def train_translation_model(data_dir, arch, extra_flags=None):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', 'translation',
data_dir,
'--save-dir', data_dir,
'--arch', arch,
'--optimizer', 'nag',
'--lr', '0.05',
'--max-tokens', '500',
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
'--source-lang', 'in',
'--target-lang', 'out',
] + (extra_flags or []),
)
train.main(train_args)
def train_language_model(data_dir, arch):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
'--task', 'language_modeling',
data_dir,
'--arch', arch,
'--optimizer', 'nag',
'--lr', '1.0',
'--criterion', 'adaptive_loss',
'--adaptive-softmax-cutoff', '5,10,15',
'--decoder-layers', '[(850, 3)] * 2 + [(1024,4)]',
'--decoder-embed-dim', '280',
'--max-tokens', '500',
'--tokens-per-sample', '500',
'--save-dir', data_dir,
'--max-epoch', '1',
'--no-progress-bar',
'--distributed-world-size', '1',
],
)
train.main(train_args)
def load_dataset_splits(task, splits):
for split in splits:
if split == 'train':
task.load_dataset(split, combine=True)
else:
for k in itertools.count():
split_k = split + (str(k) if k > 0 else '')
try:
task.load_dataset(split_k, combine=False)
except FileNotFoundError as e:
if k > 0:
break
raise e
if __name__ == '__main__':
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser)
if args.distributed_port > 0 or args.distributed_init_method is not None:
from distributed_train import main as distributed_main
distributed_main(args)
elif args.distributed_world_size > 1:
from multiprocessing_train import main as multiprocessing_main
multiprocessing_main(args)
else:
main(args)
def cli_main():
parser = options.get_training_parser()
parser.add_argument(
"--comet-logging",
action="store_true",
help="Whether to use Comet.ML for logging",
)
args = options.parse_args_and_arch(parser)
logging = getattr(args, "comet_logging", False)
config = None
if logging:
PROJECT = "machine-translation"
if not keyring.get_password("comet", PROJECT):
comet_ml_api_key = getpass("Please enter the comet.ml API key: ")
keyring.set_password("comet", PROJECT, comet_ml_api_key)
else:
comet_ml_api_key = keyring.get_password("comet", PROJECT)
experiment = Experiment(
api_key=comet_ml_api_key,
project_name="machine-translation",
workspace="machine-translation",
auto_output_logging=None,
)
config = {
"api_key": comet_ml_api_key,
"experiment_key": experiment.get_key()
}
print("Proceeding with Comet.ML logging...")
if args.distributed_init_method is None:
distributed_utils.infer_init_method(args)
if args.distributed_init_method is not None:
# distributed training
if torch.cuda.device_count() > 1 and not args.distributed_no_spawn:
start_rank = args.distributed_rank
args.distributed_rank = None # assign automatically
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, config, start_rank),
nprocs=torch.cuda.device_count(),
)
else:
distributed_main(args.device_id, args, config)
elif args.distributed_world_size > 1:
# fallback for single node with multiple GPUs
assert args.distributed_world_size <= torch.cuda.device_count()
port = random.randint(10000, 20000)
args.distributed_init_method = "tcp://localhost:{port}".format(
port=port)
args.distributed_rank = None # set based on device id
if max(args.update_freq) > 1 and args.ddp_backend != "no_c10d":
print(
"| NOTE: you may get better performance with: --ddp-backend=no_c10d"
)
torch.multiprocessing.spawn(fn=distributed_main,
args=(args, config),
nprocs=args.distributed_world_size)
else:
# single GPU training
main(args, config=config)
if config:
experiment.end()
def train_translation_model(
data_dir,
arch,
extra_flags=None,
task="translation",
run_validation=False,
lang_flags=None,
extra_valid_flags=None,
):
if lang_flags is None:
lang_flags = [
"--source-lang",
"in",
"--target-lang",
"out",
]
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--save-dir",
data_dir,
"--arch",
arch,
"--optimizer",
"nag",
"--lr",
"0.05",
"--max-tokens",
"500",
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--num-workers",
"0",
]
+ lang_flags
+ (extra_flags or []),
)
