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 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_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 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_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_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,
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_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()
