lightning/tests/callbacks/test_gpu_stats_monitor.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.
import os
from unittest import mock

import numpy as np
import pytest
import torch

from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import GPUStatsMonitor
from pytorch_lightning.loggers import CSVLogger
from pytorch_lightning.loggers.csv_logs import ExperimentWriter
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.helpers import BoringModel
from tests.helpers.runif import RunIf


@RunIf(min_gpus=1)
def test_gpu_stats_monitor(tmpdir):
    """
    Test GPU stats are logged using a logger.
    """
    model = BoringModel()
    gpu_stats = GPUStatsMonitor(intra_step_time=True)
    logger = CSVLogger(tmpdir)
    log_every_n_steps = 2

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=2,
        limit_train_batches=7,
        log_every_n_steps=log_every_n_steps,
        gpus=1,
        callbacks=[gpu_stats],
        logger=logger,
    )

    trainer.fit(model)
    assert trainer.state.finished, f"Training failed with {trainer.state}"

    path_csv = os.path.join(logger.log_dir, ExperimentWriter.NAME_METRICS_FILE)
    met_data = np.genfromtxt(path_csv, delimiter=",", names=True, deletechars="", replace_space=" ")

    batch_time_data = met_data["batch_time/intra_step (ms)"]
    batch_time_data = batch_time_data[~np.isnan(batch_time_data)]
    assert batch_time_data.shape[0] == trainer.global_step // log_every_n_steps

    fields = ["utilization.gpu", "memory.used", "memory.free", "utilization.memory"]

    for f in fields:
        assert any(f in h for h in met_data.dtype.names)


@RunIf(min_gpus=1)
def test_gpu_stats_monitor_no_queries(tmpdir):
    """
    Test GPU logger doesn't fail if no "nvidia-smi" queries are to be performed.
    """
    model = BoringModel()
    gpu_stats = GPUStatsMonitor(
        memory_utilization=False,
        gpu_utilization=False,
        intra_step_time=True,
        inter_step_time=True,
    )
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        limit_train_batches=2,
        limit_val_batches=0,
        log_every_n_steps=1,
        gpus=1,
        callbacks=[gpu_stats],
    )
    with mock.patch("pytorch_lightning.loggers.tensorboard.TensorBoardLogger.log_metrics") as log_metrics_mock:
        trainer.fit(model)

    assert log_metrics_mock.mock_calls[2:] == [
        mock.call({"batch_time/intra_step (ms)": mock.ANY}, step=0),
        mock.call({"batch_time/inter_step (ms)": mock.ANY}, step=1),
        mock.call({"batch_time/intra_step (ms)": mock.ANY}, step=1),
    ]


@pytest.mark.skipif(torch.cuda.is_available(), reason="test requires CPU machine")
def test_gpu_stats_monitor_cpu_machine(tmpdir):
    """
    Test GPUStatsMonitor on CPU machine.
    """
    with pytest.raises(MisconfigurationException, match="NVIDIA driver is not installed"):
        GPUStatsMonitor()


@RunIf(min_gpus=1)
def test_gpu_stats_monitor_no_logger(tmpdir):
    """
    Test GPUStatsMonitor with no logger in Trainer.
    """
    model = BoringModel()
    gpu_stats = GPUStatsMonitor()

    trainer = Trainer(default_root_dir=tmpdir, callbacks=[gpu_stats], max_epochs=1, gpus=1, logger=False)

    with pytest.raises(MisconfigurationException, match="Trainer that has no logger."):
        trainer.fit(model)


@RunIf(min_gpus=1)
def test_gpu_stats_monitor_no_gpu_warning(tmpdir):
    """
    Test GPUStatsMonitor raises a warning when not training on GPU device.
    """
    model = BoringModel()
    gpu_stats = GPUStatsMonitor()

    trainer = Trainer(default_root_dir=tmpdir, callbacks=[gpu_stats], max_steps=1, gpus=None)

    with pytest.raises(MisconfigurationException, match="not running on GPU"):
        trainer.fit(model)


def test_gpu_stats_monitor_parse_gpu_stats():
    logs = GPUStatsMonitor._parse_gpu_stats([1, 2], [[3, 4, 5], [6, 7]], [("gpu", "a"), ("memory", "b")])
    expected = {
        "device_id: 1/gpu (a)": 3,
        "device_id: 1/memory (b)": 4,
        "device_id: 2/gpu (a)": 6,
        "device_id: 2/memory (b)": 7,
    }
    assert logs == expected


@mock.patch.dict(os.environ, {})
@mock.patch("torch.cuda.is_available", return_value=True)
@mock.patch("torch.cuda.device_count", return_value=2)
def test_gpu_stats_monitor_get_gpu_ids_cuda_visible_devices_unset(device_count_mock, is_available_mock):
    gpu_ids = GPUStatsMonitor._get_gpu_ids([1, 0])
    expected = ["1", "0"]
    assert gpu_ids == expected


@mock.patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "3,2,4"})
@mock.patch("torch.cuda.is_available", return_value=True)
@mock.patch("torch.cuda.device_count", return_value=3)
def test_gpu_stats_monitor_get_gpu_ids_cuda_visible_devices_integers(device_count_mock, is_available_mock):
    gpu_ids = GPUStatsMonitor._get_gpu_ids([1, 2])
    expected = ["2", "4"]
    assert gpu_ids == expected


@mock.patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "GPU-01a23b4c,GPU-56d78e9f,GPU-02a46c8e"})
@mock.patch("torch.cuda.is_available", return_value=True)
@mock.patch("torch.cuda.device_count", return_value=3)
def test_gpu_stats_monitor_get_gpu_ids_cuda_visible_devices_uuids(device_count_mock, is_available_mock):
    gpu_ids = GPUStatsMonitor._get_gpu_ids([1, 2])
    expected = ["GPU-56d78e9f", "GPU-02a46c8e"]
    assert gpu_ids == expected