87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
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# 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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import numpy as np
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import pytest
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import XLAStatsMonitor
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from pytorch_lightning.loggers import CSVLogger
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from pytorch_lightning.loggers.csv_logs import ExperimentWriter
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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@RunIf(tpu=True)
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def test_xla_stats_monitor(tmpdir):
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"""Test XLA stats are logged using a logger."""
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model = BoringModel()
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xla_stats = XLAStatsMonitor()
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logger = CSVLogger(tmpdir)
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trainer = Trainer(
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default_root_dir=tmpdir, max_epochs=2, limit_train_batches=5, tpu_cores=8, callbacks=[xla_stats], logger=logger
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)
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trainer.fit(model)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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path_csv = os.path.join(logger.log_dir, ExperimentWriter.NAME_METRICS_FILE)
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met_data = np.genfromtxt(path_csv, delimiter=',', names=True, deletechars='', replace_space=' ')
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fields = ['avg. free memory (MB)', 'avg. peak memory (MB)']
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for f in fields:
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assert any([f in h for h in met_data.dtype.names])
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@RunIf(tpu=True)
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def test_xla_stats_monitor_no_logger(tmpdir):
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"""Test XLAStatsMonitor with no logger in Trainer."""
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model = BoringModel()
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xla_stats = XLAStatsMonitor()
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trainer = Trainer(
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default_root_dir=tmpdir,
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callbacks=[xla_stats],
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max_epochs=1,
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tpu_cores=[1],
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logger=False,
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)
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with pytest.raises(MisconfigurationException, match='Trainer that has no logger.'):
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trainer.fit(model)
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@RunIf(tpu=True)
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def test_xla_stats_monitor_no_tpu_warning(tmpdir):
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"""Test XLAStatsMonitor raises a warning when not training on TPUs."""
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model = BoringModel()
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xla_stats = XLAStatsMonitor()
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trainer = Trainer(
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default_root_dir=tmpdir,
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callbacks=[xla_stats],
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max_steps=1,
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tpu_cores=None,
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)
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with pytest.raises(MisconfigurationException, match='not running on TPU'):
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trainer.fit(model)
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