test_cuda_nvml_based_avail.py

# Owner(s): ["module: cuda"]

import os
import sys
import multiprocessing
import torch
import unittest
from unittest.mock import patch

# NOTE: Each of the tests in this module need to be run in a brand new process to ensure CUDA is uninitialized
# prior to test initiation.
with patch.dict(os.environ, {"PYTORCH_NVML_BASED_CUDA_CHECK": "1"}):
    # Before executing the desired tests, we need to disable CUDA initialization and fork_handler additions that would
    # otherwise be triggered by the `torch.testing._internal.common_utils` module import
    from torch.testing._internal.common_utils import (parametrize, instantiate_parametrized_tests, run_tests, TestCase,
                                                      IS_WINDOWS)
    # NOTE: Because `remove_device_and_dtype_suffixes` initializes CUDA context (triggered via the import of
    # `torch.testing._internal.common_device_type` which imports `torch.testing._internal.common_cuda`) we need
    # to bypass that method here which should be irrelevant to the parameterized tests in this module.
    torch.testing._internal.common_utils.remove_device_and_dtype_suffixes = lambda x: x

    TEST_CUDA = torch.cuda.is_available()
    if not TEST_CUDA:
        print('CUDA not available, skipping tests', file=sys.stderr)
        TestCase = object  # type: ignore[misc, assignment] # noqa: F811


class TestExtendedCUDAIsAvail(TestCase):
    SUBPROCESS_REMINDER_MSG = (
        "\n REMINDER: Tests defined in test_cuda_nvml_based_avail.py must be run in a process "
        "where there CUDA Driver API has not been initialized. Before further debugging, ensure you are either using "
        "run_test.py or have added --subprocess to run each test in a different subprocess.")

    def setUp(self):
        super().setUp()
        torch.cuda.device_count.cache_clear()  # clear the lru_cache on this method before our test

    @staticmethod
    def in_bad_fork_test() -> bool:
        _ = torch.cuda.is_available()
        return torch.cuda._is_in_bad_fork()

    # These tests validate the behavior and activation of the weaker, NVML-based, user-requested
    # `torch.cuda.is_available()` assessment. The NVML-based assessment should be attempted when
    # `PYTORCH_NVML_BASED_CUDA_CHECK` is set to 1, reverting to the default CUDA Runtime API check otherwise.
    # If the NVML-based assessment is attempted but fails, the CUDA Runtime API check should be executed
    @unittest.skipIf(IS_WINDOWS, "Needs fork")
    @parametrize("nvml_avail", [True, False])
    @parametrize("avoid_init", ['1', '0', None])
    def test_cuda_is_available(self, avoid_init, nvml_avail):
        patch_env = {"PYTORCH_NVML_BASED_CUDA_CHECK": avoid_init} if avoid_init else {}
        with patch.dict(os.environ, **patch_env):
            if nvml_avail:
                _ = torch.cuda.is_available()
            else:
                with patch.object(torch.cuda, '_device_count_nvml', return_value=-1):
                    _ = torch.cuda.is_available()
            with multiprocessing.get_context("fork").Pool(1) as pool:
                in_bad_fork = pool.apply(TestExtendedCUDAIsAvail.in_bad_fork_test)
            if os.getenv('PYTORCH_NVML_BASED_CUDA_CHECK') == '1' and nvml_avail:
                self.assertFalse(in_bad_fork, TestExtendedCUDAIsAvail.SUBPROCESS_REMINDER_MSG)
            else:
                assert in_bad_fork


instantiate_parametrized_tests(TestExtendedCUDAIsAvail)

if __name__ == '__main__':
    run_tests()