def main():
"""
Launches data-parallel multi-gpu training.
"""
args = parse_args()
if not args.cudnn:
torch.backends.cudnn.enabled = False
if args.seed is not None:
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)
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}')
if args.cuda:
torch.cuda.set_device(args.rank)
# 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 = 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)
vocab_size = tokenizer.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)
logging.info(f'Training optimizer: {opt_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,
batch_first=batch_first,
shuffle=True,
bucketing=args.bucketing,
num_workers=args.workers,
drop_last=True)
val_loader = val_data.get_loader(batch_size=args.val_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=args.workers,
drop_last=False)
test_loader = test_data.get_loader(batch_size=args.test_batch_size,
batch_first=batch_first,
shuffle=False,
num_workers=args.workers,
drop_last=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
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,
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')
for epoch in range(args.start_epoch, args.epochs):
logging.info(f'Starting epoch {epoch}')
if distributed:
train_loader.sampler.set_epoch(epoch)
trainer.epoch = epoch
train_loss, train_perf = 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, val_perf = 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)
break_training = False
if not args.disable_eval:
test_bleu, break_training = translator.run(calc_bleu=True,
epoch=epoch)
if args.rank == 0 and not args.disable_eval:
logging.info(f'Summary: Epoch: {epoch}\t'
f'Training Loss: {train_loss:.4f}\t'
f'Validation Loss: {val_loss:.4f}\t'
f'Test BLEU: {test_bleu:.2f}')
logging.info(f'Performance: Epoch: {epoch}\t'
f'Training: {train_perf:.0f} Tok/s\t'
f'Validation: {val_perf:.0f} Tok/s')
else:
logging.info(f'Summary: Epoch: {epoch}\t'
f'Training Loss {train_loss:.4f}')
logging.info(f'Performance: Epoch: {epoch}\t'
f'Training: {train_perf:.0f} Tok/s')
logging.info(f'Finished epoch {epoch}')
if break_training:
break
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():
args = parse_args()
print(args)
profile_dir = args.profile_dir
if not args.profile:
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:
print "init process group"
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)
print "distributed backend initialized"
# 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,
num_minibatches=args.num_minibatches,
cupti=args.cupti)
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,
log_dir=profile_dir)
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.profile:
if args.cuda:
break_training = torch.cuda.LongTensor([0])
else:
break_training = torch.LongTensor([0])
print("profiling finished...")
break
if args.num_minibatches > 0:
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
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():
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))
# SSY the real model
# seq2seq/models/gnmt.py
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
# SSY only the trainer seq2seq/train/trainer.py
# not the models
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)
if args.cuda:
torch.cuda.set_device(0)
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
checkpoint = torch.load(args.model, map_location={'cuda:0': 'cpu'})
vocab_size = checkpoint['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 = models.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()
tokenizer = checkpoint['tokenizer']
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=0,
max_len=150,
sort=False)
test_loader = test_data.get_loader(batch_size=args.batch_size,
batch_first=True,
shuffle=False,
num_workers=0,
drop_last=False,
distributed=False)
translator = Translator(model,
tokenizer,
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)
model.eval()
torch.cuda.empty_cache()
# only write the output to file in accuracy mode
if args.mode == 'accuracy':
test_file = open(args.output, 'w', encoding='UTF-8')
batch_time = AverageMeter(False)
tot_tok_per_sec = AverageMeter(False)
iterations = AverageMeter(False)
enc_seq_len = AverageMeter(False)
dec_seq_len = AverageMeter(False)
stats = {}
for i, (src, tgt, indices) in enumerate(test_loader):
translate_timer = time.time()
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)
