def test_update_with_gpu(self):
self.setup_gpu()
self.optimizer = chainermn.create_multi_node_optimizer(
self.actual_optimizer, self.comm)
self.optimizer.setup(self.target)
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
self.optimizer.target.a.W.grad[:] = self.comm.rank
self.optimizer.target.b.W.grad[:] = self.comm.rank + 1
self.optimizer.target.c.W.grad[:] = self.comm.rank + 2
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 1)
self.optimizer.target.a.W.update_rule.update.assert_called_once_with(
self.optimizer.target.a.W)
self.optimizer.target.b.W.update_rule.update.assert_called_once_with(
self.optimizer.target.b.W)
self.optimizer.target.c.W.update_rule.update.assert_called_once_with(
self.optimizer.target.c.W)
base = (self.comm.size - 1.0) / 2
chainer.testing.assert_allclose(self.optimizer.target.a.W.grad,
(base + 0) * np.ones((3, 2)))
chainer.testing.assert_allclose(self.optimizer.target.b.W.grad,
(base + 1) * np.ones((4, 3)))
chainer.testing.assert_allclose(self.optimizer.target.c.W.grad,
(base + 2) * np.ones((5, 4)))
def __init__(
self,
model_parameters,
# Learning rate at training step s with annealing
initial_lr=1e-4,
final_lr=1e-5,
annealing_steps=1600000,
# Learning rate as used by the Adam algorithm
beta_1=0.9,
beta_2=0.99,
# Adam regularisation parameter
eps=1e-8,
initial_training_step=0,
communicator=None):
self.initial_lr = initial_lr
self.final_lr = final_lr
self.annealing_steps = annealing_steps
self.beta_1 = beta_1
self.beta_2 = beta_2
self.eps = eps
lr = self.compute_lr_at_step(initial_training_step)
self.optimizer = optimizers.Adam(lr,
beta1=beta_1,
beta2=beta_2,
eps=eps)
self.optimizer.setup(model_parameters)
self.multi_node_optimizer = None
if communicator:
self.multi_node_optimizer = chainermn.create_multi_node_optimizer(
self.optimizer, communicator)
def __init__(
self,
model_parameters,
# Learning rate at training step s with annealing
mu_i=5.0 * 1e-4,
mu_f=5.0 * 1e-5,
n=1.6 * 1e6,
# Learning rate as used by the Adam algorithm
beta_1=0.9,
beta_2=0.99,
# Adam regularisation parameter
eps=1e-8,
communicator=None):
self.mu_i = mu_i
self.mu_f = mu_f
self.n = n
self.beta_1 = beta_1
self.beta_2 = beta_2
self.eps = eps
lr = self.mu_s(0)
self.optimizer = optimizers.Adam(lr,
beta1=beta_1,
beta2=beta_2,
eps=eps)
self.optimizer.setup(model_parameters)
self.multi_node_optimizer = None
if communicator:
self.multi_node_optimizer = chainermn.create_multi_node_optimizer(
self.optimizer, communicator)
def test_update_with_gpu(self):
self.setup_gpu()
self.optimizer = chainermn.create_multi_node_optimizer(
self.actual_optimizer, self.comm)
self.optimizer.setup(self.target)
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
self.optimizer.target.a.W.grad[:] = self.comm.rank
self.optimizer.target.b.W.grad[:] = self.comm.rank + 1
self.optimizer.target.c.W.grad[:] = self.comm.rank + 2
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 1)
self.optimizer.target.a.W.update_rule.update.assert_called_once_with(
self.optimizer.target.a.W)
self.optimizer.target.b.W.update_rule.update.assert_called_once_with(
self.optimizer.target.b.W)
self.optimizer.target.c.W.update_rule.update.assert_called_once_with(
self.optimizer.target.c.W)
base = (self.comm.size - 1.0) / 2
chainer.testing.assert_allclose(self.optimizer.target.a.W.grad,
(base + 0) * np.ones((3, 2)))
chainer.testing.assert_allclose(self.optimizer.target.b.W.grad,
(base + 1) * np.ones((4, 3)))
chainer.testing.assert_allclose(self.optimizer.target.c.W.grad,
(base + 2) * np.ones((5, 4)))
def __init__(
self,
model_parameters,
# Learning rate at training step s with annealing
lr_i=1.0 * 1e-4,
lr_f=1.0 * 1e-5,
n=10000,
# Learning rate as used by the Adam algorithm
beta_1=0.9,
beta_2=0.99,
# Adam regularisation parameter
eps=1e-8,
communicator=None):
super().__init__(lr_i, lr_f, n)
self.beta_1 = beta_1
self.beta_2 = beta_2
self.eps = eps
lr = self.mu_s(0)
self.optimizer = Eve(lr, beta1=beta_1, beta2=beta_2, eps=eps)
self.optimizer.setup(model_parameters)
if communicator:
self.multi_node_optimizer = chainermn.create_multi_node_optimizer(
self.optimizer, communicator)
def setup_optimizer(self, alpha=0.0005):
if self.comm is None:
self.optimizer = optimizers.Adam(alpha)
else:
self.optimizer = chainermn.create_multi_node_optimizer(
optimizers.Adam(alpha), self.comm)
self.optimizer.setup(self)
def setup_mnist_trainer(self, display_log=False):
batchsize = 100
n_units = 100
comm = self.communicator
model = L.Classifier(MLP(n_units, 10))
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
if comm.rank == 0:
train, test = chainer.datasets.get_mnist()
else:
train, test = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
test = chainermn.scatter_dataset(test, comm, shuffle=True)
train_iter = chainer.iterators.SerialIterator(train, batchsize)
test_iter = chainer.iterators.SerialIterator(test, batchsize,
repeat=False,
shuffle=False)
updater = training.StandardUpdater(
train_iter,
optimizer
)
return updater, optimizer, train_iter, test_iter, model
def main(args, model, x, t, valid_rate=0.2):
print('Start a training script using multiple nodes.')
comm = chainermn.create_communicator(args.communicator)
device = comm.intra_rank
assert device >= 0, 'invalid device ID: {}'.format(device)
if comm.mpi_comm.rank == 0:
print('==========================================')
print('Num process (COMM_WORLD): {}'.format(MPI.COMM_WORLD.Get_size()))
print('Using GPUs')
print('Using {} communicator'.format(args.communicator))
print('Num Minibatch-size: {}'.format(args.batchsize))
print('Num epoch: {}'.format(args.epoch))
print('==========================================')
if comm.rank == 0:
threshold = int(len(t) * (1 - valid_rate))
train = datasets.tuple_dataset.TupleDataset(x[0:threshold],
t[0:threshold])
valid = datasets.tuple_dataset.TupleDataset(x[threshold:],
t[threshold:])
datasize = len(train) * args.epoch
else:
train, valid = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
valid = chainermn.scatter_dataset(valid, comm, shuffle=True)
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
valid_iter = chainer.iterators.SerialIterator(valid,
args.batchsize,
repeat=False,
shuffle=False)
if device >= 0:
cuda.get_device_from_id(device).use()
model.to_gpu()
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.SGD(lr=2e-4), comm)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(1e-2))
updater = training.StandardUpdater(train_iter, optimizer, device=device)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
evaluator = extensions.Evaluator(valid_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
prepare_extensions(trainer, evaluator, args, comm)
trainer.run()
if comm.rank == 0:
throughput = datasize / trainer.elapsed_time
print('Throughput: {} [images/sec.] ({} / {})'.format(
throughput, datasize, trainer.elapsed_time))
model_filepath = os.path.join(args.out, 'trained.model')
chainer.serializers.save_npz(model_filepath, model)
def make_optimizer(model, comm, alpha=0.0002, beta1=0.5):
