def test_post_local_sgd_model_averaging(average_parameters_mock, tmpdir):
"""Test that when using DDP with post-localSGD, model averaging is called."""
model = BoringModel()
# test regular ddp does not call model averaging
trainer = Trainer(
fast_dev_run=True,
accelerator="gpu",
devices=2,
strategy="ddp",
default_root_dir=tmpdir,
sync_batchnorm=True,
)
trainer.fit(model)
average_parameters_mock.assert_not_called()
# test ddp with post-localSGD does call model averaging
ddp_strategy = DDPStrategy(
ddp_comm_state=post_localSGD.PostLocalSGDState(
process_group=None,
subgroup=None,
start_localSGD_iter=8,
),
ddp_comm_hook=post_localSGD.post_localSGD_hook,
model_averaging_period=4,
)
trainer = Trainer(
fast_dev_run=True,
gpus=2,
strategy=ddp_strategy,
default_root_dir=tmpdir,
sync_batchnorm=True,
)
trainer.fit(model)
average_parameters_mock.assert_called()
def test_ddp_fp16_compress_wrap_sgd_comm_hook(tmpdir):
"""Test for DDP FP16 compress wrapper for SGD hook."""
model = BoringModel()
strategy = DDPStrategy(
ddp_comm_state=powerSGD.PowerSGDState(process_group=None),
ddp_comm_hook=powerSGD.powerSGD_hook,
ddp_comm_wrapper=default.fp16_compress_wrapper,
)
trainer = Trainer(
max_epochs=1,
accelerator="gpu",
devices=2,
strategy=strategy,
default_root_dir=tmpdir,
sync_batchnorm=True,
fast_dev_run=True,
)
trainer.fit(model)
trainer_comm_hook = trainer.strategy.model.get_ddp_logging_data().comm_hook
expected_comm_hook = default.fp16_compress_wrapper(
powerSGD.powerSGD_hook).__qualname__
assert trainer_comm_hook == expected_comm_hook
assert trainer.state.finished, f"Training failed with {trainer.state}"
def test_parallel_devices_in_strategy_confilict_with_accelerator(
parallel_devices, accelerator):
with pytest.raises(MisconfigurationException,
match=r"parallel_devices set through"):
Trainer(strategy=DDPStrategy(parallel_devices=parallel_devices),
accelerator=accelerator)
@RunIf(min_gpus=2)
@mock.patch.dict(
os.environ,
{
"CUDA_VISIBLE_DEVICES": "0,1",
"SLURM_NTASKS": "2",
"SLURM_JOB_NAME": "SOME_NAME",
"SLURM_NODEID": "0",
"SLURM_PROCID": "1",
"SLURM_LOCALID": "1",
},
)
@mock.patch("pytorch_lightning.strategies.DDPStrategy.setup_distributed",
autospec=True)
@pytest.mark.parametrize("strategy", ["ddp", DDPStrategy()])
def test_strategy_choice_ddp_slurm(setup_distributed_mock, strategy):
trainer = Trainer(fast_dev_run=True, strategy=strategy, gpus=2)
assert trainer._accelerator_connector._is_slurm_managing_tasks()
assert isinstance(trainer.accelerator, GPUAccelerator)
assert isinstance(trainer.strategy, DDPStrategy)
assert isinstance(trainer.strategy.cluster_environment, SLURMEnvironment)
assert trainer.strategy.cluster_environment.local_rank() == 1
assert trainer.strategy.local_rank == 1
@mock.patch.dict(
os.environ,
{
"CUDA_VISIBLE_DEVICES": "0,1",
"SLURM_NTASKS": "2",
def process_args(args=None, return_io=False):
"""
Process arguments for running training
"""
if not isinstance(args, argparse.Namespace):
args = parse_args(args)
args.loader_kwargs = dict()
targs = dict(max_epochs=args.epochs, )
targs['accumulate_grad_batches'] = args.accumulate
env = None
if args.ipu:
targs['accelerator'] = 'ipu'
targs['devices'] = process_gpus(args.gpus)
else:
targs['gpus'] = process_gpus(args.gpus)
targs['num_nodes'] = args.num_nodes
if args.lsf:
##########################################################################################
# Currently coding against pytorch-lightning 1.4.3
##########################################################################################
if args.num_workers > 4:
print0(
"num_workers (-k) > 4 can lead to hanging on Summit -- setting to 4",
file=sys.stderr)
args.num_workers = 4
args.loader_kwargs[
'num_workers'] = 1 # Set as a default. This will get overridden elsewhere
args.loader_kwargs['multiprocessing_context'] = 'spawn'
env = LSFEnvironment()
elif args.slurm:
env = SLURMEnvironment()
if env is not None:
global RANK
global SIZE
try:
RANK = env.global_rank()
SIZE = env.world_size()
except:
print(
">>> Could not get global rank -- setting RANK to 0 and SIZE to 1",
file=sys.stderr)
RANK = 0
SIZE = 1
if targs['gpus'] is not None:
targs['accelerator'] = 'gpu'
if targs['gpus'] == 1:
targs['devices'] = 1
else:
if env is None:
raise ValueError(
'Please specify environment (--lsf or --slurm) if using more than one GPU'
)
# parallel_devices = [torch.device(i) for i in range(torch.cuda.device_count()) if i < targs['gpus']]
# precision_plugin = NativeMixedPrecisionPlugin(16, 'cuda')
torch.cuda.set_device(env.local_rank())
targs['devices'] = targs['gpus']
targs['strategy'] = DDPStrategy(
find_unused_parameters=False,
cluster_environment=env,
#accelerator=GPUAccelerator(),
#parallel_devices=parallel_devices,
#precision_plugin=precision_plugin,
)
print(
"---- Rank %s - Using GPUAccelerator with DDPStrategy" %
env.global_rank(),
file=sys.stderr)
else:
targs['accelerator'] = 'cpu'
del args.gpus
if args.sanity:
if isinstance(args.sanity, str):
args.sanity = int(args.sanity)
else:
args.sanity = 4000
targs['limit_train_batches'] = args.sanity
targs['limit_val_batches'] = args.sanity // 4
if args.lr_find:
targs['auto_lr_find'] = True
del args.lr_find
if args.checkpoint is not None:
if os.path.exists(args.checkpoint):
targs['resume_from_checkpoint'] = args.checkpoint
else:
warnings.warn(
"Ignoring -c/--checkpoint argument because {args.checkpoint} does not exist."
)
args.checkpoint = None
if args.cuda_profile:
targs['profiler'] = PyTorchProfiler(
filename=f'pytorch_prof.{RANK:0{len(str(SIZE))}}', emit_nvtx=True)
targs['replace_sampler_ddp'] = False
args.loader_kwargs = dict()
# make sure we are classifying if we are using adding classifier layers
# to a resnet features model
if args.features_checkpoint is not None:
if args.manifold:
raise ValueError(
'Cannot use manifold loss (i.e. -M) if adding classifier (i.e. -F)'
)
args.classify = True
data_mod = DeepIndexDataModule(args,
keep_open=True,
seed=args.seed + RANK,
rank=RANK,
size=SIZE)
# if classification problem, use the number of taxa as the number of outputs
if args.classify:
args.n_outputs = data_mod.dataset.n_outputs
args.input_nc = 136 if args.tnf else len(data_mod.dataset.vocab)
model = process_model(args, taxa_table=data_mod.dataset.difile.taxa_table)
if args.num_workers > 0:
data_mod.dataset.close()
ret = [model, args, targs]
if return_io:
ret.append(io)
ret.append(data_mod)
return tuple(ret)