def load_state(self):
# Load the state on rank 0:
if state is not None and hvd.rank() == 0:
self.load_state(state)
# Broadcast the global step:
self._global_step = hvd.broadcast_object(self._global_step,
root_rank=0)
# Broadcast the state of the model:
hvd.broadcast_parameters(self._net.state_dict(), root_rank=0)
# Broadcast the optimizer state:
hvd.broadcast_optimizer_state(self._opt, root_rank=0)
# Horovod doesn't actually move the optimizer onto a GPU:
if self.args.compute_mode == "GPU":
for state in self._opt.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
# Broadcast the LR Schedule state:
state_dict = hvd.broadcast_object(self.lr_scheduler.state_dict(),
root_rank=0)
self.lr_scheduler.load_state_dict(state_dict)
def load_checkpoint(self, net, opt, lr_sched, ctrls, load):
if self.is_master:
self.i_epoch = 0
# look for a checkpoint
ckpt_list = os.listdir(self.dir_saves)
if len(ckpt_list) != 0:
if load == 'best': # used when evaluating performances
ckpt_file = os.path.join(self.dir_saves, 'best.ckpt')
elif load == 'last': # used when restoring crashed experiments/resuming interrupted experiments
ckpt_file = max([os.path.join(self.dir_saves, f) for f in ckpt_list], key=os.path.getctime)
else:
ckpt_file = None
# else: # used in 'WHAT IF' experiments as an initial condition
# ckpt_id = str(load).rjust(__ALIGN_EPOCHS__, '0')
# ckpt_name = 'epoch' + ckpt_id + '.ckpt'
# ckpt_file = os.path.join(self.dir_saves, ckpt_name)
# load checkpoint from file
if self.verbose:
print('Loading checkpoint {} ...'.format(ckpt_file), end='')
ckpt = torch.load(ckpt_file)
if self.verbose:
print('done!')
# restore experiment status
self.i_epoch = ckpt['fold']['i_epoch']
self.metrics.update(ckpt['fold']['metrics'])
net.load_state_dict(ckpt['network'])
opt.load_state_dict(ckpt['training']['optimizer'])
lr_sched.load_state_dict(ckpt['training']['lr_scheduler'])
for c, sd in zip(ctrls, ckpt['training']['quantize']):
c.load_state_dict(sd)
# broadcast experiment status to worker processes
self.i_epoch = hvd.broadcast_object(self.i_epoch, root_rank=__MASTER_PROC_RANK__, name='i_epoch')
self.metrics = hvd.broadcast_object(self.metrics, root_rank=__MASTER_PROC_RANK__, name='metrics')
hvd.broadcast_parameters(net.state_dict(), root_rank=__MASTER_PROC_RANK__)
hvd.broadcast_optimizer_state(opt, root_rank=__MASTER_PROC_RANK__)
lr_sched_state_dict = hvd.broadcast_object(lr_sched.state_dict(), root_rank=__MASTER_PROC_RANK__, name='lr_sched_state_dict')
if not self.is_master:
lr_sched.load_state_dict(lr_sched_state_dict)
for i, c in enumerate(ctrls):
csd = hvd.broadcast_object(c.state_dict(), root_rank=__MASTER_PROC_RANK__, name='controller{}'.format(i))
if not self.is_master:
c.load_state_dict(csd)
def restore_model(self):
if self._rank == 0:
state = self.load_state_from_file()
else:
state = None
if state is not None and self._rank == 0:
self.restore_state(state)
if self.args.framework.distributed_mode == "horovod":
# Broadcast the global step:
self._global_step = hvd.broadcast_object(self._global_step, root_rank = 0)
# Broadcast the state of the model:
hvd.broadcast_parameters(self._net.state_dict(), root_rank = 0)
# Broadcast the optimizer state:
hvd.broadcast_optimizer_state(self._opt, root_rank = 0)
