def do_train(args):
paddle.enable_static()
if args.is_distributed:
fleet.init(is_collective=True)
assert args.device != "xpu", "xpu doesn't support distributed training"
places = [paddle.set_device("gpu")] if \
args.device == "gpu" else paddle.static.cpu_places()
trainer_count = len(places)
else:
if args.device == "gpu":
places = paddle.static.cuda_places()
elif args.device == "xpu":
places = paddle.static.xpu_places()
paddle.set_device("xpu")
else:
places = paddle.static.cpu_places()
paddle.set_device("cpu")
trainer_count = len(places)
# Set seed for CE
random_seed = eval(str(args.random_seed))
if random_seed is not None:
paddle.seed(random_seed)
# Define data loader
(train_loader), (eval_loader) = reader.create_data_loader(args,
places=places)
train_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(train_program, startup_program):
src_word = paddle.static.data(name="src_word",
shape=[None, None],
dtype=args.input_dtype)
trg_word = paddle.static.data(name="trg_word",
shape=[None, None],
dtype=args.input_dtype)
lbl_word = paddle.static.data(name="lbl_word",
shape=[None, None, 1],
dtype=args.input_dtype)
# Define model
transformer = TransformerModel(src_vocab_size=args.src_vocab_size,
trg_vocab_size=args.trg_vocab_size,
max_length=args.max_length + 1,
num_encoder_layers=args.n_layer,
num_decoder_layers=args.n_layer,
n_head=args.n_head,
d_model=args.d_model,
d_inner_hid=args.d_inner_hid,
dropout=args.dropout,
weight_sharing=args.weight_sharing,
bos_id=args.bos_idx,
eos_id=args.eos_idx)
# Define loss
criterion = CrossEntropyCriterion(args.label_smooth_eps, args.bos_idx)
logits = transformer(src_word=src_word, trg_word=trg_word)
sum_cost, avg_cost, token_num = criterion(logits, lbl_word)
scheduler = paddle.optimizer.lr.NoamDecay(args.d_model,
args.warmup_steps,
args.learning_rate,
last_epoch=0)
# Define optimizer
optimizer = paddle.optimizer.Adam(learning_rate=scheduler,
beta1=args.beta1,
beta2=args.beta2,
epsilon=float(args.eps),
parameters=transformer.parameters())
if args.is_distributed:
build_strategy = paddle.static.BuildStrategy()
exec_strategy = paddle.static.ExecutionStrategy()
dist_strategy = fleet.DistributedStrategy()
dist_strategy.build_strategy = build_strategy
dist_strategy.execution_strategy = exec_strategy
dist_strategy.fuse_grad_size_in_MB = 16
if args.use_amp:
dist_strategy.amp = True
dist_strategy.amp_configs = {
'custom_white_list': ['softmax', 'layer_norm'],
'init_loss_scaling': args.scale_loss,
'custom_black_list': ['lookup_table_v2'],
'use_pure_fp16': args.use_pure_fp16
}
optimizer = fleet.distributed_optimizer(optimizer,
strategy=dist_strategy)
else:
if args.use_amp:
amp_list = paddle.static.amp.AutoMixedPrecisionLists(
custom_white_list=['softmax', 'layer_norm'],
custom_black_list=['lookup_table_v2'])
optimizer = paddle.static.amp.decorate(
optimizer,
amp_list,
init_loss_scaling=args.scale_loss,
use_dynamic_loss_scaling=True,
use_pure_fp16=args.use_pure_fp16)
optimizer.minimize(avg_cost)
if args.is_distributed:
exe = paddle.static.Executor(places[0])
else:
exe = paddle.static.Executor()
build_strategy = paddle.static.BuildStrategy()
exec_strategy = paddle.static.ExecutionStrategy()
compiled_train_program = paddle.static.CompiledProgram(
train_program).with_data_parallel(loss_name=avg_cost.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
exe.run(startup_program)
if args.use_amp:
optimizer.amp_init(places[0])
# the best cross-entropy value with label smoothing
loss_normalizer = -(
(1. - args.label_smooth_eps) * np.log((1. - args.label_smooth_eps)) +
args.label_smooth_eps * np.log(args.label_smooth_eps /
(args.trg_vocab_size - 1) + 1e-20))
step_idx = 0
# For benchmark
reader_cost_avg = AverageStatistical()
batch_cost_avg = AverageStatistical()
batch_ips_avg = AverageStatistical()
for pass_id in range(args.epoch):
batch_id = 0
batch_start = time.time()
pass_start_time = batch_start
for data in train_loader:
# NOTE: used for benchmark and use None as default.
if args.max_iter and step_idx == args.max_iter:
break
if trainer_count == 1:
data = [data]
train_reader_cost = time.time() - batch_start
if args.is_distributed:
outs = exe.run(train_program,
feed=[{
'src_word': data[i][0],
'trg_word': data[i][1],
'lbl_word': data[i][2],
} for i in range(trainer_count)],
fetch_list=[sum_cost.name, token_num.name])
train_batch_cost = time.time() - batch_start
batch_ips_avg.record(train_batch_cost,
np.asarray(outs[1]).sum())
else:
outs = exe.run(compiled_train_program,
feed=[{
'src_word': data[i][0],
'trg_word': data[i][1],
'lbl_word': data[i][2],
} for i in range(trainer_count)],
fetch_list=[sum_cost.name, token_num.name])
train_batch_cost = time.time() - batch_start
batch_ips_avg.record(train_batch_cost,
np.asarray(outs[1]).sum() / trainer_count)
scheduler.step()
reader_cost_avg.record(train_reader_cost)
batch_cost_avg.record(train_batch_cost)
# Profile for model benchmark
if args.profiler_options is not None:
profiler.add_profiler_step(args.profiler_options)
if step_idx % args.print_step == 0 and (
args.benchmark or
(args.is_distributed and dist.get_rank() == 0)
or not args.is_distributed):
sum_cost_val, token_num_val = np.array(outs[0]), np.array(
outs[1])
# Sum the cost from multi-devices
total_sum_cost = sum_cost_val.sum()
total_token_num = token_num_val.sum()
total_avg_cost = total_sum_cost / total_token_num
if step_idx == 0:
logging.info(
"step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
"normalized loss: %f, ppl: %f" %
(step_idx, pass_id, batch_id, total_avg_cost,
total_avg_cost - loss_normalizer,
np.exp([min(total_avg_cost, 100)])))
else:
train_avg_batch_cost = args.print_step / batch_cost_avg.get_total_time(
)
logging.info(
"step_idx: %d, epoch: %d, batch: %d, avg loss: %f, "
"normalized loss: %f, ppl: %f, avg_speed: %.2f step/s, "
"batch_cost: %.5f sec, reader_cost: %.5f sec, tokens: %d, "
"ips: %.5f words/sec" %
(step_idx, pass_id, batch_id, total_avg_cost,
total_avg_cost - loss_normalizer,
np.exp([min(total_avg_cost, 100)]),
train_avg_batch_cost, batch_cost_avg.get_average(),
reader_cost_avg.get_average(),
batch_ips_avg.get_total_cnt(),
batch_ips_avg.get_average_per_sec()))
reader_cost_avg.reset()
batch_cost_avg.reset()
batch_ips_avg.reset()
if step_idx % args.save_step == 0 and step_idx != 0:
if args.save_model and dist.get_rank() == 0:
model_path = os.path.join(args.save_model,
"step_" + str(step_idx),
"transformer")
paddle.static.save(train_program, model_path)
batch_id += 1
step_idx += 1
batch_start = time.time()
