78 lines
2.8 KiB
Python
78 lines
2.8 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 pickle
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.core.optimizer import LightningOptimizer
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from pytorch_lightning.demos.boring_classes import BoringModel
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from pytorch_lightning.strategies import SingleDeviceStrategy
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from tests_pytorch.helpers.runif import RunIf
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def test_single_cpu():
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"""Tests if device is set correctly for single CPU strategy."""
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trainer = Trainer()
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assert isinstance(trainer.strategy, SingleDeviceStrategy)
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assert trainer.strategy.root_device == torch.device("cpu")
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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("cuda:0")
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self.start_cuda_memory = torch.cuda.memory_allocated()
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@RunIf(min_cuda_gpus=1, skip_windows=True)
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def test_single_gpu():
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"""Tests if device is set correctly when training and after teardown for single GPU strategy.
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Cannot run this test on MPS due to shared memory not allowing dedicated measurements of GPU memory utilization.
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"""
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trainer = Trainer(accelerator="gpu", devices=1, fast_dev_run=True)
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# assert training strategy attributes for device setting
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assert isinstance(trainer.strategy, SingleDeviceStrategy)
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assert trainer.strategy.root_device == torch.device("cuda:0")
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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 MockOptimizer:
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...
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def test_strategy_pickle():
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strategy = SingleDeviceStrategy("cpu")
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optimizer = MockOptimizer()
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strategy.optimizers = [optimizer]
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assert isinstance(strategy.optimizers[0], MockOptimizer)
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assert isinstance(strategy._lightning_optimizers[0], LightningOptimizer)
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state = pickle.dumps(strategy)
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# dumping did not get rid of the lightning optimizers
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assert isinstance(strategy._lightning_optimizers[0], LightningOptimizer)
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strategy_reloaded = pickle.loads(state)
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# loading restores the lightning optimizers
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assert isinstance(strategy_reloaded._lightning_optimizers[0], LightningOptimizer)
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