def main():
"""
Launches translation (inference).
Inference is executed on a single GPU, implementation supports beam search
with length normalization and coverage penalty.
"""
args = parse_args()
device = utils.set_device(args.cuda, args.local_rank)
utils.init_distributed(args.cuda)
args.rank = utils.get_rank()
os.makedirs(args.save_dir, exist_ok=True)
utils.setup_logging()
dllog_file = os.path.join(args.save_dir, args.dllog_file)
utils.setup_dllogger(enabled=True, filename=dllog_file)
if args.env:
utils.log_env_info()
logging.info(f'Run arguments: {args}')
dllogger.log(step='PARAMETER', data=vars(args))
if not args.cuda and torch.cuda.is_available():
warnings.warn('cuda is available but not enabled')
if not args.cudnn:
torch.backends.cudnn.enabled = False
# load checkpoint and deserialize to CPU (to save GPU memory)
if args.model:
checkpoint = torch.load(args.model, map_location={'cuda:0': 'cpu'})
# build GNMT model
tokenizer = Tokenizer()
tokenizer.set_state(checkpoint['tokenizer'])
model_config = checkpoint['model_config']
model_config['batch_first'] = args.batch_first
model_config['vocab_size'] = tokenizer.vocab_size
model = GNMT(**model_config)
model.load_state_dict(checkpoint['state_dict'])
elif args.synthetic:
model = GNMT(args.synthetic_vocab, batch_first=args.batch_first)
tokenizer = None
else:
raise RuntimeError('Specify model either with --synthetic or with --model flag')
# construct the dataset
if args.input:
data = RawTextDataset(raw_datafile=args.input,
tokenizer=tokenizer,
sort=args.sort,
)
elif args.input_text:
data = RawTextDataset(raw_data=args.input_text,
tokenizer=tokenizer,
sort=args.sort,
)
elif args.synthetic:
data = SyntheticDataset(args.synthetic_vocab, args.synthetic_len, args.batch_size[0] * args.synthetic_batches)
latency_table = tables.LatencyTable(args.percentiles)
throughput_table = tables.ThroughputTable(args.percentiles)
accuracy_table = tables.AccuracyTable('BLEU')
dtype = {
'fp32': torch.FloatTensor,
'tf32': torch.FloatTensor,
'fp16': torch.HalfTensor
}
for (math, batch_size, beam_size) in product(args.math, args.batch_size,
args.beam_size):
logging.info(f'math: {math}, batch size: {batch_size}, '
f'beam size: {beam_size}')
model.type(dtype[math])
model = model.to(device)
model.eval()
# build the data loader
loader = data.get_loader(
batch_size=batch_size,
batch_first=args.batch_first,
pad=True,
repeat=args.repeat[batch_size],
num_workers=0,
)
# build the translator object
translator = Translator(
model=model,
tokenizer=tokenizer,
loader=loader,
beam_size=beam_size,
max_seq_len=args.max_seq_len,
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,
)
# execute the inference
output, stats = translator.run(
calc_bleu=args.bleu,
eval_path=args.output,
summary=True,
warmup=args.warmup,
reference_path=args.reference,
)
# print translated outputs
if not args.synthetic and (not args.output and args.rank == 0):
logging.info(f'Translated output:')
for out in output:
print(out)
key = (batch_size, beam_size)
latency_table.add(key, {math: stats['runtimes']})
throughput_table.add(key, {math: stats['throughputs']})
accuracy_table.add(key, {math: stats['bleu']})
if args.tables:
accuracy_table.write('Inference accuracy', args.math)
if 'fp16' in args.math and 'fp32' in args.math:
relative = 'fp32'
elif 'fp16' in args.math and 'tf32' in args.math:
relative = 'tf32'
else:
relative = None
if 'fp32' in args.math:
throughput_table.write('Inference throughput', 'fp32')
if 'tf32' in args.math:
throughput_table.write('Inference throughput', 'tf32')
if 'fp16' in args.math:
throughput_table.write('Inference throughput', 'fp16',
