def main():
# is_chief indicates this machine will do shared tasks for the cluster
# such as logging and checkpointing
# is_chief must be true only for at most 1 process in training cluster
# $RANK is set by pytorch.distributed.launch
# https://github.com/pytorch/pytorch/blob/db6e4576dab097abf01d032c3326e4b285eb8499/torch/distributed/launch.py#L193
global is_chief, event_writer, global_example_count, last_recv_bytes, last_transmit_bytes, last_log_time
is_chief = (not args.distributed) or (int(os.environ['RANK'])==0)
global_example_count = 0
if is_chief:
print(f"Logging to {args.logdir}")
event_writer = SummaryWriter(args.logdir)
log_tb("first", time.time())
else:
event_writer = NoOp()
# baseline number for network bytes
last_recv_bytes, last_transmit_bytes = network_bytes()
last_log_time = time.time()
print(args)
print("~~epoch\thours\ttop1Accuracy\n")
# need to index validation directory before we start counting the time
dataloader.sort_ar(args.data+'/validation')
global reduce_function
if args.c10d:
print('Distributed: loading c10d process group')
# https://github.com/pytorch/pytorch/blob/master/torch/lib/c10d/TCPStore.hpp
torch.cuda.set_device(args.local_rank)
rank = int(os.environ['RANK'])
store = c10d.TCPStore(os.environ['MASTER_ADDR'], int(os.environ['MASTER_PORT']), rank==0) # (masterAddr, masterPort, isServer)
process_group = c10d.ProcessGroupNCCL(store, rank, args.world_size) # (store, rank, size)
reduce_function = lambda t: process_group.allreduce(t, c10d.AllreduceOptions().reduceOp)
elif args.distributed:
print('Distributed: initializing process group')
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size)
assert(args.world_size == dist.get_world_size())
reduce_function = lambda t: dist.all_reduce(t, op=dist.reduce_op.SUM)
print("Distributed: success (%d/%d)"%(args.local_rank, args.world_size))
if args.fp16: assert torch.backends.cudnn.enabled, "fp16 mode requires cudnn backend to be enabled."
print("Loading model")
if args.factorized_resnet: model = resnet.resnet50factorized(pretrained=args.pretrained)
else: model = resnet.resnet50(pretrained=args.pretrained)
model = model.cuda()
if args.init_bn0: resnet.init_dist_weights(model) # Sets batchnorm std to 0
if args.fp16: model = network_to_half(model)
best_prec5 = 93 # only save models over 92%. Otherwise it stops to save every time
# Load model from checkpoint. This must happen distributed as model is saved without it
if args.resume:
checkpoint = torch.load(args.resume, map_location = lambda storage, loc: storage.cuda(args.local_rank))
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch']
best_prec5 = checkpoint['best_prec5']
if args.c10d:
model = distributed_c10d._DistributedDataParallelC10d(model, process_group, device_ids=[args.local_rank], output_device=args.local_rank)
c10d_sanity_check()
elif args.distributed: model = nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank)
global model_params, master_params
if args.fp16: model_params, master_params = prep_param_lists(model)
else: model_params = master_params = model.parameters()
optim_params = experimental_utils.bnwd_optim_params(model, model_params, master_params) if args.no_bn_wd else master_params
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(optim_params, 0, momentum=args.momentum, weight_decay=args.weight_decay) # start with 0 lr. Scheduler will change this later
if args.resume: # we must resume optimizer params separately
checkpoint = torch.load(args.resume, map_location = lambda storage, loc: storage.cuda(args.local_rank))
optimizer.load_state_dict(checkpoint['optimizer'])
# Load data data manager and lr scheduler from phases
phases = eval(args.phases)
print("Creating data loaders (this could take 6-12 minutes)")
dm = DataManager([p for p in phases if 'bs' in p])
scheduler = Scheduler(optimizer, [p for p in phases if 'lr' in p], args.scale_lr)
start_time = datetime.now() # Loading start to after everything is loaded
if args.evaluate: return validate(dm.val_dl, model, criterion, 0, start_time)
if args.distributed:
print('Syncing machines before training')
sum_tensor(torch.tensor([1.0]).float().cuda())
print("Begin training")
estart = time.time()
for epoch in range(args.start_epoch, scheduler.tot_epochs):
estart = time.time()
dm.set_epoch(epoch)
train(dm.trn_dl, model, criterion, optimizer, scheduler, epoch)
if args.prof: break
prec5 = validate(dm.val_dl, model, criterion, epoch, start_time)
is_best = prec5 > best_prec5
