class ApexDistributeModel(object):
def __init__(self, model, criterion, optimizer, args, gpu=None):
super(ApexDistributeModel, self).__init__()
self.model = model
self.args = args
self.sync_bn = self.args.sync_bn
self.gpu = gpu
if self.gpu is not None:
assert isinstance(self.gpu, int), "GPU should is a int type."
self.criterion = criterion
self.optimizer = optimizer
self.opt_level = None
def convert(self, opt_level='O0'):
self.opt_level = opt_level
if self.sync_bn:
# synchronization batch normal
self.model = apex.parallel.convert_syncbn_model(self.model)
# assign specific gpu
self.model = self.model.cuda(self.gpu)
self.criterion = self.criterion.cuda(self.gpu)
# init model and optimizer by apex
self.model, self.optimizer = apex.amp.initialize(self.model, self.optimizer,
opt_level=self.opt_level)
# apex parallel
self.model = apex.parallel.DistributedDataParallel(self.model, delay_allreduce=True)
# self.model = apex.parallel.DistributedDataParallel(self.model)
self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[self.gpu])
return self.model, self.criterion, self.optimizer
def lars(self):
print("Enable LARS Optimizer Algorithm")
self.optimizer = LARS(self.optimizer)
def train(self, epoch, train_loader, max_batches, train_index):
"""
you must run it after the 'convert' function.
:param epoch:
:param train_loader:
:return:
"""
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
self.model.train()
train_log_file = 'train_log'
end = time.time()
epoch_batch = epoch * (len(train_loader.dataset) / self.args.batch_size)
for i, (inputs, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if train_index != -1 and i < train_index:
continue
adjust_learning_rate_epoch_poly(self.optimizer, self.args, burn_in=Darknet53.burn_in, power=Darknet53.power,
batch_num=i + epoch_batch, max_batches=max_batches)
inputs = inputs.cuda(self.gpu, non_blocking=True)
target = target.cuda(self.gpu, non_blocking=True)
# compute output
output = self.model(inputs)
loss = self.criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(acc1[0], inputs.size(0))
top5.update(acc5[0], inputs.size(0))
# compute gradient and do SGD step
self.optimizer.zero_grad()
# loss.backward()
time1 = time.time()
with apex.amp.scale_loss(loss, self.optimizer) as scale_loss:
scale_loss.backward()
time2 = time.time()
self.optimizer.step()
time3 = time.time()
print("step cost time: {}, backward cost time: {}".format(time3-time2, time2-time1))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % self.args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
if i % 1000 == 0 and i:
save_checkpoint({
'epoch': epoch,
'index': i,
'arch': "Darknet_53",
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
}, False, filename='darknet_53_init_55_pytorch_train_tmp.pth.tar')
with open(train_log_file, 'a+') as log_file:
log_file.write('Epoch:{0}, Loss{loss.avg:.4f}, Top1:{top1.avg:.3f},Top5:{top5.avg:.3f}\n'.format(
epoch, loss=losses, top1=top1, top5=top5))
class ApexDistributeModel(object):
def __init__(self, model, criterion, optimizer, args, gpu=None):
super(ApexDistributeModel, self).__init__()
self.model = model
self.args = args
self.sync_bn = self.args.sync_bn
self.gpu = gpu
if self.gpu is not None:
assert isinstance(self.gpu, int), "GPU should is a int type."
self.criterion = criterion
self.optimizer = optimizer
self.opt_level = None
def convert(self, opt_level='O0'):
self.opt_level = opt_level
if self.sync_bn:
# synchronization batch normal
self.model = apex.parallel.convert_syncbn_model(self.model)
# assign specific gpu
self.model = self.model.cuda(self.gpu)
# self.criterion = self.criterion.cuda(self.gpu)
# init model and optimizer by apex
# self.model, self.optimizer = apex.amp.initialize(self.model, self.optimizer,
# opt_level=self.opt_level)
# apex parallel
# self.model = apex.parallel.DistributedDataParallel(self.model, delay_allreduce=True)
# self.model = apex.parallel.DistributedDataParallel(self.model)
self.model = nn.parallel.DistributedDataParallel(self.model,
device_ids=[self.gpu],
bucket_cap_mb=10)
return self.model, self.criterion, self.optimizer
def lars(self):
print("Enable LARS Optimizer Algorithm")
self.optimizer = LARS(self.optimizer)
def train(self, epoch, train_loader, max_batches, train_index):
"""
you must run it after the 'convert' function.
