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Automatically set sync_batchnorm for training_type_plugin (#6536)
Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> Co-authored-by: Roger Shieh <sh.rog@protonmail.ch> Co-authored-by: Kaushik Bokka <kaushikbokka@gmail.com>
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# Copyright The PyTorch Lightning team. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
from pytorch_lightning import Trainer | ||
from pytorch_lightning.plugins import DDPPlugin | ||
from tests.helpers import BoringModel | ||
from tests.helpers.runif import RunIf | ||
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class CustomParallelPlugin(DDPPlugin): | ||
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def __init__(self, **kwargs): | ||
super().__init__(**kwargs) | ||
# Set to None so it will be overwritten by the accelerator connector. | ||
self.sync_batchnorm = None | ||
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@RunIf(skip_windows=True) | ||
def test_sync_batchnorm_set(tmpdir): | ||
"""Tests if sync_batchnorm is automatically set for custom plugin.""" | ||
model = BoringModel() | ||
plugin = CustomParallelPlugin() | ||
assert plugin.sync_batchnorm is None | ||
trainer = Trainer( | ||
max_epochs=1, | ||
plugins=[plugin], | ||
default_root_dir=tmpdir, | ||
sync_batchnorm=True, | ||
) | ||
trainer.fit(model) | ||
assert plugin.sync_batchnorm is True |