130 lines
4.8 KiB
Python
130 lines
4.8 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from unittest import mock
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import pytest
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import torch
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from torch.nn.parallel import DistributedDataParallel
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from pytorch_lightning import LightningModule, Trainer
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from pytorch_lightning.plugins.environments import LightningEnvironment
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from pytorch_lightning.strategies import DDPStrategy
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from pytorch_lightning.trainer.states import TrainerFn
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.runif import RunIf
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class BoringModelGPU(BoringModel):
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def on_train_start(self) -> None:
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# make sure that the model is on GPU when training
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assert self.device == torch.device(f"cuda:{self.trainer.strategy.local_rank}")
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self.start_cuda_memory = torch.cuda.memory_allocated()
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@RunIf(skip_windows=True, min_gpus=2, standalone=True)
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def test_ddp_with_2_gpus():
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"""Tests if device is set correctely when training and after teardown for DDPStrategy."""
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trainer = Trainer(gpus=2, strategy="ddp", fast_dev_run=True)
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# assert training type plugin attributes for device setting
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assert isinstance(trainer.strategy, DDPStrategy)
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local_rank = trainer.strategy.local_rank
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assert trainer.strategy.root_device == torch.device(f"cuda:{local_rank}")
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model = BoringModelGPU()
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trainer.fit(model)
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# assert after training, model is moved to CPU and memory is deallocated
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assert model.device == torch.device("cpu")
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cuda_memory = torch.cuda.memory_allocated()
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assert cuda_memory < model.start_cuda_memory
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class BarrierModel(BoringModel):
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def setup(self, stage=None):
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assert not isinstance(self.trainer.strategy.model, DistributedDataParallel)
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self.trainer.strategy.barrier("barrier before model is wrapped")
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def on_train_start(self):
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assert isinstance(self.trainer.strategy.model, DistributedDataParallel)
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self.trainer.strategy.barrier("barrier after model is wrapped")
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@RunIf(min_gpus=4, standalone=True)
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@mock.patch("torch.distributed.barrier")
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def test_ddp_barrier_non_consecutive_device_ids(barrier_mock, tmpdir):
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"""Test correct usage of barriers when device ids do not start at 0 or are not consecutive."""
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model = BoringModel()
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gpus = [1, 3]
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trainer = Trainer(default_root_dir=tmpdir, max_steps=1, gpus=gpus, strategy="ddp")
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trainer.fit(model)
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barrier_mock.assert_any_call(device_ids=[gpus[trainer.local_rank]])
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@mock.patch.dict(os.environ, {"LOCAL_RANK": "1"})
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def test_incorrect_ddp_script_spawning(tmpdir):
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"""Test an error message when user accidentally instructs Lightning to spawn children processes on rank > 0."""
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class WronglyImplementedEnvironment(LightningEnvironment):
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@property
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def creates_processes_externally(self):
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# returning false no matter what means Lightning would spawn also on ranks > 0 new processes
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return False
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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strategy="ddp",
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num_processes=2,
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plugins=[WronglyImplementedEnvironment()],
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)
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with pytest.raises(
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RuntimeError, match="Lightning attempted to launch new distributed processes with `local_rank > 0`."
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):
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trainer.fit(model)
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@RunIf(skip_windows=True)
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def test_ddp_configure_ddp():
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"""Tests with ddp strategy."""
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model = BoringModel()
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ddp_strategy = DDPStrategy()
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trainer = Trainer(
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max_epochs=1,
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strategy=ddp_strategy,
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)
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# test wrap the model if fitting
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trainer.state.fn = TrainerFn.FITTING
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trainer.strategy.connect(model)
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trainer.lightning_module.trainer = trainer
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trainer.strategy.setup_environment()
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assert isinstance(trainer.model, LightningModule)
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trainer.strategy.setup(trainer)
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# in DDPStrategy configure_ddp(), model wrapped by DistributedDataParallel
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assert isinstance(trainer.model, DistributedDataParallel)
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trainer = Trainer(
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max_epochs=1,
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strategy=ddp_strategy,
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)
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# test do not wrap the model if trainerFN is not fitting
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trainer.state.fn = TrainerFn.VALIDATING
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trainer.strategy.connect(model)
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trainer.lightning_module.trainer = trainer
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trainer.strategy.setup_environment()
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trainer.strategy.setup(trainer)
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# in DDPStrategy configure_ddp(), model are still LightningModule
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assert isinstance(trainer.model, LightningModule)
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