lightning/tests/accelerators/test_ddp.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 typing import Optional
from unittest import mock
from unittest.mock import patch

import pytest
import torch
from torch.nn.parallel.distributed import DistributedDataParallel

import pytorch_lightning as pl
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import Callback
from tests.accelerators import ddp_model
from tests.helpers.boring_model import BoringModel
from tests.helpers.runif import RunIf
from tests.utilities.distributed import call_training_script

CLI_ARGS = '--max_epochs 1 --gpus 2 --accelerator ddp'


@RunIf(min_gpus=2)
def test_multi_gpu_model_ddp_fit_only(tmpdir):
    # call the script
    call_training_script(ddp_model, CLI_ARGS, 'fit', tmpdir, timeout=120)

    # load the results of the script
    result_path = os.path.join(tmpdir, 'ddp.result')
    result = torch.load(result_path)

    # verify the file wrote the expected outputs
    assert result['status'] == 'complete'


@RunIf(min_gpus=2)
def test_multi_gpu_model_ddp_test_only(tmpdir):
    # call the script
    call_training_script(ddp_model, CLI_ARGS, 'test', tmpdir)

    # load the results of the script
    result_path = os.path.join(tmpdir, 'ddp.result')
    result = torch.load(result_path)

    # verify the file wrote the expected outputs
    assert result['status'] == 'complete'


@RunIf(min_gpus=2)
def test_multi_gpu_model_ddp_fit_test(tmpdir):
    # call the script
    call_training_script(ddp_model, CLI_ARGS, 'fit_test', tmpdir, timeout=20)

    # load the results of the script
    result_path = os.path.join(tmpdir, 'ddp.result')
    result = torch.load(result_path)

    # verify the file wrote the expected outputs
    assert result['status'] == 'complete'

    model_outs = result['result']
    for out in model_outs:
        assert out['test_acc'] > 0.7


@RunIf(skip_windows=True)
@pytest.mark.skipif(torch.cuda.is_available(), reason="test doesn't requires GPU machine")
def test_torch_distributed_backend_env_variables(tmpdir):
    """
    This test set `undefined` as torch backend and should raise an `Backend.UNDEFINED` ValueError.
    """
    _environ = {"PL_TORCH_DISTRIBUTED_BACKEND": "undefined", "CUDA_VISIBLE_DEVICES": "0,1", "WORLD_SIZE": "2"}
    with patch.dict(os.environ, _environ), \
         patch('torch.cuda.device_count', return_value=2):
        with pytest.raises(ValueError, match="Invalid backend: 'undefined'"):
            model = BoringModel()
            trainer = Trainer(
                default_root_dir=tmpdir,
                fast_dev_run=True,
                accelerator="ddp",
                gpus=2,
                logger=False,
            )
            trainer.fit(model)


@RunIf(skip_windows=True)
@mock.patch('torch.cuda.device_count', return_value=1)
@mock.patch('torch.cuda.is_available', return_value=True)
@mock.patch('torch.cuda.set_device')
@mock.patch.dict(os.environ, {'PL_TORCH_DISTRIBUTED_BACKEND': 'gloo'}, clear=True)
def test_ddp_torch_dist_is_available_in_setup(mock_set_device, mock_is_available, mock_device_count, tmpdir):
    """
    Test to ensure torch distributed is available within the setup hook using ddp
    """

    class TestModel(BoringModel):

        def setup(self, stage: Optional[str] = None) -> None:
            assert torch.distributed.is_initialized()
            raise SystemExit()

    model = TestModel()
    trainer = Trainer(
        default_root_dir=tmpdir,
        fast_dev_run=True,
        accelerator="ddp",
        gpus=1,
    )
    with pytest.raises(SystemExit):
        trainer.fit(model)


@RunIf(min_gpus=2, min_torch="1.8.1", special=True)
def test_ddp_wrapper(tmpdir):
    """
    Test parameters to ignore are carried over for DDP.
    """

    class WeirdModule(torch.nn.Module):

        def _save_to_state_dict(self, destination, prefix, keep_vars):
            return {"something": "something"}

    class CustomModel(BoringModel):

        def __init__(self):
            super().__init__()
            self.weird_module = WeirdModule()

            # should be skip.
            self._ddp_params_and_buffers_to_ignore = ('something')

    class CustomCallback(Callback):

        def on_train_start(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule') -> None:
            assert isinstance(trainer.training_type_plugin.model, DistributedDataParallel)
            assert trainer.training_type_plugin.model.parameters_to_ignore == ('something')
            assert trainer.training_type_plugin.model.module._ddp_params_and_buffers_to_ignore == ('something')

    model = CustomModel()
    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, accelerator="ddp", gpus=2, callbacks=CustomCallback())
    trainer.fit(model)