def test_amp_gpu_ddp_slurm_managed(tmpdir):
"""Make sure DDP + AMP work."""
# simulate setting slurm flags
tutils.set_random_main_port()
model = AMPTestModel()
# exp file to get meta
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
gpus=[0],
strategy="ddp_spawn",
precision=16,
callbacks=[checkpoint],
logger=logger,
)
trainer.fit(model)
# correct result and ok accuracy
assert trainer.state.finished, "amp + ddp model failed to complete"
# test root model address
assert isinstance(trainer.strategy.cluster_environment, SLURMEnvironment)
assert trainer.strategy.cluster_environment.resolve_root_node_address("abc") == "abc"
assert trainer.strategy.cluster_environment.resolve_root_node_address("abc[23]") == "abc23"
assert trainer.strategy.cluster_environment.resolve_root_node_address("abc[23-24]") == "abc23"
generated = trainer.strategy.cluster_environment.resolve_root_node_address("abc[23-24, 45-40, 40]")
assert generated == "abc23"
def test_amp_gpu_ddp_slurm_managed(tmpdir):
"""Make sure DDP + AMP work."""
# simulate setting slurm flags
tutils.set_random_master_port()
model = BoringModel()
# exp file to get meta
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
gpus=[0],
accelerator='ddp_spawn',
precision=16,
callbacks=[checkpoint],
logger=logger,
)
_ = trainer.fit(model)
# correct result and ok accuracy
assert trainer.state == TrainerState.FINISHED, 'amp + ddp model failed to complete'
# test root model address
assert isinstance(trainer.training_type_plugin.cluster_environment, SLURMEnvironment)
assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc') == 'abc'
assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23]') == 'abc23'
assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23-24]') == 'abc23'
generated = trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23-24, 45-40, 40]')
assert generated == 'abc23'
def run_model_test(
trainer_options,
model: LightningModule,
data: LightningDataModule = None,
on_gpu: bool = True,
version=None,
with_hpc: bool = True,
min_acc: float = 0.25,
):
reset_seed()
save_dir = trainer_options["default_root_dir"]
# logger file to get meta
logger = get_default_logger(save_dir, version=version)
trainer_options.update(logger=logger)
trainer = Trainer(**trainer_options)
initial_values = torch.tensor(
[torch.sum(torch.abs(x)) for x in model.parameters()])
trainer.fit(model, datamodule=data)
post_train_values = torch.tensor(
[torch.sum(torch.abs(x)) for x in model.parameters()])
assert trainer.state.finished, f"Training failed with {trainer.state}"
# Check that the model is actually changed post-training
change_ratio = torch.norm(initial_values - post_train_values)
assert change_ratio > 0.1, f"the model is changed of {change_ratio}"
# test model loading
pretrained_model = load_model_from_checkpoint(
logger, trainer.checkpoint_callback.best_model_path, type(model))
# test new model accuracy
test_loaders = model.test_dataloader(
) if not data else data.test_dataloader()
if not isinstance(test_loaders, list):
test_loaders = [test_loaders]
if not isinstance(model, BoringModel):
for dataloader in test_loaders:
run_prediction_eval_model_template(model,
dataloader,
min_acc=min_acc)
if with_hpc:
if trainer._distrib_type in (DistributedType.DDP,
DistributedType.DDP_SPAWN,
DistributedType.DDP2):
# on hpc this would work fine... but need to hack it for the purpose of the test
trainer.optimizers, trainer.lr_schedulers, trainer.optimizer_frequencies = trainer.init_optimizers(
pretrained_model)
# test HPC saving
trainer.checkpoint_connector.hpc_save(save_dir, logger)
# test HPC loading
checkpoint_path = trainer.checkpoint_connector.get_max_ckpt_path_from_folder(
save_dir)
trainer.checkpoint_connector.restore(checkpoint_path)
def test_strict_model_load_less_params(monkeypatch, tmpdir, tmpdir_server,
url_ckpt):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
monkeypatch.setenv('TORCH_HOME', tmpdir)
model = BoringModel()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=2,
limit_val_batches=2,
logger=logger,
callbacks=[ModelCheckpoint(dirpath=tmpdir)],
)
trainer.fit(model)
# traning complete
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
hparams_path = os.path.join(tutils.get_data_path(logger, path_dir=tmpdir),
'hparams.yaml')
hparams_url = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}'
