def run_reptile(context: str, initial_model_state=None):
args = argument_parser().parse_args()
RANDOM = random.Random(args.seed)
# TODO: Possibly implement logic using ReptileExperimentContext
reptile_args = ReptileTrainingArgs(
model_class=OmniglotLightning,
sgd=args.sgd,
inner_learning_rate=args.learning_rate,
num_inner_steps=args.inner_iters,
num_inner_steps_eval=args.eval_iters,
log_every_n_steps=3,
meta_learning_rate_initial=args.meta_step,
meta_learning_rate_final=args.meta_step_final,
num_classes_per_client=args.classes
)
experiment_logger = create_tensorboard_logger(
'reptile',
(
f"{context};seed{args.seed};"
f"train-clients{args.train_clients};"
f"{args.classes}-way{args.shots}-shot;"
f"ib{args.inner_batch}ii{args.inner_iters}"
f"ilr{str(args.learning_rate).replace('.', '')}"
f"ms{str(args.meta_step).replace('.', '')}"
f"mb{args.meta_batch}ei{args.eval_iters}"
f"{'sgd' if args.sgd else 'adam'}"
)
)
# Load and prepare Omniglot data
data_dir = REPO_ROOT / 'data' / 'omniglot'
omniglot_train_clients, omniglot_test_clients = load_omniglot_datasets(
str(data_dir.absolute()),
num_clients_train=args.train_clients,
num_clients_test=args.test_clients,
num_classes_per_client=args.classes,
num_shots_per_class=args.shots,
inner_batch_size=args.inner_batch,
random_seed=args.seed
)
# Set up clients
# Since we are doing meta-learning, we need separate sets of training and
# test clients
train_clients = initialize_clients(omniglot_train_clients, reptile_args.get_inner_model_args(), context,
experiment_logger)
test_clients = initialize_clients(omniglot_test_clients, reptile_args.get_inner_model_args(), context,
experiment_logger)
# Set up server
server = ReptileServer(
participant_name='initial_server',
model_args=reptile_args.get_meta_model_args(),
context=context, # TODO: Change to ReptileExperimentContext
initial_model_state=initial_model_state
)
# Perform training
for i in range(args.meta_iters):
if args.meta_batch == -1:
meta_batch = train_clients
else:
meta_batch = [
train_clients[k] for k in cyclerange(
i*args.meta_batch % len(train_clients),
(i+1)*args.meta_batch % len(train_clients),
len(train_clients)
)
]
# Meta training step
reptile_train_step(
aggregator=server,
participants=meta_batch,
inner_training_args=reptile_args.get_inner_training_args(),
meta_training_args=reptile_args.get_meta_training_args(
frac_done=i / args.meta_iters
)
)
# Evaluation on train and test clients
if i % args.eval_interval == 0:
# train-test set
# Pick one train client at random and test on it
k = RANDOM.randrange(len(train_clients))
client = [train_clients[k]]
reptile_train_step(
aggregator=server,
participants=client,
inner_training_args=reptile_args.get_inner_training_args(eval=True),
evaluation_mode=True
)
result = evaluate_local_models(participants=client)
experiment_logger.experiment.add_scalar(
'train-test/acc/{}/mean'.format('global_model'),
torch.mean(result.get('test/acc')),
global_step=i + 1
)
# test-test set
# Pick one test client at random and test on it
k = RANDOM.randrange(len(test_clients))
client = [test_clients[k]]
reptile_train_step(
aggregator=server,
participants=client,
inner_training_args=reptile_args.get_inner_training_args(eval=True),
evaluation_mode=True
)
result = evaluate_local_models(participants=client)
experiment_logger.experiment.add_scalar(
'test-test/acc/{}/mean'.format('global_model'),
torch.mean(result.get('test/acc')),
global_step=i + 1
)
logger.info('finished training round')
# Final evaluation on a sample of train/test clients
for label, client_set in zip(['Train', 'Test'], [train_clients, test_clients]):
eval_sample = RANDOM.sample(client_set, args.eval_samples)
reptile_train_step(
aggregator=server,
participants=eval_sample,
inner_training_args=reptile_args.get_inner_training_args(eval=True),
evaluation_mode=True
)
result = evaluate_local_models(participants=eval_sample)
log_loss_and_acc('global_model', result.get('test/loss'), result.get('test/acc'),
experiment_logger, global_step=args.meta_iters)
experiment_logger.experiment.add_scalar(
f'final_{label}_acc',
torch.mean(result.get('test/acc')),
global_step=0
)
print(f"{label} accuracy: {torch.mean(result.get('test/acc'))}")
def run_hierarchical_clustering_reptile(
seed,
name,
dataset,
num_clients,
batch_size,
num_label_limit,
use_colored_images,
sample_threshold,
hc_lr,
hc_cluster_initialization_rounds,
hc_client_fraction,
hc_local_epochs,
hc_train_args,
hc_partitioner_class,
hc_linkage_mech,
hc_criterion,
hc_dis_metric,