# Horovod doesn't actually move the optimizer onto a GPU:
if self.args.run.compute_mode == "GPU":
for state in self._opt.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.cuda()
# Broadcast the LR Schedule state:
state_dict = hvd.broadcast_object(self.lr_scheduler.state_dict(), root_rank = 0)
elif self.args.framework.distributed_mode == "DDP":
if self.args.run.compute_mode == "GPU":
self._net.cuda()
self._net = torch.nn.parallel.DistributedDataParallel(self._net)
self._global_step = MPI.COMM_WORLD.bcast(self._global_step, root=0)
state_dict = MPI.COMM_WORLD.bcast(self.lr_scheduler.state_dict(), root=0)
# Load the state dict:
self.lr_scheduler.load_state_dict(state_dict)
def run_api_experiment(input_features, output_features, dataset, **kwargs):
config = {
"input_features": input_features,
"output_features": output_features,
"combiner": {"type": "concat", "fc_size": 14},
"training": {"epochs": 2},
}
model = LudwigModel(config)
output_dir = None
try:
# Training with csv
_, _, output_dir = model.train(dataset=dataset, **kwargs)
model.predict(dataset=dataset)
# Attempt loading saved model, should broadcast successfully
model_dir = os.path.join(output_dir, "model") if output_dir else None
loaded_model = LudwigModel.load(model_dir)
# Model loading should broadcast weights from coordinator
loaded_state = loaded_model.model.state_dict()
bcast_state = hvd.broadcast_object(loaded_state)
for loaded, bcast in zip(loaded_state.values(), bcast_state.values()):
assert np.allclose(loaded, bcast)
finally:
if output_dir:
shutil.rmtree(output_dir, ignore_errors=True)
def restore_experiment_from_checkpoint(self, checkpoint, loadModelOnly=False):
"""
Restores an experiment stats, optimizer, and model from checkpoint
Experiment is restored to CPU!
When restoring a model, look for the status flag 'quantized'
If the model has been quantized, but the experiment doesn't require quantization,
then error out.
:param checkpoint: Checkpoint path
:return: None
"""
map_device = torch.device('cpu')
#Broadcast the experiment status to all workers
if self.multiprocessing is True:
if hvd.rank() == 0:
state_dict = torch.load(checkpoint, map_location=map_device)
else:
state_dict = None
state_dict = hvd.broadcast_object(state_dict, root_rank=0, name='state_dict')
else:
state_dict = torch.load(checkpoint, map_location=map_device)
experimentStatus = state_dict['experimentStatus']
#self.experimentStatus = experimentStatus
for selfKey in self.experimentStatus.keys():
if selfKey in experimentStatus.keys():
self.experimentStatus[selfKey] = experimentStatus[selfKey]
# self.experimentStatus.numEpochTrained = 0
if loadModelOnly is False:
config = state_dict['experimentConfig']
# self.config = config
for selfKey in self.config.keys():
if selfKey in config.keys():
self.config[selfKey] = config[selfKey]
# Save the optimizer state
self.optimizerStateDict = state_dict['optimizer']
else:
self.experimentStatus.numEpochTrained = 0
self.experimentStatus.numPhaseTrained = 0
# Load the model
# If pruning and quantization are both required,
# then prune before quantize.
# Otherwise the model might be pruned twice
if experimentStatus.flagPruned is True:
# If the network has been sparsified, then it no longer has
# 'weight' as a registered buffer.
# Instead, it has weight_orig and weight_mask
# We need to allocate a sparsified network before loading the model's state dict
# The target sparsity used to allocate the "sparsified network" can be random
self.prune_network(sparsityTarget=0.0)
# Check for whether it makes sense to load a quantized experiment
# If so, quantize the model before proceed with loading
if experimentStatus.flagFusedQuantized is True:
assert self.config.quantize is True, \
'Loaded experiment contains quantized model, but the experiment config does not require quantization'
self.quantize_model()
self.restore_model_from_state_dict(state_dict['model'])
def close_fold(self):
if self.is_master:
self.writer.close()
if self.verbose:
print('Fold [{}/{}] completed'.format(self.i_fold + 1, self.config['experiment']['n_folds']))
self.i_fold += 1
# communicate current fold to worker processes
self.i_fold = hvd.broadcast_object(self.i_fold, root_rank=__MASTER_PROC_RANK__, name='i_fold_new')
def get_training_status(self):
if self.is_master:
# which fold should be resumed (i.e., the last)?
folds_list = os.listdir(self.dir_exp)
try:
i_fold = max([int(f.replace('fold', '')) for f in folds_list])
except ValueError:
i_fold = 0
self.i_fold = i_fold
# communicate current fold to worker processes
self.i_fold = hvd.broadcast_object(self.i_fold, root_rank=__MASTER_PROC_RANK__, name='i_fold')
def get_speed(self, index):
if index in self.map_sec:
return self.map_sec[index]
self.grace.memory.clean()
self.grace.compressor.clean()
self.grace.memory.partition([index])
# obtain the mean speed of multiple iterations
comm_time_per_iter = self.get_avg_comm(self.benchmark_step)
# negotiate the training speed with other workers
comm_time_per_iter = hvd.broadcast_object(comm_time_per_iter,
root_rank=0)
self.map_sec[index] = comm_time_per_iter
print("benchmark time: {:.3f} ms\tindex: {}".format(
comm_time_per_iter * 1000, index))
return comm_time_per_iter
def synchronize():
global _USE_HVD
if _USE_HVD:
hvd.broadcast_object(0)
return
return comm.synchronize()
# trainer args
parser.add_argument('--skip_output',type=int,default=10,help='epoch skip for grad/vel output')
parser.add_argument('--max_epochs',type=int,default=501,help='total number of epochs')
args = parser.parse_args()
if args.device=='cuda' and torch.cuda.is_available():
torch.cuda.set_device(hvd.local_rank())
# logging
logdir=''
if hvd.rank()==0:
logger= MainLogger(name=args.name)
logdir = logger.get_logdir()
logger.print("Inversion start: %s (log_dir=%s)"%(args.name, logdir))
logger.print("hyper parameters: %s"%args)
logdir = hvd.broadcast_object(logdir, 0)
joblogger=JobLogger(hvd.rank(),logdir)
model = TimeInv(args)
model.to(args.device)
nshot,sxy = rsf.fromfile(args.fshot,"n2 data")
dataloader= time_distributed_dataloader(args.ftrue, torch.from_numpy(sxy))
optimizer = model.configure_optimizers()
model.train()
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
for epoch in range(args.max_epochs+1):
def broadcast(self, obj, src=0):
self.barrier()
obj = hvd.broadcast_object(obj, src)
return obj
config.hvd = hvd
hvd.init()
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.deterministic = True
torch.cuda.set_device(hvd.local_rank())
config.rank = hvd.rank()
config.world = hvd.size()
if hvd.local_rank() == 0:
utils.download_model(config)
hvd.broadcast_object(0, root_rank=0)
model = x.Model(config)
start_time = time.time()
print('Loading dataset')
train_data, dev_data, test_data = utils.build_dataset(config)
train_iter = utils.build_dataloader(train_data, config)
