104 lines
3.3 KiB
Python
104 lines
3.3 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 Mock
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from pytorch_lightning import Callback, Trainer
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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class TestModel(BoringModel):
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def on_pretrain_routine_end(self) -> None:
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with mock.patch("pytorch_lightning.loggers.base.LightningLoggerBase.agg_and_log_metrics") as m:
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self.trainer.logger_connector.log_metrics({"a": 2})
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logged_times = m.call_count
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expected = int(self.trainer.is_global_zero)
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msg = f"actual logger called from non-global zero, logged_times: {logged_times}, expected: {expected}"
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assert logged_times == expected, msg
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@RunIf(skip_windows=True)
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def test_global_zero_only_logging_ddp_cpu(tmpdir):
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"""
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Makes sure logging only happens from root zero
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"""
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model = TestModel()
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model.training_epoch_end = None
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trainer = Trainer(
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accelerator="ddp_cpu",
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num_processes=2,
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default_root_dir=tmpdir,
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limit_train_batches=1,
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limit_val_batches=1,
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max_epochs=1,
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weights_summary=None,
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)
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trainer.fit(model)
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@RunIf(min_gpus=2)
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def test_global_zero_only_logging_ddp_spawn(tmpdir):
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"""
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Makes sure logging only happens from root zero
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"""
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model = TestModel()
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model.training_epoch_end = None
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trainer = Trainer(
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accelerator="ddp_spawn",
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gpus=2,
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default_root_dir=tmpdir,
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limit_train_batches=1,
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limit_val_batches=1,
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max_epochs=1,
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weights_summary=None,
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)
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trainer.fit(model)
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def test_first_logger_call_in_subprocess(tmpdir):
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"""
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Test that the Trainer does not call the logger too early. Only when the worker processes are initialized
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do we have access to the rank and know which one is the main process.
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"""
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class LoggerCallsObserver(Callback):
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def on_fit_start(self, trainer, pl_module):
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# this hook is executed directly before Trainer.pre_dispatch
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# logger should not write any logs until this point
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assert not trainer.logger.method_calls
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assert not os.listdir(trainer.logger.save_dir)
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def on_train_start(self, trainer, pl_module):
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assert trainer.logger.method_call
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trainer.logger.log_hyperparams.assert_called_once()
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trainer.logger.log_graph.assert_called_once()
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logger = Mock()
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logger.version = "0"
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logger.name = "name"
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logger.save_dir = tmpdir
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=1,
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limit_val_batches=1,
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max_epochs=1,
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logger=logger,
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callbacks=[LoggerCallsObserver()],
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)
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trainer.fit(model)
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