hc_max_value_criterion, # distance threshold
hc_reallocate_clients, #
hc_threshold_min_client_cluster, # only with hc_reallocate_clients = True,
# results in clusters having at least this number of clients
hc_train_cluster_args,
rp_sgd, # True -> Use SGD as inner optimizer; False -> Use Adam
rp_adam_betas, # Used only if sgd = False
rp_meta_batch_size,
rp_num_meta_steps,
rp_meta_learning_rate_initial,
rp_meta_learning_rate_final,
rp_eval_interval,
rp_inner_learning_rate,
rp_num_inner_steps,
rp_num_inner_steps_eval):
fix_random_seeds(seed)
global_tag = 'global_performance'
if dataset == 'femnist':
if use_colored_images:
fed_dataset = load_femnist_colored_dataset(
data_dir=str((REPO_ROOT / 'data').absolute()),
num_clients=num_clients,
batch_size=batch_size,
sample_threshold=sample_threshold)
else:
fed_dataset = load_femnist_dataset(
data_dir=str((REPO_ROOT / 'data').absolute()),
num_clients=num_clients,
batch_size=batch_size,
sample_threshold=sample_threshold)
if num_label_limit != -1:
fed_dataset = scratch_labels(fed_dataset, num_label_limit)
else:
raise ValueError(f'dataset "{dataset}" unknown')
if not hasattr(hc_max_value_criterion, '__iter__'):
hc_max_value_criterion = [hc_max_value_criterion]
if not hasattr(hc_lr, '__iter__'):
hc_lr = [hc_lr]
input_channels = 3 if use_colored_images else 1
data_distribution_logged = False
for cf in hc_client_fraction:
for lr_i in hc_lr:
# Initialize experiment context parameters
fedavg_optimizer_args = OptimizerArgs(optim.SGD, lr=lr_i)
fedavg_model_args = ModelArgs(CNNLightning,
optimizer_args=fedavg_optimizer_args,
input_channels=input_channels,
only_digits=False)
fedavg_context = FedAvgExperimentContext(
name=name,
client_fraction=cf,
local_epochs=hc_local_epochs,
lr=lr_i,
batch_size=batch_size,
optimizer_args=fedavg_optimizer_args,
model_args=fedavg_model_args,
train_args=hc_train_args,
dataset_name=dataset)
reptile_context = ReptileExperimentContext(
name=name,
dataset_name=dataset,
swap_labels=False,
num_classes_per_client=0,
num_shots_per_class=0,
seed=seed,
model_class=CNNLightning,
sgd=rp_sgd,
adam_betas=rp_adam_betas,
num_clients_train=num_clients,
num_clients_test=0,
meta_batch_size=rp_meta_batch_size,
num_meta_steps=rp_num_meta_steps,
meta_learning_rate_initial=rp_meta_learning_rate_initial,
meta_learning_rate_final=rp_meta_learning_rate_final,
eval_interval=rp_eval_interval,
num_eval_clients_training=-1,
do_final_evaluation=True,
num_eval_clients_final=-1,
inner_batch_size=batch_size,
inner_learning_rate=rp_inner_learning_rate,
num_inner_steps=rp_num_inner_steps,
num_inner_steps_eval=rp_num_inner_steps_eval)
experiment_specification = f'{fedavg_context}'
experiment_logger = create_tensorboard_logger(
name, experiment_specification)
if not data_distribution_logged:
log_dataset_distribution(experiment_logger, 'full dataset',
fed_dataset)
data_distribution_logged = True
log_after_round_evaluation_fns = [
partial(log_after_round_evaluation, experiment_logger,
'fedavg'),
partial(log_after_round_evaluation, experiment_logger,
global_tag)
]
server, clients = run_fedavg(
context=fedavg_context,
num_rounds=max(hc_cluster_initialization_rounds),
dataset=fed_dataset,
save_states=True,
restore_state=True,
after_round_evaluation=log_after_round_evaluation_fns)
for init_rounds, max_value in generate_configuration(
hc_cluster_initialization_rounds, hc_max_value_criterion):
# load the model state
round_model_state = load_fedavg_state(fedavg_context,
init_rounds)
overwrite_participants_models(round_model_state, clients)
# initialize the cluster configuration
round_configuration = {
'num_rounds_init': init_rounds,
'num_rounds_cluster': 0
}
cluster_args = ClusterArgs(
hc_partitioner_class,
linkage_mech=hc_linkage_mech,
criterion=hc_criterion,
dis_metric=hc_dis_metric,
max_value_criterion=max_value,
plot_dendrogram=False,
reallocate_clients=hc_reallocate_clients,
threshold_min_client_cluster=
hc_threshold_min_client_cluster,
**round_configuration)
# create new logger for cluster experiment
experiment_specification = f'{fedavg_context}_{cluster_args}_{reptile_context}'
experiment_logger = create_tensorboard_logger(
name, experiment_specification)
fedavg_context.experiment_logger = experiment_logger
initial_train_fn = partial(run_fedavg_train_round,
round_model_state,
training_args=hc_train_cluster_args)
create_aggregator_fn = partial(FedAvgServer,
model_args=fedavg_model_args,
context=fedavg_context)
# HIERARCHICAL CLUSTERING
logger.debug('starting local training before clustering.')