ckpt_path = hparams_url if url_ckpt else new_weights_path
class CurrentModel(BoringModel):
def __init__(self):
super().__init__()
self.c_d3 = torch.nn.Linear(7, 7)
CurrentModel.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=False,
)
with pytest.raises(
RuntimeError,
match=r'Missing key\(s\) in state_dict: "c_d3.weight", "c_d3.bias"'
):
CurrentModel.load_from_checkpoint(
checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=True,
)
def test_model_saving_loading(tmpdir):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
model = BoringModel()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(
max_epochs=1,
limit_train_batches=2,
limit_val_batches=2,
logger=logger,
callbacks=[ModelCheckpoint(dirpath=tmpdir)],
default_root_dir=tmpdir,
)
trainer.fit(model)
# traning complete
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
# make a prediction
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
batch = next(iter(dataloaders[0]))
# generate preds before saving model
model.eval()
pred_before_saving = model(batch)
# save model
new_weights_path = os.path.join(tmpdir, 'save_test.ckpt')
trainer.save_checkpoint(new_weights_path)
# load new model
hparams_path = tutils.get_data_path(logger, path_dir=tmpdir)
hparams_path = os.path.join(hparams_path, 'hparams.yaml')
model_2 = BoringModel.load_from_checkpoint(
checkpoint_path=new_weights_path,
hparams_file=hparams_path,
)
model_2.eval()
# make prediction
# assert that both predictions are the same
new_pred = model_2(batch)
assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
def test_running_test_pretrained_model_distrib_ddp_spawn(tmpdir):
"""Verify `test()` on pretrained model."""
tutils.set_random_master_port()
dm = ClassifDataModule()
model = ClassificationModel()
# exp file to get meta
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
progress_bar_refresh_rate=0,
max_epochs=2,
limit_train_batches=2,
limit_val_batches=2,
callbacks=[checkpoint],
logger=logger,
gpus=[0, 1],
accelerator='ddp_spawn',
default_root_dir=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
trainer.fit(model, datamodule=dm)
log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
# correct result and ok accuracy
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
pretrained_model = ClassificationModel.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
pretrained_model.cpu()
dataloaders = dm.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tpipes.run_prediction_eval_model_template(pretrained_model,
dataloader,
min_acc=0.1)
def test_strict_model_load_more_params(monkeypatch, tmpdir, tmpdir_server,
url_ckpt):
"""Tests use case where trainer saves the model, and user loads it from tags independently."""
# set $TORCH_HOME, which determines torch hub's cache path, to tmpdir
monkeypatch.setenv("TORCH_HOME", tmpdir)
model = BoringModel()
# Extra layer
model.c_d3 = torch.nn.Linear(32, 32)
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
limit_train_batches=2,
limit_val_batches=2,
logger=logger,
callbacks=[ModelCheckpoint(dirpath=tmpdir)],
)
trainer.fit(model)
# traning complete
assert trainer.state.finished, f"Training failed with {trainer.state}"
# save model
new_weights_path = os.path.join(tmpdir, "save_test.ckpt")
trainer.save_checkpoint(new_weights_path)
# load new model
hparams_path = os.path.join(tutils.get_data_path(logger, path_dir=tmpdir),
"hparams.yaml")
hparams_url = f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}"
ckpt_path = hparams_url if url_ckpt else new_weights_path
BoringModel.load_from_checkpoint(checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=False)
with pytest.raises(
RuntimeError,
match=
r'Unexpected key\(s\) in state_dict: "c_d3.weight", "c_d3.bias"'):
BoringModel.load_from_checkpoint(checkpoint_path=ckpt_path,
hparams_file=hparams_path,
strict=True)
def test_default_args(mock_argparse, tmpdir):
"""Tests default argument parser for Trainer."""
mock_argparse.return_value = Namespace(**Trainer.default_attributes())
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
parser = ArgumentParser(add_help=False)
args = parser.parse_args()
args.logger = logger
args.max_epochs = 5
trainer = Trainer.from_argparse_args(args)
assert isinstance(trainer, Trainer)
assert trainer.max_epochs == 5
def test_running_test_pretrained_model_distrib_dp(tmpdir):
"""Verify `test()` on pretrained model."""