# NOTE: used for benchmark and use None as default.
if args.max_iter and step_idx == args.max_iter:
break
if args.save_model and dist.get_rank() == 0:
model_path = os.path.join(args.save_model, "step_final", "transformer")
paddle.static.save(train_program, model_path)
paddle.disable_static()
def do_train(args):
if args.device == "gpu":
rank = dist.get_rank()
trainer_count = dist.get_world_size()
else:
rank = 0
trainer_count = 1
paddle.set_device("cpu")
if trainer_count > 1:
dist.init_parallel_env()
# Set seed for CE
if args.seed is not None:
set_seed(args.seed)
benchmark_model = MODEL_REGISTRY[args.model]()
benchmark_optimizer = OPTIMIZER_REGISTRY[args.optimizer]()
# Define data loader
train_loader, eval_loader = benchmark_model.create_data_loader(args)
if args.max_steps is None or (args.max_steps is not None and
args.max_steps < 0):
args.max_steps = len(train_loader) * args.epoch
# Define model
model = benchmark_model.build_model(args)
if args.lr_scheduler is not None:
benchmark_lr_scheduler = LR_SCHEDULER_REGISTRY[args.lr_scheduler]()
lr = benchmark_lr_scheduler.build_scheculer(args)
else:
lr = args.learning_rate
optimizer = benchmark_optimizer.build_optimizer(args, lr, model)
# for amp training
if args.use_amp:
scaler = paddle.amp.GradScaler(
enable=True, init_loss_scaling=args.scale_loss)
model = paddle.amp.decorate(
models=model, level=args.amp_level, save_dtype='float32')
# for distributed training
if trainer_count > 1:
model = paddle.DataParallel(model)
step_id = 1
# For benchmark
reader_cost_avg = AverageStatistical()
batch_cost_avg = AverageStatistical()
batch_ips_avg = AverageStatistical()
# Train loop
for pass_id in range(args.epoch):
epoch_start = time.time()
batch_id = 0
batch_start = time.time()
for input_data in train_loader:
train_reader_cost = time.time() - batch_start
if args.use_amp:
with paddle.amp.auto_cast(
custom_black_list=args.custom_black_list
if amp_level == 'O2' else {},
level=amp_level):
loss, sample_per_cards = benchmark_model.forward(
model, args, input_data)
scaled = scaler.scale(loss)
scaled.backward()
scaler.minimize(optimizer, scaled)
if 'set_to_zero' in inspect.getfullargspec(
optimizer.clear_grad).args:
optimizer.clear_grad(set_to_zero=False)
else:
optimizer.clear_grad()
else:
loss, sample_per_cards = benchmark_model.forward(model, args,
input_data)
loss.backward()
optimizer.step()
optimizer.clear_grad()
train_batch_cost = time.time() - batch_start
reader_cost_avg.record(train_reader_cost)
batch_cost_avg.record(train_batch_cost)
batch_ips_avg.record(train_batch_cost, sample_per_cards)
if args.profiler_options is not None:
profiler.add_profiler_step(args.profiler_options)
if step_id % args.logging_steps == 0:
total_avg_loss = loss.numpy()
benchmark_model.logger(
args,
step_id=step_id,
pass_id=pass_id,
batch_id=batch_id,
loss=total_avg_loss,
batch_cost=batch_cost_avg.get_average(),
reader_cost=reader_cost_avg.get_average(),
num_samples=sample_per_cards,
ips=batch_ips_avg.get_average_per_sec())
reader_cost_avg.reset()
batch_cost_avg.reset()
batch_ips_avg.reset()
if args.max_steps and step_id == args.max_steps:
if args.save_model and rank == 0:
model_dir = args.save_model
if not os.path.exists(model_dir):
os.makedirs(model_dir)
paddle.save(model.state_dict(),
os.path.join(model_dir, "model.pdparams"))
paddle.save(optimizer.state_dict(),
os.path.join(model_dir, "model.pdopt"))
return
batch_id += 1
step_id += 1
if args.lr_scheduler is not None and not args.scheduler_update_by_epoch:
lr.step()
batch_start = time.time()
if args.lr_scheduler is not None and args.scheduler_update_by_epoch:
lr.step()
train_epoch_cost = time.time() - epoch_start
logger.info("train epoch: %d, epoch_cost: %.5f s" %
(pass_id, train_epoch_cost))
def do_train(args):
# Initialize the paddle and paddle fleet execute enviroment
paddle.enable_static()
place = paddle.set_device(args.device)
fleet.init(is_collective=True)
worker_num = fleet.worker_num()
worker_index = fleet.worker_index()
# Create the random seed for the worker
set_seed(args.seed)
worker_init = WorkerInitObj(args.seed + worker_index)
# Define the input data in the static mode
main_program = paddle.static.default_main_program()
startup_program = paddle.static.default_startup_program()
data_holders = create_data_holder(args)
[
input_ids, segment_ids, input_mask, masked_lm_positions,
masked_lm_labels, next_sentence_labels, masked_lm_scale
] = data_holders
# Define the model structure in static mode
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
config = model_class.pretrained_init_configuration[args.model_name_or_path]
if config["vocab_size"] % 8 != 0:
config["vocab_size"] += 8 - (config["vocab_size"] % 8)
model = BertForPretraining(BertModel(**config))
criterion = BertPretrainingCriterion(model.bert.config["vocab_size"])
prediction_scores, seq_relationship_score = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_positions=masked_lm_positions)
loss = criterion(prediction_scores, seq_relationship_score,
masked_lm_labels, next_sentence_labels, masked_lm_scale)
# Define the dynamic learing_reate scheduler and optimizer
num_training_steps = args.max_steps if args.max_steps > 0 else len(
train_data_loader) * args.num_train_epochs
lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps,
args.warmup_steps)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params,
multi_precision=args.use_pure_fp16)
# Use the fleet api to compile the distributed optimizer
optimizer = dist_optimizer(args, optimizer)
optimizer.minimize(loss)
# Define the Executor for running the static model
exe = paddle.static.Executor(place)
exe.run(startup_program)
state_dict = model.state_dict()
# Use the state dict to update the parameter
reset_state_dict = reset_program_state_dict(model, state_dict)
