lightning/tests/accelerators/test_accelerator_connector.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 pytest
import torch

from pytorch_lightning import Trainer
from pytorch_lightning.accelerators.accelerator import Accelerator
from pytorch_lightning.accelerators.cpu import CPUAccelerator
from pytorch_lightning.accelerators.gpu import GPUAccelerator
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.plugins import DDP2Plugin, DDPPlugin, DDPSpawnPlugin, PrecisionPlugin, SingleDevicePlugin
from pytorch_lightning.plugins.environments import ClusterEnvironment, SLURMEnvironment, TorchElasticEnvironment
from tests.helpers.boring_model import BoringModel


def test_accelerator_choice_cpu(tmpdir):
    trainer = Trainer(
        default_root_dir=tmpdir,
        fast_dev_run=True,
    )
    assert isinstance(trainer.accelerator, CPUAccelerator)
    assert isinstance(trainer.training_type_plugin, SingleDevicePlugin)


def test_accelerator_choice_ddp_cpu(tmpdir):
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp_cpu',
    )
    assert isinstance(trainer.accelerator, CPUAccelerator)
    assert isinstance(trainer.training_type_plugin, DDPSpawnPlugin)
    assert isinstance(trainer.training_type_plugin.cluster_environment, TorchElasticEnvironment)


@mock.patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "0,1"})
@mock.patch('torch.cuda.device_count', return_value=2)
@mock.patch('torch.cuda.is_available', return_value=True)
def test_accelerator_choice_ddp(cuda_available_mock, device_count_mock):
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp',
        gpus=1,
    )
    assert isinstance(trainer.accelerator, GPUAccelerator)
    assert isinstance(trainer.training_type_plugin, DDPPlugin)
    assert isinstance(trainer.training_type_plugin.cluster_environment, TorchElasticEnvironment)


@mock.patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "0,1"})
@mock.patch('torch.cuda.device_count', return_value=2)
@mock.patch('torch.cuda.is_available', return_value=True)
def test_accelerator_choice_ddp_spawn(cuda_available_mock, device_count_mock):
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp_spawn',
        gpus=1,
    )
    assert isinstance(trainer.accelerator, GPUAccelerator)
    assert isinstance(trainer.training_type_plugin, DDPSpawnPlugin)
    assert isinstance(trainer.training_type_plugin.cluster_environment, TorchElasticEnvironment)


@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@mock.patch.dict(
    os.environ, {
        "CUDA_VISIBLE_DEVICES": "0,1",
        "SLURM_NTASKS": "2",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "SLURM_LOCALID": "10"
    }
)
def test_accelerator_choice_ddp_slurm():

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert trainer.use_ddp
            assert trainer.accelerator_connector.is_slurm_managing_tasks
            assert isinstance(trainer.accelerator, GPUAccelerator)
            assert isinstance(trainer.training_type_plugin, DDPPlugin)
            assert isinstance(trainer.training_type_plugin.cluster_environment, SLURMEnvironment)
            assert trainer.training_type_plugin.cluster_environment.local_rank() == 10
            assert trainer.training_type_plugin.task_idx == 10
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp',
        gpus=2,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU")
@mock.patch.dict(
    os.environ, {
        "CUDA_VISIBLE_DEVICES": "0,1",
        "SLURM_NTASKS": "2",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "LOCAL_RANK": "0",
        "SLURM_LOCALID": "10"
    }
)
@mock.patch('torch.cuda.device_count', return_value=2)
def test_accelerator_choice_ddp2_slurm(device_count_mock):

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert trainer.use_ddp2
            assert trainer.accelerator_connector.is_slurm_managing_tasks
            assert isinstance(trainer.accelerator, GPUAccelerator)
            assert isinstance(trainer.training_type_plugin, DDP2Plugin)
            assert isinstance(trainer.training_type_plugin.cluster_environment, SLURMEnvironment)
            assert trainer.training_type_plugin.cluster_environment.local_rank() == 10
            assert trainer.training_type_plugin.task_idx == 10
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp2',
        gpus=2,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU")
@mock.patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "0,1", "WORLD_SIZE": "2", "LOCAL_RANK": "10", "NODE_RANK": "0"})
@mock.patch('torch.cuda.device_count', return_value=2)
def test_accelerator_choice_ddp_te(device_count_mock):

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert trainer.use_ddp
            assert isinstance(trainer.accelerator, GPUAccelerator)
            assert isinstance(trainer.training_type_plugin, DDPPlugin)
            assert isinstance(trainer.training_type_plugin.cluster_environment, TorchElasticEnvironment)
            assert trainer.training_type_plugin.cluster_environment.local_rank() == 10
            assert trainer.training_type_plugin.task_idx == 10
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp',
        gpus=2,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU")
@mock.patch.dict(os.environ, {"CUDA_VISIBLE_DEVICES": "0,1", "WORLD_SIZE": "2", "LOCAL_RANK": "10", "NODE_RANK": "0"})
@mock.patch('torch.cuda.device_count', return_value=2)
def test_accelerator_choice_ddp2_te(device_count_mock):