tutils.set_random_main_port()
dm = ClassifDataModule()
model = CustomClassificationModelDP(lr=0.1)
# exp file to get meta
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
enable_progress_bar=False,
max_epochs=2,
limit_train_batches=5,
limit_val_batches=5,
callbacks=[checkpoint],
logger=logger,
accelerator="gpu",
devices=[0, 1],
strategy="dp",
default_root_dir=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
trainer.fit(model, datamodule=dm)
# correct result and ok accuracy
assert trainer.state.finished, f"Training failed with {trainer.state}"
pretrained_model = CustomClassificationModelDP.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model, datamodule=dm)
pretrained_model.cpu()
dataloaders = dm.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tpipes.run_model_prediction(pretrained_model, dataloader)
def run_model_test(
trainer_options,
model: LightningModule,
data: LightningDataModule = None,
on_gpu: bool = True,
version=None,
with_hpc: bool = True,
min_acc: float = 0.25,
):
reset_seed()
save_dir = trainer_options["default_root_dir"]
# logger file to get meta
logger = get_default_logger(save_dir, version=version)
trainer_options.update(logger=logger)
trainer = Trainer(**trainer_options)
initial_values = torch.tensor([torch.sum(torch.abs(x)) for x in model.parameters()])
trainer.fit(model, datamodule=data)
post_train_values = torch.tensor([torch.sum(torch.abs(x)) for x in model.parameters()])
assert trainer.state.finished, f"Training failed with {trainer.state}"
# Check that the model is actually changed post-training
change_ratio = torch.norm(initial_values - post_train_values)
assert change_ratio > 0.03, f"the model is changed of {change_ratio}"
# test model loading
_ = load_model_from_checkpoint(logger, trainer.checkpoint_callback.best_model_path, type(model))
# test new model accuracy
test_loaders = model.test_dataloader() if not data else data.test_dataloader()
if not isinstance(test_loaders, list):
test_loaders = [test_loaders]
if not isinstance(model, BoringModel):
for dataloader in test_loaders:
run_model_prediction(model, dataloader, min_acc=min_acc)
if with_hpc:
# test HPC saving
# save logger to make sure we get all the metrics
if logger:
logger.finalize("finished")
hpc_save_path = trainer.checkpoint_connector.hpc_save_path(save_dir)
trainer.save_checkpoint(hpc_save_path)
# test HPC loading
checkpoint_path = trainer.checkpoint_connector._CheckpointConnector__get_max_ckpt_path_from_folder(save_dir)
trainer.checkpoint_connector.restore(checkpoint_path)
def test_running_test_no_val(tmpdir):
"""Verify `test()` works on a model with no `val_dataloader`.
It performs train and test only
"""
class ModelTrainTest(BoringModel):
def val_dataloader(self):
pass
def test_step(self, *args, **kwargs):
output = super().test_step(*args, **kwargs)
self.log("test_loss", output["y"])
return output
model = ModelTrainTest()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
enable_progress_bar=False,
max_epochs=1,
limit_train_batches=0.4,
limit_val_batches=0.2,
limit_test_batches=0.2,
callbacks=[checkpoint],
logger=logger,
)
trainer.fit(model)
assert trainer.state.finished, f"Training failed with {trainer.state}"
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer, key="test_loss")
def test_running_test_after_fitting(tmpdir):
"""Verify test() on fitted model."""
class ModelTrainValTest(BoringModel):
def validation_step(self, *args, **kwargs):
output = super().validation_step(*args, **kwargs)
self.log('val_loss', output['x'])
return output
def test_step(self, *args, **kwargs):
output = super().test_step(*args, **kwargs)
self.log('test_loss', output['y'])
return output
model = ModelTrainValTest()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
max_epochs=2,
limit_train_batches=0.4,
limit_val_batches=0.2,
limit_test_batches=0.2,
callbacks=[checkpoint],
logger=logger,
)
trainer.fit(model)
assert trainer.state.finished, f"Training failed with {trainer.state}"
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer, key='test_loss', thr=0.5)
def test_running_test_pretrained_model_cpu(tmpdir):
"""Verify test() on pretrained model."""
tutils.reset_seed()
dm = ClassifDataModule()
model = ClassificationModel()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
progress_bar_refresh_rate=0,
max_epochs=2,
limit_train_batches=2,
limit_val_batches=2,
limit_test_batches=2,
callbacks=[checkpoint],
logger=logger,
default_root_dir=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
trainer.fit(model, datamodule=dm)
# correct result and ok accuracy
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
pretrained_model = ClassificationModel.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model, datamodule=dm)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer, key='test_acc', thr=0.45)
def test_dp_resume(tmpdir):
"""Make sure DP continues training correctly."""
