323 lines
9.3 KiB
Python
323 lines
9.3 KiB
Python
# Copyright The Lightning AI 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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import torch
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from lightning.pytorch import Trainer, seed_everything
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from lightning.pytorch.callbacks import Callback, EarlyStopping, ModelCheckpoint
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from lightning.pytorch.demos.boring_classes import BoringModel
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import tests_pytorch.helpers.pipelines as tpipes
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import tests_pytorch.helpers.utils as tutils
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from tests_pytorch.helpers.datamodules import ClassifDataModule
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from tests_pytorch.helpers.runif import RunIf
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from tests_pytorch.helpers.simple_models import ClassificationModel
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@mock.patch("lightning.fabric.plugins.environments.slurm.SLURMEnvironment.detect", return_value=True)
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def test_cpu_slurm_save_load(_, tmp_path):
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"""Verify model save/load/checkpoint on CPU."""
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seed_everything(42)
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model = BoringModel()
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# logger file to get meta
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logger = tutils.get_default_logger(tmp_path)
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version = logger.version
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# fit model
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trainer = Trainer(
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default_root_dir=tmp_path,
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max_epochs=1,
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logger=logger,
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limit_train_batches=0.2,
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limit_val_batches=0.2,
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callbacks=[ModelCheckpoint(dirpath=tmp_path)],
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)
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trainer.fit(model)
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real_global_step = trainer.global_step
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# traning complete
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assert trainer.state.finished, "cpu model failed to complete"
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# predict with trained model before saving
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# make a prediction
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dataloaders = model.test_dataloader()
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if not isinstance(dataloaders, list):
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dataloaders = [dataloaders]
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for dataloader in dataloaders:
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for batch in dataloader:
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break
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model.eval()
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pred_before_saving = model(batch)
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# test HPC saving
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# simulate snapshot on slurm
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# save logger to make sure we get all the metrics
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if logger:
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logger.finalize("finished")
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hpc_save_path = trainer._checkpoint_connector.hpc_save_path(trainer.default_root_dir)
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trainer.save_checkpoint(hpc_save_path)
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assert os.path.exists(hpc_save_path)
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# new logger file to get meta
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logger = tutils.get_default_logger(tmp_path, version=version)
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model = BoringModel()
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class _StartCallback(Callback):
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# set the epoch start hook so we can predict before the model does the full training
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def on_train_epoch_start(self, trainer, model):
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assert trainer.global_step == real_global_step
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assert trainer.global_step > 0
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# predict with loaded model to make sure answers are the same
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mode = model.training
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model.eval()
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new_pred = model(batch)
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assert torch.eq(pred_before_saving, new_pred).all()
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model.train(mode)
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trainer = Trainer(
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default_root_dir=tmp_path,
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max_epochs=1,
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logger=logger,
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callbacks=[_StartCallback(), ModelCheckpoint(dirpath=tmp_path)],
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)
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# by calling fit again, we trigger training, loading weights from the cluster
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# and our hook to predict using current model before any more weight updates
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trainer.fit(model)
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def test_early_stopping_cpu_model(tmp_path):
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seed_everything(42)
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class ModelTrainVal(BoringModel):
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def validation_step(self, *args, **kwargs):
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output = super().validation_step(*args, **kwargs)
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self.log("val_loss", output["x"])
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return output
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stopping = EarlyStopping(monitor="val_loss", min_delta=0.1)
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trainer_options = {
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"callbacks": [stopping],
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"default_root_dir": tmp_path,
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"gradient_clip_val": 1.0,
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"enable_progress_bar": False,
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"accumulate_grad_batches": 2,
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"limit_train_batches": 0.3,
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"limit_val_batches": 0.1,
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}
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model = ModelTrainVal()
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tpipes.run_model_test(trainer_options, model)
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# test freeze on cpu
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model.freeze()
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model.unfreeze()
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@RunIf(skip_windows=True, sklearn=True)
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def test_multi_cpu_model_ddp(tmp_path):
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"""Make sure DDP works."""
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seed_everything(42)
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trainer_options = {
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"default_root_dir": tmp_path,
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"enable_progress_bar": False,
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"max_epochs": 1,
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"limit_train_batches": 0.4,
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"limit_val_batches": 0.2,
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"accelerator": "cpu",
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"devices": 2,
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"strategy": "ddp_spawn",
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}
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dm = ClassifDataModule()
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model = ClassificationModel()
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tpipes.run_model_test(trainer_options, model, data=dm)
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def test_lbfgs_cpu_model(tmp_path):
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"""Test each of the trainer options.