paddle.static.set_program_state(main_program, reset_state_dict)
if args.use_amp:
optimizer.amp_init(place)
pool = ThreadPoolExecutor(1)
global_step = 0
tic_train = time.time()
epoch = 0
while True:
files = [
os.path.join(args.input_dir, f) for f in os.listdir(args.input_dir)
if os.path.isfile(os.path.join(args.input_dir, f)) and "training" in
f
]
files.sort()
num_files = len(files)
random.Random(args.seed + epoch).shuffle(files)
f_start_id = 0
# Select one file for each worker and create the DataLoader for the file
data_file = select_dataset_file_for_each_worker(
files, f_start_id, worker_num, worker_index)
train_data_loader, _ = create_pretraining_dataset(
data_file, args.max_predictions_per_seq, args, data_holders,
worker_init, paddle.static.cuda_places())
for f_id in range(f_start_id + 1, len(files)):
data_file = select_dataset_file_for_each_worker(
files, f_id, worker_num, worker_index)
dataset_future = pool.submit(create_pretraining_dataset, data_file,
args.max_predictions_per_seq, args,
data_holders, worker_init,
paddle.static.cuda_places())
train_cost_avg = TimeCostAverage()
reader_cost_avg = TimeCostAverage()
total_samples = 0
batch_start = time.time()
for step, batch in enumerate(train_data_loader):
train_reader_cost = time.time() - batch_start
reader_cost_avg.record(train_reader_cost)
global_step += 1
train_start = time.time()
loss_return = exe.run(main_program,
feed=batch,
fetch_list=[loss])
total_samples += args.batch_size
# In the new 2.0 api, must call this function to change the learning_rate
lr_scheduler.step()
train_run_cost = time.time() - batch_start
train_cost_avg.record(train_run_cost)
# Profile for model benchmark
if args.profiler_options is not None:
profiler.add_profiler_step(args.profiler_options)
if global_step % args.logging_steps == 0:
print(
"tobal step: %d, epoch: %d, batch: %d, loss: %f, "
"avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, avg_samples: %.5f, ips: %.5f sequences/sec"
% (global_step, epoch, step, loss_return[0],
reader_cost_avg.get_average(),
train_cost_avg.get_average(), total_samples /
args.logging_steps, args.batch_size / (
reader_cost_avg.get_average() +
train_cost_avg.get_average())))
total_samples = 0
train_cost_avg.reset()
reader_cost_avg.reset()
if global_step % args.save_steps == 0:
if worker_index == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model.save_model_config(output_dir)
paddle.static.save(main_program,
os.path.join(output_dir,
"model_state"))
tokenizer.save_pretrained(output_dir)
if global_step >= args.max_steps:
reader_start = time.time()
del train_data_loader
return
batch_start = time.time()
del train_data_loader
train_data_loader, data_file = dataset_future.result(timeout=None)
epoch += 1
def do_train(args):
paddle.set_device(args.device)
nranks = paddle.distributed.get_world_size()
strategy = fleet.DistributedStrategy()
strategy.hybrid_configs = {
"dp_degree": args.dp_degree,
"mp_degree": args.mp_degree,
"pp_degree": args.pp_degree,
"sharding_degree": args.sharding_degree
}
accumulate_steps = args.local_batch_size // args.micro_batch_size
strategy.pipeline_configs = {
"accumulate_steps": accumulate_steps,
"micro_batch_size": args.micro_batch_size
}
# set control in tensor parallel
strategy.tensor_parallel_configs = {"tensor_init_seed": args.seed}
fleet.init(is_collective=True, strategy=strategy)
# obtain rank message of hybrid parallel
hcg = fleet.get_hybrid_communicate_group()
global_rank = hcg.get_global_rank()
mp_rank = hcg.get_model_parallel_rank()
pp_rank = hcg.get_stage_id()
dp_rank = hcg.get_data_parallel_rank()
sharding_rank = hcg.get_sharding_parallel_rank()
# sharding stage2/3 not support hybrid parallel
if args.sharding_stage in [2, 3]:
assert args.dp_degree == args.mp_degree == args.pp_degree == 1, "sharding stage2/3 will support hybrid parallel later"
sharding_size = hcg.get_sharding_parallel_world_size()
data_world_rank = dp_rank * sharding_size + sharding_rank
data_world_size = args.dp_degree * args.sharding_degree
local_rank = int(os.getenv("PADDLE_RANK_IN_NODE", 0))
# seed control in hybrid parallel
set_hyrbid_parallel_seed(args.seed, data_world_rank, mp_rank, pp_rank)
default_global_tokens_num = args.global_batch_size * args.max_seq_len
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
# Define log writer
log_writer_path = os.path.join(
args.output_dir, "train_log",
"{}_globalbsz_{}_pure_fp16_{}_recompute_{}_card_{}".format(
args.model_name_or_path, args.global_batch_size, args.use_pure_fp16,
False, global_rank).lower())
if os.path.exists(log_writer_path):
import shutil
shutil.rmtree(log_writer_path)
log_writer = LogWriter(log_writer_path)
pretrained_models_list = list(
model_class.pretrained_init_configuration.keys())
if args.model_name_or_path in pretrained_models_list:
model_config = model_class.pretrained_init_configuration[
args.model_name_or_path]
model_config["hidden_dropout_prob"] = args.hidden_dropout_prob
model_config[
"attention_probs_dropout_prob"] = args.attention_probs_dropout_prob
model_config['num_partitions'] = args.mp_degree
model_config['use_recompute'] = args.use_recompute
if args.pp_degree == 1:
model = GPTForPretraining(GPTModel(**model_config))
else:
model_config['topology'] = hcg.topology()
model = GPTForPretrainingPipe(**model_config)
else:
model = GPTForPretraining.from_pretrained(
args.model_name_or_path,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob)
# Create the critrion for the gpt model
criterion = GPTPretrainingCriterion()
if args.decay_steps is None:
args.decay_steps = args.max_steps
warmup_step = args.warmup_rate * args.decay_steps
lr_scheduler = None
if args.lr_decay_style == "none":
lr_scheduler = None
elif args.lr_decay_style == "cosine":