stats['total_enc_len'] = int(src_length.sum())
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)
stats['total_dec_len'] = lengths.sum().item()
stats['iters'] = counter
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)
# only write the output to file in accuracy mode
if args.mode == 'accuracy':
output = [output[indices.index(i)] for i in range(len(output))]
for line in output:
test_file.write(line)
test_file.write('\n')
# Get timing
elapsed = time.time() - translate_timer
batch_time.update(elapsed, batch_size)
total_tokens = stats['total_dec_len'] + stats['total_enc_len']
ttps = total_tokens / elapsed
tot_tok_per_sec.update(ttps, batch_size)
iterations.update(stats['iters'])
enc_seq_len.update(stats['total_enc_len'] / batch_size, batch_size)
dec_seq_len.update(stats['total_dec_len'] / batch_size, batch_size)
if i % 5 == 0:
log = []
log += 'TEST '
log += 'Time {:.3f} ({:.3f})\t'.format(batch_time.val,
batch_time.avg)
log += 'Decoder iters {:.1f} ({:.1f})\t'.format(
iterations.val, iterations.avg)
log += 'Tok/s {:.0f} ({:.0f})'.format(tot_tok_per_sec.val,
tot_tok_per_sec.avg)
log = ''.join(log)
print(log)
# summary timing
time_per_sentence = (batch_time.avg / batch_size)
log = []
log += 'TEST SUMMARY:\n'
log += 'Lines translated: {}\t'.format(len(test_loader.dataset))
log += 'Avg total tokens/s: {:.0f}\n'.format(tot_tok_per_sec.avg)
log += 'Avg time per batch: {:.3f} s\t'.format(batch_time.avg)
log += 'Avg time per sentence: {:.3f} ms\n'.format(1000 *
time_per_sentence)
log += 'Avg encoder seq len: {:.2f}\t'.format(enc_seq_len.avg)
log += 'Avg decoder seq len: {:.2f}\t'.format(dec_seq_len.avg)
log += 'Total decoder iterations: {}'.format(int(iterations.sum))
log = ''.join(log)
print(log)
# only write the output to file in accuracy mode
if args.mode == 'accuracy':
test_file.close()
test_path = args.output
# run moses detokenizer
detok_path = os.path.join(args.dataset_dir, config.DETOKENIZER)
detok_test_path = test_path + '.detok'
with open(detok_test_path, 'w') as detok_test_file, \
open(test_path, 'r') as test_file:
subprocess.run(['perl', detok_path],
stdin=test_file,
stdout=detok_test_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_test_path, reference_path)
],
stdout=subprocess.PIPE,
shell=True)
bleu = float(sacrebleu.stdout.strip())
print('BLEU on test dataset: {}'.format(bleu))
print('Finished evaluation on test set')
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():
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_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():
execution_timer = time.time()
tfiargs = tfiParser.getParser()
args = tfiargs.parse_args()
# import os
# os.environ['CUDA_LAUNCH_BLOCKING']='1'
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
print("Use GPU: {} for training".format(args.gpu))
checkpoint = torch.load(args.model, map_location={'cuda:0': 'cpu'})
vocab_size = checkpoint['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 = models.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.gpu is not None:
model = model.cuda()
tokenizer = checkpoint['tokenizer']
test_data = ParallelDataset(src_fname=os.path.join(args.data,
config.SRC_TEST_FNAME),
tgt_fname=os.path.join(args.data,
config.TGT_TEST_FNAME),
tokenizer=tokenizer,
min_len=0,
max_len=150,
sort=False)
test_loader = test_data.get_loader(batch_size=args.batch_size,
batch_first=True,
shuffle=False,
num_workers=0,
drop_last=False,
distributed=False)
translator = Translator(model,
tokenizer,
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.gpu is not None)
model.eval()
# torch.cuda.empty_cache()
if args.record_prefix is not None:
record = Record('GNMTv2',
batch_size=args.batch_size,
injection=args.injection,
fiLayer=args.layer,
fiFeatures=args.fiFeats,
fiWeights=args.fiWeights)
# Faulty Run
if args.faulty:
fi = FI(model,
record=record,
fiMode=args.injection,
fiLayer=args.layer,
fiBit=args.bit,
fiFeatures=args.fiFeats,
fiWeights=args.fiWeights,
log=args.log)
traverse_time = AverageMeter()
start = time.time()
fi.traverseModel(model)
traverse_time.update(time.time() - start)
displayConfig(args)
fi.injectionMode = True
print("\n Number of new layers: #%d \n" % fi.numNewLayers)
elif args.golden:
import distiller.modules as dist
model = dist.convert_model_to_distiller_lstm(model)
if args.quantize:
overrides_yaml = """
.*att_rnn.attn.*:
clip_acts: NONE # Quantize without clipping
decoder.classifier.classifier:
clip_acts: NONE # Quantize without clipping
"""
