def test_sync_reduce_ddp():
"""Make sure sync-reduce works with DDP"""
tutils.reset_seed()
tutils.set_random_master_port()
worldsize = 2
mp.spawn(_ddp_test_fn, args=(worldsize,), nprocs=worldsize)
def test_amp_gpu_ddp_slurm_managed(tmpdir):
"""Make sure DDP + AMP work."""
# simulate setting slurm flags
tutils.set_random_master_port()
os.environ['SLURM_LOCALID'] = str(0)
model = EvalModelTemplate(tutils.get_default_hparams())
# 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(
max_epochs=1,
gpus=[0],
distributed_backend='ddp',
precision=16,
checkpoint_callback=checkpoint,
logger=logger,
)
trainer.is_slurm_managing_tasks = True
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'amp + ddp model failed to complete'
# test root model address
assert trainer.resolve_root_node_address('abc') == 'abc'
assert trainer.resolve_root_node_address('abc[23]') == 'abc23'
assert trainer.resolve_root_node_address('abc[23-24]') == 'abc23'
assert trainer.resolve_root_node_address(
'abc[23-24, 45-40, 40]') == 'abc23'
def run_test_from_config(trainer_options):
"""Trains the default model with the given config."""
tutils.reset_seed()
tutils.set_random_master_port()
ckpt_path = trainer_options['default_root_dir']
trainer_options['checkpoint_callback'] = ModelCheckpoint(ckpt_path)
model, hparams = tutils.get_default_model()
tutils.run_model_test(trainer_options, model, version=0, with_hpc=False)
# Horovod should be initialized following training. If not, this will raise an exception.
assert hvd.size() == 2
def test_running_test_pretrained_model_distrib(tmpdir, backend):
"""Verify `test()` on pretrained model."""
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# 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,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend=backend,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger,
trainer.checkpoint_callback.dirpath,
module_class=LightningTestModel)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tutils.run_prediction(dataloader, pretrained_model)
def test_multi_cpu_model_ddp(tmpdir):
"""Make sure DDP works."""
tutils.set_random_master_port()
trainer_options = dict(default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=None,
num_processes=2,
distributed_backend='ddp_cpu')
model = EvalModelTemplate()
tutils.run_model_test(trainer_options, model, on_gpu=False)
def test_amp_gpu_ddp(tmpdir):
"""Make sure DDP + AMP work."""
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
gpus=2,
distributed_backend='ddp',
precision=16)
tutils.run_model_test(trainer_options, model)
def test_multi_gpu_model_ddp2(tmpdir):
"""Make sure DDP2 works."""
tutils.reset_seed()
tutils.set_random_master_port()
model, hparams = tutils.get_default_model()
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=2,
weights_summary=None,
distributed_backend='ddp2')
tutils.run_model_test(trainer_options, model)
def test_multi_gpu_model_ddp(tmpdir):
"""Make sure DDP works."""
tutils.reset_seed()
tutils.set_random_master_port()
model, hparams = tutils.get_default_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=[0, 1],
distributed_backend='ddp')
tutils.run_model_test(trainer_options, model)
def run_test_from_config(trainer_options):
"""Trains the default model with the given config."""
set_random_master_port()
ckpt_path = trainer_options['default_root_dir']
trainer_options.update(checkpoint_callback=ModelCheckpoint(ckpt_path))
model = EvalModelTemplate()
run_model_test(trainer_options, model, on_gpu=args.on_gpu, version=0, with_hpc=False)
# Horovod should be initialized following training. If not, this will raise an exception.
assert hvd.size() == 2
if args.on_gpu:
trainer = Trainer(gpus=1, distributed_backend='horovod', max_epochs=1)
# Test the root_gpu property
assert trainer.root_gpu == hvd.local_rank()
def test_amp_multi_gpu(tmpdir, backend):
"""Make sure DP/DDP + AMP work."""