relative=relative)
if 'fp32' in args.math:
latency_table.write('Inference latency', 'fp32')
if 'tf32' in args.math:
latency_table.write('Inference latency', 'tf32')
if 'fp16' in args.math:
latency_table.write('Inference latency', 'fp16',
relative=relative, reverse_speedup=True)
avg_throughput = np.array(stats['throughputs']).mean()
avg_latency = np.array(stats['runtimes']).mean()
summary = {
'eval_throughput': avg_throughput,
'eval_bleu': stats['bleu'],
'eval_avg_latency': avg_latency,
}
for p in args.percentiles:
summary[f'eval_{p}%_latency'] = 1000 * np.percentile(stats['runtimes'], p)
dllogger.log(step=tuple(), data=summary)
passed = utils.benchmark(stats['bleu'], args.target_bleu,
stats['tokens_per_sec'], args.target_perf)
return passed
def main():
mlperf_log.ROOT_DIR_GNMT = os.path.dirname(os.path.abspath(__file__))
mlperf_log.LOGGER.propagate = False
mlperf_log.gnmt_print(key=mlperf_log.RUN_START)
args = exp.get_arguments(parse_args(), show=True)
device = exp.get_device()
chrono = exp.chrono()
if not args.cudnn:
torch.backends.cudnn.enabled = False
# initialize distributed backend
distributed = args.world_size > 1
if distributed:
backend = 'nccl' if args.cuda else 'gloo'
dist.init_process_group(backend=backend,
rank=args.rank,
init_method=args.dist_url,
world_size=args.world_size)
# create directory for results
save_path = os.environ.get('OUTPUT_DIRECTORY')
if save_path is None:
save_path = '/tmp'
if args.save is not None:
save_path = os.path.join(args.results_dir, args.save)
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))
if args.cuda:
torch.cuda.set_device(args.rank)
# build tokenizer
tokenizer = Tokenizer(os.path.join(args.dataset_dir, config.VOCAB_FNAME))
train_data = ParallelDataset(
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)
mlperf_log.gnmt_print(key=mlperf_log.PREPROC_NUM_TRAIN_EXAMPLES,
value=len(train_data))
vocab_size = tokenizer.vocab_size
mlperf_log.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 = models.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)
# 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
},
opt_config=opt_config,
batch_first=batch_first,
keep_checkpoints=args.keep_checkpoints,
math=args.math,
print_freq=args.print_freq,
cuda=args.cuda,
distributed=distributed)
trainer_options['model'] = model
trainer = trainers.Seq2SeqTrainer(**trainer_options, number=args.number)
translator = Translator(model,
tokenizer,
beam_size=args.beam_size,
max_seq_len=args.max_length_val,
len_norm_factor=args.len_norm_factor,
len_norm_const=args.len_norm_const,
cov_penalty_factor=args.cov_penalty_factor,
cuda=args.cuda)
num_parameters = sum([l.nelement() for l in model.parameters()])
# get data loaders
train_loader = train_data.get_loader(batch_size=args.batch_size,
batch_first=batch_first,
shuffle=True,
bucket=args.bucketing,
num_workers=args.workers,
drop_last=True,
distributed=distributed)
mlperf_log.gnmt_print(key=mlperf_log.INPUT_BATCH_SIZE,
value=args.batch_size * args.world_size)
mlperf_log.gnmt_print(key=mlperf_log.INPUT_SIZE,
value=train_loader.sampler.num_samples)
# training loop
best_loss = float('inf')
mlperf_log.gnmt_print(key=mlperf_log.TRAIN_LOOP)
for epoch in range(0, args.repeat):
with chrono.time('train') as t:
if distributed:
train_loader.sampler.set_epoch(epoch)
trainer.epoch = epoch
train_loss = trainer.optimize(train_loader)
exp.log_epoch_loss(train_loss)
exp.show_eta(epoch, t)
exp.report()
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():
"""
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 translation (inference).
