def main():
"""
Launches data-parallel multi-gpu training.
"""
training_start = time.time()
args = parse_args()
device = utils.set_device(args.cuda, args.local_rank)
utils.init_distributed(args.cuda)
args.rank = utils.get_rank()
if not args.cudnn:
torch.backends.cudnn.enabled = False
# create directory for results
os.makedirs(args.save_dir, exist_ok=True)
# setup logging
log_filename = f'log_rank_{utils.get_rank()}.log'
utils.setup_logging(args.log_all_ranks,
os.path.join(args.save_dir, log_filename))
if args.env:
utils.log_env_info()
logging.info(f'Saving results to: {args.save_dir}')
logging.info(f'Run arguments: {args}')
args.train_iter_size = set_iter_size(args.train_iter_size,
args.train_global_batch_size,
args.train_batch_size)
worker_seeds, shuffling_seeds = utils.setup_seeds(args.seed, args.epochs,
device)
worker_seed = worker_seeds[args.rank]
logging.info(f'Worker {args.rank} is using worker seed: {worker_seed}')
torch.manual_seed(worker_seed)
# build tokenizer
pad_vocab = utils.pad_vocabulary(args.math)
tokenizer = Tokenizer(args.vocab, args.bpe_codes, args.lang, pad_vocab)
# build datasets
train_data = LazyParallelDataset(
src_fname=args.train_src,
tgt_fname=args.train_tgt,
tokenizer=tokenizer,
min_len=args.train_min_length,
max_len=args.train_max_length,
sort=False,
max_size=args.train_max_size,
)
val_data = ParallelDataset(
src_fname=args.val_src,
tgt_fname=args.val_tgt,
tokenizer=tokenizer,
min_len=args.val_min_length,
max_len=args.val_max_length,
sort=True,
)
test_data = TextDataset(
src_fname=args.test_src,
tokenizer=tokenizer,
min_len=args.test_min_length,
max_len=args.test_max_length,
sort=True,
)
vocab_size = tokenizer.vocab_size
# build GNMT model
model_config = {
'hidden_size': args.hidden_size,
'vocab_size': vocab_size,
'num_layers': args.num_layers,
'dropout': args.dropout,
'batch_first': False,
'share_embedding': args.share_embedding,
}
model = GNMT(**model_config).to(device)
logging.info(model)
batch_first = model.batch_first
# define loss function (criterion) and optimizer
criterion = build_criterion(vocab_size, config.PAD,
args.smoothing).to(device)
opt_config = {'optimizer': args.optimizer, 'lr': args.lr}
opt_config.update(literal_eval(args.optimizer_extra))
logging.info(f'Training optimizer config: {opt_config}')
scheduler_config = {
'warmup_steps': args.warmup_steps,
'remain_steps': args.remain_steps,
'decay_interval': args.decay_interval,
'decay_steps': args.decay_steps,
'decay_factor': args.decay_factor
}
logging.info(f'Training LR schedule config: {scheduler_config}')
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info(f'Number of parameters: {num_parameters}')
batching_opt = {
'shard_size': args.shard_size,
'num_buckets': args.num_buckets
}
# get data loaders
train_loader = train_data.get_loader(batch_size=args.train_batch_size,
seeds=shuffling_seeds,
batch_first=batch_first,
shuffle=True,
batching=args.batching,
batching_opt=batching_opt,
num_workers=args.train_loader_workers)
val_loader = val_data.get_loader(batch_size=args.val_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=args.val_loader_workers)
test_loader = test_data.get_loader(batch_size=args.test_batch_size,
batch_first=batch_first,
shuffle=False,
pad=True,
num_workers=args.test_loader_workers)
translator = Translator(
model=model,
tokenizer=tokenizer,
loader=test_loader,
beam_size=args.beam_size,
max_seq_len=args.test_max_length,
len_norm_factor=args.len_norm_factor,
len_norm_const=args.len_norm_const,
cov_penalty_factor=args.cov_penalty_factor,
print_freq=args.print_freq,
reference=args.test_tgt,
)
# create trainer
total_train_iters = len(train_loader) // args.train_iter_size * args.epochs
save_info = {
'model_config': model_config,
'config': args,
'tokenizer': tokenizer.get_state()
}
loss_scaling = {
'init_scale': args.init_scale,
'upscale_interval': args.upscale_interval
}
trainer_options = dict(
model=model,
criterion=criterion,
grad_clip=args.grad_clip,
iter_size=args.train_iter_size,
save_dir=args.save_dir,
save_freq=args.save_freq,
save_info=save_info,
opt_config=opt_config,
scheduler_config=scheduler_config,
train_iterations=total_train_iters,
keep_checkpoints=args.keep_checkpoints,
math=args.math,
loss_scaling=loss_scaling,
print_freq=args.print_freq,
intra_epoch_eval=args.intra_epoch_eval,
translator=translator,
prealloc_mode=args.prealloc_mode,
)
trainer = trainers.Seq2SeqTrainer(**trainer_options)
# optionally resume from a checkpoint
if args.resume:
checkpoint_file = args.resume
if os.path.isdir(checkpoint_file):
checkpoint_file = os.path.join(checkpoint_file, 'model_best.pth')
if os.path.isfile(checkpoint_file):
trainer.load(checkpoint_file)
else:
