306 lines
11 KiB
Python
306 lines
11 KiB
Python
# Copyright The Lightning AI 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 datetime import timedelta
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from unittest import mock
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from unittest.mock import Mock
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import pytest
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import torch
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from torch.distributed.optim import ZeroRedundancyOptimizer
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from torch.nn.parallel import DistributedDataParallel
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from lightning.fabric.plugins.environments import ClusterEnvironment, LightningEnvironment
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from lightning.pytorch import LightningModule, Trainer
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from lightning.pytorch.demos.boring_classes import BoringModel
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from lightning.pytorch.strategies import DDPStrategy
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from lightning.pytorch.strategies.launchers import _SubprocessScriptLauncher
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from lightning.pytorch.trainer.states import TrainerFn
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from tests_pytorch.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(min_cuda_gpus=2, skip_windows=True, standalone=True)
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def test_ddp_with_2_gpus():
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"""Tests if device is set correctly when training and after teardown for DDPStrategy."""
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trainer = Trainer(
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accelerator="gpu",
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devices=2,
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strategy="ddp",
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fast_dev_run=True,
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enable_progress_bar=False,
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enable_model_summary=False,
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)
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# assert strategy 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_cuda_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(
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default_root_dir=tmpdir,
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max_steps=1,
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accelerator="gpu",
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devices=gpus,
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strategy="ddp",
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enable_progress_bar=False,
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enable_model_summary=False,
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)
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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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accelerator="cpu",
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devices=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(mps_count_0):
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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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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 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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@RunIf(min_cuda_gpus=1)
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@pytest.mark.parametrize("trainer_fn", (TrainerFn.VALIDATING, TrainerFn.TESTING, TrainerFn.PREDICTING))
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def test_ddp_dont_configure_sync_batchnorm(trainer_fn):
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model = BoringModelGPU()
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model.layer = torch.nn.BatchNorm1d(10)
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ddp_strategy = DDPStrategy()
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trainer = Trainer(accelerator="gpu", devices=1, strategy=ddp_strategy, sync_batchnorm=True)
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trainer.state.fn = trainer_fn
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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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# because TrainerFn is not FITTING, model is not configured with sync batchnorm
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assert not isinstance(trainer.strategy.model.layer, torch.nn.modules.batchnorm.SyncBatchNorm)
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class CheckOptimizerDeviceModel(BoringModel):
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def configure_optimizers(self):
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assert all(param.device.type == "cuda" for param in self.parameters())
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super().configure_optimizers()
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@RunIf(min_cuda_gpus=1)
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@pytest.mark.parametrize("strategy", ("ddp", "ddp_spawn"))
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def test_model_parameters_on_device_for_optimizer(strategy):
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"""Test that the strategy has moved the parameters to the device by the time the optimizer gets created."""
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model = CheckOptimizerDeviceModel()
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trainer = Trainer(
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default_root_dir=os.getcwd(),
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fast_dev_run=1,
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accelerator="gpu",
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devices=1,
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strategy=strategy,
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)
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trainer.fit(model)
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def test_configure_launcher_create_processes_externally():
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class MyClusterEnvironment(ClusterEnvironment):
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@property
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def creates_processes_externally(self):
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return True
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@property
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def main_address(self):
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return ""
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@property
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def main_port(self):
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return 8080
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@staticmethod
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def detect():
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return True
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def world_size(self):
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return 1
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def set_world_size(self):
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pass
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def global_rank(self):
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return 0
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def set_global_rank(self):
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pass
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def local_rank(self):
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return 0
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def node_rank(self):
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return 0
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ddp_strategy = DDPStrategy(cluster_environment=MyClusterEnvironment())
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assert ddp_strategy.launcher is None
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ddp_strategy._configure_launcher()
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assert isinstance(ddp_strategy.launcher, _SubprocessScriptLauncher)
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ddp_strategy.launcher._call_children_scripts = Mock()
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launch_fn = Mock()
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ddp_strategy.launcher.launch(launch_fn)
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ddp_strategy.launcher._call_children_scripts.assert_not_called()
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launch_fn.assert_called_once()
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@mock.patch("torch.distributed.init_process_group")
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def test_ddp_strategy_set_timeout(mock_init_process_group):
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"""Test that the timeout gets passed to the ``torch.distributed.init_process_group`` function."""
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test_timedelta = timedelta(seconds=30)
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model = BoringModel()
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ddp_strategy = DDPStrategy(timeout=test_timedelta)
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trainer = Trainer(
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max_epochs=1,
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accelerator="cpu",
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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.strategy.connect(model)
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trainer.lightning_module.trainer = trainer
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trainer.strategy.setup_environment()
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process_group_backend = trainer.strategy._get_process_group_backend()
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global_rank = trainer.strategy.cluster_environment.global_rank()
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world_size = trainer.strategy.cluster_environment.world_size()
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mock_init_process_group.assert_called_with(
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process_group_backend, rank=global_rank, world_size=world_size, timeout=test_timedelta
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)
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class BoringZeroRedundancyOptimizerModel(BoringModel):
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def configure_optimizers(self):
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return ZeroRedundancyOptimizer(self.layer.parameters(), optimizer_class=torch.optim.Adam, lr=0.1)
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@RunIf(min_cuda_gpus=2, skip_windows=True)
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@pytest.mark.parametrize("strategy", (pytest.param("ddp", marks=RunIf(standalone=True)), "ddp_spawn"))
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def test_ddp_strategy_checkpoint_zero_redundancy_optimizer(tmpdir, strategy):
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"""Test to ensure that checkpoint is saved correctly when using zero redundancy optimizer."""
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model = BoringZeroRedundancyOptimizerModel()
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trainer = Trainer(accelerator="gpu", devices=2, strategy=strategy, max_steps=1)
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trainer.fit(model)
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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# need to broadcast because tmpdir is different on each process
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checkpoint_path = trainer.strategy.broadcast(checkpoint_path)
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trainer.save_checkpoint(checkpoint_path)
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trainer.strategy.barrier() # ensure the checkpoint is saved before load
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saved_model = BoringModel.load_from_checkpoint(checkpoint_path)
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# Assert model parameters are identical after loading
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for trained_param, loaded_param in zip(model.parameters(), saved_model.parameters()):
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assert torch.equal(trained_param.to("cpu"), loaded_param)
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class UnusedParametersModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.intermediate_layer = torch.nn.Linear(32, 32)
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def training_step(self, batch, batch_idx):
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with torch.no_grad():
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batch = self.intermediate_layer(batch)
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return super().training_step(batch, batch_idx)
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def test_ddp_strategy_find_unused_parameters_exception():
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"""Test that the DDP strategy can change PyTorch's error message so that it's more useful for Lightning
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users."""
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trainer = Trainer(accelerator="cpu", devices=1, strategy="ddp", max_steps=2)
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with pytest.raises(RuntimeError, match="It looks like your LightningModule has parameters that were not used in"):
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trainer.fit(UnusedParametersModel())
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