train.main(train_args)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
"--num-workers",
"0",
]
+ lang_flags
+ (extra_valid_flags or []),
)
validate.main(validate_args)
def _quantize_language_model(data_dir,
arch,
extra_flags=None,
run_validation=False):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
0,
] + (extra_flags or []),
)
train.main(train_args)
# try scalar quantization
scalar_quant_train_parser = options.get_training_parser()
scalar_quant_train_args = options.parse_args_and_arch(
scalar_quant_train_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-update",
"3",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
0,
"--quant-noise-scalar",
"0.5",
] + (extra_flags or []),
)
train.main(scalar_quant_train_args)
# try iterative PQ quantization
quantize_parser = options.get_training_parser()
quantize_args = options.parse_args_and_arch(
quantize_parser,
[
"--task",
"language_modeling",
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--criterion",
"adaptive_loss",
"--adaptive-softmax-cutoff",
"5,10,15",
"--max-tokens",
"50",
"--tokens-per-sample",
"50",
"--max-update",
"6",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--ddp-backend",
"no_c10d",
"--num-workers",
0,
"--restore-file",
os.path.join(data_dir, "checkpoint_last.pt"),
"--reset-optimizer",
"--quantization-config-path",
os.path.join(os.path.dirname(__file__),
"transformer_quantization_config.yaml"),
] + (extra_flags or []),
)
train.main(quantize_args)
def train_language_model(
data_dir,
arch,
extra_flags=None,
run_validation=False,
extra_valid_flags=None,
task="language_modeling",
world_size=1,
):
train_parser = options.get_training_parser()
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
task,
data_dir,
"--arch",
arch,
"--optimizer",
"adam",
"--lr",
"0.0001",
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
str(world_size),
"--ddp-backend",
"no_c10d",
"--num-workers",
"0",
] + (extra_flags or []),
)
cfg = convert_namespace_to_omegaconf(train_args)
distributed_utils.call_main(cfg, train.main)
if run_validation:
# test validation
validate_parser = options.get_validation_parser()
validate_args = options.parse_args_and_arch(
validate_parser,
[
"--task",
task,
data_dir,
"--path",
os.path.join(data_dir, "checkpoint_last.pt"),
"--valid-subset",
"valid",
"--max-tokens",
"500",
"--no-progress-bar",
"--num-workers",
"0",
] + (extra_valid_flags or []),
)
validate.main(validate_args)
def train_main(alpha, beta, save_path):
parser = options.get_training_parser()
input_args = [
data_set, '--share-decoder-input-output-embed', '--arch',
'transformer_iwslt_de_en', '--max-tokens', '4000', '--lr', '5e-4',
'--save-interval', '2', '--max-epoch', '85', '--patience', '5',
'--optimizer', 'adam', '--adam-betas', '(0.9, 0.98)', '--clip-norm',
'0.0', '--weight-decay', '0.0001', '--dropout', '0.3',
'--lr-scheduler', 'inverse_sqrt', '--warmup-updates', '4000',
'--keep-last-epochs', '4', '--criterion', 'jensen_cross_entropy',
'--alpha',
str(alpha), '--beta',
str(beta), '--use-uniform', '--fp16', '--save-dir', save_path
]
args = options.parse_args_and_arch(parser, input_args=input_args)
if args.distributed_init_method is None:
distributed_utils.infer_init_method(args)
if args.distributed_init_method is not None:
# distributed training
if torch.cuda.device_count() > 1 and not args.distributed_no_spawn:
start_rank = args.distributed_rank
args.distributed_rank = None # assign automatically
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, start_rank),
nprocs=torch.cuda.device_count(),
)
else:
distributed_main(args.device_id, args)
elif args.distributed_world_size > 1:
# fallback for single node with multiple GPUs
assert args.distributed_world_size <= torch.cuda.device_count()
port = random.randint(10000, 20000)
args.distributed_init_method = 'tcp://localhost:{port}'.format(
port=port)
args.distributed_rank = None # set based on device id
if max(args.update_freq) > 1 and args.ddp_backend != 'no_c10d':
print(
'| NOTE: you may get better performance with: --ddp-backend=no_c10d'
)