# Create a multi node optimizer from a standard Chainer optimizer.
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(alpha=alpha, beta1=beta1), comm)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001), 'hook_dec')
return optimizer
def make_optimizer(model, comm, alpha=0.0002, beta1=0.5):
# Create a multi node optimizer from a standard Chainer optimizer.
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(alpha=alpha, beta1=beta1), comm)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001), 'hook_dec')
return optimizer
def setup_mnist_trainer(self, display_log=False, use_chx=False):
batchsize = 100
n_units = 100
comm = self.communicator
model = L.Classifier(MLP(n_units, 10))
model.to_device(get_device(None, use_chx))
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
if comm.rank == 0:
train, test = chainer.datasets.get_mnist()
else:
train, test = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
test = chainermn.scatter_dataset(test, comm, shuffle=True)
train_iter = chainer.iterators.SerialIterator(train, batchsize)
test_iter = chainer.iterators.SerialIterator(test,
batchsize,
repeat=False,
shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer)
return updater, optimizer, train_iter, test_iter, model
def _prepare_multinode_snapshot(n, result):
n_units = 100
batchsize = 10
comm = create_communicator('naive')
model = L.Classifier(MLP(n_units, 10))
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
if comm.rank == 0:
train, _ = chainer.datasets.get_mnist()
else:
train, _ = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
train_iter = chainer.iterators.SerialIterator(train, batchsize)
updater = StandardUpdater(train_iter, optimizer)
trainer = Trainer(updater, out=result)
snapshot = extensions.snapshot(target=updater, autoload=True)
replica_sets = []
mn_snapshot = multi_node_snapshot(comm, snapshot, replica_sets)
mn_snapshot.initialize(trainer)
for _ in range(n):
updater.update()
return updater, mn_snapshot, trainer
def objective(trial, comm):
# Sample an architecture.
model = L.Classifier(create_model(trial))
# Setup optimizer.
optimizer = chainer.optimizers.MomentumSGD()
optimizer.setup(model)
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
# Setup dataset and iterator. Only worker 0 loads the whole dataset.
# The dataset of worker 0 is evenly split and distributed to all workers.
if comm.rank == 0:
train, valid = chainer.datasets.get_mnist()
rng = np.random.RandomState(0)
train = chainer.datasets.SubDataset(
train, 0, N_TRAIN_EXAMPLES, order=rng.permutation(len(train))
)
valid = chainer.datasets.SubDataset(
valid, 0, N_VALID_EXAMPLES, order=rng.permutation(len(valid))
)
else:
train, valid = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
valid = chainermn.scatter_dataset(valid, comm)
train_iter = chainer.iterators.SerialIterator(train, BATCHSIZE, shuffle=True)
valid_iter = chainer.iterators.SerialIterator(valid, BATCHSIZE, repeat=False, shuffle=False)
# Setup trainer.
updater = chainer.training.StandardUpdater(train_iter, optimizer)
trainer = chainer.training.Trainer(updater, (EPOCH, "epoch"))
# Add Chainer extension for pruners.
trainer.extend(
optuna.integration.ChainerPruningExtension(
trial, "validation/main/accuracy", (PRUNER_INTERVAL, "epoch")
)
)
evaluator = chainer.training.extensions.Evaluator(valid_iter, model)
trainer.extend(chainermn.create_multi_node_evaluator(evaluator, comm))
log_report_extension = chainer.training.extensions.LogReport(log_name=None)
trainer.extend(log_report_extension)
if comm.rank == 0:
trainer.extend(chainer.training.extensions.ProgressBar())
# Run training.
# Please set show_loop_exception_msg False to inhibit messages about TrialPruned exception.
# ChainerPruningExtension raises TrialPruned exception to stop training, and
# trainer shows some messages every time it receive TrialPruned.
trainer.run(show_loop_exception_msg=False)
# Evaluate.
evaluator = chainer.training.extensions.Evaluator(valid_iter, model)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
report = evaluator()
return report["main/accuracy"]
def run_test_observation_aggregator(comm, xp, use_chainer_variable,
communicate_interval, use_gpu):
model = DummyChain()
if use_gpu:
# Use CuPy's Device class to force call cudaSetDevice()
chainer.cuda.get_device_from_id(comm.intra_rank).use()
device = get_device(comm.intra_rank if use_gpu else None, xp == chainerx)
if xp == chainerx:
train = xp.array(np.random.rand(10, 1).astype(np.float32))
else:
train = xp.random.rand(10, 1).astype(np.float32)
model.to_device(device)
train_iter = chainer.iterators.SerialIterator(train,
batch_size=1,
repeat=True,
shuffle=True)
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
updater = chainer.training.StandardUpdater(train_iter,
optimizer,
device=device)
trainer = chainer.training.Trainer(updater, (1, 'epoch'))
@extension.make_extension(trigger=(1, 'iteration'),
priority=extension.PRIORITY_WRITER)
def rank_reporter(trainer_):
tmp = xp.asarray(comm.rank, dtype=np.float32)
if use_chainer_variable:
tmp = chainer.Variable(tmp)
trainer_.observation['rank'] = tmp
@extension.make_extension(trigger=(communicate_interval, 'iteration'),
priority=extension.PRIORITY_READER)
def aggregated_rank_checker(trainer_):
actual = trainer_.observation['rank-aggregated']
if use_chainer_variable:
actual = actual.data
expected = (comm.size - 1) / 2
chainer.testing.assert_allclose(actual, expected)
trainer.extend(rank_reporter)
trainer.extend(
ObservationAggregator(comm,
'rank',
'rank-aggregated',
comm_trigger=(communicate_interval,
'iteration')))
trainer.extend(aggregated_rank_checker)
trainer.run()
def make_optimizer(model, comm, config):
# Select from https://docs.chainer.org/en/stable/reference/optimizers.html.
# NOTE: The order of the arguments for optimizers follows their definitions.
opt_algorithm = yaml_utils.load_optimizer(config.optimizer['algorithm'],
args=config.optimizer['args'])
optimizer = chainermn.create_multi_node_optimizer(opt_algorithm, comm)
optimizer.setup(model)
return optimizer
def make_adam(model, lr=0.0002, beta1=0.9, beta2=0.999):
optimizer = chainer.optimizers.Adam(alpha=lr, beta1=beta1, beta2=beta2)
if chainer.config.using_chainermn:
optimizer = chainermn.create_multi_node_optimizer(
optimizer, chainer.config.communicator)
optimizer.setup(model)
return optimizer
def objective(trial, comm):
# Sample an architecture.
model = L.Classifier(create_model(trial))
# Setup optimizer.
optimizer = chainer.optimizers.MomentumSGD()
optimizer.setup(model)
