def get_model(args):
# ResNet
if args.model.lower() == "resnet20":
model = resnet.resnet20()
elif args.model.lower() == "resnet32":
model = resnet.resnet32()
elif args.model.lower() == "resnet44":
model = resnet.resnet44()
elif args.model.lower() == "resnet56":
model = resnet.resnet56()
elif args.model.lower() == "resnet110":
model = resnet.resnet110()
if args.cuda:
model.cuda()
# Optimizer
criterion = nn.CrossEntropyLoss()
args.base_lr = args.base_lr * hvd.size()
optimizer = optim.SGD(model.parameters(),
lr=args.base_lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.use_kfac:
KFAC = kfac.get_kfac_module(args.kfac_name)
preconditioner = KFAC(model,
lr=args.base_lr,
factor_decay=args.stat_decay,
damping=args.damping,
kl_clip=args.kl_clip,
fac_update_freq=args.kfac_cov_update_freq,
kfac_update_freq=args.kfac_update_freq,
exclude_parts=args.exclude_parts)
kfac_param_scheduler = kfac.KFACParamScheduler(
preconditioner,
damping_alpha=1,
damping_schedule=None,
update_freq_alpha=1,
update_freq_schedule=None)
else:
preconditioner = None
# Distributed Optimizer
compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
optimizer = hvd.DistributedOptimizer(
optimizer,
named_parameters=model.named_parameters(),
compression=compression,
op=hvd.Average,
backward_passes_per_step=1)
if hvd.size() > 1:
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
# Learning Rate Schedule
lrs = create_lr_schedule(hvd.size(), args.warmup_epochs, args.lr_decay)
lr_scheduler = [LambdaLR(optimizer, lrs)]
if preconditioner is not None:
lr_scheduler.append(LambdaLR(preconditioner, lrs))
lr_scheduler.append(kfac_param_scheduler)
return model, optimizer, preconditioner, lr_scheduler, criterion
def main(args):
logfilename = 'convergence_cifar10_{}_kfac{}_gpu{}_bs{}_{}_lr{}_sr{}_wp{}.log'.format(
args.model, args.kfac_update_freq, hvd.size(), args.batch_size,
args.kfac_name, args.base_lr, args.sparse_ratio, args.warmup_epochs)
if hvd.rank() == 0:
wandb.init(project='kfac',
entity='hkust-distributedml',
name=logfilename,
config=args)
logfile = './logs/' + logfilename
#logfile = './logs/sparse_cifar10_{}_kfac{}_gpu{}_bs{}.log'.format(args.model, args.kfac_update_freq, hvd.size(), args.batch_size)
#logfile = './logs/cifar10_{}_kfac{}_gpu{}_bs{}.log'.format(args.model, args.kfac_update_freq, hvd.size(), args.batch_size)
hdlr = logging.FileHandler(logfile)
hdlr.setFormatter(formatter)
logger.addHandler(hdlr)
logger.info(args)
torch.manual_seed(args.seed)
verbose = True if hvd.rank() == 0 else False
args.verbose = 1 if hvd.rank() == 0 else 0
if args.cuda:
torch.cuda.set_device(hvd.local_rank())
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
args.log_dir = os.path.join(
args.log_dir, "cifar10_{}_kfac{}_gpu_{}_{}".format(
args.model, args.kfac_update_freq, hvd.size(),
datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')))
#os.makedirs(args.log_dir, exist_ok=True)
#log_writer = SummaryWriter(args.log_dir) if verbose else None
log_writer = None
# Horovod: limit # of CPU threads to be used per worker.
torch.set_num_threads(1)
kwargs = {'num_workers': 8, 'pin_memory': True} if args.cuda else {}
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
download = True if hvd.local_rank() == 0 else False
if not download: hvd.allreduce(torch.tensor(1), name="barrier")
train_dataset = datasets.CIFAR10(root=args.dir,
train=True,
download=download,
transform=transform_train)
test_dataset = datasets.CIFAR10(root=args.dir,
train=False,
download=download,
transform=transform_test)
if download: hvd.allreduce(torch.tensor(1), name="barrier")