from distiller.utils import yaml_ordered_load
overrides = yaml_ordered_load(
overrides_yaml) # Basic quantizer defintion
stats_file = '/home/bfgoldstein/torchfi/examples/wmt16/model_stats.yaml'
quantizer = tfi.FIPostTraLinearQuantizer(
model,
mode=args.quant_mode,
bits_activations=args.quant_bacts,
bits_parameters=args.quant_bwts,
bits_accum=args.quant_baccum,
per_channel_wts=args.quant_channel,
clip_acts=args.quant_cacts,
model_activation_stats=args.quant_stats_file,
overrides=overrides,
clip_n_stds=args.quant_cnstds,
scale_approx_mult_bits=args.quant_scalebits)
quantizer.prepare_model()
# model = quantizer.model
if args.faulty:
fi.setQuantParams(args)
print(model._modules.items())
# Setting model to evaluation mode and cuda (if enabled) after FI traverse
model.eval()
if args.gpu is not None:
model = model.cuda()
test_file = open(args.record_prefix +
getRecordPrefix(args, 'fp32', faulty=args.faulty) +
".tok",
'w',
encoding='UTF-8')
batch_time = AverageMeter(False)
tot_tok_per_sec = AverageMeter(False)
iterations = AverageMeter(False)
enc_seq_len = AverageMeter(False)
dec_seq_len = AverageMeter(False)
bleu_score = AverageMeter(False)
score_time = AverageMeter(False)
stats = {}
reference_content = readReferenceFile(args)
for batch_idx, (input, target, indices) in enumerate(test_loader):
translate_timer = time.time()
input_data, input_lenght = input
if translator.batch_first:
batch_size = input_data.size(0)
else:
batch_size = input_data.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)
input_lenght = torch.LongTensor(input_lenght)
stats['total_enc_len'] = int(input_lenght.sum())
if args.gpu is not None:
input_data = input_data.cuda(args.gpu, non_blocking=True)
input_lenght = input_lenght.cuda(args.gpu, non_blocking=True)
bos = bos.cuda(args.gpu, non_blocking=True)
with torch.no_grad():
context = translator.model.encode(input_data, input_lenght)
context = [context, input_lenght, None]
if beam_size == 1:
generator = translator.generator.greedy_search
else:
generator = translator.generator.beam_search
preds, lengths, counter = generator(batch_size, bos, context)
if args.faulty:
fi.injectionMode = True
stats['total_dec_len'] = lengths.sum().item()
stats['iters'] = counter
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_idx, line in enumerate(output):
score_timer = time.time()
detok_sentence = detokenizeSentence(args, line)
chunk = (batch_idx * batch_size) + line_idx
score = scoreBleuSentence(args, detok_sentence,
reference_content[chunk])
bleu_score.update(score)
record.addBleuScores(score)
# Get timing
elapsed = time.time() - score_timer
score_time.update(elapsed)
test_file.write(line)
test_file.write('\n')
# Get timing
elapsed = time.time() - translate_timer
batch_time.update(elapsed, batch_size)
total_tokens = stats['total_dec_len'] + stats['total_enc_len']
ttps = total_tokens / elapsed
tot_tok_per_sec.update(ttps, batch_size)
iterations.update(stats['iters'])
enc_seq_len.update(stats['total_enc_len'] / batch_size, batch_size)
dec_seq_len.update(stats['total_dec_len'] / batch_size, batch_size)
if batch_idx % args.print_freq == 0:
print('[Test {}] Time: {:.3f} ({:.3f})\t \
Decoder iters {:.1f} ({:.1f})\t \
Tok/s {:.0f} ({:.0f})\n \
Bleu score: {:.2f} ({:.2f})\t \
Bleu time: {:.3f} ({:.3f})'.format(
batch_idx, batch_time.val, batch_time.avg, iterations.val,
iterations.avg, tot_tok_per_sec.val, tot_tok_per_sec.avg,
bleu_score.val, bleu_score.avg, score_time.val,
score_time.avg))
# summary timing
time_per_sentence = (batch_time.avg / batch_size)
print('[Test] Summary \n \
Lines translated: {}\t \
Avg total tokens/s: {:.0f}\n \
Avg time per batch: {:.3f} s\t \
Avg time per sentence: {:.3f} ms\n \
Avg encoder seq len: {:.2f}\t \
Avg decoder seq len: {:.2f}\t \
Total decoder iterations: {}\n \
Traverse time : {:.3f} s\t \
Total number of injections: {}'.format(
len(test_loader.dataset), tot_tok_per_sec.avg, batch_time.avg,
1000 * time_per_sentence, enc_seq_len.avg, dec_seq_len.avg,
int(iterations.sum), traverse_time.val if args.faulty else 0.0,
int(fi.numInjections) if args.faulty else 0))
test_file.close()
detok = detokenizeFile(args)
bleu = scoreBleuFile(args, detok)
record.setBleuScoreAvg(bleu)
saveRecord(
args.record_prefix + getRecordPrefix(args, 'fp32', faulty=args.faulty),
record)
print('BLEU on test dataset: {}'.format(bleu))
# Get timing
execution_elapsed = time.time() - execution_timer
print('Finished evaluation on test set in {:.2f} seconds'.format(
execution_elapsed))
