, n_threads)
            distinct = set(arr)
            try:
                self.assertGreater(len(distinct), 1, distinct)
            except AssertionError as e:
                failure = e
            else:
                return
        raise failure

    def test_gil_held(self):
        """
        Test the GIL is held by default, by checking serialized runs
        produce deterministic results.
        """
        cfunc = jit(f_sig, nopython=True)(f)
        self.check_gil_held(cfunc)

    def test_gil_released(self):
        """
        Test releasing the GIL, by checking parallel runs produce
        unpredictable results.
        """
        cfunc = jit(f_sig, nopython=True, nogil=True)(f)
        self.check_gil_released(cfunc)

    def test_gil_released_inside_lifted_loop(self):
        """
        Test the GIL can by released by a lifted loop even though the
        surrounding code uses object mode.
        """
        cfunc = jit(f_sig, forceobj=True, nogil=True)(lifted_f)
        self.check_gil_released(cfunc)

    def test_gil_released_by_caller(self):
        """
        Releasing the GIL in the caller is sufficient to have it
        released in a callee.
        """
        compiled_f = jit(f_sig, nopython=True)(f)
        @jit(f_sig, nopython=True, nogil=True)
        def caller(a, i):
            compiled_f(a, i)
        self.check_gil_released(caller)

    def test_gil_released_by_caller_and_callee(self):
        """
        Same, but with both caller and callee asking to release the GIL.
        """
        compiled_f = jit(f_sig, nopython=True, nogil=True)(f)
        @jit(f_sig, nopython=True, nogil=True)
        def caller(a, i):
            compiled_f(a, i)
        self.check_gil_released(caller)

    def test_gil_ignored_by_callee(self):
        """
        When only the callee asks to release the GIL, it gets ignored.
        """
        compiled_f = jit(f_sig, nopython=True, nogil=True)(f)
        @jit(f_sig, nopython=True)
        def caller(a, i):
            compiled_f(a, i)
        self.check_gil_held(caller)

    def test_object_mode(self):
        """
        When the function is compiled in object mode, a warning is
        printed out.
        """
        with warnings.catch_warnings(record=True) as wlist:
            warnings.simplefilter('always', errors.NumbaWarning)
            cfunc = jit(f_sig, forceobj=True, nogil=True)(object_f)
        self.assertTrue(any(w.category is errors.NumbaWarning
                            and "Code running in object mode won't allow parallel execution" in str(w.message)
                            for w in wlist), wlist)
        # Just check it doesn't crash.
        self.run_in_threads(cfunc, 2)


if __name__ == '__main__':
    unittest.main()