:param epoch:
:param train_loader:
:return:
"""
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
self.model.train()
train_log_file = 'train_log'
end = time.time()
epoch_batch = epoch * (len(train_loader.dataset) /
self.args.batch_size)
for i, (inputs, target) in enumerate(train_loader):
data_time.update(time.time() - end)
print("data iteration cost time: {}ms".format(data_time.val *
1000))
# print(target)
gpu_1 = time.time()
inputs = inputs.cuda(self.gpu, non_blocking=True)
target = target.cuda(self.gpu, non_blocking=True)
gpu_2 = time.time()
print("convert datasets to gpu cost time: {}ms".format(
(gpu_2 - gpu_1) * 1000))
inference_1 = time.time()
output = self.model(inputs)
inference_2 = time.time()
print("inference time cost: {}ms".format(
(inference_2 - inference_1) * 1000))
loss_1 = time.time()
loss = self.criterion(output, target)
loss_2 = time.time()
print("loss cost time: {}ms".format((loss_2 - loss_1) * 1000))
zero_1 = time.time()
self.optimizer.zero_grad()
zero_2 = time.time()
print("zero cost time: {}ms".format((zero_2 - zero_1) * 1000))
backward_1 = time.time()
loss.backward()
backward_2 = time.time()
print("backward cost time: {}ms".format(
(backward_2 - backward_1) * 1000))
step_1 = time.time()
self.optimizer.step()
step_2 = time.time()
print("step cost time: {}ms".format((step_2 - step_1) * 1000))
batch_time.update(time.time() - end)
print("total cost time is: {}s".format(batch_time.val))
# item_1 = time.time()
# print("loss is {}".format(loss.item()))
# item_2 = time.time()
# print("loss item cost: {}s".format(item_2 - item_1))
print("==================================")
end = time.time()
class ApexDistributeModel(object):
def __init__(self, model, criterion, optimizer, config, gpu=None):
super(ApexDistributeModel, self).__init__()
self.model = model
self.config = config
self.sync_bn = self.config['sync_bn']
self.gpu = gpu
if self.gpu is not None:
assert isinstance(self.gpu, int), "GPU should is a int type."
self.criterion = criterion
self.optimizer = optimizer
self.opt_level = None
def convert(self, opt_level='O0'):
self.opt_level = opt_level
if self.sync_bn:
# synchronization batch normal
self.model = apex.parallel.convert_syncbn_model(self.model)
# assign specific gpu
self.model = self.model.cuda(self.gpu)
self.criterion = self.criterion.cuda(self.gpu)
# init model and optimizer by apex
self.model, self.optimizer = apex.amp.initialize(
self.model, self.optimizer, opt_level=self.opt_level)
# apex parallel
self.model = apex.parallel.DistributedDataParallel(
self.model, delay_allreduce=True)
return self.model, self.criterion, self.optimizer
def lars(self):
self.optimizer = LARS(self.optimizer)
def train(self, epoch, train_loader):
"""
you must run it after the 'convert' function.
:param epoch:
:param train_loader:
:return:
"""
self.model.train()
print("Epoch is {}".format(epoch))
train_iter = iter(train_loader)
inputs, target = next(train_iter)
step = 0
start_time = time.time()
while inputs is not None:
step += 1
inputs = inputs.cuda(self.gpu, non_blocking=True)
target = target.cuda(self.gpu, non_blocking=True)
output = self.model(inputs)
loss = self.criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
self.optimizer.zero_grad()
with apex.amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
self.optimizer.step()
inputs, target = next(train_iter, (None, None))
if step % 10 == 0:
end_time = time.time()
print("Step is {}, cost time: {}, loss: {}, acc1: {}, acc5:{}".
format(step, (end_time - start_time), loss.item(),
acc1.item(), acc5.item()))
start_time = time.time()
def main(is_distributed, rank, ip, sync_bn):
world_size = 1
if is_distributed:
world_size = 2
torch.distributed.init_process_group(backend='nccl',
init_method=ip,
world_size=world_size,
rank=rank)
assert torch.backends.cudnn.enabled, "Amp requires cudnn backend to be enabled."
print("Connect")
# set hyper parameters
batch_size = 128
lr = 0.01 # base on batch size 256
momentum = 0.9
weight_decay = 0.0001
epoch = 100
# recompute lr
lr = lr * world_size
# create model
model = AlexNet(10)
# synchronization batch normal
if sync_bn:
model = apex.parallel.convert_syncbn_model(model)
model = model.cuda()
# define loss function
criterion = nn.CrossEntropyLoss().cuda()
# define optimizer strategy
optimizer = torch.optim.SGD(model.parameters(),
lr,
momentum=momentum,
weight_decay=weight_decay)
model, optimizer = apex.amp.initialize(model, optimizer, opt_level='O0')
optimizer = LARS(optimizer)
if is_distributed:
# for distribute training
model = nn.parallel.DistributedDataParallel(model)
# model = apex.parallel.DistributedDataParallel(model, delay_allreduce=True)
# load train data
data_path = '~/datasets/cifar10/train'
train_set = LoadClassifyDataSets(data_path, 227)
train_sampler = None
if is_distributed:
train_sampler = distributed.DistributedSampler(train_set)
train_loader = DataLoader(train_set,
batch_size,
shuffle=(train_sampler is None),
num_workers=4,
pin_memory=True,
sampler=train_sampler,
collate_fn=collate_fn)
for epoch in range(100):
# for distribute
if is_distributed:
train_sampler.set_epoch(epoch)
model.train()
# train_iter = iter(train_loader)
# inputs, target = next(train_iter)
prefetcher = DataPrefetcher(train_loader)
inputs, target = prefetcher.next()
step = 0
print("Epoch is {}".format(epoch))
while inputs is not None:
step += 1
print("Step is {}".format(step))
time_model_1 = time.time()
output = model(inputs)
time_model_2 = time.time()
print("model time: {}".format(time_model_2 - time_model_1))
time_loss_1 = time.time()
loss = criterion(output, target.cuda(async=True))
time_loss_2 = time.time()
print("loss time: {}".format(time_loss_2 - time_loss_1))
optimizer.zero_grad()
time_back_1 = time.time()
# loss.backward()
with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
time_back_2 = time.time()
print("back time: {}".format(time_back_2 - time_back_1))
optimizer.step()
# if step % 10 == 0:
# print("loss is : {}", loss.item())
# inputs, target = next(train_iter, (None, None))
inputs, target = prefetcher.next()