Inference is executed on a single GPU, implementation supports beam search
with length normalization and coverage penalty.
"""
args = parse_args()
utils.set_device(args.cuda, args.local_rank)
utils.init_distributed(args.cuda)
setup_logging()
if args.env:
utils.log_env_info()
logging.info(f'Run arguments: {args}')
if not args.cuda and torch.cuda.is_available():
warnings.warn('cuda is available but not enabled')
if not args.cudnn:
torch.backends.cudnn.enabled = False
# load checkpoint and deserialize to CPU (to save GPU memory)
checkpoint = torch.load(args.model, map_location={'cuda:0': 'cpu'})
# build GNMT model
tokenizer = Tokenizer()
tokenizer.set_state(checkpoint['tokenizer'])
vocab_size = tokenizer.vocab_size
model_config = checkpoint['model_config']
model_config['batch_first'] = args.batch_first
model = GNMT(vocab_size=vocab_size, **model_config)
model.load_state_dict(checkpoint['state_dict'])
for (math, batch_size, beam_size) in product(args.math, args.batch_size,
args.beam_size):
logging.info(f'math: {math}, batch size: {batch_size}, '
f'beam size: {beam_size}')
if math == 'fp32':
dtype = torch.FloatTensor
if math == 'fp16':
dtype = torch.HalfTensor
model.type(dtype)
if args.cuda:
model = model.cuda()
model.eval()
# construct the dataset
test_data = TextDataset(src_fname=args.input,
tokenizer=tokenizer,
sort=args.sort)
# build the data loader
test_loader = test_data.get_loader(batch_size=batch_size,
batch_first=args.batch_first,
shuffle=False,
pad=True,
num_workers=0)
# build the translator object
translator = Translator(model=model,
tokenizer=tokenizer,
loader=test_loader,
beam_size=beam_size,
max_seq_len=args.max_seq_len,
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)
# execute the inference
translator.run(calc_bleu=args.bleu,
eval_path=args.output,
reference_path=args.reference,
summary=True)
def main():
"""
Launches translation (inference).
Inference is executed on a single GPU, implementation supports beam search
with length normalization and coverage penalty.
"""
args = parse_args()
# initialize distributed backend
distributed = args.world_size > 1
if distributed:
backend = 'nccl' if args.cuda else 'gloo'
dist.init_process_group(backend=backend, rank=args.rank,
init_method=args.dist_url,
world_size=args.world_size)
setup_logging()
logging.info(f'Run arguments: {args}')
if args.cuda:
torch.cuda.set_device(args.rank)
if not args.cuda and torch.cuda.is_available():
warnings.warn('cuda is available but not enabled')
if args.math == 'fp16' and not args.cuda:
raise RuntimeError('fp16 requires cuda')
if not args.cudnn:
torch.backends.cudnn.enabled = False
# load checkpoint and deserialize to CPU (to save GPU memory)
checkpoint = torch.load(args.model, map_location={'cuda:0': 'cpu'})
# build GNMT model
tokenizer = Tokenizer()
tokenizer.set_state(checkpoint['tokenizer'])
vocab_size = tokenizer.vocab_size
model_config = dict(vocab_size=vocab_size, math=checkpoint['config'].math,
**literal_eval(checkpoint['config'].model_config))
model_config['batch_first'] = args.batch_first
model = GNMT(**model_config)
state_dict = checkpoint['state_dict']
if checkpoint_from_distributed(state_dict):
state_dict = unwrap_distributed(state_dict)
model.load_state_dict(state_dict)
if args.math == 'fp32':
dtype = torch.FloatTensor
if args.math == 'fp16':
dtype = torch.HalfTensor
model.type(dtype)
if args.cuda:
model = model.cuda()
model.eval()
# construct the dataset
test_data = TextDataset(src_fname=args.input,
tokenizer=tokenizer,
sort=False)