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
# Setup dataset and iterator. Only worker 0 loads the whole dataset.
# The dataset of worker 0 is evenly split and distributed to all workers.
if comm.rank == 0:
train, test = chainer.datasets.get_mnist()
rng = np.random.RandomState(0)
train = chainer.datasets.SubDataset(train,
0,
N_TRAIN_EXAMPLES,
order=rng.permutation(len(train)))
test = chainer.datasets.SubDataset(test,
0,
N_TEST_EXAMPLES,
order=rng.permutation(len(test)))
else:
train, test = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
test = chainermn.scatter_dataset(test, comm)
train_iter = chainer.iterators.SerialIterator(train,
BATCHSIZE,
shuffle=True)
test_iter = chainer.iterators.SerialIterator(test,
BATCHSIZE,
repeat=False,
shuffle=False)
# Setup trainer.
updater = chainer.training.StandardUpdater(train_iter, optimizer)
trainer = chainer.training.Trainer(updater, (EPOCH, 'epoch'))
if comm.rank == 0:
trainer.extend(chainer.training.extensions.ProgressBar())
# Run training.
trainer.run()
# Evaluate.
evaluator = chainer.training.extensions.Evaluator(test_iter, model)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
report = evaluator()
# The following line mitigates the memory problem in CircleCI
# (see https://github.com/pfnet/optuna/pull/325 for more details).
gc.collect()
return 1.0 - report['main/accuracy']
def test_can_create_valid_wrapper_for_chainermn(self):
optimizer = create_marked_profile_optimizer(
chainermn.create_multi_node_optimizer(optimizers.SGD(lr=1.0),
None),
sync=True)
self.assertIsNotNone(optimizer)
np.testing.assert_allclose([optimizer.lr], [1.0])
self.assertIsInstance(optimizer, _MarkedProfileOptimizerForMN)
self.assertNotIsInstance(optimizer.actual_optimizer, chainer.Optimizer)
self.assertIsInstance(optimizer.actual_optimizer.actual_optimizer,
chainer.Optimizer)
def make_optimizer(model, comm, alpha=0.001, beta1=0.9, beta2=0.999, chmn=False, add_decay=False):
# # 12/2018: problem in minoas, probably related with openmpi.
if chmn:
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(alpha=alpha, beta1=beta1, beta2=beta2), comm)
else:
optimizer = chainer.optimizers.Adam(alpha=alpha, beta1=beta1, beta2=beta2)
optimizer.setup(model)
if add_decay:
optimizer.add_hook(chainer.optimizer.WeightDecay(0.00001), 'hook_dec')
#optimizer.add_hook(chainer.optimizer_hooks.GradientClipping(0.1), 'hook_clip')
return optimizer
def get_optimizer(args, comm, model):
if args.optimizer == 'momentum_sgd':
actual_optimizer = chainer.optimizers.MomentumSGD()
elif args.optimizer == 'adam':
actual_optimizer = chainer.optimizers.Adam()
elif args.optimizer == 'rmsprop_warmup':
actual_optimizer = dlframeworks.chainer.optimizers.RMSpropWarmup()
else:
actual_optimizer = chainer.optimizers.RMSprop()
optimizer = chainermn.create_multi_node_optimizer(actual_optimizer, comm)
optimizer.setup(model)
return optimizer
def objective(trial, comm):
# Sample an architecture.
model = L.Classifier(create_model(trial))
# Setup optimizer.
optimizer = chainer.optimizers.MomentumSGD()
optimizer.setup(model)
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
# Setup dataset and iterator. Only worker 0 loads the whole dataset.
# The dataset of worker 0 is evenly split and distributed to all workers.
if comm.rank == 0:
train, valid = chainer.datasets.get_mnist()
rng = np.random.RandomState(0)
train = chainer.datasets.SubDataset(train,
0,
N_TRAIN_EXAMPLES,
order=rng.permutation(len(train)))
valid = chainer.datasets.SubDataset(valid,
0,
N_VALID_EXAMPLES,
order=rng.permutation(len(valid)))
else:
train, valid = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
valid = chainermn.scatter_dataset(valid, comm)
train_iter = chainer.iterators.SerialIterator(train,
BATCHSIZE,
shuffle=True)
valid_iter = chainer.iterators.SerialIterator(valid,
BATCHSIZE,
repeat=False,
shuffle=False)
# Setup trainer.
updater = chainer.training.StandardUpdater(train_iter, optimizer)
trainer = chainer.training.Trainer(updater, (EPOCH, "epoch"))
if comm.rank == 0:
trainer.extend(chainer.training.extensions.ProgressBar())
# Run training.
trainer.run()
# Evaluate.
evaluator = chainer.training.extensions.Evaluator(valid_iter, model)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
report = evaluator()
return report["main/accuracy"]
def make_optimizer(self, model, alpha, beta1, beta2):
self.print_log(
'Use Adam Optimizer with alpah = {}, beta1 = {}, beta2 = {}'.
format(alpha, beta1, beta2))
optimizer = chainer.optimizers.Adam(alpha=alpha,
beta1=beta1,
beta2=beta2)
if self.use_mpi:
self.print_log('Use Optimizer with MPI')
optimizer = chainermn.create_multi_node_optimizer(
optimizer, self.comm)
optimizer.setup(model)
return optimizer
def objective(trial, comm):
device = comm.intra_rank
chainer.cuda.get_device_from_id(device).use()
# Sample an architecture.
model = L.Classifier(create_model(trial))
model.to_gpu()
# Setup optimizer.
optimizer = chainer.optimizers.MomentumSGD()
optimizer.setup(model)
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
# Setup dataset and iterator. Only worker 0 loads the whole dataset.
# The dataset of worker 0 is evenly split and distributed to all workers.
if comm.rank == 0:
train, test = chainer.datasets.get_mnist()
rng = np.random.RandomState(0)
train = chainer.datasets.SubDataset(
train, 0, N_TRAIN_EXAMPLES, order=rng.permutation(len(train)))
test = chainer.datasets.SubDataset(
test, 0, N_TEST_EXAMPLES, order=rng.permutation(len(test)))
else:
train, test = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
test = chainermn.scatter_dataset(test, comm)
train_iter = chainer.iterators.SerialIterator(
train, BATCHSIZE, shuffle=True)
test_iter = chainer.iterators.SerialIterator(
test, BATCHSIZE, repeat=False, shuffle=False)
# Setup trainer.
updater = chainer.training.StandardUpdater(
train_iter, optimizer, device=device)
trainer = chainer.training.Trainer(updater, (EPOCH, 'epoch'))
if comm.rank == 0:
trainer.extend(chainer.training.extensions.ProgressBar())
# Run training.
trainer.run()
# Evaluate.
evaluator = chainer.training.extensions.Evaluator(
test_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
report = evaluator()
return report['main/accuracy']
def test_mnist(self, display_log=True):
# This test file is intended to be run on Travis-CI and
# GPU is not used for now.
epoch = 5
batchsize = 100
n_units = 100
comm = chainermn.create_communicator('naive')
model = L.Classifier(MLP(n_units, 10))
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
if comm.rank == 0:
train, test = chainer.datasets.get_mnist()
else:
train, test = None, None
train = chainermn.scatter_dataset(train, comm)
test = chainermn.scatter_dataset(test, comm)
train_iter = chainer.iterators.SerialIterator(train, batchsize)
test_iter = chainer.iterators.SerialIterator(test,
batchsize,
repeat=False,
shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer)
trainer = training.Trainer(updater, (epoch, 'epoch'))
# Wrap standard Chainer evaluators by MultiNodeEvaluator.
evaluator = extensions.Evaluator(test_iter, model)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
trainer.extend(evaluator)