lr_scheduler = lr.CosineAnnealingWithWarmupDecay(
max_lr=args.max_lr,
min_lr=args.min_lr,
warmup_step=warmup_step,
decay_step=args.decay_steps)
clip = None
if args.grad_clip > 0:
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=args.grad_clip)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
if args.sharding_stage == 1 and args.sharding_degree > 1:
optimizer = DygraphShardingOptimizer(
hcg=fleet.get_hybrid_communicate_group(),
user_defined_strategy=strategy,
params=model.parameters(),
inner_optimizer_class=paddle.optimizer.AdamW,
learning_rate=lr_scheduler
if lr_scheduler is not None else args.max_lr,
beta1=args.adam_beta1,
beta2=args.adam_beta2,
epsilon=args.adam_epsilon,
weight_decay=args.weight_decay,
grad_clip=clip,
apply_decay_param_fun=lambda x: x in decay_params)
else:
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler
if lr_scheduler is not None else args.max_lr,
beta1=args.adam_beta1,
beta2=args.adam_beta2,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
grad_clip=clip,
apply_decay_param_fun=lambda x: x in decay_params,
# TODO: remove 'multi_precision' in definition of optimizer
# and add it to 'paddle.amp.decorate'
multi_precision=args.use_pure_fp16)
if args.use_pure_fp16:
scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
# level O2 means converting the network to FP16
if args.sharding_stage not in [2, 3]:
scaler = fleet.distributed_scaler(scaler)
model = paddle.amp.decorate(
models=model, level='O2', save_dtype='float32')
# wrap sharding stage2/3 and add collective group
# TODO(Baibaifan): combine ShardingStage1/2/3 and fleet.distributed_model in feature
if args.sharding_stage in [2, 3]:
scaler = scaler if args.use_pure_fp16 else None
model, optimizer, scaler = wrap_sharding_2_3(model, optimizer, scaler,
args.sharding_offload)
elif paddle.distributed.get_world_size() > 1:
model = fleet.distributed_model(model)
optimizer = fleet.distributed_optimizer(optimizer)
if args.model_name_or_path not in pretrained_models_list:
logger.info("Try to load checkpoint from %s " % args.model_name_or_path)
opt_path = os.path.join(args.model_name_or_path, "model_state.pdopt")
if os.path.exists(opt_path):
opt_dict = paddle.load(opt_path)
optimizer.set_state_dict(opt_dict)
else:
logger.warning("No optimizer checkpoint file found in %s." %
opt_path)
global_step = 0
tic_train = time.time()
for epoch in range(args.num_train_epochs):
files = get_train_data_file(args)
files.sort()
num_files = len(files)
for f_id in range(num_files):
data_file = files[f_id]
train_data_loader, valid_data_loader, test_data_loader = create_pretrained_dataset(
args, [data_file],
local_rank=local_rank,
data_world_size=data_world_size,
data_world_rank=data_world_rank,
eos_id=tokenizer.eos_token_id)
# Bug fix, if not call valid_data_loader, the enumerate will call valid_data_loader
# many times. and start a new random dataloader.
valid_data_loader = valid_data_loader()
test_data_loader = test_data_loader()
# time count
train_reader_cost = 0.0
train_run_cost = 0.0
reader_start = time.time()
for step, batch in enumerate(train_data_loader()):
train_reader_cost += time.time() - reader_start
train_start = time.time()
global_step += 1
tokens, loss_mask, position_ids, labels = batch
loss_mask.stop_gradient = True
labels.stop_gradient = True
position_ids.stop_gradient = True
if args.pp_degree == 1:
# In ParallelMode of DataParallel, 'no_sync' can be used for improving
# performance of model by gradient accumulation.
loss = 0.0
for i in range(accumulate_steps):
start_index = i * args.micro_batch_size
end_index = start_index + args.micro_batch_size
with paddle.amp.auto_cast(
args.use_pure_fp16,
custom_black_list=[
"reduce_sum",
"c_softmax_with_cross_entropy",
"elementwise_div"
],
level='O2'):
preds = model(
tokens[start_index:end_index, :],
position_ids[start_index:end_index, :])
loss_mbs = criterion(
preds, labels[start_index:end_index, :],
loss_mask[start_index:end_index, :])
loss_mbs = loss_mbs / accumulate_steps
if args.use_pure_fp16:
scaler.scale(loss_mbs).backward()
else:
loss_mbs.backward()
loss = loss + loss_mbs
if args.use_pure_fp16:
if args.sharding_stage in [2, 3]:
scaler.step(optimizer)
scaler.update()
else:
scaler.minimize(optimizer, loss)
else:
optimizer.step()
if lr_scheduler is not None:
lr_scheduler.step()
optimizer.clear_grad()
else:
data = [(tokens, position_ids), (labels, loss_mask)]
with paddle.amp.auto_cast(
args.use_pure_fp16,
custom_black_list=[
"reduce_sum", "c_softmax_with_cross_entropy",
"elementwise_div"
],
level='O2'):
loss = model.train_batch(
data,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
scaler=scaler if args.use_pure_fp16 else None)
# Sync for profile time, delete it may be a little faster
paddle.device.cuda.synchronize()
train_run_cost += time.time() - train_start
# Profile for model benchmark
profiler.add_profiler_step(args.profiler_options)
if global_step % args.logging_freq == 0:
avg_loss = loss.numpy()
speed = args.logging_freq / (
train_reader_cost + train_run_cost)
avg_reader_cost = train_reader_cost / args.logging_freq
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %.9f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, speed: %.2f step/s, ips: %.0f tokens/s, ips_per_card: %.0f tokens/s, learning rate: %.5e"
% (global_step, epoch, step, avg_loss, avg_reader_cost,
1. / speed, speed, speed * default_global_tokens_num,
speed * default_global_tokens_num / nranks,
optimizer.get_lr()))
log_writer.add_scalar("loss", float(loss), global_step)
log_writer.add_scalar("learning_rate",
optimizer.get_lr(), global_step)
tic_train = time.time()
train_reader_cost = 0.0
train_run_cost = 0.0
if args.check_accuracy:
if global_step >= args.max_steps:
return
else:
continue
if global_step % args.eval_freq == 0:
# Since the valid data broardcast to all devices, we do evaluate on all device.
run_evaluate(args, valid_data_loader, model, criterion,
args.eval_iters, log_writer, global_step,
epoch, "valid")
# TODO: 1. merge paramters while saving model. 2. ensure that the model is saved and loaded correctly
# only dp_rank = 0 save model
if (global_step % args.save_steps == 0 or
global_step >= args.max_steps) and dp_rank == 0:
model_to_save = model._layers if paddle.distributed.get_world_size(
) > 1 and args.sharding_stage not in [2, 3] else model
output_dir = os.path.join(args.output_dir,
"step_%d" % global_step)
os.makedirs(output_dir, exist_ok=True)
logger.info("Save model to %s" % output_dir)
if args.pp_degree > 1:
if mp_rank == 0 and sharding_rank == 0 and pp_rank == 0:
tokenizer.save_pretrained(output_dir)
model_to_save.save_state_dict(output_dir)
paddle.save(
optimizer.state_dict(),
os.path.join(
output_dir,
"model_state_mp_{:0>2d}_sharding_{:0>2d}_pp_{:0>2d}.pdopt".
format(mp_rank, sharding_rank, pp_rank)))
else:
if args.sharding_stage == 3:
# If parameter need to convert to cpu, please add convert2cpu=True
model_to_save.get_all_parameters(convert2cpu=False)
if mp_rank == 0 and sharding_rank == 0:
tokenizer.save_pretrained(output_dir)
model_to_save.save_pretrained(output_dir)
paddle.save(
optimizer.state_dict(),
os.path.join(
output_dir,
"model_state_mp_{:0>2d}_sharding_{:0>2d}.pdopt".
format(mp_rank, sharding_rank)))
if global_step >= args.max_steps:
run_evaluate(args, test_data_loader, model, criterion,
args.test_iters, log_writer, global_step,
epoch, "test")
logger.info("The training process is complete.")
del train_data_loader
return
reader_start = time.time()
del train_data_loader
def do_train(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args)
global final_res
args.task_name = args.task_name.lower()
metric_class = METRIC_CLASSES[args.task_name]
model_class, tokenizer_class = XLNetForSequenceClassification, XLNetTokenizer
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
if args.task_name == "mnli":
train_data_loader, dev_data_loader_matched, dev_data_loader_mismatched, train_ds, dev_ds_matched, dev_ds_mismatched = create_data_loader(
args, tokenizer)
else:
train_data_loader, dev_data_loader, train_ds, dev_ds = create_data_loader(
args, tokenizer)
num_classes = 1 if train_ds.label_list is None else len(
train_ds.label_list)
model = XLNetForSequenceClassification.from_pretrained(
args.model_name_or_path, num_classes=num_classes)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
if args.max_steps > 0:
num_training_steps = args.max_steps
num_train_epochs = ceil(num_training_steps / len(train_data_loader))
else:
num_training_steps = len(train_data_loader) * args.num_train_epochs
num_train_epochs = args.num_train_epochs
warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion
lr_scheduler = LinearDecayWithWarmup(args.learning_rate,
num_training_steps, warmup)
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=args.max_grad_norm)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "layer_norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
beta1=0.9,
beta2=0.999,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
grad_clip=clip,
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
loss_fct = paddle.nn.loss.CrossEntropyLoss(
) if train_ds.label_list else paddle.nn.loss.MSELoss()
metric = metric_class()
global_step = 0
model.train()
train_reader_cost = 0.0
train_run_cost = 0.0
reader_start = time.time()
for epoch in range(num_train_epochs):
for step, batch in enumerate(train_data_loader):
train_reader_cost += time.time() - reader_start
train_start = time.time()
global_step += 1
input_ids, token_type_ids, attention_mask, labels = batch
logits = model(input_ids, token_type_ids, attention_mask)
loss = loss_fct(logits, labels)
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
train_run_cost += time.time() - train_start
# Profile for model benchmark
profiler.add_profiler_step(args.profiler_options)
if global_step % args.logging_steps == 0:
speed = args.logging_steps / (train_reader_cost +
train_run_cost)
avg_reader_cost = train_reader_cost / args.logging_steps
print(
"global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s, avg_reader_cost: %.4f sec, avg_batch_cost: %.4f sec, avg_samples: %d, avg_ips: %.4f sequences/sec"
% (
global_step,
num_training_steps,
epoch,
step,
paddle.distributed.get_rank(),
loss,
optimizer.get_lr(),
speed,
avg_reader_cost,
1.0 / speed,
args.batch_size,
speed * args.batch_size,
))
train_reader_cost = 0.0
train_run_cost = 0.0
if global_step % args.save_steps == 0 or global_step == num_training_steps:
tic_eval = time.time()
if args.task_name == "mnli":
print("matched ", end="")
evaluate(model, loss_fct, metric, dev_data_loader_matched)
final_res1 = "matched " + final_res
print("mismatched ", end="")
evaluate(model, loss_fct, metric,
dev_data_loader_mismatched)
final_res2 = "mismatched " + final_res
final_res = final_res1 + "\r\n" + final_res2
print("eval done total : %s s" % (time.time() - tic_eval))
else:
evaluate(model, loss_fct, metric, dev_data_loader)
print("eval done total : %s s" % (time.time() - tic_eval))
if (not paddle.distributed.get_world_size() > 1
) or paddle.distributed.get_rank() == 0:
output_dir = os.path.join(
args.output_dir,
"%s_ft_model_%d" % (args.task_name, global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if global_step == num_training_steps:
print(final_res)
exit(0)
reader_start = time.time()
def on_batch_end(self, mode, step=None, logs=None):
if mode == 'train':
profiler.add_profiler_step(self.profiler_options)
def do_train(args):
# Initialize the paddle and paddle fleet execute environment
paddle.enable_static()
fleet.init(is_collective=True)
# Create the random seed for the worker
random.seed(args.seed)
np.random.seed(args.seed)
paddle.seed(args.seed)
get_rng_state_tracker().add('global_seed', args.seed)
get_rng_state_tracker().add('local_seed',
args.seed + fleet.worker_index() + 2021)
if args.use_amp and args.amp_level == "O2":
assert (args.mp_degree == 1 and args.pp_degree == 1
), "When amp level is O2, mp_degree and pp_degree should be 1."
assert (args.use_sharding == False
), "When amp level is O2, use_sharding should be False."
assert args.device in [
"cpu", "gpu", "xpu"
], "Invalid device! Available device should be cpu, gpu, or xpu."