trained_participants = initial_train_fn(clients)
if len(trained_participants) != len(clients):
raise ValueError(
'not all clients successfully participated in the clustering round'
)
# Clustering of participants by model updates
partitioner = cluster_args()
cluster_clients_dic = partitioner.cluster(clients, server)
_cluster_clients_dic = dict()
for cluster_id, participants in cluster_clients_dic.items():
_cluster_clients_dic[cluster_id] = [
c._name for c in participants
]
log_cluster_distribution(experiment_logger,
cluster_clients_dic, 62)
# Initialize cluster models
cluster_server_dic = {}
for cluster_id, participants in cluster_clients_dic.items():
intermediate_cluster_server = create_aggregator_fn(
'cluster_server' + cluster_id)
intermediate_cluster_server.aggregate(participants)
cluster_server = ReptileServer(
participant_name=f'cluster_server{cluster_id}',
model_args=reptile_context.meta_model_args,
context=reptile_context,
initial_model_state=intermediate_cluster_server.model.
state_dict())
#create_aggregator_fn('cluster_server' + cluster_id)
#cluster_server.aggregate(participants)
cluster_server_dic[cluster_id] = cluster_server
# REPTILE TRAINING INSIDE CLUSTERS
after_round_evaluation = [log_after_round_evaluation]
RANDOM = random.Random(seed)
# Perform training
for i in range(reptile_context.num_meta_steps):
for cluster_id, participants in cluster_clients_dic.items(
):
if reptile_context.meta_batch_size == -1:
meta_batch = participants
else:
meta_batch = [
participants[k] for k in cyclerange(
start=i * reptile_context.meta_batch_size %
len(participants),
interval=reptile_context.meta_batch_size,
total_len=len(participants))
]
# Meta training step
reptile_train_step(
aggregator=cluster_server_dic[cluster_id],
participants=meta_batch,
inner_training_args=reptile_context.
get_inner_training_args(),
meta_training_args=reptile_context.
get_meta_training_args(
frac_done=i / reptile_context.num_meta_steps))
# Evaluation on train and test clients
if i % reptile_context.eval_interval == 0:
global_step = init_rounds + i
global_loss, global_acc = [], []
for cluster_id, participants in cluster_clients_dic.items(
):
# Test on all clients inside clusters
reptile_train_step(
aggregator=cluster_server_dic[cluster_id],
participants=participants,
inner_training_args=reptile_context.
get_inner_training_args(eval=True),
evaluation_mode=True)
result = evaluate_local_models(
participants=participants)
loss = result.get('test/loss')
acc = result.get('test/acc')
# Log
if after_round_evaluation is not None:
for c in after_round_evaluation:
c(experiment_logger,
f'cluster_{cluster_id}', loss, acc,
global_step)
loss_list = loss.tolist()
acc_list = acc.tolist()
global_loss.extend(loss_list if isinstance(
loss_list, list) else [loss_list])
global_acc.extend(acc_list if isinstance(
acc_list, list) else [acc_list])
if after_round_evaluation is not None:
for c in after_round_evaluation:
c(experiment_logger, 'mean_over_all_clients',
Tensor(global_loss), Tensor(global_acc),
global_step)
logger.info(f'Finished Reptile training round {i}')
# Final evaluation at end of training
if reptile_context.do_final_evaluation:
global_loss, global_acc = [], []
for cluster_id, participants in cluster_clients_dic.items(
):
# Final evaluation on train and test clients
# Test on all clients inside clusters
reptile_train_step(
aggregator=cluster_server_dic[cluster_id],
participants=participants,
inner_training_args=reptile_context.