# Some display and output extensions are necessary only for one worker.
# (Otherwise, there would just be repeated outputs.)
if comm.rank == 0 and display_log:
trainer.extend(extensions.LogReport(trigger=(1, 'epoch')),
trigger=(1, 'epoch'))
trainer.extend(extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/accuracy',
'validation/main/accuracy', 'elapsed_time'
],
out=sys.stderr),
trigger=(1, 'epoch'))
trainer.run()
err = evaluator()['validation/main/accuracy']
self.assertGreaterEqual(err, 0.95)
def test_update(self):
self.setup_gpu()
self.optimizer = chainermn.create_multi_node_optimizer(
self.actual_optimizer, self.comm, double_buffering=True)
opt = self.optimizer.setup(self.target)
assert opt is self.optimizer
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
self.optimizer.target.a.W.grad[:] = self.comm.rank
self.optimizer.target.b.W.grad[:] = self.comm.rank + 1
self.optimizer.target.c.W.grad[:] = self.comm.rank + 2
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 0)
base = (self.comm.size - 1.0) / 2
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad,
(base + 0) * np.ones((3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad,
(base + 1) * np.ones((4, 3)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.c.W.grad,
(base + 2) * np.ones((5, 4)))
self.optimizer.target.a.W.grad[:] = self.comm.rank + 3
self.optimizer.target.b.W.grad[:] = self.comm.rank + 4
self.optimizer.target.c.W.grad[:] = self.comm.rank + 5
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 1)
self.optimizer.target.a.W.update_rule.update.assert_called_once_with(
self.optimizer.target.a.W)
self.optimizer.target.b.W.update_rule.update.assert_called_once_with(
self.optimizer.target.b.W)
self.optimizer.target.c.W.update_rule.update.assert_called_once_with(
self.optimizer.target.c.W)
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad,
(base + 3) * np.ones((3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad,
(base + 4) * np.ones((4, 3)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.c.W.grad,
(base + 5) * np.ones((5, 4)))
# barrier() requires before destructor of PureNcclCommunicator
# because communication may not be finished.
self.comm.mpi_comm.barrier()
def test_update(self):
self.setup_gpu()
self.optimizer = chainermn.create_multi_node_optimizer(
self.actual_optimizer, self.comm, double_buffering=True)
opt = self.optimizer.setup(self.target)
assert opt is self.optimizer
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
self.optimizer.target.a.W.grad[:] = self.comm.rank
self.optimizer.target.b.W.grad[:] = self.comm.rank + 1
self.optimizer.target.c.W.grad[:] = self.comm.rank + 2
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 0)
base = (self.comm.size - 1.0) / 2
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad, (base + 0) * np.ones(
(3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad, (base + 1) * np.ones(
(4, 3)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.c.W.grad, (base + 2) * np.ones(
(5, 4)))
self.optimizer.target.a.W.grad[:] = self.comm.rank + 3
self.optimizer.target.b.W.grad[:] = self.comm.rank + 4
self.optimizer.target.c.W.grad[:] = self.comm.rank + 5
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 1)
self.optimizer.target.a.W.update_rule.update.assert_called_once_with(
self.optimizer.target.a.W)
self.optimizer.target.b.W.update_rule.update.assert_called_once_with(
self.optimizer.target.b.W)
self.optimizer.target.c.W.update_rule.update.assert_called_once_with(
self.optimizer.target.c.W)
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad, (base + 3) * np.ones(
(3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad, (base + 4) * np.ones(
(4, 3)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.c.W.grad, (base + 5) * np.ones(
(5, 4)))
# barrier() requires before destructor of PureNcclCommunicator
# because communication may not be finished.
self.comm.mpi_comm.barrier()
def make_optimizer(model):
if args.optimizer in [
'SGD', 'Momentum', 'CMomentum', 'AdaGrad', 'RMSprop',
'NesterovAG', 'LBFGS'
]:
optimizer = optim[args.optimizer](lr=args.learning_rate)
elif args.optimizer in ['AdaDelta']:
optimizer = optim[args.optimizer]()
elif args.optimizer in ['Adam', 'AdaBound', 'Eve']:
optimizer = optim[args.optimizer](
alpha=args.learning_rate, weight_decay_rate=args.weight_decay)
if args.mpi:
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
optimizer.setup(model)
return optimizer
def check_update(self, batched_copy):
self.setup(batched_copy)
self.optimizer = chainermn.create_multi_node_optimizer(
self.actual_optimizer, self.comm, double_buffering=True)
opt = self.optimizer.setup(self.target)
assert opt is self.optimizer
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
self.optimizer.target.a.W.grad[:] = self.comm.rank
self.optimizer.target.b.W.grad[:] = self.comm.rank + 1
self.optimizer.target.c.W.grad[:] = self.comm.rank + 2
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 0)
base = (self.comm.size - 1.0) / 2
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad,
(base + 0) * np.ones((3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad,
(base + 1) * np.ones((4, 3)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.c.W.grad,
(base + 2) * np.ones((5, 4)))
self.optimizer.target.a.W.grad[:] = self.comm.rank + 3
self.optimizer.target.b.W.grad[:] = self.comm.rank + 4
self.optimizer.target.c.W.grad[:] = self.comm.rank + 5
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 1)
self.optimizer.target.a.W.update_rule.update.assert_called_once_with(
self.optimizer.target.a.W)
self.optimizer.target.b.W.update_rule.update.assert_called_once_with(
self.optimizer.target.b.W)
self.optimizer.target.c.W.update_rule.update.assert_called_once_with(
self.optimizer.target.c.W)
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad,
(base + 3) * np.ones((3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad,
(base + 4) * np.ones((4, 3)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.c.W.grad,
(base + 5) * np.ones((5, 4)))
self.comm.finalize()
def objective(trial, comm):
# Sample an architecture.
model = L.Classifier(create_model(trial))
# Setup optimizer.
optimizer = chainer.optimizers.MomentumSGD()
optimizer.setup(model)
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
# Setup dataset and iterator.
train, test = chainer.datasets.get_mnist()
rng = np.random.RandomState(0)
train = chainer.datasets.SubDataset(train,
0,
N_TRAIN_EXAMPLES,
order=rng.permutation(len(train)))
test = chainer.datasets.SubDataset(test,
0,
N_TEST_EXAMPLES,
order=rng.permutation(len(test)))
train = chainermn.scatter_dataset(train, comm, shuffle=True)
test = chainermn.scatter_dataset(test, comm)
train_iter = chainer.iterators.SerialIterator(train,
BATCHSIZE,
shuffle=True)
test_iter = chainer.iterators.SerialIterator(test,
BATCHSIZE,
repeat=False,
shuffle=False)
# Setup trainer.
updater = chainer.training.StandardUpdater(train_iter, optimizer)
trainer = chainer.training.Trainer(updater, (EPOCH, 'epoch'))
if comm.rank == 0:
trainer.extend(chainer.training.extensions.ProgressBar())
# Run training.
trainer.run()
# Evaluate.
evaluator = chainer.training.extensions.Evaluator(test_iter, model)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
report = evaluator()
return 1.0 - report['main/accuracy']
def make_optimizer(self, model, alpha, beta1, beta2):
self.print_log(
'Use Adam Optimizer with alpah = {}, beta1 = {}, beta2 = {}'.