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, ),
nprocs=args.distributed_world_size,
)
else:
# single GPU training
main(args)
ckpts = os.listdir(args.save_dir)
try:
ckpts.remove('checkpoint_last.pt')
except ValueError:
print("no checkpoint_last.pt in folder", args.save_dir)
f = open(os.path.join(args.save_dir, "final_entropies.txt"), "a+")
results = {}
entropies = {}
for ckpt in ckpts:
if '.pt' in ckpt:
path = os.path.join(args.save_dir, ckpt)
f.write(path + '\n')
run_generation(path, results, entropies)
f.write('{entropy: ' + str(entropies[path]) + ', bleu: ' +
str(results[path]) + '}\n')
f.close()
return results
def cli_main():
parser = options.get_training_parser()
parser.add_argument(
'--config',
type=str,
nargs='*',
help=
'paths to JSON files of experiment configurations, from high to low priority',
)
parser.add_argument('--exp-name',
type=str,
default='',
help='name of the experiment')
parser.add_argument(
'--debug',
default=False,
action='store_true',
help='run training in the debugging mode',
)
parser.add_argument('--path-attributes',
type=str,
nargs='*',
default=['task', 'arch', 'lr'])
parser.add_argument(
'--filter_best_last_ckpts',
type=str,
default=False,
help=
'whether to filter out checkpoint_best and checkpoint_last from checkpoint list'
)
parser.add_argument('--log_valid_progress',
type=str,
default=False,
help='whether to log validation progress')
pre_parsed_args, unknown = parser.parse_known_args()
config_dict = {}
for config_path in pre_parsed_args.config:
config_dict = update_config(config_dict, compose_configs(config_path))
parser_modifier = modify_factory(config_dict)
args = options.parse_args_and_arch(parser, modify_parser=parser_modifier)
update_namespace(args, config_dict)
if args.distributed_init_method is None:
distributed_utils.infer_init_method(args)
if args.distributed_init_method is not None:
# distributed training
if torch.cuda.device_count() > 1 and not args.distributed_no_spawn:
start_rank = args.distributed_rank
args.distributed_rank = None # assign automatically
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, start_rank),
nprocs=torch.cuda.device_count(),
)
else:
distributed_main(args.device_id, args)
elif args.distributed_world_size > 1:
# fallback for single node with multiple GPUs
assert args.distributed_world_size <= torch.cuda.device_count()
port = random.randint(10000, 20000)
args.distributed_init_method = 'tcp://localhost:{port}'.format(
port=port)
args.distributed_rank = None # set based on device id
if (args.update_freq is not None and max(args.update_freq) > 1
and args.ddp_backend != 'no_c10d'):
logger.info(
'NOTE: you may get faster training with: --ddp-backend=no_c10d'
)
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, ),
nprocs=args.distributed_world_size,
)
else:
# single GPU training
main(args)
def setup():
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser)
# make sure everything is reset before loading the model
args.reset_optimizer = True
args.reset_meters = True
args.reset_dataloader = True
args.reset_lr_scheduler = True
args.path = args.restore_file
args.max_sentences_valid = 1 # We attack batch size 1 at the moment
args.beam = 1 # beam size 1 for inference on the model, could use higher
utils.import_user_module(args)
torch.manual_seed(args.seed)
# setup task, model, loss function, and trainer
task = tasks.setup_task(args)
if not args.interactive_attacks:
for valid_sub_split in args.valid_subset.split(
','): # load validation data
task.load_dataset(valid_sub_split, combine=False, epoch=0)
models, _ = checkpoint_utils.load_model_ensemble(args.path.split(':'),
arg_overrides={},
task=task)
assert len(
models) == 1 # Make sure you didn't pass an ensemble of models in
model = models[0]
if torch.cuda.is_available() and not args.cpu:
assert torch.cuda.device_count() == 1 # only works on 1 GPU for now
torch.cuda.set_device(0)
model.cuda()
args.beam = 1 # beam size 1 for now
model.make_generation_fast_(beamable_mm_beam_size=args.beam,
need_attn=False)
criterion = task.build_criterion(args)
trainer = Trainer(args, task, model, criterion)