dev_iter = utils.build_dataloader(dev_data, config)
test_iter = utils.build_dataloader(test_data, config)
time_dif = utils.get_time_dif(start_time)
print("Prepare data time: ", time_dif)
# Train, eval, test
model = model.to(config.device)
def distribute_optimizer_state(optimizer: torch.optim.Optimizer):
"""Distributes the optimizer state if horovod is available"""
if HAVE_HOROVOD:
state_dict = hvd.broadcast_object(optimizer.state_dict(), root_rank=0)
if hvd.rank() > 0:
optimizer.load_state_dict(state_dict)
def startup(gpu, args, config):
hvd.init()
torch.cuda.set_device(hvd.local_rank())
torch.manual_seed(7)
args.rank = hvd.rank()
# args.rank = args.nr * args.gpus + gpu
# print('rank', args.rank, 'gpu', gpu, 'worldsize', args.world_size)
# distributed.init_process_group(
# backend='nccl', init_method='env://', world_size=args.world_size, rank=args.rank)
torch.set_num_threads(1)
experiment = setup_comet_ml(args, args.rank)
# model
model = Transducer(config)
if config.model.random_init:
for param in model.parameters():
torch.nn.init.uniform(param, -0.1, 0.1)
model.preload(config.model.preload_from)
model.preload_lm(config.model.dec.pretrain_file)
model.cuda()
# torch.cuda.set_device(gpu)
# model = parallel.DistributedDataParallel(model, device_ids=[gpu])
# data
d_params = Data.parameters
d_params['freq_mask'] = config.data.freq_mask
d_params['time_mask'] = config.data.time_mask
train_dataset = Data(
mean=config.data.mean, std=config.data.std,
json_path=config.data.train_json,
order_time_feature=True,
tokenizer=config.model.tokenizer,
bpe_size=config.model.bpe_size,
cache_dir=config.model.bpe_cache_dir,
adaptive_specaug=config.data.adaptive_specaug,
time_repeats=config.data.time_repeats,
**d_params
)
test_dataset = Data(
mean=config.data.mean, std=config.data.std,
json_path=config.data.valid_json,
order_time_feature=True,
tokenizer=config.model.tokenizer,
bpe_size=config.model.bpe_size,
cache_dir=config.model.bpe_cache_dir,
adaptive_specaug=config.data.adaptive_specaug,
time_repeats=config.data.time_repeats,
**d_params, valid=True
)
train_sampler = data.distributed.DistributedSampler(train_dataset,
num_replicas=hvd.size(),
rank=hvd.rank())
test_sampler = data.distributed.DistributedSampler(test_dataset,
num_replicas=hvd.size(),
rank=hvd.rank())
train_loader = data.DataLoader(dataset=train_dataset,
batch_size=config.dist_train.batch_size,
shuffle=train_sampler is None,
num_workers=args.data_workers,
pin_memory=True,
collate_fn=collate_fn_padd if config.model.type == "BasicRNNT" else collate_fn_padd_order,
drop_last=True,
sampler=train_sampler)
test_loader = data.DataLoader(dataset=test_dataset,
batch_size=config.dist_train.batch_size,
shuffle=False,
num_workers=args.data_workers,
collate_fn=collate_fn_padd if config.model.type == "BasicRNNT" else collate_fn_padd_order,
drop_last=True,
pin_memory=True,
sampler=test_sampler)
# freeze encoder if specified
if config.model.enc.freeze:
for param in model.encoder.parameters():
param.requires_grad = False
# define optimizer and loss
optimizer = optim.AdamW(
filter(lambda p: p.requires_grad, model.parameters()),
lr=config.dist_train.learning_rate,
weight_decay=config.dist_train.weight_decay
)
scheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=config.dist_train.learning_rate,
steps_per_epoch=int(len(train_loader) / config.dist_train.grad_acc_steps),
epochs=config.dist_train.epochs,
div_factor=config.dist_train.div_factor,
pct_start=config.dist_train.pct_start,
anneal_strategy='linear')
optimizer = hvd.DistributedOptimizer(
optimizer, named_parameters=model.named_parameters(),