model = BoringModel()
trainer_options = dict(max_epochs=1,
gpus=2,
accelerator='dp',
default_root_dir=tmpdir)
# get logger
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# add these to the trainer options
trainer_options['logger'] = logger
trainer_options['callbacks'] = [checkpoint]
# fit model
trainer = Trainer(**trainer_options)
trainer.is_slurm_managing_tasks = True
trainer.fit(model)
# track epoch before saving. Increment since we finished the current epoch, don't want to rerun
real_global_epoch = trainer.current_epoch + 1
# correct result and ok accuracy
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
# ---------------------------
# HPC LOAD/SAVE
# ---------------------------
# save
trainer.checkpoint_connector.hpc_save(tmpdir, logger)
# init new trainer
new_logger = tutils.get_default_logger(tmpdir, version=logger.version)
trainer_options['logger'] = new_logger
trainer_options['callbacks'] = [ModelCheckpoint(dirpath=tmpdir)]
trainer_options['limit_train_batches'] = 0.5
trainer_options['limit_val_batches'] = 0.2
trainer_options['max_epochs'] = 1
new_trainer = Trainer(**trainer_options)
class CustomModel(BoringModel):
def __init__(self):
super().__init__()
self.on_train_start_called = False
# set the epoch start hook so we can predict before the model does the full training
def on_train_start(self):
assert self.trainer.current_epoch == real_global_epoch and self.trainer.current_epoch > 0
# if model and state loaded correctly, predictions will be good even though we
# haven't trained with the new loaded model
dp_model = new_trainer.model
dp_model.eval()
dp_model.module.module.running_stage = RunningStage.EVALUATING
dataloader = self.train_dataloader()
tpipes.run_prediction(self.trainer.lightning_module, dataloader)
self.on_train_start_called = True
# new model
model = CustomModel()
# fit new model which should load hpc weights
new_trainer.fit(model)
assert model.on_train_start_called
# test freeze on gpu
model.freeze()
model.unfreeze()
def test_running_test_pretrained_model_distrib_dp(tmpdir):
"""Verify `test()` on pretrained model."""
tutils.set_random_master_port()
class CustomClassificationModelDP(ClassificationModel):
def _step(self, batch, batch_idx):
x, y = batch
logits = self(x)
return {'logits': logits, 'y': y}
def training_step(self, batch, batch_idx):
_, y = batch
out = self._step(batch, batch_idx)
loss = F.cross_entropy(out['logits'], y)
return loss
def validation_step(self, batch, batch_idx):
return self._step(batch, batch_idx)
def test_step(self, batch, batch_idx):
return self._step(batch, batch_idx)
def validation_step_end(self, outputs):
self.log('val_acc', self.valid_acc(outputs['logits'],
outputs['y']))
dm = ClassifDataModule()
model = CustomClassificationModelDP(lr=0.1)
# exp file to get meta
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
progress_bar_refresh_rate=0,
max_epochs=2,
limit_train_batches=5,
limit_val_batches=5,
callbacks=[checkpoint],
logger=logger,
gpus=[0, 1],
accelerator='dp',
default_root_dir=tmpdir,
)
# fit model
trainer = Trainer(**trainer_options)
trainer.fit(model, datamodule=dm)
# correct result and ok accuracy
assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}"
pretrained_model = ClassificationModel.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
pretrained_model.cpu()
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tpipes.run_prediction(pretrained_model, dataloader)
def test_dp_resume(tmpdir):
"""Make sure DP continues training correctly."""
model = CustomClassificationModelDP(lr=0.1)
dm = ClassifDataModule()
trainer_options = dict(max_epochs=1,
accelerator="gpu",
devices=2,
strategy="dp",
default_root_dir=tmpdir)
# get logger
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# add these to the trainer options
trainer_options["logger"] = logger
trainer_options["callbacks"] = [checkpoint]
# fit model
trainer = Trainer(**trainer_options)
trainer.fit(model, datamodule=dm)
# track epoch before saving
real_global_epoch = trainer.current_epoch
# correct result and ok accuracy
assert trainer.state.finished, f"Training failed with {trainer.state}"
# ---------------------------
# HPC LOAD/SAVE
# ---------------------------
# save
# save logger to make sure we get all the metrics
if logger:
logger.finalize("finished")
hpc_save_path = trainer._checkpoint_connector.hpc_save_path(tmpdir)
trainer.save_checkpoint(hpc_save_path)
# init new trainer
new_logger = tutils.get_default_logger(tmpdir, version=logger.version)
trainer_options["logger"] = new_logger
trainer_options["callbacks"] = [ModelCheckpoint(dirpath=tmpdir)]
trainer_options["limit_train_batches"] = 0.5
trainer_options["limit_val_batches"] = 0.2
trainer_options["max_epochs"] = 1
new_trainer = Trainer(**trainer_options)
class CustomModel(CustomClassificationModelDP):
def __init__(self):
super().__init__()
self.on_train_start_called = False
def on_validation_start(self):
assert self.trainer.current_epoch == real_global_epoch and self.trainer.current_epoch > 0
dataloader = dm.val_dataloader()
tpipes.run_model_prediction(self.trainer.lightning_module,
dataloader=dataloader)
# new model
model = CustomModel()
# validate new model which should load hpc weights
new_trainer.validate(model, datamodule=dm, ckpt_path=hpc_save_path)
# test freeze on gpu
model.freeze()
model.unfreeze()
def test_dp_resume(tmpdir):
"""Make sure DP continues training correctly."""