# Horovod: use DistributedSampler to partition the training data.
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, num_replicas=hvd.size(), rank=hvd.rank())
#train_loader = torch.utils.data.DataLoader(train_dataset,
train_loader = MultiEpochsDataLoader(train_dataset,
batch_size=args.batch_size *
args.batches_per_allreduce,
sampler=train_sampler,
**kwargs)
# Horovod: use DistributedSampler to partition the test data.
test_sampler = torch.utils.data.distributed.DistributedSampler(
test_dataset, num_replicas=hvd.size(), rank=hvd.rank())
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.test_batch_size,
sampler=test_sampler,
**kwargs)
if args.model.lower() == "resnet20":
model = resnet.resnet20()
elif args.model.lower() == "resnet32":
model = resnet.resnet32()
elif args.model.lower() == "resnet44":
model = resnet.resnet44()
elif args.model.lower() == "resnet56":
model = resnet.resnet56()
elif args.model.lower() == "resnet110":
model = resnet.resnet110()
if args.cuda:
model.cuda()
#if verbose:
# summary(model, (3, 32, 32))
criterion = nn.CrossEntropyLoss()
args.base_lr = args.base_lr * hvd.size()
use_kfac = True if args.kfac_update_freq > 0 else False
optimizer = optim.SGD(model.parameters(),
lr=args.base_lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if use_kfac:
KFAC = kfac.get_kfac_module(args.kfac_name)
preconditioner = KFAC(
model,
lr=args.base_lr,
factor_decay=args.stat_decay,
damping=args.damping,
kl_clip=args.kl_clip,
fac_update_freq=args.kfac_cov_update_freq,
kfac_update_freq=args.kfac_update_freq,
diag_blocks=args.diag_blocks,
diag_warmup=args.diag_warmup,
distribute_layer_factors=args.distribute_layer_factors,
sparse_ratio=args.sparse_ratio)
kfac_param_scheduler = kfac.KFACParamScheduler(
preconditioner,
damping_alpha=args.damping_alpha,
damping_schedule=args.damping_schedule,
update_freq_alpha=args.kfac_update_freq_alpha,
update_freq_schedule=args.kfac_update_freq_schedule)
# KFAC guarentees grads are equal across ranks before opt.step() is called
# so if we do not use kfac we need to wrap the optimizer with horovod
compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
optimizer = hvd.DistributedOptimizer(
optimizer,
named_parameters=model.named_parameters(),
compression=compression,
op=hvd.Average,
backward_passes_per_step=args.batches_per_allreduce)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
lrs = create_lr_schedule(hvd.size(), args.warmup_epochs, args.lr_decay)
lr_scheduler = [LambdaLR(optimizer, lrs)]
if use_kfac:
lr_scheduler.append(LambdaLR(preconditioner, lrs))
def train(epoch):
model.train()
train_sampler.set_epoch(epoch)
train_loss = Metric('train_loss')
train_accuracy = Metric('train_accuracy')
if STEP_FIRST:
for scheduler in lr_scheduler:
scheduler.step()
if use_kfac:
kfac_param_scheduler.step(epoch)
# with tqdm(total=len(train_loader),
# desc='Epoch {:3d}/{:3d}'.format(epoch + 1, args.epochs),
# disable=not verbose) as t:
display = 20
avg_time = 0.0
io_time = 0.0
if True:
for batch_idx, (data, target) in enumerate(train_loader):
stime = time.time()
if args.cuda:
data, target = data.cuda(non_blocking=True), target.cuda(
non_blocking=True)
io_time += time.time() - stime
optimizer.zero_grad()
for i in range(0, len(data), args.batch_size):
data_batch = data[i:i + args.batch_size]
target_batch = target[i:i + args.batch_size]
output = model(data_batch)
loss = criterion(output, target_batch)
with torch.no_grad():
train_loss.update(loss)
train_accuracy.update(accuracy(output, target_batch))
loss.div_(math.ceil(float(len(data)) / args.batch_size))
loss.backward()
optimizer.synchronize()
if use_kfac:
preconditioner.step(epoch=epoch)
with optimizer.skip_synchronize():
optimizer.step()
#t.set_postfix_str("loss: {:.4f}, acc: {:.2f}%".format(
#train_loss.avg.item(), 100*train_accuracy.avg.item()))
#t.update(1)
avg_time += (time.time() - stime)
if batch_idx > 0 and batch_idx % display == 0:
if args.verbose:
logger.info(
"[%d][%d] train loss: %.4f, acc: %.3f, time: %.3f [io: %.3f], speed: %.3f images/s"
% (epoch, batch_idx, train_loss.avg.item(), 100 *
train_accuracy.avg.item(), avg_time / display,
io_time / display, args.batch_size /
(avg_time / display)))
avg_time = 0.0
io_time = 0.0
if hvd.rank() == 0:
wandb.log({"loss": loss, "epoch": epoch})
if args.verbose:
logger.info("[%d] epoch train loss: %.4f, acc: %.3f" %
(epoch, train_loss.avg.item(),
100 * train_accuracy.avg.item()))
if not STEP_FIRST:
for scheduler in lr_scheduler:
scheduler.step()
if use_kfac:
kfac_param_scheduler.step(epoch)
if log_writer:
log_writer.add_scalar('train/loss', train_loss.avg, epoch)
log_writer.add_scalar('train/accuracy', train_accuracy.avg, epoch)
def test(epoch):
model.eval()
test_loss = Metric('val_loss')
test_accuracy = Metric('val_accuracy')
#with tqdm(total=len(test_loader),
# bar_format='{l_bar}{bar}|{postfix}',
# desc=' '.format(epoch + 1, args.epochs),
# disable=not verbose) as t:
if True:
with torch.no_grad():
for i, (data, target) in enumerate(test_loader):
if args.cuda:
data, target = data.cuda(), target.cuda()
output = model(data)
test_loss.update(criterion(output, target))
test_accuracy.update(accuracy(output, target))
if args.verbose:
logger.info("[%d][0] evaluation loss: %.4f, acc: %.3f" %
(epoch, test_loss.avg.item(),
100 * test_accuracy.avg.item()))
if hvd.rank() == 0:
wandb.log({
"val top-1 acc": test_accuracy.avg.item(),
"epoch": epoch
})
#t.update(1)
#if i + 1 == len(test_loader):
# t.set_postfix_str("\b\b test_loss: {:.4f}, test_acc: {:.2f}%".format(
# test_loss.avg.item(), 100*test_accuracy.avg.item()),
# refresh=False)
if log_writer:
log_writer.add_scalar('test/loss', test_loss.avg, epoch)
log_writer.add_scalar('test/accuracy', test_accuracy.avg, epoch)
start = time.time()
for epoch in range(args.epochs):
if args.verbose:
logger.info("[%d] epoch train starts" % (epoch))
train(epoch)
test(epoch)
if verbose:
logger.info("Training time: %s",
str(datetime.timedelta(seconds=time.time() - start)))
pass
def get_model(args):
if args.model.lower() == 'resnet34':
model = models.resnet34()
elif args.model.lower() == 'resnet50':
model = models.resnet50()
elif args.model.lower() == 'resnet101':
model = models.resnet101()
elif args.model.lower() == 'resnet152':
model = models.resnet152()
elif args.model.lower() == 'resnext50':
model = models.resnext50_32x4d()
elif args.model.lower() == 'resnext101':
model = models.resnext101_32x8d()
else:
raise ValueError('Unknown model \'{}\''.format(args.model))
if args.cuda:
model.cuda()
# Horovod: scale learning rate by the number of GPUs.
args.base_lr = args.base_lr * hvd.size() * args.batches_per_allreduce
optimizer = optim.SGD(model.parameters(),
lr=args.base_lr,
momentum=args.momentum,
weight_decay=args.wd)
if args.kfac_update_freq > 0:
KFAC = kfac.get_kfac_module(args.kfac_name)
preconditioner = KFAC(
model,
lr=args.base_lr,
factor_decay=args.stat_decay,
damping=args.damping,
kl_clip=args.kl_clip,
fac_update_freq=args.kfac_cov_update_freq,
kfac_update_freq=args.kfac_update_freq,
diag_blocks=args.diag_blocks,
diag_warmup=args.diag_warmup,
distribute_layer_factors=args.distribute_layer_factors,
exclude_parts=args.exclude_parts)
kfac_param_scheduler = kfac.KFACParamScheduler(
preconditioner,
damping_alpha=args.damping_alpha,
damping_schedule=args.damping_decay,
update_freq_alpha=args.kfac_update_freq_alpha,
update_freq_schedule=args.kfac_update_freq_decay,
start_epoch=args.resume_from_epoch)
else:
preconditioner = None
compression = hvd.Compression.fp16 if args.fp16_allreduce \
else hvd.Compression.none
optimizer = hvd.DistributedOptimizer(
optimizer,
named_parameters=model.named_parameters(),
compression=compression,
op=hvd.Average,
backward_passes_per_step=args.batches_per_allreduce)
# Restore from a previous checkpoint, if initial_epoch is specified.
# Horovod: restore on the first worker which will broadcast weights
# to other workers.
if args.resume_from_epoch > 0 and hvd.rank() == 0:
filepath = args.checkpoint_format.format(epoch=args.resume_from_epoch)
checkpoint = torch.load(filepath)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
# Horovod: broadcast parameters & optimizer state.
if hvd.size() > 1:
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
lrs = create_lr_schedule(hvd.size(), args.warmup_epochs, args.lr_decay)
lr_scheduler = [LambdaLR(optimizer, lrs)]
if preconditioner is not None:
lr_scheduler.append(LambdaLR(preconditioner, lrs))
lr_scheduler.append(kfac_param_scheduler)
loss_func = LabelSmoothLoss(args.label_smoothing)
return model, optimizer, preconditioner, lr_scheduler, lrs, loss_func