generator = task.build_generator(args)
bpe_vocab_size = trainer.get_model().encoder.embed_tokens.weight.shape[0]
add_hooks(trainer.get_model(),
bpe_vocab_size) # add gradient hooks to embeddings
embedding_weight = get_embedding_weight(
trainer.get_model(), bpe_vocab_size) # save the embedding matrix
if not args.interactive_attacks:
subset = args.valid_subset.split(',')[
0] # only one validation subset handled
itr = trainer.task.get_batch_iterator(
dataset=trainer.task.dataset(subset),
max_tokens=args.max_tokens_valid,
max_sentences=args.max_sentences_valid,
max_positions=utils.resolve_max_positions(
trainer.task.max_positions(),
trainer.get_model().max_positions(),
),
ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=args.required_batch_size_multiple,
seed=args.seed,
num_shards=args.distributed_world_size,
shard_id=args.distributed_rank,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
else:
itr = [
None
] * 100000 # a fake dataset to go through, overwritten when doing interactive attacks
# Handle BPE
bpe = encoders.build_bpe(args)
assert bpe is not None
return args, trainer, generator, embedding_weight, itr, bpe
def fairseq_train(
preprocessed_dir,
exp_dir,
ngpus=None,
max_tokens=2000,
arch='fconv_iwslt_de_en',
pretrained_emb_path=None,
embeddings_dim=None,
# Transformer (decoder is the same as encoder for now)
encoder_embed_dim=512,
encoder_layers=6,
encoder_attention_heads=8,
# encoder_decoder_dim_ratio=1,
# share_embeddings=True,
max_epoch=50,
warmup_updates=None,
lr=0.1,
min_lr=1e-9,
dropout=0.2,
label_smoothing=0.1,
lr_scheduler='fixed',
weight_decay=0.0001,
criterion='label_smoothed_cross_entropy',
optimizer='nag',
validations_before_sari_early_stopping=10,
fp16=False):
exp_dir = Path(exp_dir)
with log_stdout(exp_dir / 'fairseq_train.stdout'):
preprocessed_dir = Path(preprocessed_dir)
exp_dir.mkdir(exist_ok=True, parents=True)
# Copy dictionaries to exp_dir for generation
shutil.copy(preprocessed_dir / 'dict.complex.txt', exp_dir)
shutil.copy(preprocessed_dir / 'dict.simple.txt', exp_dir)
train_parser = options.get_training_parser()
# if share_embeddings:
# assert encoder_decoder_dim_ratio == 1
args = [
'--task',
'translation',
preprocessed_dir,
'--raw-text',
'--source-lang',
'complex',
'--target-lang',
'simple',
'--save-dir',
os.path.join(exp_dir, 'checkpoints'),
'--clip-norm',
0.1,
'--criterion',
criterion,
'--no-epoch-checkpoints',
'--save-interval-updates',
5000, # Validate every n updates
'--validations-before-sari-early-stopping',
validations_before_sari_early_stopping,
'--arch',
arch,
# '--decoder-out-embed-dim', int(embeddings_dim * encoder_decoder_dim_ratio), # Output dim of decoder
'--max-tokens',
max_tokens,
'--max-epoch',
max_epoch,
'--lr-scheduler',
lr_scheduler,
'--dropout',
dropout,
'--lr',
lr,
'--lr-shrink',
0.5, # For reduce lr on plateau scheduler
'--min-lr',
min_lr,
'--weight-decay',
weight_decay,
'--optimizer',
optimizer,
'--label-smoothing',
label_smoothing,
'--seed',
random.randint(1, 1000),
# '--force-anneal', '200',
# '--distributed-world-size', '1',
]
if arch == 'transformer':
args.extend([
'--encoder-embed-dim',
encoder_embed_dim,
'--encoder-ffn-embed-dim',
4 * encoder_embed_dim,
'--encoder-layers',
encoder_layers,
'--encoder-attention-heads',
encoder_attention_heads,
'--decoder-layers',
encoder_layers,
'--decoder-attention-heads',
encoder_attention_heads,
])
if pretrained_emb_path is not None:
args.extend([
'--encoder-embed-path',
pretrained_emb_path if pretrained_emb_path is not None else ''
])
args.extend([
'--decoder-embed-path',
pretrained_emb_path if pretrained_emb_path is not None else ''
])
if embeddings_dim is not None:
args.extend(['--encoder-embed-dim',
embeddings_dim]) # Input and output dim of encoder
args.extend(['--decoder-embed-dim',
embeddings_dim]) # Input dim of decoder
if ngpus is not None:
args.extend(['--distributed-world-size', ngpus])
# if share_embeddings:
# args.append('--share-input-output-embed')
if fp16:
args.append('--fp16')
if warmup_updates is not None:
args.extend(['--warmup-updates', warmup_updates])