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Testing LBFGS optimizer
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"""
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seed_everything(42)
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class ModelSpecifiedOptimizer(BoringModel):
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def __init__(self, optimizer_name, learning_rate):
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super().__init__()
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self.optimizer_name = optimizer_name
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self.learning_rate = learning_rate
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self.save_hyperparameters()
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trainer_options = {
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"default_root_dir": tmp_path,
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"max_epochs": 1,
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"enable_progress_bar": False,
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"limit_train_batches": 0.2,
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"limit_val_batches": 0.2,
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}
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model = ModelSpecifiedOptimizer(optimizer_name="LBFGS", learning_rate=0.004)
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tpipes.run_model_test_without_loggers(trainer_options, model, min_acc=0.01)
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def test_default_logger_callbacks_cpu_model(tmp_path):
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"""Test each of the trainer options."""
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seed_everything(42)
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trainer_options = {
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"default_root_dir": tmp_path,
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"max_epochs": 1,
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"gradient_clip_val": 1.0,
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"overfit_batches": 0.20,
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"enable_progress_bar": False,
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"limit_train_batches": 0.01,
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"limit_val_batches": 0.01,
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}
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model = BoringModel()
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tpipes.run_model_test_without_loggers(trainer_options, model, min_acc=0.01)
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# test freeze on cpu
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model.freeze()
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model.unfreeze()
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def test_running_test_after_fitting(tmp_path):
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"""Verify test() on fitted model."""
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seed_everything(42)
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class ModelTrainValTest(BoringModel):
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def validation_step(self, *args, **kwargs):
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output = super().validation_step(*args, **kwargs)
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self.log("val_loss", output["x"])
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return output
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def test_step(self, *args, **kwargs):
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output = super().test_step(*args, **kwargs)
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self.log("test_loss", output["y"])
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return output
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model = ModelTrainValTest()
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# logger file to get meta
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logger = tutils.get_default_logger(tmp_path)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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# fit model
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trainer = Trainer(
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default_root_dir=tmp_path,
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enable_progress_bar=False,
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max_epochs=2,
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limit_train_batches=0.4,
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limit_val_batches=0.2,
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limit_test_batches=0.2,
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callbacks=[checkpoint],
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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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trainer.test()
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# test we have good test accuracy
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tutils.assert_ok_model_acc(trainer, key="test_loss", thr=0.5)
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def test_running_test_no_val(tmp_path):
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"""Verify `test()` works on a model with no `val_dataloader`.
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It performs train and test only
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"""
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seed_everything(42)
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class ModelTrainTest(BoringModel):
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def test_step(self, *args, **kwargs):
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output = super().test_step(*args, **kwargs)
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self.log("test_loss", output["y"])
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return output
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val_dataloader = None
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model = ModelTrainTest()
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# logger file to get meta
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logger = tutils.get_default_logger(tmp_path)
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# logger file to get weights
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checkpoint = tutils.init_checkpoint_callback(logger)
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# fit model
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trainer = Trainer(
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default_root_dir=tmp_path,
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enable_progress_bar=False,
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max_epochs=1,
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limit_train_batches=0.4,
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limit_val_batches=0.2,
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limit_test_batches=0.2,
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callbacks=[checkpoint],
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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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trainer.test()
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# test we have good test accuracy
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tutils.assert_ok_model_acc(trainer, key="test_loss")
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def test_cpu_model(tmp_path):
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"""Make sure model trains on CPU."""
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seed_everything(42)
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trainer_options = {
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"default_root_dir": tmp_path,
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"enable_progress_bar": False,
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"max_epochs": 1,
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"limit_train_batches": 4,
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"limit_val_batches": 4,
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model)
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def test_all_features_cpu_model(tmp_path):
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"""Test each of the trainer options."""
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seed_everything(42)
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trainer_options = {
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"default_root_dir": tmp_path,
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"gradient_clip_val": 1.0,
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"overfit_batches": 0.20,
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"enable_progress_bar": False,
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"accumulate_grad_batches": 2,
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"max_epochs": 1,
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"limit_train_batches": 0.4,
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"limit_val_batches": 0.4,
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}
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model = BoringModel()
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tpipes.run_model_test(trainer_options, model)
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