best_prec5 = max(prec5, best_prec5)
if args.local_rank == 0:
if is_best: save_checkpoint(epoch, model, best_prec5, optimizer, is_best=True, filename='model_best.pth.tar')
phase = dm.get_phase(epoch)
if phase:save_checkpoint(epoch, model, best_prec5, optimizer, filename=f'sz{phase["bs"]}_checkpoint.path.tar')
event_writer.export_scalars_to_json(args.logdir+'/scalars.json')
event_writer.close()
def allreduce(x, op):
opts = c10d.AllreduceOptions()
opts.reduceOp = op
work = pg.allreduce([x], opts)
work.wait()
def __init__(self,
module,
device_ids=None,
output_device=None,
dim=0,
broadcast_buffers=True,
process_group=None,
bucket_cap_mb=25):
super(_DistributedDataParallelC10d, self).__init__()
# Use all devices by default
if device_ids is None:
device_ids = list(range(torch.cuda.device_count()))
if output_device is None:
output_device = device_ids[0]
if process_group is None:
self.process_group = c10d.get_default_group()
else:
self.process_group = process_group
self.dim = dim
self.module = module
self.device_ids = device_ids
self.output_device = output_device
self.broadcast_buffers = broadcast_buffers
self.allreduce_opts = c10d.AllreduceOptions()
MB = 1024 * 1024
# used for intra-node param sync and inter-node sync as well
self.broadcast_bucket_size = 25 * MB
# Sync params and buffers
module_states = list(self.module.state_dict().values())
if len(module_states) > 0:
self._dist_broadcast_coalesced(module_states,
self.broadcast_bucket_size)
if len(device_ids) > 1:
# TODO: we don't need to replicate params in here. they're always going to
# be broadcasted using larger blocks in broadcast_coalesced, so it might be
# better to not pollute the caches with these small blocks
self._module_copies = replicate(self.module,
self.device_ids,
detach=True)
self._module_copies[0] = self.module
for module_copy in self._module_copies[1:]:
for param, copy_param in zip(self.module.parameters(),
module_copy.parameters()):
copy_param.requires_grad = param.requires_grad
else:
self._module_copies = [self.module]
# .data() of each parameter for each model replica
self.modules_params_data = [[] for _ in range(len(self.device_ids))]
# .data() of each buffer for each model replica
self.modules_buffers_data = [[] for _ in range(len(self.device_ids))]
for dev_idx, module in enumerate(self._module_copies):
self.modules_params_data[dev_idx] = [
p.data for p in module.parameters()
]
self.modules_buffers_data[dev_idx] = [
b.data for b in module.buffers()
]
bucket_bytes_cap = bucket_cap_mb * MB
# This is a triply-nested list where the "dimensions" are: devices, buckets, bucket_elems
param_buckets = []
# Split the parameters into buckets and by types as well
param_buckets = [
list(_take_tensors(m.parameters(), bucket_bytes_cap))
for m in self._module_copies
]
self.bucket_sizes = []
self.bucket_map = {}
# We transpose param_buckets, so the loop is over buckets.
# param_buckets_tuple is a doubly-nested list with "dims": devices, bucket_elems
for bucket_idx, param_buckets_tuple in enumerate(zip(*param_buckets)):
self.bucket_sizes.append(0)
# Now, we transpose again, so we iterate over bucket_elems, but getting tuples
# of params from each device.
for idx, param_tuple in enumerate(zip(*param_buckets_tuple)):
if not param_tuple[0].requires_grad:
continue
for p in param_tuple:
self.bucket_map[p] = (bucket_idx, idx)
self.bucket_sizes[bucket_idx] += 1
self.buckets = [[[None for _ in range(self.bucket_sizes[i])]
for _ in range(len(self.device_ids))]
for i in range(len(self.bucket_sizes))]
# The number of params ready in each bucket
self.buckets_ready_size = [[0 for _ in range(len(self.device_ids))]
for i in range(len(self.bucket_sizes))]
# coalesced bucket for only device 0
self.buckets_coalesced = [[] for _ in range(len(self.bucket_sizes))]
# We will always reduce the bucket following the reverse order
# that is, alway reduces following the order of: n - 1, n - 2, ..., 0
self.next_bucket = len(self.bucket_sizes) - 1
self.ready_buckets_not_reduced = set()
self.reduction_works = [None for _ in range(len(self.bucket_sizes))]
self.devs_ready = [0 for _ in range(len(self.bucket_sizes))]
# default stream tracking to launch nccl reduce kernels
self.default_streams = []
for dev_id in self.device_ids:
with torch.cuda.device(dev_id):
self.default_streams.append(torch.cuda.current_stream())
self._register_grad_hooks()
def allreduce(tensors, op):
opts = c10d.AllreduceOptions()
opts.reduceOp = op
work = pg.allreduce(tensors, opts)
work.wait()