place = paddle.set_device(args.device)
worker_num = fleet.worker_num()
worker_index = fleet.worker_index()
local_rank = 0 if fleet.local_rank() is None else int(fleet.local_rank())
topo = Topology(
device_rank=worker_index,
world_size=worker_num,
dp_degree=args.dp_degree,
pp_degree=args.pp_degree,
sharding_degree=args.sharding_degree,
mp_degree=args.mp_degree)
logger.info("The topo of hybrid parallelism:\n{}".format(topo))
dist_strategy = dist_optimizer(args, topo)
# Create log write, train results show on last card of pipeline.
if topo.is_last:
log_writer_path = os.path.join(
args.output_dir, "train_log",
"{}_globalbsz_{}_amp_{}_recompute_{}_card_{}".format(
args.model_name_or_path, args.global_batch_size, args.use_amp,
args.use_recompute, worker_index).lower())
if os.path.exists(log_writer_path):
import shutil
shutil.rmtree(log_writer_path)
log_writer = LogWriter(log_writer_path)
# Define the input data in the static mode
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
pretrained_models_list = list(
model_class.pretrained_init_configuration.keys())
data_file = get_train_data_file(args)
main_program = paddle.static.default_main_program()
startup_program = paddle.static.default_startup_program()
with paddle.static.program_guard(main_program, startup_program):
with paddle.utils.unique_name.guard():
with paddle.static.device_guard('gpu:0'):
data_holders = create_data_holder(args)
[tokens, loss_mask, position_ids, labels] = data_holders
tokenizer = tokenizer_class.from_pretrained(
args.model_name_or_path)
eos_id = tokenizer.eos_token_id
train_data_loader, valid_data_loader, test_data_loader = create_pretrained_dataset(
args,
data_file,
local_rank=local_rank,
data_world_size=topo.data_info.size,
data_world_rank=topo.data_info.rank,
eos_id=eos_id,
max_seq_len=args.max_seq_len,
places=paddle.static.cuda_places(),
data_holders=data_holders,
pipeline_mode=False, )
if args.model_name_or_path in pretrained_models_list:
model_config = model_class.pretrained_init_configuration[
args.model_name_or_path]
model_config[
"hidden_dropout_prob"] = args.hidden_dropout_prob
model_config[
"attention_probs_dropout_prob"] = args.attention_probs_dropout_prob
model_config["topo"] = topo
model = guard(f'gpu:{args.pp_degree -1}')(
GPTForPretraining)(guard(f'gpu:0')(GPTModel)(
**model_config))
else:
model, _ = GPTForPretraining.from_pretrained(
args.model_name_or_path,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.
attention_probs_dropout_prob,
topo=topo)
# Create the model for the gpt pretrain
preds = model(tokens, position_ids)
criterion = guard(f'gpu:{args.pp_degree -1}')(
GPTPretrainingCriterion)(topo)
loss = criterion(preds, labels, loss_mask)
# Create the learning_rate sheduler and optimizer
if args.decay_steps is None:
args.decay_steps = args.max_steps
warmup_step = args.warmup_rate * args.decay_steps
# TODO @ZHUI Use paddle network to support lr scheduler
lr_scheduler = lr.CosineAnnealingWithWarmupDecay(
max_lr=args.max_lr,
min_lr=args.min_lr,
warmup_step=warmup_step,
decay_step=args.decay_steps)
clip = None
if args.grad_clip > 0:
clip = paddle.fluid.clip.GradientClipByGlobalNorm(
clip_norm=args.grad_clip)
decay_param = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
beta1=args.adam_beta1,
beta2=args.adam_beta2,
epsilon=args.adam_epsilon,
grad_clip=clip,
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_param)
# alias
optimizer.apply_optimize = optimizer._apply_optimize
if args.use_recompute:
dist_strategy.recompute = True
dist_strategy.recompute_configs = {
"checkpoints": model.gpt.checkpoints
}
# Use the fleet api to compile the distributed optimizer
optimizer = fleet.distributed_optimizer(
optimizer, strategy=dist_strategy)
optimizer.minimize(loss)
logger.info(f'final strategy: {fleet._final_strategy()}')
logger.info("The training meta optimizer is/are %s" %
fleet._get_applied_meta_list())
program_desc_dir = os.path.join(args.output_dir, "program_desc")
if not os.path.isdir(program_desc_dir):
os.mkdir(program_desc_dir)
with open(program_desc_dir + "/main_program.txt.%d" % worker_index,
'w') as f:
f.write(str(main_program))
with open(program_desc_dir + "/startup_program.txt.%d" % worker_index,
'w') as f:
f.write(str(startup_program))
# Define the Executor for running the static model
exe = paddle.static.Executor(place)
exe.run(startup_program)
test_program = main_program.clone(for_test=True)
if args.use_amp and args.amp_level == "O2":
optimizer.amp_init(place)
if args.model_name_or_path not in pretrained_models_list:
logger.info("Try to load checkpoint from %s " % args.model_name_or_path)
dygrah_path = os.path.join(args.model_name_or_path,
"model_state.pdparams")
static_path = os.path.join(args.model_name_or_path, "static_vars")
flag_loaded = False
if os.path.exists(static_path):
if args.mp_degree > 1:
logger.warning("MP should init with dygraph params")
else:
logger.info("Loading parameters from %s" % static_path)
paddle.static.load(main_program, static_path, exe)
flag_loaded = True
if not flag_loaded and os.path.exists(dygrah_path):
if args.sharding_degree > 1:
logger.warning("Sharding should init with static vars")
else:
logger.info("Loading parameters from %s" % dygrah_path)
init_static_with_params(
model,
paddle.load(
dygrah_path, return_numpy=True),
topo,
main_program)
flag_loaded = True
if not flag_loaded:
logger.error("No checkpoint load.")