logging.error(f'No checkpoint found at {args.resume}')
# training loop
best_loss = float('inf')
training_perf = []
break_training = False
test_bleu = None
for epoch in range(args.start_epoch, args.epochs):
logging.info(f'Starting epoch {epoch}')
train_loader.sampler.set_epoch(epoch)
trainer.epoch = epoch
train_loss, train_perf = trainer.optimize(train_loader)
training_perf.append(train_perf)
# evaluate on validation set
if args.eval:
logging.info(f'Running validation on dev set')
val_loss, val_perf = trainer.evaluate(val_loader)
# remember best prec@1 and save checkpoint
if args.rank == 0:
is_best = val_loss < best_loss
best_loss = min(val_loss, best_loss)
trainer.save(save_all=args.save_all, is_best=is_best)
if args.eval:
utils.barrier()
eval_fname = f'eval_epoch_{epoch}'
eval_path = os.path.join(args.save_dir, eval_fname)
_, eval_stats = translator.run(
calc_bleu=True,
epoch=epoch,
eval_path=eval_path,
)
test_bleu = eval_stats['bleu']
if args.target_bleu and test_bleu >= args.target_bleu:
logging.info(f'Target accuracy reached')
break_training = True
acc_log = []
acc_log += [f'Summary: Epoch: {epoch}']
acc_log += [f'Training Loss: {train_loss:.4f}']
if args.eval:
acc_log += [f'Validation Loss: {val_loss:.4f}']
acc_log += [f'Test BLEU: {test_bleu:.2f}']
perf_log = []
perf_log += [f'Performance: Epoch: {epoch}']
perf_log += [f'Training: {train_perf:.0f} Tok/s']
if args.eval:
perf_log += [f'Validation: {val_perf:.0f} Tok/s']
if args.rank == 0:
logging.info('\t'.join(acc_log))
logging.info('\t'.join(perf_log))
logging.info(f'Finished epoch {epoch}')
if break_training:
break
utils.barrier()
training_stop = time.time()
training_time = training_stop - training_start
logging.info(f'Total training time {training_time:.0f} s')
table = TrainingTable()
avg_training_perf = sum(training_perf) / len(training_perf)
table.add(utils.get_world_size(), args.train_batch_size, test_bleu,
avg_training_perf, training_time)
if utils.get_rank() == 0:
table.write('Training Summary', args.math)
passed = utils.benchmark(test_bleu, args.target_bleu, train_perf,
args.target_perf)
if not passed:
sys.exit(1)
def main():
"""
Launches data-parallel multi-gpu training.
"""
mlperf_log.ROOT_DIR_GNMT = os.path.dirname(os.path.abspath(__file__))
mlperf_log.LOGGER.propagate = False
args = parse_args()
if args.cuda:
torch.cuda.set_device(args.local_rank)
device = torch.device('cuda')
else:
device = torch.device('cpu')
# initialize distributed backend
distributed = False
if 'WORLD_SIZE' in os.environ:
distributed = int(os.environ['WORLD_SIZE']) > 1
if distributed:
assert args.cuda
'''Initialize distributed communication'''
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
assert torch.distributed.is_initialized()
gnmt_print(key=mlperf_log.RUN_START)
args.rank = get_rank()
if not args.cudnn:
torch.backends.cudnn.enabled = False
# create directory for results
save_path = os.path.join(args.results_dir, args.save)
args.save_path = save_path
os.makedirs(save_path, exist_ok=True)
# setup logging
log_filename = f'log_gpu_{args.rank}.log'
setup_logging(os.path.join(save_path, log_filename))
logging.info(f'Saving results to: {save_path}')
logging.info(f'Run arguments: {args}')
# setup L2 promotion
if args.cuda:
l2_promote()
gnmt_print(key=mlperf_log.RUN_SET_RANDOM_SEED)
# https://github.com/mlperf/policies/issues/120#issuecomment-431111348
if args.seed is None:
# random master seed, random.SystemRandom() uses /dev/urandom on Unix
master_seed = random.SystemRandom().randint(0, 2**32 - 1)
if get_rank() == 0:
# master seed is reported only from rank=0 worker, it's to avoid
# confusion, seeds from rank=0 are later broadcasted to other
# workers
logging.info(f'Using random master seed: {master_seed}')
else:
# master seed was specified from command line
master_seed = args.seed
logging.info(f'Using master seed from command line: {master_seed}')
# initialize seeding RNG
seeding_rng = random.Random(master_seed)
# generate worker seeds, one seed for every distributed worker
worker_seeds = generate_seeds(seeding_rng, get_world_size())
# generate seeds for data shuffling, one seed for every epoch
shuffling_seeds = generate_seeds(seeding_rng, args.epochs)
# broadcast seeds from rank=0 to other workers
worker_seeds = broadcast_seeds(worker_seeds, device)
shuffling_seeds = broadcast_seeds(shuffling_seeds, device)
# set worker seed
worker_seed = worker_seeds[args.rank]
logging.info(f'Worker {args.rank} is using worker seed: {worker_seed}')
torch.manual_seed(worker_seed)
# build tokenizer
tokenizer = Tokenizer(os.path.join(args.dataset_dir, config.VOCAB_FNAME))
# build datasets
gnmt_print(key=mlperf_log.PREPROC_TOKENIZE_TRAINING)