format(alpha, beta1, beta2))
optimizer = chainer.optimizers.Adam(alpha=alpha,
beta1=beta1,
beta2=beta2)
if self.use_mpi:
self.print_log('Use Optimizer with MPI')
optimizer = chainermn.create_multi_node_optimizer(
optimizer, self.comm)
# if self.nvprof:
# optimizer = create_marked_profile_optimizer(optimizer, sync=True, sync_level=2)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(5))
return optimizer
def run_test_observation_aggregator(comm, xp, use_chainer_variable,
communicate_interval, use_cupy):
model = DummyChain()
if use_cupy:
model.to_gpu()
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
train = xp.random.rand(10, 1).astype(np.float32)
train_iter = chainer.iterators.SerialIterator(train,
batch_size=1,
repeat=True,
shuffle=True)
updater = chainer.training.StandardUpdater(train_iter, optimizer)
trainer = chainer.training.Trainer(updater, (1, 'epoch'))
@extension.make_extension(trigger=(1, 'iteration'),
priority=extension.PRIORITY_WRITER)
def rank_reporter(trainer):
tmp = xp.asarray(comm.rank, dtype=np.float32)
if use_chainer_variable:
tmp = chainer.Variable(tmp)
trainer.observation['rank'] = tmp
@extension.make_extension(trigger=(communicate_interval, 'iteration'),
priority=extension.PRIORITY_READER)
def aggregated_rank_checker(trainer):
actual = trainer.observation['rank-aggregated']
if use_chainer_variable:
actual = actual.data
expected = (comm.size - 1) / 2
chainer.testing.assert_allclose(actual, expected)
trainer.extend(rank_reporter)
trainer.extend(
ObservationAggregator(comm,
'rank',
'rank-aggregated',
comm_trigger=(communicate_interval,
'iteration')))
trainer.extend(aggregated_rank_checker)
trainer.run()
def train(x_data,
t_data,
batchsize=128,
layer=1,
in_units=1,
hidden_units=5,
out_units=1):
comm = chainermn.create_communicator('naive')
# Iterator
batchsize = batchsize
x_data = chainermn.scatter_dataset(x_data, comm)
t_data = chainermn.scatter_dataset(t_data, comm)
train_iter = iterators.SerialIterator(x_data, batchsize)
test_iter = iterators.SerialIterator(t_data,
batchsize,
repeat=False,
shuffle=False)
# setup model
model = LSTM(in_units, hidden_units, out_units)
# setup optimizer
optimizer = chainermn.create_multi_node_optimizer(optimizers.Adam(), comm)
optimizer.setup(model)
updater = training.StandardUpdater(train_iter, optimizer, MyConverter)
trainer = training.Trainer(updater, (20, 'epoch'), out='result')
if comm.rank == 0:
trainer.extend(extensions.LogReport())
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.observe_lr())
trainer.extend(extensions.Evaluator(test_iter, model, MyConverter),
name='val')
trainer.extend(
extensions.PrintReport(
['epoch', 'main/loss', 'val/main/loss', 'elapsed_time', 'lr']))
trainer.extend(
extensions.PlotReport(['main/loss', 'val/main/loss'],
x_key='epoch',
file_name='loss.png'))
# trainer.extend(extensions.ProgressBar())
trainer.run()
def _setup_optimizer(config, model, comm):
optimizer_name = config['optimizer']
lr = float(config['init_lr'])
weight_decay = float(config['weight_decay'])
if optimizer_name == 'Adam':
optimizer = Adam(alpha=lr, weight_decay_rate=weight_decay)
elif optimizer_name in \
('SGD', 'MomentumSGD', 'CorrectedMomentumSGD', 'RMSprop'):
optimizer = eval(optimizer_name)(lr=lr)
if weight_decay > 0.:
optimizer.add_hook(WeightDecay(weight_decay))
else:
raise ValueError('Invalid optimizer: {}'.format(optimizer_name))
if comm is not None:
optimizer = chainermn.create_multi_node_optimizer(optimizer, comm)
optimizer.setup(model)
return optimizer
def __init__(
self,
model_parameters,
# Learning rate at training step s with annealing
lr_i=1.0 * 1e-4,
lr_f=1.0 * 1e-5,
n=10000,
communicator=None):
super().__init__(lr_i, lr_f, n)
lr = self.mu_s(0)
self.optimizer = optimizers.MomentumSGD(lr)
self.optimizer.setup(model_parameters)
self.multi_node_optimizer = None
if communicator:
self.multi_node_optimizer = chainermn.create_multi_node_optimizer(
self.optimizer, communicator)
def test_update_with_gpu(self):
self.setup_gpu()
self.optimizer = chainermn.create_multi_node_optimizer(
self.actual_optimizer, self.comm)
opt = self.optimizer.setup(self.target)
assert opt is self.optimizer
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
with self.target.init_scope():
c = chainer.links.Linear(4, 4)
c.to_gpu()
self.target.c = c
if self.comm.rank == 0:
self.target.c.W.data[:] = self.comm.rank + 2
self.optimizer.setup(self.target)
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
send_buf = chainer.cuda.to_cpu(self.optimizer.target.c.W.data)
recv_buf = self.comm.mpi_comm.allgather(send_buf)
for i in range(1, self.comm.size):
chainer.testing.assert_allclose(recv_buf[0], recv_buf[i])
self.optimizer.target.a.W.grad[:] = self.comm.rank
self.optimizer.target.b.W.grad[:] = self.comm.rank + 1
self.optimizer.target.c.W.grad[:] = self.comm.rank + 2
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 1)
self.optimizer.target.a.W.update_rule.update.assert_called_once_with(
self.optimizer.target.a.W)
self.optimizer.target.b.W.update_rule.update.assert_called_once_with(
self.optimizer.target.b.W)
self.optimizer.target.c.W.update_rule.update.assert_called_once_with(
self.optimizer.target.c.W)
base = (self.comm.size - 1.0) / 2
chainer.testing.assert_allclose(self.optimizer.target.a.W.grad,
(base + 0) * np.ones((3, 2)))
chainer.testing.assert_allclose(self.optimizer.target.b.W.grad,
(base + 1) * np.ones((4, 3)))
chainer.testing.assert_allclose(self.optimizer.target.c.W.grad,
(base + 2) * np.ones((4, 4)))
def test_update(self):
self.setup_gpu()
self.optimizer = chainermn.create_multi_node_optimizer(
self.actual_optimizer, self.comm, double_buffering=True)
opt = self.optimizer.setup(self.target)
assert opt is self.optimizer
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
self.optimizer.target.a.W.grad[:] = self.comm.rank
self.optimizer.target.b.W.grad[:] = self.comm.rank + 1
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 0)
base = (self.comm.size - 1.0) / 2
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad,
(base + 0) * np.ones((3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad,
(base + 1) * np.ones((4, 3)))
self.optimizer.target.a.W.grad[:] = self.comm.rank + 3
self.optimizer.target.b.W.grad[:] = self.comm.rank + 4
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 1)
self.optimizer.target.a.W.update_rule.update.assert_called_once_with(
self.optimizer.target.a.W)
self.optimizer.target.b.W.update_rule.update.assert_called_once_with(
self.optimizer.target.b.W)
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad,
(base + 3) * np.ones((3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad,
(base + 4) * np.ones((4, 3)))
with self.target.init_scope():
c = chainer.links.Linear(4, 4)
c.to_gpu()
self.target.c = c
if self.comm.rank == 0:
self.target.c.W.data[:] = self.comm.rank + 2
self.optimizer.setup(self.target)
self.optimizer.update()
self.assertEqual(self.actual_optimizer.t, 0)
send_buf = chainer.cuda.to_cpu(self.optimizer.target.c.W.data)
recv_buf = self.comm.mpi_comm.allgather(send_buf)
for i in range(1, self.comm.size):
chainer.testing.assert_allclose(recv_buf[0], recv_buf[i])
self.optimizer.target.a.W.grad[:] = self.comm.rank + 6
self.optimizer.target.b.W.grad[:] = self.comm.rank + 7
self.optimizer.target.c.W.grad[:] = self.comm.rank + 8
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 0)
base = (self.comm.size - 1.0) / 2
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad,
(base + 6) * np.ones((3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad,
(base + 7) * np.ones((4, 3)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.c.W.grad,
(base + 8) * np.ones((4, 4)))
self.optimizer.target.a.W.grad[:] = self.comm.rank + 9
self.optimizer.target.b.W.grad[:] = self.comm.rank + 10
self.optimizer.target.c.W.grad[:] = self.comm.rank + 11
self.optimizer.update()
self.optimizer.wait()
self.assertEqual(self.actual_optimizer.t, 1)
self.optimizer.target.a.W.update_rule.update.assert_called_once_with(
self.optimizer.target.a.W)
self.optimizer.target.b.W.update_rule.update.assert_called_once_with(
self.optimizer.target.b.W)
self.optimizer.target.c.W.update_rule.update.assert_called_once_with(
self.optimizer.target.c.W)
chainer.testing.assert_allclose(
self.optimizer.communicated_target.a.W.grad,
(base + 9) * np.ones((3, 2)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.b.W.grad,
(base + 10) * np.ones((4, 3)))
chainer.testing.assert_allclose(
self.optimizer.communicated_target.c.W.grad,
(base + 11) * np.ones((4, 4)))
