test: refactor process-based tests to use concurrent futures in order to simplify tests

numba_cuda/numba/cuda/tests/cudadrv/test_init.py

@@ -1,6 +1,7 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: BSD-2-Clause
 
+import concurrent.futures
 import multiprocessing as mp
 import os
 
@@ -20,88 +21,83 @@ def cuInit_raising(arg):
 # not assigned until we attempt to initialize - mock.patch.object cannot locate
 # the non-existent original method, and so fails. Instead we patch
 # driver.cuInit with our raising version prior to any attempt to initialize.
-def cuInit_raising_test(result_queue):
+def cuInit_raising_test():
     driver.cuInit = cuInit_raising
 
-    success = False
-    msg = None
-
     try:
         # A CUDA operation that forces initialization of the device
         cuda.device_array(1)
     except CudaSupportError as e:
         success = True
         msg = e.msg
+    else:
+        success = False
+        msg = None
 
-    result_queue.put((success, msg))
+    return success, msg
 
 
 # Similar to cuInit_raising_test above, but for testing that the string
 # returned by cuda_error() is as expected.
-def initialization_error_test(result_queue):
+def initialization_error_test():
     driver.cuInit = cuInit_raising
 
-    success = False
-    msg = None
-
     try:
         # A CUDA operation that forces initialization of the device
         cuda.device_array(1)
     except CudaSupportError:
         success = True
+    else:
+        success = False
 
-    msg = cuda.cuda_error()
-    result_queue.put((success, msg))
+    return success, cuda.cuda_error()
 
 
 # For testing the path where Driver.__init__() catches a CudaSupportError
-def cuda_disabled_test(result_queue):
-    success = False
-    msg = None
-
+def cuda_disabled_test():
     try:
         # A CUDA operation that forces initialization of the device
         cuda.device_array(1)
     except CudaSupportError as e:
         success = True
         msg = e.msg
+    else:
+        success = False
+        msg = None
 
-    result_queue.put((success, msg))
+    return success, msg
 
 
 # Similar to cuda_disabled_test, but checks cuda.cuda_error() instead of the
 # exception raised on initialization
-def cuda_disabled_error_test(result_queue):
-    success = False
-    msg = None
-
+def cuda_disabled_error_test():
     try:
         # A CUDA operation that forces initialization of the device
         cuda.device_array(1)
     except CudaSupportError:
         success = True
+    else:
+        success = False
 
-    msg = cuda.cuda_error()
-    result_queue.put((success, msg))
+    return success, cuda.cuda_error()
 
 
 @skip_on_cudasim("CUDA Simulator does not initialize driver")
 class TestInit(CUDATestCase):
     def _test_init_failure(self, target, expected):
         # Run the initialization failure test in a separate subprocess
-        ctx = mp.get_context("spawn")
-        result_queue = ctx.Queue()
-        proc = ctx.Process(target=target, args=(result_queue,))
-        proc.start()
-        proc.join(30)  # should complete within 30s
-        success, msg = result_queue.get()
+        with concurrent.futures.ProcessPoolExecutor(
+            mp_context=mp.get_context("spawn")
+        ) as exe:
+            # should complete within 30s
+            success, msg = exe.submit(target).result(timeout=30)
 
         # Ensure the child process raised an exception during initialization
         # before checking the message
         if not success:
-            self.fail("CudaSupportError not raised")
+            assert "CudaSupportError not raised" in msg
 
-        self.assertIn(expected, msg)
+        assert expected in msg
 
     def test_init_failure_raising(self):
         expected = "Error at driver init: CUDA_ERROR_UNKNOWN (999)"

numba_cuda/numba/cuda/tests/cudadrv/test_runtime.py

@@ -1,21 +1,18 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: BSD-2-Clause
 
+import concurrent.futures
 import multiprocessing
 import os
 from numba.cuda.testing import unittest
 
 
-def set_visible_devices_and_check(q):
-    try:
-        from numba import cuda
-        import os
+def set_visible_devices_and_check():
+    from numba import cuda
+    import os
 
-        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-        q.put(len(cuda.gpus.lst))
-    except:  # noqa: E722
-        # Sentinel value for error executing test code
-        q.put(-1)
+    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+    return len(cuda.gpus.lst)
 
 
 class TestVisibleDevices(unittest.TestCase):
@@ -38,22 +35,13 @@ def test_visible_devices_set_after_import(self):
             msg = "Cannot test when CUDA_VISIBLE_DEVICES already set"
             self.skipTest(msg)
 
-        ctx = multiprocessing.get_context("spawn")
-        q = ctx.Queue()
-        p = ctx.Process(target=set_visible_devices_and_check, args=(q,))
-        p.start()
-        try:
-            visible_gpu_count = q.get()
-        finally:
-            p.join()
-
-        # Make an obvious distinction between an error running the test code
-        # and an incorrect number of GPUs in the list
-        msg = "Error running set_visible_devices_and_check"
-        self.assertNotEqual(visible_gpu_count, -1, msg=msg)
-
-        # The actual check that we see only one GPU
-        self.assertEqual(visible_gpu_count, 1)
+        with concurrent.futures.ProcessPoolExecutor(
+            mp_context=multiprocessing.get_context("spawn")
+        ) as exe:
+            future = exe.submit(set_visible_devices_and_check)
+
+        visible_gpu_count = future.result()
+        assert visible_gpu_count == 1
 
 
 if __name__ == "__main__":

numba_cuda/numba/cuda/tests/cudapy/test_multiprocessing.py

@@ -3,47 +3,36 @@
 
 import os
 import multiprocessing as mp
+import pytest
+import concurrent.futures
 
 import numpy as np
 
 from numba import cuda
 from numba.cuda.testing import skip_on_cudasim, CUDATestCase
 import unittest
 