backward_passes_per_step=config.dist_train.grad_acc_steps,
op=hvd.Adasum if args.use_adasum else hvd.Average)
if args.load_model_from and args.rank == 0:
print("LOADING MODEL FROM", args.load_model_from)
checkpoint = torch.load(args.load_model_from)
model.load_state_dict(checkpoint['model_state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
# logging in terminal and comet
h_params = {}
h_params.update({
"batch_size": config.dist_train.batch_size,
"grad_acc": config.dist_train.grad_acc_steps,
"virtual_batch_size": config.dist_train.batch_size * config.dist_train.grad_acc_steps,
"learning_rate": config.dist_train.learning_rate,
"optimizer": optimizer.__class__.__name__,
"scheduler": scheduler.__class__.__name__,
})
num_params = sum([param.nelement() for param in model.parameters()])
if args.rank == 0:
print(model)
print(h_params)
print(d_params)
print('number of model parameters: ', num_params)
print("\n train dataset summary \n", train_dataset.describe())
print("\n test dataset summary \n", test_dataset.describe())
print("\n data transforms \n", train_dataset.audio_transforms, test_dataset.audio_transforms)
if args.rank == 0:
experiment.log_parameters(h_params)
experiment.log_parameters(d_params)
experiment.set_name(config.comet_info.exp_name) # experiment name
experiment.log_others(vars(args))
experiment.log_other('train_summary', str(train_dataset.describe()))
experiment.log_other('test_summary', str(test_dataset.describe()))
experiment.log_other('train_data_transforms', str(train_dataset.audio_transforms))
experiment.log_other('valid_data_transforms', str(test_dataset.audio_transforms))
experiment.log_other('n_model_params', num_params)
# save args to file
if args.rank == 0:
ckpt_dir = os.path.join(config.dist_train.checkpoint_dir, config.comet_info.exp_name)
if not os.path.isdir(ckpt_dir):
os.makedirs(ckpt_dir)
params_file = os.path.join(ckpt_dir, "args.txt")
pretty_json = json.dumps(vars(args), sort_keys=True, indent=4)
with open(params_file, 'w+') as f:
f.write(pretty_json)
print(pretty_json)
# # resume from checkpoint
# distributed.barrier() # block processes until enter loading
args.start_epoch = 1
args.start_step = 1
args.total_iter = 0
if args.resume_from:
if args.rank == 0:
print("LOADING FROM CHECKPOINT...", args.resume_from)
checkpoint = torch.load(args.resume_from, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['model_state_dict'])
model.cuda()
# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
args.start_epoch = checkpoint['epoch']
if args.resume_step > 0:
args.start_step = args.resume_step
else:
args.start_step = checkpoint['step']
args.total_iter = checkpoint['total_iter']
# distributed.barrier() # block process until finish loading
print('broadcasting model state')
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
print('broadcasting optimizer state..')
# new_optimizer_state = hvd.broadcast_object(optimizer.state_dict(), root_rank=0)
# optimizer.load_state_dict(new_optimizer_state)
print('broadcasting scheduler state..')
new_scheduler_state = hvd.broadcast_object(scheduler.state_dict(), root_rank=0)
scheduler.load_state_dict(new_scheduler_state)
print('broadcasting other args')
args.start_epoch = hvd.broadcast_object(args.start_epoch, root_rank=0)
args.start_step = hvd.broadcast_object(args.start_step, root_rank=0)
args.total_iter = hvd.broadcast_object(args.total_iter, root_rank=0)
train(args, args.rank, experiment, model, optimizer, scheduler, train_loader, test_loader, train_sampler, config)
def __init__(self, configFile, multiprocessing=False):
"""
Initialize the basic configuration of an experiment object.
