88 lines
3.6 KiB
Python
88 lines
3.6 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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import pytest
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import torch
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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.plugins import SingleDevicePlugin
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from pytorch_lightning.plugins.io.torch_plugin import TorchCheckpointIO
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from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
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from tests.helpers.boring_model import BoringModel
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@pytest.mark.parametrize("delay_dispatch", [True, False])
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def test_plugin_setup_optimizers_in_pre_dispatch(tmpdir, delay_dispatch):
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"""Test when using a custom training type plugin that delays setup optimizers, we do not call setup optimizers
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till ``pre_dispatch``."""
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class TestModel(BoringModel):
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def on_fit_start(self):
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if delay_dispatch:
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# Ensure we haven't setup optimizers if we've delayed dispatch
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assert len(self.trainer.optimizers) == 0
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else:
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assert len(self.trainer.optimizers) > 0
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def on_fit_end(self):
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assert len(self.trainer.optimizers) > 0
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class CustomPlugin(SingleDevicePlugin):
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@property
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def setup_optimizers_in_pre_dispatch(self) -> bool:
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return delay_dispatch
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, strategy=CustomPlugin(device=torch.device("cpu")))
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trainer.fit(model)
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def test_restore_checkpoint_after_pre_dispatch_default():
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"""Assert default for restore_checkpoint_after_pre_dispatch is False."""
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plugin = SingleDevicePlugin(torch.device("cpu"))
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accelerator = CPUAccelerator(training_type_plugin=plugin, precision_plugin=PrecisionPlugin())
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assert not accelerator.training_type_plugin.restore_checkpoint_after_pre_dispatch
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assert not plugin.restore_checkpoint_after_pre_dispatch
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@pytest.mark.parametrize("restore_after_pre_dispatch", [True, False])
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def test_restore_checkpoint_after_pre_dispatch(tmpdir, restore_after_pre_dispatch):
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"""Test to ensure that if restore_checkpoint_after_pre_dispatch is True, then we only load the state after pre-
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dispatch is called."""
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class TestPlugin(SingleDevicePlugin):
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predispatched_called = False
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def pre_dispatch(self) -> None:
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super().pre_dispatch()
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self.predispatched_called = True
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@property
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def restore_checkpoint_after_pre_dispatch(self) -> bool:
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return restore_after_pre_dispatch
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def load_checkpoint(self, checkpoint_path: Union[str, Path]) -> Dict[str, Any]:
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assert self.predispatched_called == restore_after_pre_dispatch
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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(torch.device("cpu"), checkpoint_io=TorchCheckpointIO())
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accelerator = CPUAccelerator(training_type_plugin=plugin, precision_plugin=PrecisionPlugin())
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assert accelerator.training_type_plugin.restore_checkpoint_after_pre_dispatch == restore_after_pre_dispatch
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assert plugin.restore_checkpoint_after_pre_dispatch == restore_after_pre_dispatch
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trainer = Trainer(default_root_dir=tmpdir, accelerator=accelerator, 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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accelerator.training_type_plugin.predispatched_called = False
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func(model, ckpt_path=checkpoint_path)
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