# build the data loader
test_loader = test_data.get_loader(batch_size=args.batch_size,
batch_first=args.batch_first,
shuffle=False,
pad=True,
num_workers=0,
drop_last=False)
# build the translator object
translator = Translator(model=model,
tokenizer=tokenizer,
loader=test_loader,
beam_size=args.beam_size,
max_seq_len=args.max_seq_len,
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)
# execute the inference
translator.run(calc_bleu=args.bleu, eval_path=args.output,
reference_path=args.reference, summary=True)
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)
def main():
mlperf_log.ROOT_DIR_GNMT = os.path.dirname(os.path.abspath(__file__))
mlperf_log.LOGGER.propagate = False
mlperf_log.gnmt_print(key=mlperf_log.RUN_START)
args = parse_args()
print(args)
if not args.cudnn:
torch.backends.cudnn.enabled = False
mlperf_log.gnmt_print(key=mlperf_log.RUN_SET_RANDOM_SEED)
if args.seed:
torch.manual_seed(args.seed + args.rank)
# initialize distributed backend
distributed = args.world_size > 1
if distributed:
backend = 'nccl' if args.cuda else 'gloo'
dist.init_process_group(backend=backend,
rank=args.rank,
init_method=args.dist_url,
world_size=args.world_size)
# create directory for results
save_path = os.path.join(args.results_dir, args.save)
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}')
if args.cuda:
torch.cuda.set_device(args.rank)
# build tokenizer
tokenizer = Tokenizer(os.path.join(args.dataset_dir, config.VOCAB_FNAME))
# build datasets
mlperf_log.gnmt_print(key=mlperf_log.PREPROC_TOKENIZE_TRAINING)
mlperf_log.gnmt_print(key=mlperf_log.TRAIN_HP_MAX_SEQ_LEN,
value=args.max_length_train)
train_data = ParallelDataset(
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)
mlperf_log.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)
mlperf_log.gnmt_print(key=mlperf_log.PREPROC_TOKENIZE_EVAL)
test_data = ParallelDataset(src_fname=os.path.join(args.dataset_dir,
config.SRC_TEST_FNAME),
tgt_fname=os.path.join(args.dataset_dir,
config.TGT_TEST_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_val,
max_len=args.max_length_val,
sort=False)
mlperf_log.gnmt_print(key=mlperf_log.PREPROC_NUM_EVAL_EXAMPLES,
value=len(test_data))
vocab_size = tokenizer.vocab_size
mlperf_log.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 = models.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)
logging.info(f'Training optimizer: {opt_config}')
# 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
},
opt_config=opt_config,
batch_first=batch_first,
keep_checkpoints=args.keep_checkpoints,
math=args.math,
print_freq=args.print_freq,
cuda=args.cuda,
distributed=distributed)
trainer_options['model'] = model
trainer = trainers.Seq2SeqTrainer(**trainer_options)
translator = Translator(model,
tokenizer,
beam_size=args.beam_size,
max_seq_len=args.max_length_val,
len_norm_factor=args.len_norm_factor,
len_norm_const=args.len_norm_const,
cov_penalty_factor=args.cov_penalty_factor,
cuda=args.cuda)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info(f'Number of parameters: {num_parameters}')
# 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}')
# get data loaders
train_loader = train_data.get_loader(batch_size=args.batch_size,
batch_first=batch_first,
shuffle=True,
bucket=args.bucketing,
num_workers=args.workers,
drop_last=True,
distributed=distributed)
mlperf_log.gnmt_print(key=mlperf_log.INPUT_BATCH_SIZE,
value=args.batch_size * args.world_size)
mlperf_log.gnmt_print(key=mlperf_log.INPUT_SIZE,
value=train_loader.sampler.num_samples)