-has_mp_get_context = hasattr(mp, "get_context")
-is_unix = os.name == "posix"
-
-
-def fork_test(q):
-    from numba.cuda.cudadrv.error import CudaDriverError
-
-    try:
-        cuda.to_device(np.arange(1))
-    except CudaDriverError as e:
-        q.put(e)
-    else:
-        q.put(None)
-
 
 @skip_on_cudasim("disabled for cudasim")
 class TestMultiprocessing(CUDATestCase):
-    @unittest.skipUnless(has_mp_get_context, "requires mp.get_context")
-    @unittest.skipUnless(is_unix, "requires Unix")
+    @unittest.skipUnless(hasattr(mp, "get_context"), "requires mp.get_context")
+    @unittest.skipUnless(os.name == "posix", "requires Unix")
     def test_fork(self):
         """
         Test fork detection.
         """
+        from numba.cuda.cudadrv.error import CudaDriverError
+
         cuda.current_context()  # force cuda initialize
-        # fork in process that also uses CUDA
-        ctx = mp.get_context("fork")
-        q = ctx.Queue()
-        proc = ctx.Process(target=fork_test, args=[q])
-        proc.start()
-        exc = q.get()
-        proc.join()
-        # there should be an exception raised in the child process
-        self.assertIsNotNone(exc)
-        self.assertIn("CUDA initialized before forking", str(exc))
+        with concurrent.futures.ProcessPoolExecutor(
+            mp_context=mp.get_context("fork")
+        ) as exe:
+            future = exe.submit(cuda.to_device, np.arange(1))
+
+        with pytest.raises(
+            CudaDriverError, match="CUDA initialized before forking"
+        ):
+            future.result()
 
 
 if __name__ == "__main__":

numba_cuda/numba/cuda/tests/cudapy/test_multithreads.py

@@ -1,7 +1,6 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: BSD-2-Clause
 
-import traceback
 import threading
 import multiprocessing
 import numpy as np
@@ -13,12 +12,7 @@
 )
 import unittest
 
-try:
-    from concurrent.futures import ThreadPoolExecutor
-except ImportError:
-    has_concurrent_futures = False
-else:
-    has_concurrent_futures = True
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
 
 
 has_mp_get_context = hasattr(multiprocessing, "get_context")
@@ -41,52 +35,34 @@ def use_foo(x):
             np.testing.assert_equal(ary, expected)
 
 
-def spawn_process_entry(q):
-    try:
-        check_concurrent_compiling()
-    # Catch anything that goes wrong in the threads
-    except:  # noqa: E722
-        msg = traceback.format_exc()
-        q.put("\n".join(["", "=" * 80, msg]))
-    else:
-        q.put(None)
-
-
 @skip_under_cuda_memcheck("Hangs cuda-memcheck")
 @skip_on_cudasim("disabled for cudasim")
 class TestMultiThreadCompiling(CUDATestCase):
-    @unittest.skipIf(not has_concurrent_futures, "no concurrent.futures")
     def test_concurrent_compiling(self):
         check_concurrent_compiling()
 
     @unittest.skipIf(not has_mp_get_context, "no multiprocessing.get_context")
     def test_spawn_concurrent_compilation(self):
         # force CUDA context init
         cuda.get_current_device()
-        # use "spawn" to avoid inheriting the CUDA context
-        ctx = multiprocessing.get_context("spawn")
-
-        q = ctx.Queue()
-        p = ctx.Process(target=spawn_process_entry, args=(q,))
-        p.start()
-        try:
-            err = q.get()
-        finally:
-            p.join()
-        if err is not None:
-            raise AssertionError(err)
-        self.assertEqual(p.exitcode, 0, "test failed in child process")
+
+        with ProcessPoolExecutor(
+            # use "spawn" to avoid inheriting the CUDA context
+            mp_context=multiprocessing.get_context("spawn")
+        ) as exe:
+            future = exe.submit(check_concurrent_compiling)
+        future.result()
 
     def test_invalid_context_error_with_d2h(self):
         def d2h(arr, out):
             out[:] = arr.copy_to_host()
 
         arr = np.arange(1, 4)
         out = np.zeros_like(arr)
-        darr = cuda.to_device(arr)
-        th = threading.Thread(target=d2h, args=[darr, out])
-        th.start()
-        th.join()
+
+        with ThreadPoolExecutor() as exe:
+            exe.submit(d2h, cuda.to_device(arr), out)
+
         np.testing.assert_equal(arr, out)
 
     def test_invalid_context_error_with_d2d(self):