gnmt_print(key=mlperf_log.TRAIN_HP_MAX_SEQ_LEN,
value=args.max_length_train)
train_data = LazyParallelDataset(
src_fname=os.path.join(args.dataset_dir, config.SRC_TRAIN_FNAME),
tgt_fname=os.path.join(args.dataset_dir, config.TGT_TRAIN_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_train,
max_len=args.max_length_train,
sort=False,
max_size=args.max_size)
gnmt_print(key=mlperf_log.PREPROC_NUM_TRAIN_EXAMPLES,
value=len(train_data))
val_data = ParallelDataset(src_fname=os.path.join(args.dataset_dir,
config.SRC_VAL_FNAME),
tgt_fname=os.path.join(args.dataset_dir,
config.TGT_VAL_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_val,
max_len=args.max_length_val,
sort=True)
gnmt_print(key=mlperf_log.PREPROC_TOKENIZE_EVAL)
test_data = TextDataset(src_fname=os.path.join(args.dataset_dir,
config.SRC_TEST_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_test,
max_len=args.max_length_test,
sort=False)
gnmt_print(key=mlperf_log.PREPROC_NUM_EVAL_EXAMPLES, value=len(test_data))
vocab_size = tokenizer.vocab_size
# size of the vocabulary has been padded to a multiple of 8
gnmt_print(key=mlperf_log.PREPROC_VOCAB_SIZE, value=vocab_size)
# build GNMT model
model_config = dict(vocab_size=vocab_size,
math=args.math,
**literal_eval(args.model_config))
model = GNMT(**model_config)
logging.info(model)
batch_first = model.batch_first
# define loss function (criterion) and optimizer
criterion = build_criterion(vocab_size, config.PAD, args.smoothing)
opt_config = literal_eval(args.optimization_config)
scheduler_config = literal_eval(args.scheduler_config)
logging.info(f'Training optimizer: {opt_config}')
logging.info(f'Training LR Schedule: {scheduler_config}')
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info(f'Number of parameters: {num_parameters}')
# get data loaders
train_loader = train_data.get_loader(batch_size=args.batch_size,
seeds=shuffling_seeds,
batch_first=batch_first,
shuffle=True,
bucketing=args.bucketing,
num_workers=args.train_loader_workers)
gnmt_print(key=mlperf_log.INPUT_BATCH_SIZE,
value=args.batch_size * get_world_size())
gnmt_print(key=mlperf_log.INPUT_SIZE,
value=train_loader.sampler.num_samples)
val_loader = val_data.get_loader(batch_size=args.val_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=args.val_loader_workers)
test_loader = test_data.get_loader(batch_size=args.test_batch_size,
batch_first=batch_first,
shuffle=False,
pad=True,
num_workers=args.test_loader_workers)
gnmt_print(key=mlperf_log.EVAL_SIZE, value=len(test_loader.dataset))
translator = Translator(model=model,
tokenizer=tokenizer,
loader=test_loader,
beam_size=args.beam_size,
max_seq_len=args.max_length_test,
len_norm_factor=args.len_norm_factor,
len_norm_const=args.len_norm_const,
cov_penalty_factor=args.cov_penalty_factor,
cuda=args.cuda,
print_freq=args.print_freq,
dataset_dir=args.dataset_dir,
target_bleu=args.target_bleu,
save_path=args.save_path)
# create trainer
trainer_options = dict(
criterion=criterion,
grad_clip=args.grad_clip,
save_path=save_path,
save_freq=args.save_freq,
save_info={
'config': args,
'tokenizer': tokenizer.get_state()
},
opt_config=opt_config,
scheduler_config=scheduler_config,
batch_first=batch_first,
keep_checkpoints=args.keep_checkpoints,
math=args.math,
print_freq=args.print_freq,
cuda=args.cuda,
distributed=distributed,
distributed_overlap_allreduce=args.enable_apex_allreduce_overlap,
distributed_overlap_allreduce_messagesize=args.apex_message_size,
intra_epoch_eval=args.intra_epoch_eval,
translator=translator,
arch=args.arch)
trainer_options['model'] = model
trainer = trainers.Seq2SeqTrainer(**trainer_options)
# optionally resume from a checkpoint
if args.resume:
checkpoint_file = args.resume
if os.path.isdir(checkpoint_file):
checkpoint_file = os.path.join(checkpoint_file, 'model_best.pth')
if os.path.isfile(checkpoint_file):
trainer.load(checkpoint_file)
else:
logging.error(f'No checkpoint found at {args.resume}')
# training loop
# best_loss = float('inf')
gnmt_print(key=mlperf_log.TRAIN_LOOP)
for epoch in range(1):
logging.info(f'Starting epoch {epoch}')
gnmt_print(key=mlperf_log.TRAIN_EPOCH, value=epoch)
if distributed:
train_loader.sampler.set_epoch(epoch)
trainer.epoch = epoch
train_loss, train_perf = trainer.optimize(train_loader)
logging.info(f'Finished epoch {epoch}')
# Save the checkpoint at the end of the training loop, after the RUN_STOP
# tag
# https://github.com/mlperf/policies/issues/55#issuecomment-428335773
if not args.disable_eval:
gnmt_print(key=mlperf_log.TRAIN_CHECKPOINT)
if get_rank() == 0:
trainer.save(save_all=args.save_all, is_best=True)
gnmt_print(key=mlperf_log.RUN_FINAL)
def main():
"""
Launches data-parallel multi-gpu training.