args = [str(arg) for arg in args]
train_args = options.parse_args_and_arch(train_parser, args)
train.main(train_args)
def test_masks_token_spans(self):
with TemporaryDirectory() as dirname:
# prep input file
raw_file = os.path.join(dirname, "raw")
data = make_data(out_file=raw_file)
vocab = build_vocab(data)
# binarize
binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False)
split = "train"
bin_file = os.path.join(dirname, split)
dataset_impl = "mmap"
FileBinarizer.multiprocess_dataset(
input_file=raw_file,
binarizer=binarizer,
dataset_impl=dataset_impl,
vocab_size=len(vocab),
output_prefix=bin_file,
)
# adding sentinel tokens
for i in range(100):
vocab.add_symbol(f"<extra_id_{i}>")
# setup task
train_args = options.parse_args_and_arch(
options.get_training_parser(),
[
"--task",
"span_masked_lm",
"--arch",
"bart_base",
"--seed",
"42",
dirname,
],
)
cfg = convert_namespace_to_omegaconf(train_args)
task = SpanMaskedLMTask(cfg.task, binarizer.dict)
# load datasets
original_dataset = task._load_dataset_split(bin_file, 1, False)
task.load_dataset(split)
masked_dataset = task.dataset(split)
iterator = task.get_batch_iterator(
dataset=masked_dataset,
max_tokens=65_536,
max_positions=4_096,
).next_epoch_itr(shuffle=False)
num_tokens = len(vocab)
for batch in iterator:
for sample in range(len(batch)):
sample_id = batch["id"][sample]
original_tokens = original_dataset[sample_id]
masked_src_tokens = batch["net_input"]["src_tokens"][
sample]
masked_src_length = batch["net_input"]["src_lengths"][
sample]
masked_tgt_tokens = batch["target"][sample]
original_offset = 0
masked_tgt_offset = 0
extra_id_token = len(vocab) - 1
for masked_src_token in masked_src_tokens[:
masked_src_length]:
if masked_src_token == extra_id_token:
assert (masked_src_token ==
masked_tgt_tokens[masked_tgt_offset])
extra_id_token -= 1
masked_tgt_offset += 1
while (original_offset < len(original_tokens)
and masked_tgt_tokens[masked_tgt_offset] !=
extra_id_token):
assert (original_tokens[original_offset] ==
masked_tgt_tokens[masked_tgt_offset])
original_offset += 1
masked_tgt_offset += 1
else:
assert original_tokens[
original_offset] == masked_src_token
original_offset += 1
def test_denoising(self):
with TemporaryDirectory() as dirname:
# prep input file
raw_file = os.path.join(dirname, "raw")
data = make_data(out_file=raw_file)
vocab = build_vocab(data)
# binarize
binarizer = VocabularyDatasetBinarizer(vocab, append_eos=False)
split = "train"
bin_file = os.path.join(dirname, split)
dataset_impl = "mmap"
FileBinarizer.multiprocess_dataset(
input_file=raw_file,
binarizer=binarizer,
dataset_impl=dataset_impl,
vocab_size=len(vocab),
output_prefix=bin_file,
)
# setup task
train_args = options.parse_args_and_arch(
options.get_training_parser(),
[
"--task",
"denoising",
"--arch",
"bart_base",
"--seed",
"42",
"--mask-length",
"word",
"--permute-sentences",
"1",
"--rotate",
"0",
"--replace-length",
"-1",
"--mask",
"0.2",
dirname,
],
)
cfg = convert_namespace_to_omegaconf(train_args)
task = DenoisingTask(cfg.task, binarizer.dict)
# load datasets
original_dataset = task._load_dataset_split(bin_file, 1, False)
task.load_dataset(split)
masked_dataset = task.dataset(split)
iterator = task.get_batch_iterator(
dataset=masked_dataset,
max_tokens=65_536,
max_positions=4_096,
).next_epoch_itr(shuffle=False)
mask_index = task.source_dictionary.index("<mask>")
for batch in iterator:
for sample in range(len(batch)):
net_input = batch["net_input"]
masked_src_tokens = net_input["src_tokens"][sample]
masked_src_length = net_input["src_lengths"][sample]
masked_tgt_tokens = batch["target"][sample]
sample_id = batch["id"][sample]
original_tokens = original_dataset[sample_id]
original_tokens = original_tokens.masked_select(
masked_src_tokens[:masked_src_length] == mask_index
)
masked_tokens = masked_tgt_tokens.masked_select(
masked_src_tokens == mask_index
)
assert masked_tokens.equal(original_tokens)
def train_masked_language_model(data_dir, arch, extra_args=()):
train_parser = options.get_training_parser()