# barrier() requires before destructor of PureNcclCommunicator
# because communication may not be finished.
self.comm.mpi_comm.barrier()
def main():
parser = argparse.ArgumentParser(description='ChainerMN example: MNIST')
parser.add_argument('--batchsize', '-b', type=int, default=100,
help='Number of images in each mini-batch')
parser.add_argument('--communicator', type=str,
default='hierarchical', help='Type of communicator')
parser.add_argument('--epoch', '-e', type=int, default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu', '-g', action='store_true',
help='Use GPU')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
parser.add_argument('--unit', '-u', type=int, default=1000,
help='Number of units')
args = parser.parse_args()
# Prepare ChainerMN communicator.
if args.gpu:
if args.communicator == 'naive':
print("Error: 'naive' communicator does not support GPU.\n")
exit(-1)
comm = chainermn.create_communicator(args.communicator)
device = comm.intra_rank
else:
if args.communicator != 'naive':
print('Warning: using naive communicator '
'because only naive supports CPU-only execution')
comm = chainermn.create_communicator('naive')
device = -1
if comm.rank == 0:
print('==========================================')
print('Num process (COMM_WORLD): {}'.format(comm.size))
if args.gpu:
print('Using GPUs')
print('Using {} communicator'.format(args.communicator))
print('Num unit: {}'.format(args.unit))
print('Num Minibatch-size: {}'.format(args.batchsize))
print('Num epoch: {}'.format(args.epoch))
print('==========================================')
model = L.Classifier(MLP(args.unit, 10))
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu()
# Create a multi node optimizer from a standard Chainer optimizer.
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
# Split and distribute the dataset. Only worker 0 loads the whole dataset.
# Datasets of worker 0 are evenly split and distributed to all workers.
if comm.rank == 0:
train, test = chainer.datasets.get_mnist()
else:
train, test = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
test = chainermn.scatter_dataset(test, comm, shuffle=True)
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer, device=device)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
# Create a multi node evaluator from a standard Chainer evaluator.
evaluator = extensions.Evaluator(test_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
trainer.extend(evaluator)
# Some display and output extensions are necessary only for one worker.
# (Otherwise, there would just be repeated outputs.)
if comm.rank == 0:
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
trainer.extend(extensions.ProgressBar())
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
def main():
# Check if GPU is available
# (ImageNet example does not support CPU execution)
if not chainer.cuda.available:
raise RuntimeError("ImageNet requires GPU support.")
archs = {
'alex': alex.Alex,
'googlenet': googlenet.GoogLeNet,
'googlenetbn': googlenetbn.GoogLeNetBN,
'nin': nin.NIN,
'resnet50': resnet50.ResNet50,
}
parser = argparse.ArgumentParser(
description='Learning convnet from ILSVRC2012 dataset')
parser.add_argument('train', help='Path to training image-label list file')
parser.add_argument('val', help='Path to validation image-label list file')
parser.add_argument('--arch', '-a', choices=archs.keys(), default='nin',
help='Convnet architecture')
parser.add_argument('--batchsize', '-B', type=int, default=32,
help='Learning minibatch size')
parser.add_argument('--epoch', '-E', type=int, default=10,
help='Number of epochs to train')
parser.add_argument('--initmodel',
help='Initialize the model from given file')
parser.add_argument('--loaderjob', '-j', type=int,
help='Number of parallel data loading processes')
parser.add_argument('--mean', '-m', default='mean.npy',
help='Mean file (computed by compute_mean.py)')
parser.add_argument('--resume', '-r', default='',
help='Initialize the trainer from given file')
parser.add_argument('--out', '-o', default='result',
help='Output directory')
parser.add_argument('--root', '-R', default='.',
help='Root directory path of image files')
parser.add_argument('--val_batchsize', '-b', type=int, default=250,
help='Validation minibatch size')
parser.add_argument('--test', action='store_true')
parser.add_argument('--communicator', default='hierarchical')
parser.set_defaults(test=False)
args = parser.parse_args()
# Prepare ChainerMN communicator.
comm = chainermn.create_communicator(args.communicator)
device = comm.intra_rank
if comm.rank == 0:
print('==========================================')
print('Num process (COMM_WORLD): {}'.format(comm.size))
print('Using {} communicator'.format(args.communicator))
print('Using {} arch'.format(args.arch))
print('Num Minibatch-size: {}'.format(args.batchsize))
print('Num epoch: {}'.format(args.epoch))
print('==========================================')
model = archs[args.arch]()
if args.initmodel:
print('Load model from', args.initmodel)
chainer.serializers.load_npz(args.initmodel, model)
chainer.cuda.get_device_from_id(device).use() # Make the GPU current
model.to_gpu()
# Split and distribute the dataset. Only worker 0 loads the whole dataset.
# Datasets of worker 0 are evenly split and distributed to all workers.
mean = np.load(args.mean)
if comm.rank == 0:
train = PreprocessedDataset(args.train, args.root, mean, model.insize)
val = PreprocessedDataset(
args.val, args.root, mean, model.insize, False)
else:
train = None
val = None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
val = chainermn.scatter_dataset(val, comm)
# We need to change the start method of multiprocessing module if we are
# using InfiniBand and MultiprocessIterator. This is because processes
# often crash when calling fork if they are using Infiniband.
# (c.f., https://www.open-mpi.org/faq/?category=tuning#fork-warning )
multiprocessing.set_start_method('forkserver')
train_iter = chainer.iterators.MultiprocessIterator(
train, args.batchsize, n_processes=args.loaderjob)
val_iter = chainer.iterators.MultiprocessIterator(
val, args.val_batchsize, repeat=False, n_processes=args.loaderjob)