"""
mlperf_log.ROOT_DIR_GNMT = os.path.dirname(os.path.abspath(__file__))
mlperf_log.LOGGER.propagate = False
args = parse_args()
device = utils.set_device(args.cuda, args.local_rank)
distributed = utils.init_distributed(args.cuda)
gnmt_print(key=mlperf_log.RUN_START, sync=True)
args.rank = utils.get_rank()
if not args.cudnn:
torch.backends.cudnn.enabled = False
# create directory for results
save_path = os.path.join(args.results_dir, args.save)
args.save_path = save_path
os.makedirs(save_path, exist_ok=True)
# setup logging
log_filename = f'log_rank_{utils.get_rank()}.log'
utils.setup_logging(os.path.join(save_path, log_filename))
if args.env:
utils.log_env_info()
logging.info(f'Saving results to: {save_path}')
logging.info(f'Run arguments: {args}')
# automatically set train_iter_size based on train_global_batch_size,
# world_size and per-worker train_batch_size
if args.train_global_batch_size is not None:
global_bs = args.train_global_batch_size
bs = args.train_batch_size
world_size = utils.get_world_size()
assert global_bs % (bs * world_size) == 0
args.train_iter_size = global_bs // (bs * world_size)
logging.info(f'Global batch size was set in the config, '
f'Setting train_iter_size to {args.train_iter_size}')
worker_seeds, shuffling_seeds = utils.setup_seeds(args.seed, args.epochs,
device)
worker_seed = worker_seeds[args.rank]
logging.info(f'Worker {args.rank} is using worker seed: {worker_seed}')
torch.manual_seed(worker_seed)
# build tokenizer
pad_vocab = utils.pad_vocabulary(args.math)
tokenizer = Tokenizer(os.path.join(args.dataset_dir, config.VOCAB_FNAME),
pad_vocab)
# build datasets
gnmt_print(key=mlperf_log.PREPROC_TOKENIZE_TRAINING, sync=False)
gnmt_print(key=mlperf_log.TRAIN_HP_MAX_SEQ_LEN,
value=args.max_length_train,
sync=False)
train_data = LazyParallelDataset(
src_fname=os.path.join(args.dataset_dir, config.SRC_TRAIN_FNAME),
tgt_fname=os.path.join(args.dataset_dir, config.TGT_TRAIN_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_train,
max_len=args.max_length_train,
sort=False,
max_size=args.max_size)
gnmt_print(key=mlperf_log.PREPROC_NUM_TRAIN_EXAMPLES,
value=len(train_data),
sync=False)
val_data = ParallelDataset(src_fname=os.path.join(args.dataset_dir,
config.SRC_VAL_FNAME),
tgt_fname=os.path.join(args.dataset_dir,
config.TGT_VAL_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_val,
max_len=args.max_length_val,
sort=True)
gnmt_print(key=mlperf_log.PREPROC_TOKENIZE_EVAL, sync=False)
test_data = TextDataset(src_fname=os.path.join(args.dataset_dir,
config.SRC_TEST_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_test,
max_len=args.max_length_test,
sort=True)
gnmt_print(key=mlperf_log.PREPROC_NUM_EVAL_EXAMPLES,
value=len(test_data),
sync=False)
vocab_size = tokenizer.vocab_size
gnmt_print(key=mlperf_log.PREPROC_VOCAB_SIZE, value=vocab_size, sync=False)
# build GNMT model
model_config = {
'hidden_size': args.hidden_size,
'num_layers': args.num_layers,
'dropout': args.dropout,
'batch_first': False,
'share_embedding': args.share_embedding
}
model = GNMT(vocab_size=vocab_size, **model_config)
logging.info(model)
batch_first = model.batch_first
# define loss function (criterion) and optimizer
criterion = build_criterion(vocab_size, config.PAD, args.smoothing)
opt_config = {'optimizer': args.optimizer, 'lr': args.lr}
opt_config.update(literal_eval(args.optimizer_extra))
logging.info(f'Training optimizer config: {opt_config}')
scheduler_config = {
'warmup_steps': args.warmup_steps,
'remain_steps': args.remain_steps,
'decay_interval': args.decay_interval,
'decay_steps': args.decay_steps,
'decay_factor': args.decay_factor
}
logging.info(f'Training LR schedule config: {scheduler_config}')
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info(f'Number of parameters: {num_parameters}')
batching_opt = {
'shard_size': args.shard_size,
'num_buckets': args.num_buckets
}
# get data loaders
train_loader = train_data.get_loader(batch_size=args.train_batch_size,
seeds=shuffling_seeds,
batch_first=batch_first,
shuffle=True,
batching=args.batching,
batching_opt=batching_opt,
num_workers=args.train_loader_workers)
gnmt_print(key=mlperf_log.INPUT_BATCH_SIZE,
value=args.train_batch_size * utils.get_world_size(),
sync=False)
gnmt_print(key=mlperf_log.INPUT_SIZE,
value=train_loader.sampler.num_samples,
sync=False)
val_loader = val_data.get_loader(batch_size=args.val_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=args.val_loader_workers)
test_loader = test_data.get_loader(batch_size=args.test_batch_size,
batch_first=batch_first,
