80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
import os
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from pathlib import Path
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from typing import Any, Dict, Union
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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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import pytorch_lightning as pl
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from lightning_lite.plugins import TorchCheckpointIO
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from pytorch_lightning import Trainer
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from pytorch_lightning.accelerators import CPUAccelerator
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from pytorch_lightning.demos.boring_classes import BoringModel
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from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
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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_restore_checkpoint_after_pre_setup_default():
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"""Assert default for restore_checkpoint_after_setup is False."""
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plugin = SingleDeviceStrategy(
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accelerator=CPUAccelerator(), device=torch.device("cpu"), precision_plugin=PrecisionPlugin()
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)
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assert not plugin.restore_checkpoint_after_setup
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def test_availability():
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assert CPUAccelerator.is_available()
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@RunIf(psutil=True)
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def test_get_device_stats(tmpdir):
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gpu_stats = CPUAccelerator().get_device_stats(Mock())
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fields = ["cpu_vm_percent", "cpu_percent", "cpu_swap_percent"]
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for f in fields:
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assert any(f in h for h in gpu_stats.keys())
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@pytest.mark.parametrize("restore_after_pre_setup", [True, False])
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def test_restore_checkpoint_after_pre_setup(tmpdir, restore_after_pre_setup):
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"""Test to ensure that if restore_checkpoint_after_setup is True, then we only load the state after pre-
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dispatch is called."""
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class TestPlugin(SingleDeviceStrategy):
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setup_called = False
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def setup(self, trainer: "pl.Trainer") -> None:
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super().setup(trainer)
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self.setup_called = True
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@property
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def restore_checkpoint_after_setup(self) -> bool:
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return restore_after_pre_setup
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def load_checkpoint(self, checkpoint_path: Union[str, Path]) -> Dict[str, Any]:
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assert self.setup_called == restore_after_pre_setup
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return super().load_checkpoint(checkpoint_path)
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
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trainer.fit(model)
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checkpoint_path = os.path.join(tmpdir, "model.pt")
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trainer.save_checkpoint(checkpoint_path)
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plugin = TestPlugin(
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accelerator=CPUAccelerator(),
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precision_plugin=PrecisionPlugin(),
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device=torch.device("cpu"),
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checkpoint_io=TorchCheckpointIO(),
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)
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assert plugin.restore_checkpoint_after_setup == restore_after_pre_setup
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trainer = Trainer(default_root_dir=tmpdir, strategy=plugin, fast_dev_run=True)
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trainer.fit(model, ckpt_path=checkpoint_path)
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for func in (trainer.test, trainer.validate, trainer.predict):
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plugin.setup_called = False
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func(model, ckpt_path=checkpoint_path)
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