global_step = 0
tic_train = time.time()
epoch = 0
learning_rate = main_program.global_block().vars["learning_rate_0"]
while True:
fetchs = []
if topo.is_last:
fetchs = [loss, learning_rate]
# Bug fix, if not call valid_data_loader, the enumerate will call valid_data_loader
# many times. and start a new random dataloader.
valid_data_loader = valid_data_loader()
test_data_loader = test_data_loader()
train_reader_cost = 0.0
train_run_cost = 0.0
reader_start = time.time()
for step, batch in enumerate(train_data_loader()):
train_reader_cost += time.time() - reader_start
train_start = time.time()
global_step += 1
ret = exe.run(main_program,
feed=batch,
fetch_list=fetchs,
use_program_cache=True)
# In the new 2.0 api, must call this function to change the learning_rate
lr_scheduler.step()
train_run_cost += time.time() - train_start
# Profile for model benchmark
profiler.add_profiler_step(args.profiler_options)
if global_step % args.logging_freq == 0:
if topo.is_last:
loss_return, lr_return = ret
#speed = args.logging_freq / (time.time() - tic_train)
speed = args.logging_freq / (
train_reader_cost + train_run_cost)
avg_reader_cost = train_reader_cost / args.logging_freq
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %.9f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, speed: %.2f steps/s, ips: %.0f tokens/s, ips_per_card: %.0f tokens/s, learning rate: %.5e"
% (global_step, epoch, step, loss_return[0],
avg_reader_cost, 1. / speed, speed,
speed * args.global_batch_size * args.max_seq_len,
speed * args.global_batch_size * args.max_seq_len /
worker_num, lr_return[0]))
log_writer.add_scalar("loss", loss_return[0], global_step)
log_writer.add_scalar("learning_rate", lr_return[0],
global_step)
tic_train = time.time()
train_reader_cost = 0.0
train_run_cost = 0.0
if args.check_accuracy:
if global_step >= args.max_steps:
return
else:
continue
if global_step % args.eval_freq == 0:
# TODO, check the input data of validation
eval_fetch = []
if topo.is_last:
eval_fetch = [loss]
run_evaluate(valid_data_loader, exe, test_program,
args.eval_iters, log_writer, global_step, args,
epoch, topo.is_last, eval_fetch, "valid")
tic_train = time.time()
if global_step % args.save_steps == 0 or global_step >= args.max_steps:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
logger.debug("saving models to {}".format(output_dir))
save_persistables(exe,
os.path.join(output_dir, "static_vars"),
main_program)
if global_step <= args.save_steps:
model.init_config["init_args"][0].init_config.pop("topo",
None)
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
tic_train = time.time()
if global_step >= args.max_steps:
eval_fetch = []
if topo.is_last:
eval_fetch = [loss]
run_evaluate(test_data_loader, exe, test_program,
args.test_iters, log_writer, global_step, args,
epoch, topo.is_last, eval_fetch, "test")
del train_data_loader
return
reader_start = time.time()
epoch += 1
def do_train(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args)
worker_init = WorkerInitObj(args.seed + paddle.distributed.get_rank())
args.model_type = args.model_type.lower()
base_class, model_class, criterion_class, tokenizer_class = MODEL_CLASSES[
args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
pretrained_models_list = list(
model_class.pretrained_init_configuration.keys())
if args.model_name_or_path in pretrained_models_list:
model = model_class(
base_class(**model_class.pretrained_init_configuration[
args.model_name_or_path]))
else:
model = model_class.from_pretrained(args.model_name_or_path)
criterion = criterion_class(
getattr(model, model_class.base_model_prefix).config["vocab_size"])
# decorate @to_static for benchmark, skip it by default.
if args.to_static:
specs = create_input_specs()
model = paddle.jit.to_static(model, input_spec=specs)
logger.info("Successfully to apply @to_static with specs: {}".format(
specs))
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
# If use default last_epoch, lr of the first iteration is 0.
# Use `last_epoch = 0` to be consistent with nv bert.
num_training_steps = args.max_steps if args.max_steps > 0 else len(
train_data_loader) * args.num_train_epochs
lr_scheduler = LinearDecayWithWarmup(
args.learning_rate, num_training_steps, args.warmup_steps, last_epoch=0)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
if args.use_amp:
scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
pool = ThreadPoolExecutor(1)
global_step = 0
tic_train = time.time()
for epoch in range(args.num_train_epochs):
files = [
os.path.join(args.input_dir, f) for f in os.listdir(args.input_dir)
if os.path.isfile(os.path.join(args.input_dir, f)) and "train" in f
]
files.sort()
num_files = len(files)
random.Random(args.seed + epoch).shuffle(files)
f_start_id = 0
shared_file_list = {}
if paddle.distributed.get_world_size() > num_files:
remainder = paddle.distributed.get_world_size() % num_files
data_file = files[(
f_start_id * paddle.distributed.get_world_size() +
paddle.distributed.get_rank() + remainder * f_start_id) %
num_files]
else:
data_file = files[(f_start_id * paddle.distributed.get_world_size()
+ paddle.distributed.get_rank()) % num_files]
previous_file = data_file
train_data_loader, _ = create_pretraining_dataset(
data_file, args.max_predictions_per_seq, shared_file_list, args,
worker_init)
# TODO(guosheng): better way to process single file
single_file = True if f_start_id + 1 == len(files) else False
for f_id in range(f_start_id, len(files)):
if not single_file and f_id == f_start_id:
continue
if paddle.distributed.get_world_size() > num_files:
data_file = files[(
f_id * paddle.distributed.get_world_size() +
paddle.distributed.get_rank() + remainder * f_id) %
num_files]
else:
data_file = files[(f_id * paddle.distributed.get_world_size() +
paddle.distributed.get_rank()) % num_files]
previous_file = data_file
dataset_future = pool.submit(create_pretraining_dataset, data_file,
args.max_predictions_per_seq,
shared_file_list, args, worker_init)
train_cost_avg = TimeCostAverage()
reader_cost_avg = TimeCostAverage()
total_samples = 0
batch_start = time.time()
for step, batch in enumerate(train_data_loader):
train_reader_cost = time.time() - batch_start
reader_cost_avg.record(train_reader_cost)
global_step += 1
(input_ids, segment_ids, input_mask, masked_lm_positions,
masked_lm_labels, next_sentence_labels,
masked_lm_scale) = batch
with paddle.amp.auto_cast(
args.use_amp,
custom_white_list=["layer_norm", "softmax", "gelu"]):
prediction_scores, seq_relationship_score = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_positions=masked_lm_positions)
loss = criterion(prediction_scores, seq_relationship_score,
masked_lm_labels, next_sentence_labels,
masked_lm_scale)
if args.use_amp:
scaler.scale(loss).backward()
scaler.minimize(optimizer, loss)
else:
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
total_samples += args.batch_size
train_run_cost = time.time() - batch_start
train_cost_avg.record(train_run_cost)
# Profile for model benchmark
if args.profiler_options is not None:
profiler.add_profiler_step(args.profiler_options)
if global_step % args.logging_steps == 0:
if paddle.distributed.get_rank() == 0:
logger.info(
"global step: %d, epoch: %d, batch: %d, loss: %f, "
"avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, avg_samples: %.5f, ips: %.5f sequences/sec"
% (global_step, epoch, step, loss,
reader_cost_avg.get_average(),
train_cost_avg.get_average(), total_samples /
args.logging_steps, total_samples / (
args.logging_steps *
train_cost_avg.get_average())))
total_samples = 0
train_cost_avg.reset()
reader_cost_avg.reset()
if global_step % args.save_steps == 0:
if paddle.distributed.get_rank() == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(
optimizer.state_dict(),
os.path.join(output_dir, "model_state.pdopt"))
if global_step >= args.max_steps:
del train_data_loader
return
batch_start = time.time()
del train_data_loader
train_data_loader, data_file = dataset_future.result(timeout=None)
def do_train(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
worker_index = paddle.distributed.get_rank()
worker_num = paddle.distributed.get_world_size()
local_rank = int(os.getenv("PADDLE_RANK_IN_NODE", 0))
set_seed(args)