tutils.set_random_master_port()
model = EvalModelTemplate(tutils.get_default_hparams())
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
# gpus=2,
gpus='0, 1', # test init with gpu string
distributed_backend=backend,
precision=16)
# tutils.run_model_test(trainer_options, model)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result
def test_amp_gpu_ddp_slurm_managed(tmpdir):
"""Make sure DDP + AMP work."""
if not tutils.can_run_gpu_test():
return
tutils.reset_seed()
# simulate setting slurm flags
tutils.set_random_master_port()
os.environ['SLURM_LOCALID'] = str(0)
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(show_progress_bar=True,
max_epochs=1,
gpus=[0],
distributed_backend='ddp',
precision=16)
# exp file to get meta
logger = tutils.get_default_testtube_logger(tmpdir, False)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
# add these to the trainer options
trainer_options['checkpoint_callback'] = checkpoint
trainer_options['logger'] = logger
# fit model
trainer = Trainer(**trainer_options)
trainer.is_slurm_managing_tasks = True
result = trainer.fit(model)
# correct result and ok accuracy
assert result == 1, 'amp + ddp model failed to complete'
# test root model address
assert trainer.resolve_root_node_address('abc') == 'abc'
assert trainer.resolve_root_node_address('abc[23]') == 'abc23'
assert trainer.resolve_root_node_address('abc[23-24]') == 'abc23'
assert trainer.resolve_root_node_address(
'abc[23-24, 45-40, 40]') == 'abc23'
def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
"""Make sure DDP works with dataloaders passed to fit()"""
tutils.set_random_master_port()
trainer_options = dict(default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=[0, 1],
distributed_backend='ddp')
model = EvalModelTemplate()
fit_options = dict(train_dataloader=model.train_dataloader(),
val_dataloaders=model.val_dataloader())
trainer = Trainer(**trainer_options)
result = trainer.fit(model, **fit_options)
assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
"""Make sure DDP works with dataloaders passed to fit()"""
tutils.reset_seed()
tutils.set_random_master_port()
model, hparams = tutils.get_default_model()
trainer = Trainer(default_root_dir=tmpdir,
progress_bar_refresh_rate=0,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=[0, 1],
distributed_backend='ddp')
result = trainer.fit(model,
train_dataloader=model.train_dataloader(),
val_dataloaders=model.val_dataloader())
assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
def test_multi_gpu_model(tmpdir, backend):
"""Make sure DDP works."""
tutils.set_random_master_port()
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=[0, 1],
distributed_backend=backend,
)
model = EvalModelTemplate()
# tutils.run_model_test(trainer_options, model)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result
# test memory helper functions
memory.get_memory_profile('min_max')
def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
"""Make sure DDP works with dataloaders passed to fit()"""
tutils.reset_seed()
tutils.set_random_master_port()
model, hparams = tutils.get_default_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
gpus=[0, 1],
distributed_backend='ddp')
fit_options = dict(train_dataloader=model.train_dataloader(),
val_dataloaders=model.val_dataloader())
trainer = Trainer(**trainer_options)
result = trainer.fit(model, **fit_options)
assert result == 1, "DDP doesn't work with dataloaders passed to fit()."
def test_multi_gpu_wandb(tmpdir, backend):
"""Make sure DP/DDP + AMP work."""
from pytorch_lightning.loggers import WandbLogger
tutils.set_random_master_port()
model = EvalModelTemplate()
logger = WandbLogger(name='utest')
trainer_options = dict(
default_root_dir=tmpdir,
max_epochs=1,
gpus=2,
distributed_backend=backend,
precision=16,
logger=logger,
)
# tutils.run_model_test(trainer_options, model)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result
trainer.test(model)
def test_numpy_metric_ddp():
tutils.reset_seed()
tutils.set_random_master_port()
world_size = 2
mp.spawn(_ddp_test_numpy_metric, args=(world_size,), nprocs=world_size)
