lightning/tests/tests_lite/accelerators/test_cuda.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from unittest import mock

import pytest
import torch
from tests_lite.helpers.runif import RunIf

from lightning_lite.accelerators.cuda import CUDAAccelerator, is_cuda_available, num_cuda_devices


@mock.patch("lightning_lite.accelerators.cuda.num_cuda_devices", return_value=2)
def test_auto_device_count(_):
    assert CUDAAccelerator.auto_device_count() == 2


@RunIf(min_cuda_gpus=1)
def test_gpu_availability():
    assert CUDAAccelerator.is_available()


def test_init_device_with_wrong_device_type():
    with pytest.raises(ValueError, match="Device should be CUDA"):
        CUDAAccelerator().setup_device(torch.device("cpu"))


@pytest.mark.parametrize(
    "devices,expected",
    [
        ([], []),
        ([1], [torch.device("cuda", 1)]),
        ([3, 1], [torch.device("cuda", 3), torch.device("cuda", 1)]),
    ],
)
def test_get_parallel_devices(devices, expected):
    assert CUDAAccelerator.get_parallel_devices(devices) == expected


@mock.patch("torch.cuda.set_device")
def test_set_cuda_device(set_device_mock):
    CUDAAccelerator().setup_device(torch.device("cuda", 1))
    set_device_mock.assert_called_once_with(torch.device("cuda", 1))


@mock.patch("lightning_lite.accelerators.cuda._device_count_nvml", return_value=-1)
@mock.patch("torch.cuda.device_count", return_value=100)
def test_num_cuda_devices_without_nvml(*_):
    """Test that if NVML can't be loaded, our helper functions fall back to the default implementation for
    determining CUDA availability."""
    num_cuda_devices.cache_clear()
    assert is_cuda_available()
    assert num_cuda_devices() == 100