val_loader = val_data.get_loader(batch_size=args.eval_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=args.workers,
drop_last=False,
distributed=False)
test_loader = test_data.get_loader(batch_size=args.eval_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=0,
drop_last=False,
distributed=False)
mlperf_log.gnmt_print(key=mlperf_log.EVAL_SIZE,
value=len(test_loader.sampler))
# training loop
best_loss = float('inf')
mlperf_log.gnmt_print(key=mlperf_log.TRAIN_LOOP)
for epoch in range(args.start_epoch, args.epochs):
mlperf_log.gnmt_print(key=mlperf_log.TRAIN_EPOCH, value=epoch)
logging.info(f'Starting epoch {epoch}')
if distributed:
train_loader.sampler.set_epoch(epoch)
trainer.epoch = epoch
train_loss = trainer.optimize(train_loader)
# evaluate on validation set
if args.rank == 0 and not args.disable_eval:
logging.info(f'Running validation on dev set')
val_loss = trainer.evaluate(val_loader)
# remember best prec@1 and save checkpoint
is_best = val_loss < best_loss
best_loss = min(val_loss, best_loss)
mlperf_log.gnmt_print(key=mlperf_log.TRAIN_CHECKPOINT)
trainer.save(save_all=args.save_all, is_best=is_best)
logging.info(f'Epoch: {epoch}\t'
f'Training Loss {train_loss:.4f}\t'
f'Validation Loss {val_loss:.4f}')
else:
logging.info(f'Epoch: {epoch}\t' f'Training Loss {train_loss:.4f}')
if args.cuda:
break_training = torch.cuda.LongTensor([0])
else:
break_training = torch.LongTensor([0])
if args.rank == 0 and not args.disable_eval:
logging.info(f'Running evaluation on test set')
mlperf_log.gnmt_print(key=mlperf_log.EVAL_START, value=epoch)
model.eval()
torch.cuda.empty_cache()
eval_path = os.path.join(save_path, f'eval_epoch_{epoch}')
eval_file = open(eval_path, 'w')
for i, (src, tgt, indices) in enumerate(test_loader):
src, src_length = src
if translator.batch_first:
batch_size = src.size(0)
else:
batch_size = src.size(1)
beam_size = args.beam_size
bos = [translator.insert_target_start] * \
(batch_size * beam_size)
bos = torch.LongTensor(bos)
if translator.batch_first:
bos = bos.view(-1, 1)
else:
bos = bos.view(1, -1)
src_length = torch.LongTensor(src_length)
if args.cuda:
src = src.cuda()
src_length = src_length.cuda()
bos = bos.cuda()
with torch.no_grad():
context = translator.model.encode(src, src_length)
context = [context, src_length, None]
if beam_size == 1:
generator = translator.generator.greedy_search
else:
generator = translator.generator.beam_search
preds, lengths, counter = generator(
batch_size, bos, context)
preds = preds.cpu()
lengths = lengths.cpu()
output = []
for idx, pred in enumerate(preds):
end = lengths[idx] - 1
pred = pred[1:end]
pred = pred.tolist()
out = translator.tok.detokenize(pred)
output.append(out)
output = [output[indices.index(i)] for i in range(len(output))]
for line in output:
eval_file.write(line)
eval_file.write('\n')
eval_file.close()
# run moses detokenizer
detok_path = os.path.join(args.dataset_dir, config.DETOKENIZER)
detok_eval_path = eval_path + '.detok'
with open(detok_eval_path, 'w') as detok_eval_file, \
open(eval_path, 'r') as eval_file:
subprocess.run(['perl', f'{detok_path}'],
stdin=eval_file,
stdout=detok_eval_file,
stderr=subprocess.DEVNULL)
# run sacrebleu
reference_path = os.path.join(args.dataset_dir,
config.TGT_TEST_TARGET_FNAME)
sacrebleu = subprocess.run([
f'sacrebleu --input {detok_eval_path} \
{reference_path} --score-only -lc --tokenize intl'
],
stdout=subprocess.PIPE,
shell=True)
bleu = float(sacrebleu.stdout.strip())
logging.info(f'Finished evaluation on test set')
logging.info(f'BLEU on test dataset: {bleu}')