shuffle=False,
pad=True,
num_workers=args.test_loader_workers)
gnmt_print(key=mlperf_log.EVAL_SIZE,
value=len(test_loader.dataset),
sync=False)
translator = Translator(model=model,
tokenizer=tokenizer,
loader=test_loader,
beam_size=args.beam_size,
max_seq_len=args.max_length_test,
len_norm_factor=args.len_norm_factor,
len_norm_const=args.len_norm_const,
cov_penalty_factor=args.cov_penalty_factor,
cuda=args.cuda,
print_freq=args.print_freq,
dataset_dir=args.dataset_dir,
target_bleu=args.target_bleu,
save_path=args.save_path)
# create trainer
total_train_iters = len(train_loader) // args.train_iter_size * args.epochs
save_info = {
'model_config': model_config,
'config': args,
'tokenizer': tokenizer.get_state()
}
trainer_options = dict(criterion=criterion,
grad_clip=args.grad_clip,
iter_size=args.train_iter_size,
save_path=save_path,
save_freq=args.save_freq,
save_info=save_info,
opt_config=opt_config,
scheduler_config=scheduler_config,
train_iterations=total_train_iters,
batch_first=batch_first,
keep_checkpoints=args.keep_checkpoints,
math=args.math,
print_freq=args.print_freq,
cuda=args.cuda,
distributed=distributed,
intra_epoch_eval=args.intra_epoch_eval,
translator=translator)
trainer_options['model'] = model
trainer = trainers.Seq2SeqTrainer(**trainer_options)
# optionally resume from a checkpoint
if args.resume:
checkpoint_file = args.resume
if os.path.isdir(checkpoint_file):
checkpoint_file = os.path.join(checkpoint_file, 'model_best.pth')
if os.path.isfile(checkpoint_file):
trainer.load(checkpoint_file)
else:
logging.error(f'No checkpoint found at {args.resume}')
# training loop
best_loss = float('inf')
break_training = False
test_bleu = None
gnmt_print(key=mlperf_log.TRAIN_LOOP, sync=True)
for epoch in range(args.start_epoch, args.epochs):
logging.info(f'Starting epoch {epoch}')
gnmt_print(key=mlperf_log.TRAIN_EPOCH, value=epoch, sync=True)
train_loader.sampler.set_epoch(epoch)
trainer.epoch = epoch
train_loss, train_perf = trainer.optimize(train_loader)
# evaluate on validation set
if args.eval:
logging.info(f'Running validation on dev set')
val_loss, val_perf = trainer.evaluate(val_loader)
# remember best prec@1 and save checkpoint
gnmt_print(key=mlperf_log.TRAIN_CHECKPOINT, sync=False)
if args.rank == 0:
is_best = val_loss < best_loss
best_loss = min(val_loss, best_loss)
trainer.save(save_all=args.save_all, is_best=is_best)
if args.eval:
gnmt_print(key=mlperf_log.EVAL_START, value=epoch, sync=True)
test_bleu, break_training = translator.run(calc_bleu=True,
epoch=epoch)
gnmt_print(key=mlperf_log.EVAL_ACCURACY,
value={
"epoch": epoch,
"value": round(test_bleu, 2)
},
sync=False)
gnmt_print(key=mlperf_log.EVAL_TARGET,
value=args.target_bleu,
sync=False)
gnmt_print(key=mlperf_log.EVAL_STOP, sync=True)
acc_log = []
acc_log += [f'Summary: Epoch: {epoch}']
acc_log += [f'Training Loss: {train_loss:.4f}']
if args.eval:
acc_log += [f'Validation Loss: {val_loss:.4f}']
acc_log += [f'Test BLEU: {test_bleu:.2f}']
perf_log = []
perf_log += [f'Performance: Epoch: {epoch}']
perf_log += [f'Training: {train_perf:.0f} Tok/s']
if args.eval:
perf_log += [f'Validation: {val_perf:.0f} Tok/s']
if args.rank == 0:
logging.info('\t'.join(acc_log))
logging.info('\t'.join(perf_log))
logging.info(f'Finished epoch {epoch}')
if break_training:
break
gnmt_print(key=mlperf_log.RUN_STOP,
value={"success": bool(break_training)},
sync=True)
gnmt_print(key=mlperf_log.RUN_FINAL, sync=False)
def main():
global args, best_prec1
args = parser.parse_args()
# Special case handling for GNMT model
l2_promote()
torch.cuda.set_device(args.local_rank)
# build tokenizer
tokenizer = Tokenizer(os.path.join(args.data_dir, config.VOCAB_FNAME))
# define loss function
criterion = build_gnmt_criterion(vocab_size=tokenizer.vocab_size,
padding_idx=config.PAD,
smoothing=0.1)
# create stages of the model
module = importlib.import_module(args.module)
args.arch = module.arch()
model = module.model(criterion)
input_size = [args.max_length_train, args.batch_size]
training_tensor_shapes = {
"input0": input_size,
"input1": [args.batch_size],
"input2": input_size,
"target": [args.max_length_train * args.batch_size],
"target_length": [args.batch_size]
}
dtypes = {
"input0": torch.int64,
"input1": torch.int64,
"input2": torch.int64,
"target": torch.int64,
"target_length": torch.int32
}
inputs_module_destinations = {"input0": 0, "input1": 0, "input2": 0}
target_tensor_names = {"target", "target_length"}
for module_id, (stage, inputs, outputs) in enumerate(
model[:-1]): # Skip last layer (loss).