# Create a multi node optimizer from a standard Chainer optimizer.
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.MomentumSGD(lr=0.01, momentum=0.9), comm)
optimizer.setup(model)
# Set up a trainer
updater = training.StandardUpdater(train_iter, optimizer, device=device)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), args.out)
checkpoint_interval = (10, 'iteration') if args.test else (1, 'epoch')
val_interval = (10, 'iteration') if args.test else (1, 'epoch')
log_interval = (10, 'iteration') if args.test else (1, 'epoch')
checkpointer = chainermn.create_multi_node_checkpointer(
name='imagenet-example', comm=comm)
checkpointer.maybe_load(trainer, optimizer)
trainer.extend(checkpointer, trigger=checkpoint_interval)
# Create a multi node evaluator from an evaluator.
evaluator = TestModeEvaluator(val_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
trainer.extend(evaluator, trigger=val_interval)
# Some display and output extensions are necessary only for one worker.
# (Otherwise, there would just be repeated outputs.)
if comm.rank == 0:
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.LogReport(trigger=log_interval))
trainer.extend(extensions.observe_lr(), trigger=log_interval)
trainer.extend(extensions.PrintReport([
'epoch', 'iteration', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'lr'
]), trigger=log_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
comm = chainermn.create_communicator(communicator)
device = comm.intra_rank if args.num_gpus > 0 else -1
print('==========================================')
print('Using {} communicator'.format(comm))
print('Num unit: {}'.format(args.units))
print('Num Minibatch-size: {}'.format(args.batch_size))
print('Num epoch: {}'.format(args.epochs))
print('==========================================')
model = L.Classifier(MLP(args.units, 10))
if device >= 0:
chainer.cuda.get_device(device).use()
# Create a multi node optimizer from a standard Chainer optimizer.
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
train_file = np.load(os.path.join(args.train, 'train.npz'))
test_file = np.load(os.path.join(args.test, 'test.npz'))
preprocess_mnist_options = {
'withlabel': True,
'ndim': 1,
'scale': 1.,
'image_dtype': np.float32,
'label_dtype': np.int32,
'rgb_format': False
}
train_dataset = _preprocess_mnist(train_file, **preprocess_mnist_options)
def main():
parser = argparse.ArgumentParser(description='Chainer example: seq2seq')
parser.add_argument('--batchsize', '-b', type=int, default=64,
help='Number of images in each mini-batch')
parser.add_argument('--bleu', action='store_true', default=False,
help='Report BLEU score')
parser.add_argument('--gpu', '-g', action='store_true',
help='Use GPU')
parser.add_argument('--cache', '-c', default=None,
help='Directory to cache pre-processed dataset')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
parser.add_argument('--unit', '-u', type=int, default=1024,
help='Number of units')
parser.add_argument('--communicator', default='hierarchical',
help='Type of communicator')
parser.add_argument('--stop', '-s', type=str, default='15e',
help='Stop trigger (ex. "500i", "15e")')
parser.add_argument('--input', '-i', type=str, default='wmt',
help='Input directory')
parser.add_argument('--optimizer', type=str, default='adam()',
help='Optimizer and its argument')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
args = parser.parse_args()
# Prepare ChainerMN communicator
if args.gpu:
comm = chainermn.create_communicator('hierarchical')
dev = comm.intra_rank
else:
comm = chainermn.create_communicator('naive')
dev = -1
if comm.rank == 0:
print('==========================================')
print('Num process (COMM_WORLD): {}'.format(comm.size))
if args.gpu:
print('Using GPUs')
print('Using {} communicator'.format(args.communicator))
print('Num unit: {}'.format(args.unit))
print('Num Minibatch-size: {}'.format(args.batchsize))
print('==========================================')
# Rank 0 prepares all data
if comm.rank == 0:
if args.cache and not os.path.exists(args.cache):
os.mkdir(args.cache)
# Read source data
bt = time.time()
if args.cache:
cache_file = os.path.join(args.cache, 'source.pickle')
source_vocab, source_data = cached_call(cache_file,
read_source,
args.input, args.cache)
else:
source_vocab, source_data = read_source(args.input, args.cache)
et = time.time()
print('RD source done. {:.3f} [s]'.format(et - bt))
sys.stdout.flush()
# Read target data
bt = time.time()
if args.cache:
cache_file = os.path.join(args.cache, 'target.pickle')
target_vocab, target_data = cached_call(cache_file,
read_target,
args.input, args.cache)
else:
target_vocab, target_data = read_target(args.input, args.cache)
et = time.time()
print('RD target done. {:.3f} [s]'.format(et - bt))
sys.stdout.flush()
print('Original training data size: %d' % len(source_data))
train_data = [(s, t)
for s, t in six.moves.zip(source_data, target_data)
if 0 < len(s) < 50 and 0 < len(t) < 50]
print('Filtered training data size: %d' % len(train_data))
en_path = os.path.join(args.input, 'dev', 'newstest2013.en')
source_data = europal.make_dataset(en_path, source_vocab)
fr_path = os.path.join(args.input, 'dev', 'newstest2013.fr')
target_data = europal.make_dataset(fr_path, target_vocab)
assert(len(source_data) == len(target_data))
test_data = [(s, t) for s, t
in six.moves.zip(source_data, target_data)
if 0 < len(s) and 0 < len(t)]
source_ids = {word: index
for index, word in enumerate(source_vocab)}
target_ids = {word: index
for index, word in enumerate(target_vocab)}
else:
# target_data, source_data = None, None
train_data, test_data = None, None
target_ids, source_ids = None, None
# Print GPU id
for i in range(0, comm.size):
if comm.rank == i:
print('Rank {} GPU: {}'.format(comm.rank, dev))
sys.stdout.flush()
comm.mpi_comm.Barrier()
# broadcast id- > word dictionary
source_ids = comm.bcast_obj(source_ids, root=0)
target_ids = comm.bcast_obj(target_ids, root=0)
target_words = {i: w for w, i in target_ids.items()}
source_words = {i: w for w, i in source_ids.items()}
if comm.rank == 0:
print('target_words : {}'.format(len(target_words)))
print('source_words : {}'.format(len(source_words)))
model = Seq2seq(3, len(source_ids), len(target_ids), args.unit)
if dev >= 0:
chainer.cuda.get_device_from_id(dev).use()
model.to_gpu(dev)
# determine the stop trigger
m = re.match(r'^(\d+)e$', args.stop)
if m:
trigger = (int(m.group(1)), 'epoch')
else:
m = re.match(r'^(\d+)i$', args.stop)
if m:
trigger = (int(m.group(1)), 'iteration')
else:
if comm.rank == 0:
sys.stderr.write('Error: unknown stop trigger: {}'.format(
args.stop))
exit(-1)
if comm.rank == 0:
print('Trigger: {}'.format(trigger))
optimizer = chainermn.create_multi_node_optimizer(
create_optimizer(args.optimizer), comm)
optimizer.setup(model)
# Broadcast dataset
# Sanity check of train_data
train_data = chainermn.scatter_dataset(train_data, comm)
test_data = chainermn.scatter_dataset(test_data, comm)
train_iter = chainer.iterators.SerialIterator(train_data,
args.batchsize,
shuffle=False)
updater = training.StandardUpdater(
train_iter, optimizer, converter=convert, device=dev)
trainer = training.Trainer(updater,
trigger,
out=args.out)
trainer.extend(chainermn.create_multi_node_evaluator(
BleuEvaluator(model, test_data, device=dev, comm=comm),
comm))
def translate_one(source, target):
words = europal.split_sentence(source)
print('# source : ' + ' '.join(words))
x = model.xp.array(
[source_ids.get(w, 1) for w in words], numpy.int32)
ys = model.translate([x])[0]
words = [target_words[y] for y in ys]
print('# result : ' + ' '.join(words))
print('# expect : ' + target)
# @chainer.training.make_extension(trigger=(200, 'iteration'))
def translate(trainer):
translate_one(
'Who are we ?',
'Qui sommes-nous?')