if args.target_bleu:
if bleu >= args.target_bleu:
logging.info(f'Target accuracy reached')
break_training[0] = 1
torch.cuda.empty_cache()
mlperf_log.gnmt_print(key=mlperf_log.EVAL_ACCURACY,
value={
"epoch": epoch,
"value": bleu
})
mlperf_log.gnmt_print(key=mlperf_log.EVAL_TARGET,
value=args.target_bleu)
mlperf_log.gnmt_print(key=mlperf_log.EVAL_STOP)
if distributed:
dist.broadcast(break_training, 0)
logging.info(f'Finished epoch {epoch}')
if break_training:
break
mlperf_log.gnmt_print(key=mlperf_log.RUN_STOP,
value={"success": bool(break_training)})
mlperf_log.gnmt_print(key=mlperf_log.RUN_FINAL)
def main():
args = parse_args()
print(args)
profile_dir = args.profile_dir
if not args.nsight and not args.cupti:
profile_dir = None
if not args.cudnn:
torch.backends.cudnn.enabled = False
if args.seed:
torch.manual_seed(args.seed + args.rank)
if args.cuda:
torch.cuda.set_device(args.gpu_rank)
# initialize distributed backend
distributed = args.world_size > 1
if distributed:
if args.ps:
backend = 'gloo'
else:
backend = 'nccl' if args.cuda else 'gloo'
dist.init_process_group(backend=backend, rank=args.rank,
init_method=args.dist_url,
world_size=args.world_size)
# create directory for results
save_path = os.path.join(args.results_dir, args.save)
if not os.path.exists(save_path):
os.makedirs(save_path)
# setup logging
log_filename = 'log_gpu_{}.log'.format(args.rank)
setup_logging(os.path.join(save_path, log_filename))
logging.info('Saving results to: {}'.format(save_path))
logging.info('Run arguments: {}'.format(args))
# build tokenizer
tokenizer = Tokenizer(os.path.join(args.dataset_dir, config.VOCAB_FNAME))
# build datasets
train_data = ParallelDataset(
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)
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)
test_data = ParallelDataset(
src_fname=os.path.join(args.dataset_dir, config.SRC_TEST_FNAME),
tgt_fname=os.path.join(args.dataset_dir, config.TGT_TEST_FNAME),
tokenizer=tokenizer,
min_len=args.min_length_val,
max_len=args.max_length_val,
sort=False)
vocab_size = tokenizer.vocab_size
# build GNMT model
model_config = dict(vocab_size=vocab_size, math=args.math,
**literal_eval(args.model_config))
model = models.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)
logging.info('Training optimizer: {}'.format(opt_config))
# 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},
opt_config=opt_config,
batch_first=batch_first,
keep_checkpoints=args.keep_checkpoints,
math=args.math,
print_freq=args.print_freq,
cuda=args.cuda,
distributed=distributed,
log_dir=profile_dir,
ps=args.ps,
world_size=args.world_size,
rank=args.rank,
cupti=args.cupti,
nsight=args.nsight,
profile_start=args.profile_start,
profile_stop=args.profile_stop
)
trainer_options['model'] = model
trainer = trainers.Seq2SeqTrainer(**trainer_options)
translator = Translator(model,
tokenizer,
beam_size=args.beam_size,
max_seq_len=args.max_length_val,
len_norm_factor=args.len_norm_factor,
len_norm_const=args.len_norm_const,
cov_penalty_factor=args.cov_penalty_factor,
cuda=args.cuda)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info('Number of parameters: {}'.format(num_parameters))
# 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('No checkpoint found at {}'.format(args.resume))
# get data loaders
train_loader = train_data.get_loader(batch_size=args.batch_size,
batch_first=batch_first,
shuffle=True,
bucket=args.bucketing,
num_workers=args.workers,
drop_last=True,
distributed=distributed)