model = CustomClassificationModelDP(lr=0.1)
dm = ClassifDataModule()
trainer_options = dict(max_epochs=1,
gpus=2,
strategy="dp",
default_root_dir=tmpdir)
# get logger
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# add these to the trainer options
trainer_options["logger"] = logger
trainer_options["callbacks"] = [checkpoint]
# fit model
trainer = Trainer(**trainer_options)
trainer._is_slurm_managing_tasks = True
trainer.fit(model, datamodule=dm)
# track epoch before saving. Increment since we finished the current epoch, don't want to rerun
real_global_epoch = trainer.current_epoch + 1
# correct result and ok accuracy
assert trainer.state.finished, f"Training failed with {trainer.state}"
# ---------------------------
# HPC LOAD/SAVE
# ---------------------------
# save
trainer.checkpoint_connector.hpc_save(tmpdir, logger)
# init new trainer
new_logger = tutils.get_default_logger(tmpdir, version=logger.version)
trainer_options["logger"] = new_logger
trainer_options["callbacks"] = [ModelCheckpoint(dirpath=tmpdir)]
trainer_options["limit_train_batches"] = 0.5
trainer_options["limit_val_batches"] = 0.2
trainer_options["max_epochs"] = 1
new_trainer = Trainer(**trainer_options)
class CustomModel(CustomClassificationModelDP):
def __init__(self):
super().__init__()
self.on_pretrain_routine_end_called = False
# set the epoch start hook so we can predict before the model does the full training
def on_pretrain_routine_end(self):
assert self.trainer.current_epoch == real_global_epoch and self.trainer.current_epoch > 0
# if model and state loaded correctly, predictions will be good even though we
# haven't trained with the new loaded model
new_trainer.state.stage = RunningStage.VALIDATING
dataloader = dm.train_dataloader()
tpipes.run_prediction_eval_model_template(
self.trainer.lightning_module, dataloader=dataloader)
self.on_pretrain_routine_end_called = True
# new model
model = CustomModel()
# fit new model which should load hpc weights
new_trainer.fit(model, datamodule=dm)
assert model.on_pretrain_routine_end_called
# test freeze on gpu
model.freeze()
model.unfreeze()
def test_cpu_slurm_save_load(tmpdir):
"""Verify model save/load/checkpoint on CPU."""
model = BoringModel()
# logger file to get meta
logger = tutils.get_default_logger(tmpdir)
version = logger.version
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
logger=logger,
limit_train_batches=0.2,
limit_val_batches=0.2,
callbacks=[ModelCheckpoint(dirpath=tmpdir)],
)
trainer.fit(model)
real_global_step = trainer.global_step
# traning complete
assert trainer.state.finished, "cpu model failed to complete"
# predict with trained model before saving
# make a prediction
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
for batch in dataloader:
break
model.eval()
pred_before_saving = model(batch)
# test HPC saving
# simulate snapshot on slurm
# save logger to make sure we get all the metrics
if logger:
logger.finalize("finished")
hpc_save_path = trainer.checkpoint_connector.hpc_save_path(
trainer.weights_save_path)
trainer.save_checkpoint(hpc_save_path)
assert os.path.exists(hpc_save_path)
# new logger file to get meta
logger = tutils.get_default_logger(tmpdir, version=version)
model = BoringModel()
class _StartCallback(Callback):
# set the epoch start hook so we can predict before the model does the full training
def on_train_epoch_start(self, trainer, model):
assert trainer.global_step == real_global_step and trainer.global_step > 0
# predict with loaded model to make sure answers are the same
mode = model.training
model.eval()
new_pred = model(batch)
assert torch.eq(pred_before_saving, new_pred).all()
model.train(mode)
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
logger=logger,
callbacks=[_StartCallback(),
ModelCheckpoint(dirpath=tmpdir)],
)
# by calling fit again, we trigger training, loading weights from the cluster
# and our hook to predict using current model before any more weight updates
trainer.fit(model)