Children initialization implementations should also
1) Instantiate and initialize model
:param configFile:
:param multiprocessing:
"""
# TODO: Children should provide their own init method
# 1) Load configuration file
# 2) Instantiate data loader and samplers
# 3) Instantiate train and validation meters
# Experiment states initialization
status = generate_experiment_status()
self.experimentStatus = status
self.multiprocessing = multiprocessing
qatRoundedConfig = torch.quantization.FakeQuantize.with_args(
observer=custom_quant.RoundedMovingAverageMinMaxObserver,
quant_min=-128,
quant_max=127,
averaging_constant=0.01
)
self.qatConfig = torch.quantization.QConfig(
activation=qatRoundedConfig
, weight=qatRoundedConfig
)
# Placeholder reference to optimizer state
# To be populated if restoring the experiment from checkpoint
self.optimizerStateDict = None
# TODO: Initialize these in the concrete __init__() method of each derived class
self.model = None
self.trainDataSet = None
self.trainDataLoader = None
self.trainDataSampler = None
self.valDataSet = None
self.valDataLoader = None
self.valDataSampler = None
self.logWriter = None
self.trainMeter = None
self.valMeter = None
self.trainTimeMeter = None
# Load experiment setting from config file
config = generate_base_config()
if (multiprocessing is False) or (multiprocessing is True and hvd.rank() == 0):
try:
file = open(configFile, "r")
except IOError:
raise ValueError("The provided configuration file cannot be opened.")
with file:
yamlConfig = yaml.load(file, Loader=yaml.FullLoader)
config = edict(yamlConfig)
# Broadcast the configuration to the workers during multiprocessing
if multiprocessing is True:
config = hvd.broadcast_object(obj=config, root_rank=0, name='config')
self.config = config
torch.manual_seed(self.config.seed)
# Set intra-op parallelism threads
torch.set_num_threads(self.config.numThreadsPerWorker)
def broadcast(self, obj, src=0):
obj = hvd.broadcast_object(obj, src)
return obj
def _setup_experiment(self, exp_id):
"""Get pointers to the data and experiment folders.
Args:
exp_id (str): The decimal literal identifying the experiment.
"""
if self.is_master:
QUANT_HOME = sys.path[0]
# get pointers to HARD SHARED resources
HARD_STORAGE = os.path.join(QUANT_HOME, 'cfg', 'hard_storage.json')
with open(HARD_STORAGE, 'r') as fp:
d = json.load(fp)
# data
HARD_HOME_DATA = os.path.join(d['data'], 'Quant')
HARD_DIR_DATA = os.path.join(HARD_HOME_DATA, 'problems', self.problem, 'data')
if not os.path.isdir(HARD_DIR_DATA):
raise FileNotFoundError('{} hard directory (data) not found: {}'.format(self.problem, HARD_DIR_DATA))
# log
HARD_HOME_LOGS = os.path.join(d['logs'], 'Quant')
HARD_DIR_LOGS = os.path.join(HARD_HOME_LOGS, 'problems', self.problem, 'logs')
if not os.path.isdir(HARD_DIR_LOGS):
raise FileNotFoundError('{} hard directory (logs) not found: {}'.format(self.problem, HARD_DIR_LOGS))
# get pointers to SOFT SHARED resources (which are redirected to HARD ones using symlinks)
DIR_PROBLEM = os.path.join(QUANT_HOME, 'problems', self.problem)
dir_data = os.path.join(DIR_PROBLEM, 'data')
if not os.path.isdir(dir_data):
os.symlink(HARD_DIR_DATA, dir_data)
dir_logs = os.path.join(DIR_PROBLEM, 'logs')
if not os.path.isdir(dir_logs):
os.symlink(HARD_DIR_LOGS, dir_logs)
# get pointers to PRIVATE experiment resources
if exp_id:
# retrieve an existing report
exp_id = int(exp_id)
else:
# create a new report
exp_folders = [f for f in os.listdir(dir_logs) if f.startswith('exp')]
if len(exp_folders) == 0:
exp_id = 0
else:
exp_id = max(int(f.replace('exp', '')) for f in exp_folders) + 1
dir_exp = os.path.join(dir_logs, 'exp'+str(exp_id).rjust(__ALIGN_EXP__, '0'))
if not os.path.isdir(dir_exp):
os.mkdir(dir_exp)
self.dir_data = dir_data
self.dir_exp = dir_exp
if self.verbose:
# print setup message
message = 'EXPERIMENT LOGBOOK\n'
message += 'Problem: {}\n'.format(self.problem)
message += 'Network topology: {}\n'.format(self.topology)
message += 'Data directory: {}\n'.format(self.dir_data)
message += 'Experiment directory: {}\n'.format(self.dir_exp)
def print_message(message):
"""Print a nice delimiter around a multiline message."""