input_tensors = []
for module_input in inputs:
if module_input in inputs_module_destinations:
inputs_module_destinations[module_input] = module_id
input_tensor = torch.ones(tuple(
training_tensor_shapes[module_input]),
dtype=dtypes[module_input]).cuda()
input_tensors.append(input_tensor)
stage.cuda()
# PyTorch should not maintain metadata for a backward pass on
# synthetic inputs. Without the following line, the runtime is
# as much as 1.5x slower in a full DP configuration.
with torch.no_grad():
output_tensors = stage(*tuple(input_tensors))
if not type(output_tensors) is tuple:
output_tensors = [output_tensors]
for output, output_tensor in zip(outputs, list(output_tensors)):
training_tensor_shapes[output] = list(output_tensor.size())
dtypes[output] = output_tensor.dtype
eval_tensor_shapes = {}
for key in training_tensor_shapes:
eval_tensor_shapes[key] = tuple(training_tensor_shapes[key])
training_tensor_shapes[key] = tuple(training_tensor_shapes[key])
configuration_maps = {
'module_to_stage_map': None,
'stage_to_rank_map': None,
'stage_to_depth_map': None
}
if args.config_path is not None:
json_config_file = json.load(open(args.config_path, 'r'))
configuration_maps['module_to_stage_map'] = json_config_file.get(
"module_to_stage_map", None)
configuration_maps['stage_to_rank_map'] = json_config_file.get(
"stage_to_rank_map", None)
configuration_maps['stage_to_rank_map'] = {
int(k): v
for (k, v) in configuration_maps['stage_to_rank_map'].items()
}
configuration_maps['stage_to_depth_map'] = json_config_file.get(
"stage_to_depth_map", None)
r = runtime.StageRuntime(
model=model,
distributed_backend=args.distributed_backend,
fp16=args.fp16,
loss_scale=args.loss_scale,
training_tensor_shapes=training_tensor_shapes,
eval_tensor_shapes=eval_tensor_shapes,
training_tensor_dtypes=dtypes,
inputs_module_destinations=inputs_module_destinations,
target_tensor_names=target_tensor_names,
configuration_maps=configuration_maps,
master_addr=args.master_addr,
rank=args.rank,
local_rank=args.local_rank,
num_ranks_in_server=args.num_ranks_in_server,
verbose_freq=args.verbose_frequency,
model_type=runtime.TRANSLATION,
enable_recompute=args.recompute)
# stage needed to determine if current stage is the first stage
# num_stages needed to determine if current stage is the last stage
# num_ranks needed to determine number of warmup_minibatches in case of pipelining
args.stage = r.stage
args.num_stages = r.num_stages
args.num_ranks = r.num_ranks
if not is_first_stage():
args.synthetic_data = True
# define optimizer
if args.no_input_pipelining:
num_versions = 1
else:
# number of versions is the total number of machines following the current
# stage, shared amongst all replicas in this stage
num_versions = r.num_warmup_minibatches + 1
# if specified, resume from checkpoint
if args.resume:
checkpoint_file_path = "%s.%d.pth.tar" % (args.resume, r.stage)
assert os.path.isfile(checkpoint_file_path)
print("=> loading checkpoint '{}'".format(checkpoint_file_path))
checkpoint = torch.load(checkpoint_file_path)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
r.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_file_path, checkpoint['epoch']))
# TODO: make this configurable by args
use_adam_optimizer = True
if use_adam_optimizer:
optimizer = adam.AdamWithWeightStashing(
modules=r.modules(),
master_parameters=r.master_parameters,
model_parameters=r.model_parameters,
loss_scale=args.loss_scale,
num_versions=num_versions,
lr=args.lr,
betas=(0.9, 0.999),
weight_decay=args.weight_decay,
verbose_freq=args.verbose_frequency,
macrobatch=args.macrobatch)
else:
optimizer = sgd.SGDWithWeightStashing(
modules=r.modules(),
master_parameters=r.master_parameters,
model_parameters=r.model_parameters,
loss_scale=args.loss_scale,
num_versions=num_versions,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
verbose_freq=args.verbose_frequency)
if args.resume:
optimizer.load_state_dict(checkpoint['optimizer'])
cudnn.benchmark = True
train_dataset = LazyParallelDataset(
src_fname=os.path.join(args.data_dir, config.SRC_TRAIN_FNAME),
tgt_fname=os.path.join(args.data_dir, config.TGT_TRAIN_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_train,
max_len=args.max_length_train,
sort=False,
max_size=None)
val_dataset = ParallelDataset(
src_fname=os.path.join(args.data_dir, config.SRC_VAL_FNAME),
tgt_fname=os.path.join(args.data_dir, config.TGT_VAL_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_train,
max_len=args.max_length_train,
sort=True)
distributed_sampler = False
if configuration_maps['stage_to_rank_map'] is not None:
num_ranks_in_first_stage = len(
configuration_maps['stage_to_rank_map'][0])
if num_ranks_in_first_stage > 1:
distributed_sampler = True
# TODO: fix random seeds
train_loader = train_dataset.get_loader(
batch_size=args.batch_size,
seeds=range(args.epochs),
batch_first=False,
shuffle=True,
bucketing=not args.no_bucketing,
num_workers=args.workers,
world_size=r.num_ranks_in_first_stage,
rank=r.rank_in_stage if r.stage == 0 else 0)
val_loader = val_dataset.get_loader(
batch_size=args.batch_size,
batch_first=False,
shuffle=True,
num_workers=args.workers,
world_size=r.num_ranks_in_first_stage,
seeds=range(args.epochs),
rank=r.rank_in_stage if r.stage == 0 else 0)
# if checkpoint is loaded, start by running validation
if args.resume:
assert args.start_epoch > 0
validate(val_loader, r, args.start_epoch - 1)
for epoch in range(args.start_epoch, args.epochs):
if distributed_sampler:
train_loader.sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch, args.epochs, r, args.lr_policy)
# train or run forward pass only for one epoch
if args.forward_only:
validate(val_loader, r, epoch)
else:
train(train_loader, r, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, r, epoch)
if r.stage != r.num_stages: prec1 = 0
# remember best prec@1 and save checkpoint
best_prec1 = max(prec1, best_prec1)
should_save_checkpoint = args.checkpoint_dir_not_nfs or r.rank_in_stage == 0
if args.checkpoint_dir and should_save_checkpoint:
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': r.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
'tokenizer': tokenizer.get_state()
}, args.checkpoint_dir, r.stage, epoch)
def main():
"""
Launches data-parallel multi-gpu training.