# TODO: langs should be in and out right?
train_args = options.parse_args_and_arch(
train_parser,
[
"--task",
"cross_lingual_lm",
data_dir,
"--arch",
arch,
# Optimizer args
"--optimizer",
"adam",
"--lr-scheduler",
"reduce_lr_on_plateau",
"--lr-shrink",
"0.5",
"--lr",
"0.0001",
"--min-lr",
"1e-09",
# dropout, attention args
"--dropout",
"0.1",
"--attention-dropout",
"0.1",
# MLM args
"--criterion",
"masked_lm_loss",
"--masked-lm-only",
"--monolingual-langs",
"in,out",
"--num-segment",
"5",
# Transformer args: use a small transformer model for fast training
"--encoder-layers",
"1",
"--encoder-embed-dim",
"32",
"--encoder-attention-heads",
"1",
"--encoder-ffn-embed-dim",
"32",
# Other training args
"--max-tokens",
"500",
"--tokens-per-sample",
"500",
"--save-dir",
data_dir,
"--max-epoch",
"1",
"--no-progress-bar",
"--distributed-world-size",
"1",
"--dataset-impl",
"raw",
] + list(extra_args),
)
train.main(train_args)
def cli_main():
parser = options.get_training_parser()
parser.add_argument(
'--train-subtransformer',
action='store_true',
default=False,
help='whether train SuperTransformer or SubTransformer')
parser.add_argument(
'--sub-configs',
required=False,
is_config_file=True,
help=
'when training SubTransformer, use --configs to specify architecture and --sub-configs to specify other settings'
)
# for profiling
parser.add_argument('--profile-flops',
action='store_true',
help='measure the FLOPs of a SubTransformer')
parser.add_argument('--latgpu',
action='store_true',
help='measure SubTransformer latency on GPU')
parser.add_argument('--latcpu',
action='store_true',
help='measure SubTransformer latency on CPU')
parser.add_argument(
'--latiter',
type=int,
default=300,
help='how many iterations to run when measure the latency')
parser.add_argument('--latsilent',
action='store_true',
help='keep silent when measure latency')
parser.add_argument(
'--validate-subtransformer',
action='store_true',
help='evaluate the SubTransformer on the validation set')
options.add_generation_args(parser)
args = options.parse_args_and_arch(parser)
if args.latcpu:
args.cpu = True
args.fp16 = False
if args.latgpu or args.latcpu or args.profile_flops:
args.distributed_world_size = 1
#if args.distributed_init_method is None:
# distributed_utils.infer_init_method(args)
if args.distributed_init_method is not None:
# distributed training
#if torch.cuda.device_count() > 1 and not args.distributed_no_spawn:
if not args.distributed_no_spawn:
start_rank = args.distributed_rank
args.distributed_rank = None # assign automatically
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, start_rank),
#nprocs=torch.cuda.device_count(),
nprocs=8, #Use all TPU cores
)
else:
distributed_main(args.device_id, args)
elif args.distributed_world_size > 1:
# fallback for single node with multiple GPUs
#assert args.distributed_world_size <= torch.cuda.device_count()
import torch_xla.distributed.xla_multiprocessing as xmp
torch.multiprocessing.set_sharing_strategy("file_system")
port = random.randint(10000, 20000)
args.distributed_init_method = 'tcp://localhost:{port}'.format(
port=port)
args.distributed_rank = None # set based on device id
if max(args.update_freq) > 1 and args.ddp_backend != 'no_c10d':
print(
'| NOTE: you may get better performance with: --ddp-backend=no_c10d'
)
xmp.spawn(
fn=distributed_main,
args=(args, ),
nprocs=8, # use all 8 TPU cores
)
#torch.multiprocessing.spawn(
# fn=distributed_main,
# args=(args, ),
# nprocs=args.distributed_world_size,
#)
else:
# single GPU training
main(args)
def main(args):
if args.distributed_init_method is None and args.distributed_port > 0:
