def test_single_gpu_batch_parse():
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
trainer = Trainer()
# batch is just a tensor
batch = torch.rand(2, 3)
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'
# tensor list
batch = [torch.rand(2, 3), torch.rand(2, 3)]
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch[0].device.index == 0 and batch[0].type(
) == 'torch.cuda.FloatTensor'
assert batch[1].device.index == 0 and batch[1].type(
) == 'torch.cuda.FloatTensor'
# tensor list of lists
batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch[0][0].device.index == 0 and batch[0][0].type(
) == 'torch.cuda.FloatTensor'
assert batch[0][1].device.index == 0 and batch[0][1].type(
) == 'torch.cuda.FloatTensor'
# tensor dict
batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch[0]['a'].device.index == 0 and batch[0]['a'].type(
) == 'torch.cuda.FloatTensor'
assert batch[0]['b'].device.index == 0 and batch[0]['b'].type(
) == 'torch.cuda.FloatTensor'
# tuple of tensor list and list of tensor dict
batch = ([torch.rand(2, 3) for _ in range(2)], [{
'a': torch.rand(2, 3),
'b': torch.rand(2, 3)
} for _ in range(2)])
batch = trainer.transfer_batch_to_gpu(batch, 0)
assert batch[0][0].device.index == 0 and batch[0][0].type(
) == 'torch.cuda.FloatTensor'
assert batch[1][0]['a'].device.index == 0
assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
assert batch[1][0]['b'].device.index == 0
assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
def test_running_test_pretrained_model_ddp(tmpdir):
"""Verify `test()` on pretrained model."""
if not tutils.can_run_gpu_test():
return
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_testtube_logger(tmpdir, False)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(show_progress_bar=False,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend='ddp')
# 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)
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tutils.run_prediction(dataloader, pretrained_model)
def test_multi_gpu_none_backend(tmpdir):
"""Make sure when using multiple GPUs the user can't use `distributed_backend = None`."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
model, hparams = tutils.get_default_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
max_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus='-1')
with pytest.warns(UserWarning):
tutils.run_model_test(trainer_options, model)
def test_multi_gpu_model_ddp(tmpdir):
"""Make sure DDP works."""
if not tutils.can_run_gpu_test():
return
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 test_amp_single_gpu(tmpdir):
"""Make sure DDP + AMP work."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=True,
max_epochs=1,
gpus=1,
distributed_backend='ddp',
precision=16)
tutils.run_model_test(trainer_options, model)
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_amp_gpu_dp(tmpdir):
"""Make sure DP + AMP work."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
model, hparams = tutils.get_default_model()
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
gpus='0, 1', # test init with gpu string
distributed_backend='dp',
precision=16)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1
def test_multi_gpu_model_dp(tmpdir):
"""Make sure DP works."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
model, hparams = tutils.get_default_model()
trainer_options = dict(default_save_path=tmpdir,
show_progress_bar=False,
distributed_backend='dp',
max_epochs=1,
train_percent_check=0.1,
val_percent_check=0.1,
gpus='-1')
tutils.run_model_test(trainer_options, model)
# test memory helper functions
memory.get_memory_profile('min_max')
def test_running_test_pretrained_model_dp(tmpdir):
"""Verify test() on pretrained model."""
tutils.reset_seed()
if not tutils.can_run_gpu_test():
return
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# logger file to get meta
logger = tutils.get_default_testtube_logger(tmpdir, False)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(show_progress_bar=True,
max_epochs=4,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend='dp')
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# 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)
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
"""Make sure DDP works with dataloaders passed to fit()"""
if not tutils.can_run_gpu_test():
return
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_dp_resume(tmpdir):
"""Make sure DP continues training correctly."""
if not tutils.can_run_gpu_test():
return
tutils.reset_seed()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
trainer_options = dict(
show_progress_bar=True,
max_epochs=3,
gpus=2,
distributed_backend='dp',
)
# get logger
logger = tutils.get_default_testtube_logger(tmpdir, debug=False)
# 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['checkpoint_callback'] = checkpoint
# fit model
trainer = Trainer(**trainer_options)
trainer.is_slurm_managing_tasks = True
result = 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 result == 1, 'amp + dp model failed to complete'
# ---------------------------
# HPC LOAD/SAVE
# ---------------------------
# save
trainer.hpc_save(tmpdir, logger)
# init new trainer
new_logger = tutils.get_default_testtube_logger(tmpdir,
version=logger.version)
trainer_options['logger'] = new_logger
trainer_options['checkpoint_callback'] = ModelCheckpoint(tmpdir)
trainer_options['train_percent_check'] = 0.5
trainer_options['val_percent_check'] = 0.2
trainer_options['max_epochs'] = 1
new_trainer = Trainer(**trainer_options)
# set the epoch start hook so we can predict before the model does the full training
def assert_good_acc():
assert new_trainer.current_epoch == real_global_epoch and new_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()
dataloader = trainer.train_dataloader
tutils.run_prediction(dataloader, dp_model, dp=True)
# new model
model = LightningTestModel(hparams)
model.on_train_start = assert_good_acc
# fit new model which should load hpc weights
new_trainer.fit(model)
# test freeze on gpu
model.freeze()
model.unfreeze()