"""
mlperf_compliance.mlperf_log.LOGGER.propagate = False
mlperf_compliance.mlperf_log.setdefault(
root_dir=os.path.dirname(os.path.abspath(__file__)),
benchmark=mlperf_compliance.constants.GNMT,
stack_offset=1,
extra_print=False
)
mlperf_print(key=mlperf_compliance.constants.INIT_START,
log_all_ranks=True)
args = parse_args()
device = utils.set_device(args.cuda, args.local_rank)
distributed = utils.init_distributed(args.cuda)
# preinit and warmup streams/ groups for apex DDP communicators
allreduce_communicators=None
if distributed and args.apex_num_allreduce_streams > 1:
bucket_pgs = [torch.distributed.new_group() for _ in range(args.apex_num_allreduce_streams)]
bucket_streams = [torch.cuda.Stream() for _ in range(args.apex_num_allreduce_streams)]
for pg, stream in zip(bucket_pgs,bucket_streams):
with torch.cuda.stream(stream):
torch.distributed.all_reduce(torch.cuda.FloatTensor(1), group=pg)
allreduce_communicators=(bucket_pgs,bucket_streams)
args.rank = utils.get_rank()
if not args.cudnn:
torch.backends.cudnn.enabled = False
# create directory for results
save_path = os.path.join(args.results_dir, args.save)
args.save_path = save_path
os.makedirs(save_path, exist_ok=True)
# setup logging
log_filename = f'log_rank_{utils.get_rank()}.log'
utils.setup_logging(args.log_all_ranks,
os.path.join(save_path, log_filename))
if args.env:
utils.log_env_info()
logging.info(f'Saving results to: {save_path}')
logging.info(f'Run arguments: {args}')
# automatically set train_iter_size based on train_global_batch_size,
# world_size and per-worker train_batch_size
if args.train_global_batch_size is not None:
global_bs = args.train_global_batch_size
bs = args.train_batch_size
world_size = utils.get_world_size()
assert global_bs % (bs * world_size) == 0
args.train_iter_size = global_bs // (bs * world_size)
logging.info(f'Global batch size was set in the config, '
f'Setting train_iter_size to {args.train_iter_size}')
# setup L2 promotion
if args.cuda:
utils.l2_promote()
worker_seeds, shuffling_seeds = utils.setup_seeds(args.seed, args.epochs,
device)
worker_seed = worker_seeds[args.rank]
logging.info(f'Worker {args.rank} is using worker seed: {worker_seed}')
torch.manual_seed(worker_seed)
# build tokenizer
# https://github.com/mlperf/policies/issues/201
pad_vocab = utils.pad_vocabulary(args.math)
tokenizer = Tokenizer(os.path.join(args.dataset_dir, config.VOCAB_FNAME),
pad_vocab)
vocab_size = tokenizer.vocab_size
# build GNMT model
model_config = {'hidden_size': args.hidden_size,
'num_layers': args.num_layers,
'dropout': args.dropout, 'batch_first': False,
'share_embedding': args.share_embedding,
'fusion': args.fused_attention}
model = GNMT(vocab_size=vocab_size, **model_config)
logging.info(model)
# define loss function (criterion) and optimizer
criterion = build_criterion(vocab_size, config.PAD, args.smoothing,
args.fused_xentropy)
opt_config = {'optimizer': args.optimizer, 'lr': args.lr}
opt_config.update(literal_eval(args.optimizer_extra))
logging.info(f'Training optimizer config: {opt_config}')
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info(f'Number of parameters: {num_parameters}')
# create trainer
save_info = {'model_config': model_config, 'config': args, 'tokenizer':
tokenizer.get_state()}
loss_scaling = {'init_scale': args.init_scale, 'upscale_interval':
args.upscale_interval}
trainer_options = dict(
criterion=criterion,
grad_clip=args.grad_clip,
iter_size=args.train_iter_size,
save_path=save_path,
save_freq=args.save_freq,
save_info=save_info,
opt_config=opt_config,
batch_first=model.batch_first,
keep_checkpoints=args.keep_checkpoints,
math=args.math,
loss_scaling=loss_scaling,
print_freq=args.print_freq,
cuda=args.cuda,
distributed=distributed,
distributed_overlap_allreduce=args.enable_apex_allreduce_overlap,
distributed_overlap_num_allreduce_streams=args.apex_num_allreduce_streams,
distributed_overlap_allreduce_messagesize=args.apex_message_size,
distributed_overlap_allreduce_communicators=allreduce_communicators,
intra_epoch_eval=args.intra_epoch_eval,
prealloc_mode=args.prealloc_mode)
trainer_options['model'] = model
trainer = trainers.Seq2SeqTrainer(**trainer_options)
trainer.preallocate(args.train_batch_size, args.max_length_train,
training=True)
mlperf_print(key=mlperf_compliance.constants.INIT_STOP,
sync=True)
mlperf_print(key=mlperf_compliance.constants.RUN_START,
sync=True)
utils.barrier()