val_loader = val_data.get_loader(batch_size=args.eval_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=args.workers,
drop_last=False,
distributed=False)
test_loader = test_data.get_loader(batch_size=args.eval_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=0,
drop_last=False,
distributed=False)
# training loop
best_loss = float('inf')
for epoch in range(args.start_epoch, args.epochs):
logging.info('Starting epoch {}'.format(epoch))
if distributed:
train_loader.sampler.set_epoch(epoch)
trainer.epoch = epoch
train_loss = trainer.optimize(train_loader)
if args.cupti or args.nsight:
break
# evaluate on validation set
if args.rank == 0 and not args.disable_eval:
logging.info('Running validation on dev set')
val_loss = trainer.evaluate(val_loader)
# remember best prec@1 and save checkpoint
is_best = val_loss < best_loss
best_loss = min(val_loss, best_loss)
trainer.save(save_all=args.save_all, is_best=is_best)
logging.info('Epoch: {}\tTraining Loss {:.4f}\tValidation Loss {:.4f}'.format(
epoch, train_loss, val_loss))
else:
logging.info('Epoch: {}\tTraining Loss {:.4f}'.format(
epoch, train_loss))
if args.cuda:
break_training = torch.cuda.LongTensor([0])
else:
break_training = torch.LongTensor([0])
if args.rank == 0 and not args.disable_eval:
logging.info('Running evaluation on test set')
model.eval()
torch.cuda.empty_cache()
eval_path = os.path.join(save_path, 'eval_epoch_{}'.format(epoch))
eval_file = open(eval_path, 'w')
for i, (src, tgt, indices) in enumerate(test_loader):
src, src_length = src
if translator.batch_first:
batch_size = src.size(0)
else:
batch_size = src.size(1)
beam_size = args.beam_size
bos = [translator.insert_target_start] * (batch_size * beam_size)
bos = torch.LongTensor(bos)
if translator.batch_first:
bos = bos.view(-1, 1)
else:
bos = bos.view(1, -1)
src_length = torch.LongTensor(src_length)
if args.cuda:
src = src.cuda()
src_length = src_length.cuda()
bos = bos.cuda()
with torch.no_grad():
context = translator.model.encode(src, src_length)
context = [context, src_length, None]
if beam_size == 1:
generator = translator.generator.greedy_search
else:
generator = translator.generator.beam_search
preds, lengths, counter = generator(batch_size, bos, context)
preds = preds.cpu()
lengths = lengths.cpu()
output = []
for idx, pred in enumerate(preds):
end = lengths[idx] - 1
pred = pred[1: end]
pred = pred.tolist()
out = translator.tok.detokenize(pred)
output.append(out)
output = [output[indices.index(i)] for i in range(len(output))]
for line in output:
eval_file.write(line)
eval_file.write('\n')
eval_file.close()
# run moses detokenizer
detok_path = os.path.join(args.dataset_dir, config.DETOKENIZER)
detok_eval_path = eval_path + '.detok'
with open(detok_eval_path, 'w') as detok_eval_file, \
open(eval_path, 'r') as eval_file:
subprocess.run(['perl', '{}'.format(detok_path)], stdin=eval_file,
stdout=detok_eval_file, stderr=subprocess.DEVNULL)
# run sacrebleu
reference_path = os.path.join(args.dataset_dir, config.TGT_TEST_TARGET_FNAME)
sacrebleu = subprocess.run(['sacrebleu --input {} {} --score-only -lc --tokenize intl'.format(
detok_eval_path, reference_path)],
stdout=subprocess.PIPE, shell=True)
bleu = float(sacrebleu.stdout.strip())
logging.info('Finished evaluation on test set')
logging.info('BLEU on test dataset: {}'.format(bleu))
if args.target_bleu:
if bleu >= args.target_bleu:
logging.info('Target accuracy reached')
break_training[0] = 1
torch.cuda.empty_cache()
if distributed:
dist.broadcast(break_training, 0)
logging.info('Finished epoch {}'.format(epoch))
if break_training:
break