# We can determine the init method automatically for Slurm.
node_list = os.environ.get('SLURM_JOB_NODELIST')
if node_list is not None:
try:
hostnames = subprocess.check_output(['scontrol', 'show', 'hostnames', node_list])
args.distributed_init_method = 'tcp://{host}:{port}'.format(
host=hostnames.split()[0].decode('utf-8'),
port=args.distributed_port)
args.distributed_rank = int(os.environ.get('SLURM_PROCID'))
args.device_id = int(os.environ.get('SLURM_LOCALID'))
except subprocess.CalledProcessError as e: # scontrol failed
raise e
except FileNotFoundError as e: # Slurm is not installed
pass
if args.distributed_init_method is None:
raise ValueError('--distributed-init-method or --distributed-port '
'must be specified for distributed training')
args.distributed_rank = distributed_utils.distributed_init(args)
print('| initialized host {} as rank {}'.format(socket.gethostname(), args.distributed_rank))
single_process_main(args)
if __name__ == '__main__':
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser)
main(args)
def cli_main():
parser = options.get_training_parser()
parser.add_argument(
'--config',
type=str,
nargs='*',
help=
'paths to JSON files of experiment configurations, from high to low priority',
)
parser.add_argument('--exp-name',
type=str,
default='',
help='name of the experiment')
parser.add_argument(
'--debug',
default=False,
action='store_true',
help='run training in the debugging mode',
)
parser.add_argument('--path-attributes',
type=str,
nargs='*',
default=['task', 'arch', 'lr'])
parser.add_argument('--torch-file-system', action='store_true')
pre_parsed_args, unknown = parser.parse_known_args()
config_dict = {}
for config_path in pre_parsed_args.config:
config_dict = update_config(config_dict, compose_configs(config_path))
parser_modifier = modify_factory(config_dict)
args = options.parse_args_and_arch(parser, modify_parser=parser_modifier)
update_namespace(args, config_dict)
# set sharing strategy file system in case /dev/shm/ limits are small
if args.torch_file_system:
torch.multiprocessing.set_sharing_strategy('file_system')
training_name = get_training_name(args)
base_save_dir = generate_save_dir(args, training_name, sys.argv[1:])
setattr(args, 'training_name', training_name)
setattr(args, 'save_dir', os.path.join(base_save_dir, 'checkpoints'))
setattr(args, 'tensorboard_logdir',
os.path.join(base_save_dir, 'tensorboard'))
save_config(vars(args), base_save_dir)
if args.distributed_init_method is None:
distributed_utils.infer_init_method(args)
if args.distributed_init_method is not None:
# distributed training
if torch.cuda.device_count() > 1 and not args.distributed_no_spawn:
start_rank = args.distributed_rank
args.distributed_rank = None # assign automatically
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, start_rank),
nprocs=torch.cuda.device_count(),
)
else:
distributed_main(args.device_id, args)
elif args.distributed_world_size > 1:
# fallback for single node with multiple GPUs
assert args.distributed_world_size <= torch.cuda.device_count()
port = random.randint(10000, 20000)
args.distributed_init_method = 'tcp://localhost:{port}'.format(
port=port)
args.distributed_rank = None # set based on device id
if (args.update_freq is not None and max(args.update_freq) > 1
and args.ddp_backend != 'no_c10d'):
logger.info(
'NOTE: you may get faster training with: --ddp-backend=no_c10d'
)
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, ),
nprocs=args.distributed_world_size,
)
else:
# single GPU training
main(args)