104 lines
3.7 KiB
Python
104 lines
3.7 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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from unittest.mock import MagicMock
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import pytest
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import torch
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from torch.utils.data import DataLoader
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from pytorch_lightning import Trainer
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from pytorch_lightning.plugins.training_type import TPUSpawnPlugin
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from pytorch_lightning.trainer.connectors.data_connector import DataConnector
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers.boring_model import BoringModel, RandomDataset
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from tests.helpers.dataloaders import CustomNotImplementedErrorDataloader
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from tests.helpers.runif import RunIf
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from tests.helpers.utils import pl_multi_process_test
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class BoringModelNoDataloaders(BoringModel):
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def train_dataloader(self):
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raise NotImplementedError
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def val_dataloader(self):
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raise NotImplementedError
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def test_dataloader(self):
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raise NotImplementedError
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def predict_dataloader(self):
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raise NotImplementedError
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_loader = DataLoader(RandomDataset(32, 64))
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_loader_no_len = CustomNotImplementedErrorDataloader(_loader)
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@pytest.mark.parametrize(
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"train_dataloaders, val_dataloaders, test_dataloaders, predict_dataloaders",
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[
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(_loader_no_len, None, None, None),
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(None, _loader_no_len, None, None),
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(None, None, _loader_no_len, None),
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(None, None, None, _loader_no_len),
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(None, [_loader, _loader_no_len], None, None),
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],
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)
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@mock.patch("pytorch_lightning.plugins.training_type.tpu_spawn.xm")
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def test_error_patched_iterable_dataloaders(
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_, tmpdir, train_dataloaders, val_dataloaders, test_dataloaders, predict_dataloaders
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):
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model = BoringModelNoDataloaders()
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connector = DataConnector(MagicMock())
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connector.attach_dataloaders(
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model,
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train_dataloaders=train_dataloaders,
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val_dataloaders=val_dataloaders,
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test_dataloaders=test_dataloaders,
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predict_dataloaders=predict_dataloaders,
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)
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with pytest.raises(MisconfigurationException, match="TPUs do not currently support"):
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TPUSpawnPlugin(MagicMock()).connect(model)
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@mock.patch("pytorch_lightning.plugins.training_type.tpu_spawn.xm")
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def test_error_process_iterable_dataloader(_):
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with pytest.raises(MisconfigurationException, match="TPUs do not currently support"):
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TPUSpawnPlugin(MagicMock()).process_dataloader(_loader_no_len)
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class BoringModelTPU(BoringModel):
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def on_train_start(self) -> None:
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assert self.device == torch.device("xla")
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assert os.environ.get("PT_XLA_DEBUG") == "1"
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@RunIf(tpu=True)
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@pl_multi_process_test
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def test_model_tpu_one_core():
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"""Tests if device/debug flag is set correctely when training and after teardown for TPUSpawnPlugin."""
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trainer = Trainer(tpu_cores=1, fast_dev_run=True, plugin=TPUSpawnPlugin(debug=True))
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# assert training type plugin attributes for device setting
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assert isinstance(trainer.training_type_plugin, TPUSpawnPlugin)
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assert not trainer.training_type_plugin.on_gpu
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assert trainer.training_type_plugin.on_tpu
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assert trainer.training_type_plugin.root_device == torch.device("xla")
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model = BoringModelTPU()
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trainer.fit(model)
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assert "PT_XLA_DEBUG" not in os.environ
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