get_inner_training_args(eval=True),
evaluation_mode=True)
result = evaluate_local_models(
participants=participants)
loss = result.get('test/loss')
acc = result.get('test/acc')
print(
f'Cluster {cluster_id} ({len(participants)} part.): loss = {loss}, acc = {acc}'
)
loss_list = loss.tolist()
acc_list = acc.tolist()
global_loss.extend(loss_list if isinstance(
loss_list, list) else [loss_list])
global_acc.extend(acc_list if isinstance(
acc_list, list) else [acc_list])
# Log
if after_round_evaluation is not None:
for c in after_round_evaluation:
c(experiment_logger, f'cluster_{cluster_id}',
loss, acc, reptile_context.num_meta_steps)
log_loss_and_acc('overall_mean', Tensor(global_loss),
Tensor(global_acc), experiment_logger, 0)
def run_reptile(context: ReptileExperimentContext,
dataset_train: FederatedDatasetData,
dataset_test: FederatedDatasetData,
initial_model_state,
after_round_evaluation):
RANDOM = random.Random(context.seed)
# Randomly swap labels
if context.swap_labels:
dataset_train = swap_labels(
fed_dataset=dataset_train,
max_classes_per_client=62,
random_seed=context.seed
)
if dataset_test is not None:
dataset_test = swap_labels(
fed_dataset=dataset_test,
max_classes_per_client=62,
random_seed=context.seed
)
# Set up clients
train_clients = initialize_clients(
dataset=dataset_train,
model_args=context.inner_model_args,
context=context.name,
experiment_logger=context.experiment_logger
)
if dataset_test is not None:
test_clients = initialize_clients(
dataset=dataset_test,
model_args=context.inner_model_args,
context=context.name,
experiment_logger=context.experiment_logger
)
else:
test_clients = None
# Set up server
server = ReptileServer(
participant_name='initial_server',
model_args=context.meta_model_args,
context=context.name,
initial_model_state=initial_model_state
)
# Perform training
for i in range(context.num_meta_steps):
if context.meta_batch_size == -1:
meta_batch = train_clients
else:
meta_batch = [
train_clients[k] for k in cyclerange(
start=i*context.meta_batch_size % len(train_clients),
interval=context.meta_batch_size,
total_len=len(train_clients)
)
]
# Meta training step
reptile_train_step(
aggregator=server,
participants=meta_batch,
inner_training_args=context.get_inner_training_args(),
meta_training_args=context.get_meta_training_args(
frac_done=i / context.num_meta_steps
)
)
# Evaluation on train and test clients
if i % context.eval_interval == 0:
# Pick train / test clients at random and test on them
losses, accs = [], []
for client_set in [train_clients, test_clients]:
if client_set:
if context.num_eval_clients_training == -1:
clients = client_set
else:
clients = RANDOM.sample(
client_set, context.num_eval_clients_training
)
reptile_train_step(
aggregator=server,
participants=clients,
inner_training_args=context.get_inner_training_args(eval=True),
evaluation_mode=True
)
result = evaluate_local_models(participants=clients)
losses.append(result.get('test/loss'))
accs.append(result.get('test/acc'))
else:
losses.append(None)
accs.append(None)
# Log
if after_round_evaluation is not None:
for c in after_round_evaluation:
c('', losses[0], accs[0], losses[1], accs[1], i)
logger.info('finished training round')
if context.do_final_evaluation:
# Final evaluation on subsample of train / test clients
losses, accs = [], []
for client_set in [train_clients, test_clients]:
if client_set:
if context.num_eval_clients_final == -1:
eval_clients = client_set
else:
eval_clients = RANDOM.sample(
client_set, context.num_eval_clients_final
)
reptile_train_step(
aggregator=server,
participants=eval_clients,
inner_training_args=context.get_inner_training_args(eval=True),
evaluation_mode=True
)
result = evaluate_local_models(participants=eval_clients)
losses.append(result.get('test/loss'))
accs.append(result.get('test/acc'))
else:
losses.append(None)
accs.append(None)
# Log
if after_round_evaluation is not None:
for c in after_round_evaluation:
c('final_', losses[0], accs[0], losses[1], accs[1], 0)