mlperf_print(key=mlperf_compliance.constants.MAX_SEQUENCE_LENGTH,
value=args.max_length_train,
metadata={'method': 'discard'})
if args.use_preproc_data:
train_data = PreprocessedDataset(
min_len=args.min_length_train,
max_len=args.max_length_train,
vocab_size=tokenizer.vocab_size,
)
train_data.read_data(
os.path.join(args.preproc_data_dir, 'training.bin'),
tokenizer.vocab_size,
)
train_data.prepare()
else:
train_data = LazyParallelDataset(
src_fname=os.path.join(args.dataset_dir, config.SRC_TRAIN_FNAME),
tgt_fname=os.path.join(args.dataset_dir, config.TGT_TRAIN_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_train,
max_len=args.max_length_train,
sort=False,
max_size=args.max_size,
)
test_data = TextDataset(
src_fname=os.path.join(args.dataset_dir, config.SRC_TEST_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_test,
max_len=args.max_length_test,
sort=True)
batching_opt = {'shard_size': args.shard_size,
'num_buckets': args.num_buckets}
# get data loaders
train_loader = train_data.get_loader(batch_size=args.train_batch_size,
seeds=shuffling_seeds,
batch_first=model.batch_first,
shuffle=True,
batching=args.batching,
batching_opt=batching_opt,
num_workers=args.train_loader_workers)
mlperf_print(key=mlperf_compliance.constants.GLOBAL_BATCH_SIZE,
value=args.train_batch_size * utils.get_world_size(),
sync=False)
test_loader = test_data.get_loader(batch_size=args.test_batch_size,
batch_first=model.batch_first,
shuffle=False,
num_workers=args.test_loader_workers)
translator = Translator(model=model,
tokenizer=tokenizer,
loader=test_loader,
beam_size=args.beam_size,
max_seq_len=args.max_length_test,
len_norm_factor=args.len_norm_factor,
len_norm_const=args.len_norm_const,
cov_penalty_factor=args.cov_penalty_factor,
cuda=args.cuda,
print_freq=args.print_freq,
dataset_dir=args.dataset_dir,
target_bleu=args.target_bleu,
save_path=args.save_path)
total_train_iters = len(train_loader) // args.train_iter_size * args.epochs
scheduler_config = {'warmup_steps': args.warmup_steps,
'remain_steps': args.remain_steps,
'decay_interval': args.decay_interval,
'decay_steps': args.decay_steps,
'decay_factor': args.decay_factor}
logging.info(f'Training LR schedule config: {scheduler_config}')
scheduler = WarmupMultiStepLR(trainer.optimizer, total_train_iters,
**scheduler_config)
trainer.scheduler = scheduler
trainer.translator = translator
# optionally resume from a checkpoint
if args.resume:
checkpoint_file = args.resume
if os.path.isdir(checkpoint_file):
checkpoint_file = os.path.join(
checkpoint_file, 'model_best.pth')
if os.path.isfile(checkpoint_file):
trainer.load(checkpoint_file)
else:
logging.error(f'No checkpoint found at {args.resume}')
# training loop
break_training = False
test_bleu = None
for epoch in range(args.start_epoch, args.epochs):
mlperf_print(key=mlperf_compliance.constants.BLOCK_START,
metadata={'first_epoch_num': epoch + 1,
'epoch_count': 1},
sync=True)
mlperf_print(key=mlperf_compliance.constants.EPOCH_START,
metadata={'epoch_num': epoch + 1},
sync=True)
logging.info(f'Starting epoch {epoch}')
train_loader.sampler.set_epoch(epoch)
trainer.epoch = epoch
train_loss, train_perf = trainer.optimize(train_loader)
mlperf_print(key=mlperf_compliance.constants.EPOCH_STOP,
metadata={'epoch_num': epoch + 1},
sync=True)
if args.eval:
mlperf_print(key=mlperf_compliance.constants.EVAL_START,
metadata={'epoch_num': epoch + 1},
sync=True)
test_bleu, break_training = translator.run(calc_bleu=True,
epoch=epoch)
mlperf_print(key=mlperf_compliance.constants.EVAL_ACCURACY,
value=test_bleu,
metadata={'epoch_num': epoch + 1},
sync=False)
mlperf_print(key=mlperf_compliance.constants.EVAL_STOP,
metadata={'epoch_num': epoch + 1},
sync=True)
acc_log = []
acc_log += [f'Summary: Epoch: {epoch}']
acc_log += [f'Training Loss: {train_loss:.4f}']
if args.eval:
acc_log += [f'Test BLEU: {test_bleu:.2f}']
perf_log = []
perf_log += [f'Performance: Epoch: {epoch}']
perf_log += [f'Training: {train_perf:.0f} Tok/s']
if args.rank == 0:
logging.info('\t'.join(acc_log))
logging.info('\t'.join(perf_log))
logging.info(f'Finished epoch {epoch}')
mlperf_print(key=mlperf_compliance.constants.BLOCK_STOP,
metadata={'first_epoch_num': epoch + 1},
sync=True)
if break_training:
break
if args.use_preproc_data:
train_data.finalize()
status = 'success' if break_training else 'aborted'
mlperf_print(key=mlperf_compliance.constants.RUN_STOP,
metadata={'status': status},
sync=True)
