42 lines
1.4 KiB
Python
42 lines
1.4 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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from pytorch_lightning import Trainer
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from pytorch_lightning.plugins import DDPPlugin
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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class CustomParallelPlugin(DDPPlugin):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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# Set to None so it will be overwritten by the accelerator connector.
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self.sync_batchnorm = None
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@RunIf(skip_windows=True)
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def test_sync_batchnorm_set(tmpdir):
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"""Tests if sync_batchnorm is automatically set for custom plugin."""
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model = BoringModel()
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plugin = CustomParallelPlugin()
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assert plugin.sync_batchnorm is None
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trainer = Trainer(
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max_epochs=1,
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plugins=[plugin],
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default_root_dir=tmpdir,
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sync_batchnorm=True,
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)
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trainer.fit(model)
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assert plugin.sync_batchnorm is True
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