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert trainer.use_ddp2
            assert isinstance(trainer.accelerator, GPUAccelerator)
            assert isinstance(trainer.training_type_plugin, DDP2Plugin)
            assert isinstance(trainer.training_type_plugin.cluster_environment, TorchElasticEnvironment)
            assert trainer.training_type_plugin.cluster_environment.local_rank() == 10
            assert trainer.training_type_plugin.task_idx == 10
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp2',
        gpus=2,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)


@mock.patch.dict(os.environ, {
    "WORLD_SIZE": "1",
    "LOCAL_RANK": "10",
    "NODE_RANK": "0",
})
@mock.patch('torch.cuda.device_count', return_value=0)
def test_accelerator_choice_ddp_cpu_te(device_count_mock):

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert trainer.use_ddp
            assert isinstance(trainer.accelerator, CPUAccelerator)
            assert isinstance(trainer.training_type_plugin, DDPPlugin)
            assert isinstance(trainer.training_type_plugin.cluster_environment, TorchElasticEnvironment)
            assert trainer.training_type_plugin.cluster_environment.local_rank() == 10
            assert trainer.training_type_plugin.task_idx == 10
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp_cpu',
        num_processes=2,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)


@mock.patch.dict(
    os.environ, {
        "SLURM_NTASKS": "2",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "LOCAL_RANK": "0",
        "SLURM_LOCALID": "0"
    }
)
@mock.patch('torch.cuda.device_count', return_value=0)
def test_accelerator_choice_ddp_cpu_slurm(device_count_mock):

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert trainer.use_ddp
            assert trainer.accelerator_connector.is_slurm_managing_tasks
            assert isinstance(trainer.accelerator, CPUAccelerator)
            assert isinstance(trainer.training_type_plugin, DDPPlugin)
            assert isinstance(trainer.training_type_plugin.cluster_environment, SLURMEnvironment)
            assert trainer.training_type_plugin.task_idx == 0
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp_cpu',
        num_processes=2,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)


@mock.patch.dict(
    os.environ, {
        "SLURM_NTASKS": "2",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "LOCAL_RANK": "0",
        "SLURM_LOCALID": "0"
    }
)
@mock.patch('torch.cuda.device_count', return_value=0)
def test_accelerator_choice_ddp_cpu_custom_cluster(device_count_mock):
    """
    Test that we choose the custom cluster even when SLURM or TE flags are around
    """

    class CustomCluster(ClusterEnvironment):

        def master_address(self):
            return 'asdf'

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert trainer.use_ddp
            assert isinstance(trainer.accelerator, CPUAccelerator)
            assert isinstance(trainer.training_type_plugin, DDPPlugin)
            assert isinstance(trainer.training_type_plugin.cluster_environment, CustomCluster)
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        plugins=[CustomCluster()],
        fast_dev_run=True,
        accelerator='ddp_cpu',
        num_processes=2,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)


@mock.patch.dict(
    os.environ, {
        "SLURM_NTASKS": "2",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "LOCAL_RANK": "0",
        "SLURM_LOCALID": "0"
    }
)
@mock.patch('torch.cuda.device_count', return_value=0)
def test_custom_accelerator(device_count_mock):

    class Accel(Accelerator):
        pass

    class Prec(PrecisionPlugin):
        pass

    class TrainTypePlugin(SingleDevicePlugin):
        pass

    accelerator = Accel(
        training_type_plugin=TrainTypePlugin(device=torch.device("cpu")),
        precision_plugin=Prec(),
    )
    trainer = Trainer(
        accelerator=accelerator,
        fast_dev_run=True,
        num_processes=2,
    )
    assert isinstance(trainer.accelerator, Accel)
    assert isinstance(trainer.training_type_plugin, TrainTypePlugin)
    assert isinstance(trainer.precision_plugin, Prec)


@mock.patch.dict(
    os.environ, {
        "SLURM_NTASKS": "2",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "LOCAL_RANK": "0",
        "SLURM_LOCALID": "0"
    }
)
@mock.patch('torch.cuda.device_count', return_value=0)
def test_dist_backend_accelerator_mapping(device_count_mock):

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert isinstance(trainer.accelerator, CPUAccelerator)
            assert isinstance(trainer.training_type_plugin, DDPPlugin)
            assert trainer.training_type_plugin.task_idx == 0
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        accelerator='ddp_cpu',
        num_processes=2,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)