lines = message.splitlines()
tab_size = 4
width = max(len(l) for l in lines) + tab_size
print('+' + '-' * width + '+')
for l in lines:
print(l)
print('+' + '-' * width + '+')
print_message(message)
# load configuration
private_config_file = os.path.join(self.dir_exp, 'config.json')
if not os.path.isfile(private_config_file):
# no configuration in the experiment folder: look for global one
shared_config_file = os.path.join(os.path.dirname(self.lib.__file__), 'config.json')
if not os.path.isfile(shared_config_file):
raise FileNotFoundError('Configuration file not found: {}'.format(shared_config_file))
shutil.copyfile(shared_config_file, private_config_file)
# generate seed for experiment
with open(private_config_file, 'r+') as fp:
config = json.load(fp)
config['experiment']['seed'] = torch.randint(__MAX_SEED__, (1,)).item()
fp.seek(0)
json.dump(config, fp, indent=4)
fp.truncate()
with open(private_config_file, 'r') as fp:
self.config = json.load(fp)
# communicate data pointer and experiment configuration to worker processes
self.dir_data = hvd.broadcast_object(self.dir_data, root_rank=__MASTER_PROC_RANK__, name='dir_data')
self.config = hvd.broadcast_object(self.config, root_rank=__MASTER_PROC_RANK__, name='config')
def broadcast(self, obj: object, src: int = 0) -> object:
obj = hvd.broadcast_object(obj, src)
return obj
def train(self):
dset = ConcatDataset(
[eval(cls)(**params) for cls, params in self.dataset])
# eval(cls) means to call the Dataset,e.g:DAVISDataset
# (**params) means to delivery the initial params[dict] into Dataset. e.g:DAVISDataset(params)
# Finally, concat these Datasets.
# Partition dataset among workers using DistributedSampler
train_sampler = torch.utils.data.distributed.DistributedSampler(
dset, num_replicas=hvd.size(), rank=hvd.rank())
loader = DataLoader(dset,
batch_size=self.batch_size,
sampler=train_sampler,
num_workers=self.num_workers,
pin_memory=True,
shuffle=False)
# Add Horovod Distributed Optimizer
backward_passes_per_step = dset.datasets[
0].sample_size - 1 # e.g:3 frames has 2 backward()
self.optimizer = hvd.DistributedOptimizer(
self.optimizer,
named_parameters=self.model.named_parameters(),
backward_passes_per_step=backward_passes_per_step)
# Broadcast parameters from rank 0 to all other processes.
hvd.broadcast_parameters(self.model.state_dict(), root_rank=0)
for epoch in range(self.epoch + 1, self.max_epochs + 1):
self.epoch = epoch
self.stats = ddict(AverageMeter)
t0 = None
runtime = AverageMeter()
for i, batch in enumerate(loader, 1):
t0 = time(
) if t0 is None else t0 # Ignore loader startup pause
self.optimizer.zero_grad()
stats = self.model(*batch)
self.optimizer.step()
runtime.update(time() - t0)
t0 = time()
stats['stats/lr'] = self.scheduler.get_last_lr()[0]
self.update_stats(stats,
i,
len(loader),
runtime,
do_print=True)
if hvd.rank() == 0:
self.log_stats() # tensorboard
self.scheduler.step()
lr_dict = hvd.broadcast_object(self.scheduler.state_dict(), 0)
if hvd.rank() > 0:
self.scheduler.load_state_dict(lr_dict)
if self.epoch % self.save_interval == 0 and hvd.rank() == 0:
self.save_checkpoint()
print("%s done" % self.name)