translate_one(
'And it often costs over a hundred dollars ' +
'to obtain the required identity card .',
'Or, il en coûte souvent plus de cent dollars ' +
'pour obtenir la carte d\'identité requise.')
source, target = test_data[numpy.random.choice(len(test_data))]
source = ' '.join([source_words.get(i, '') for i in source])
target = ' '.join([target_words.get(i, '') for i in target])
translate_one(source, target)
if comm.rank == 0:
trainer.extend(extensions.LogReport(trigger=(1, 'epoch')),
trigger=(1, 'epoch'))
report = extensions.PrintReport(['epoch',
'iteration',
'main/loss',
'main/perp',
'validation/main/bleu',
'elapsed_time'])
trainer.extend(report, trigger=(1, 'epoch'))
comm.mpi_comm.Barrier()
if comm.rank == 0:
print('start training')
sys.stdout.flush()
trainer.run()
# comm.inter_rank gives the rank of the node. This should only print on one node.
if comm.inter_rank == 0:
print('# Minibatch-size: {}'.format(args.batch_size))
print('# epoch: {}'.format(args.epochs))
print('# communicator: {}'.format(args.communicator))
# Set up a neural network to train.
# Classifier reports softmax cross entropy loss and accuracy at every
# iteration, which will be used by the PrintReport extension below.
# comm.intra_rank gives the rank of the process on a given node.
device = comm.intra_rank if num_gpus > 0 else -1
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
optimizer = chainermn.create_multi_node_optimizer(chainer.optimizers.MomentumSGD(args.learning_rate), comm)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(5e-4))
num_loaders = 2
train_iter = chainer.iterators.MultiprocessIterator(train, args.batch_size, n_processes=num_loaders)
test_iter = chainer.iterators.MultiprocessIterator(test, args.batch_size, repeat=False, n_processes=num_loaders)
# Set up a trainer
updater = training.StandardUpdater(train_iter, optimizer, device=device)
trainer = training.Trainer(updater, (args.epochs, 'epoch'), out=args.output_data_dir)
# Evaluate the model with the test dataset for each epoch
evaluator = extensions.Evaluator(test_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
def main():
parser = argparse.ArgumentParser(
description='ChainerMN example: pipelined neural network')
parser.add_argument('--batchsize', '-b', type=int, default=100,
help='Number of images in each mini-batch')
parser.add_argument('--epoch', '-e', type=int, default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gpu', '-g', action='store_true',
help='Use GPU')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--unit', '-u', type=int, default=1000,
help='Number of units')
args = parser.parse_args()
# Prepare ChainerMN communicator.
if args.gpu:
comm = chainermn.create_communicator('hierarchical')
data_axis, model_axis = comm.rank % 2, comm.rank // 2
data_comm = comm.split(data_axis, comm.rank)
model_comm = comm.split(model_axis, comm.rank)
device = comm.intra_rank
else:
comm = chainermn.create_communicator('naive')
data_axis, model_axis = comm.rank % 2, comm.rank // 2
data_comm = comm.split(data_axis, comm.rank)
model_comm = comm.split(model_axis, comm.rank)
device = -1
if model_comm.size != 2:
raise ValueError(
'This example can only be executed on the even number'
'of processes.')
if comm.rank == 0:
print('==========================================')
if args.gpu:
print('Using GPUs')
print('Num unit: {}'.format(args.unit))
print('Num Minibatch-size: {}'.format(args.batchsize))
print('Num epoch: {}'.format(args.epoch))
print('==========================================')
if data_axis == 0:
model = L.Classifier(MLP0(model_comm, args.unit))
elif data_axis == 1:
model = MLP1(model_comm, args.unit, 10)
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu()
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), data_comm)
optimizer.setup(model)
# Original dataset on worker 0 and 1.
# Datasets of worker 0 and 1 are split and distributed to all workers.
if model_axis == 0:
train, test = chainer.datasets.get_mnist()
if data_axis == 1:
train = chainermn.datasets.create_empty_dataset(train)
test = chainermn.datasets.create_empty_dataset(test)
else:
train, test = None, None
train = chainermn.scatter_dataset(train, data_comm, shuffle=True)
test = chainermn.scatter_dataset(test, data_comm, shuffle=True)
train_iter = chainer.iterators.SerialIterator(
train, args.batchsize, shuffle=False)
test_iter = chainer.iterators.SerialIterator(
test, args.batchsize, repeat=False, shuffle=False)
updater = training.StandardUpdater(train_iter, optimizer, device=device)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
evaluator = extensions.Evaluator(test_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, data_comm)
trainer.extend(evaluator)
# Some display and output extentions are necessary only for worker 0.
if comm.rank == 0:
trainer.extend(extensions.DumpGraph('main/loss'))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
trainer.extend(extensions.ProgressBar())
trainer.run()
def check_mnist(gpu, display_log=True):
epoch = 5
batchsize = 100
n_units = 100
comm = chainermn.create_communicator('naive')
if gpu:
device = comm.intra_rank
chainer.cuda.get_device_from_id(device).use()
else:
device = -1
model = L.Classifier(MLP(n_units, 10))
if gpu:
model.to_gpu()
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.Adam(), comm)
optimizer.setup(model)
if comm.rank == 0:
train, test = chainer.datasets.get_mnist()
else:
train, test = None, None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
test = chainermn.scatter_dataset(test, comm, shuffle=True)
train_iter = chainer.iterators.SerialIterator(train, batchsize)
test_iter = chainer.iterators.SerialIterator(test, batchsize,
repeat=False,
shuffle=False)
updater = training.StandardUpdater(
train_iter,
optimizer,
device=device
)
trainer = training.Trainer(updater, (epoch, 'epoch'))
# Wrap standard Chainer evaluators by MultiNodeEvaluator.
evaluator = extensions.Evaluator(test_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
trainer.extend(evaluator)
# Add checkpointer. This is just to check checkpointing runs
# without errors
path = tempfile.mkdtemp(dir='/tmp', prefix=__name__ + "-tmp-")
checkpointer = create_multi_node_checkpointer(name=__name__, comm=comm,
path=path)
trainer.extend(checkpointer, trigger=(1, 'epoch'))
# Some display and output extensions are necessary only for one worker.
# (Otherwise, there would just be repeated outputs.)
if comm.rank == 0 and display_log:
trainer.extend(extensions.LogReport(trigger=(1, 'epoch')),
trigger=(1, 'epoch'))
trainer.extend(extensions.PrintReport(['epoch',
'main/loss',
'validation/main/loss',
'main/accuracy',
'validation/main/accuracy',
'elapsed_time'],
out=sys.stderr),
trigger=(1, 'epoch'))
trainer.run()
err = evaluator()['validation/main/accuracy']
assert err > 0.95
# Check checkpointer successfully finalized snapshot directory
assert [] == os.listdir(path)
os.removedirs(path)