# Now, we only support data parallel in dygraph mode for now.
topo = Topology(
device_rank=worker_index, world_size=worker_num, dp_degree=worker_num)
default_global_batch_size = topo.data_info.size * args.micro_batch_size
default_global_tokens_num = default_global_batch_size * args.max_seq_len
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
# Define log writer
log_writer_path = os.path.join(
args.output_dir, "train_log",
"{}_globalbsz_{}_amp_{}_recompute_{}_card_{}".format(
args.model_name_or_path, args.micro_batch_size *
topo.data_info.size, False, False, worker_index).lower())
if os.path.exists(log_writer_path):
import shutil
shutil.rmtree(log_writer_path)
log_writer = LogWriter(log_writer_path)
pretrained_models_list = list(
model_class.pretrained_init_configuration.keys())
if args.model_name_or_path in pretrained_models_list:
model_config = model_class.pretrained_init_configuration[
args.model_name_or_path]
model_config["hidden_dropout_prob"] = args.hidden_dropout_prob
model_config[
"attention_probs_dropout_prob"] = args.attention_probs_dropout_prob
model = GPTForPretraining(GPTModel(**model_config))
else:
model = GPTForPretraining.from_pretrained(
args.model_name_or_path,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob)
# Create the critrion for the gpt model
criterion = GPTPretrainingCriterion()
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
if args.decay_steps is None:
args.decay_steps = args.max_steps
warmup_step = args.warmup_rate * args.decay_steps
lr_scheduler = None
if args.lr_decay_style == "none":
lr_scheduler = None
elif args.lr_decay_style == "cosine":
lr_scheduler = lr.CosineAnnealingWithWarmupDecay(
max_lr=args.max_lr,
min_lr=args.min_lr,
warmup_step=warmup_step,
decay_step=args.decay_steps)
clip = None
if args.grad_clip > 0:
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=args.grad_clip)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler if lr_scheduler is not None else args.max_lr,
beta1=args.adam_beta1,
beta2=args.adam_beta2,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
grad_clip=clip,
apply_decay_param_fun=lambda x: x in decay_params)
if args.use_amp:
scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
if args.model_name_or_path not in pretrained_models_list:
logger.info("Try to load checkpoint from %s " % args.model_name_or_path)
opt_path = os.path.join(args.model_name_or_path, "model_state.pdopt")
if os.path.exists(opt_path):
opt_dict = paddle.load(opt_path)
optimizer.set_state_dict(opt_dict)
else:
logger.warning("No optimizer checkpoint file found in %s." %
opt_path)
global_step = 0
epoch = 0
tic_train = time.time()
while True:
files = get_train_data_file(args)
files.sort()
num_files = len(files)
for f_id in range(num_files):
data_file = files[f_id]
train_data_loader, valid_data_loader, test_data_loader = create_pretrained_dataset(
args, [data_file],
local_rank=local_rank,
data_world_size=topo.data_info.size,
data_world_rank=topo.data_info.rank,
eos_id=tokenizer.eos_token_id)
# Bug fix, if not call valid_data_loader, the enumerate will call valid_data_loader
# many times. and start a new random dataloader.
valid_data_loader = valid_data_loader()
test_data_loader = test_data_loader()
# time count
train_reader_cost = 0.0
train_run_cost = 0.0
reader_start = time.time()
for step, batch in enumerate(train_data_loader()):
train_reader_cost += time.time() - reader_start
train_start = time.time()
global_step += 1
tokens, loss_mask, attention_mask, position_ids, labels = batch
loss_mask.stop_gradient = True
attention_mask.stop_gradient = True
with paddle.amp.auto_cast(
args.use_amp,
custom_white_list=["layer_norm", "softmax", "gelu"],
custom_black_list=[
"reduce_sum", "c_softmax_with_cross_entropy",
"c_embedding"
]):
preds = model(tokens, position_ids, attention_mask)
loss = criterion(preds, labels, loss_mask)
if args.use_amp:
scaler.scale(loss).backward()
scaler.minimize(optimizer, loss)
else:
loss.backward()
optimizer.step()
if lr_scheduler is not None:
lr_scheduler.step()
optimizer.clear_grad()
loss_numpy = loss.numpy()
train_run_cost += time.time() - train_start
# Profile for model benchmark
profiler.add_profiler_step(args.profiler_options)
if global_step % args.logging_freq == 0:
speed = args.logging_freq / (
train_reader_cost + train_run_cost)
avg_reader_cost = train_reader_cost / args.logging_freq
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %.9f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, speed: %.2f step/s, ips: %.0f tokens/s, ips_per_card: %.0f tokens/s, learning rate: %.5e"
%
(global_step, epoch, step, loss_numpy, avg_reader_cost,
1. / speed, speed, speed * default_global_tokens_num,
speed * default_global_tokens_num / worker_num,
optimizer.get_lr()))
log_writer.add_scalar("loss", loss_numpy, global_step)
log_writer.add_scalar("learning_rate",
optimizer.get_lr(), global_step)
tic_train = time.time()
train_reader_cost = 0.0
train_run_cost = 0.0
if args.check_accuracy:
if global_step >= args.max_steps:
return
else:
continue
if global_step % args.eval_freq == 0:
# Since the valid data broardcast to all devices, we do evaluate on all device.
run_evaluate(valid_data_loader, model, criterion,
args.eval_iters, log_writer, global_step,
epoch, "valid")
if global_step % args.save_steps == 0 or global_step >= args.max_steps:
if worker_index == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
logger.info("Save model to %s" % output_dir)
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
paddle.save(optimizer.state_dict(),
os.path.join(output_dir,
"model_state.pdopt"))
if global_step >= args.max_steps:
run_evaluate(test_data_loader, model, criterion,
args.test_iters, log_writer, global_step,
epoch, "test")
logger.info("The training process is complete.")
del train_data_loader
return
reader_start = time.time()
del train_data_loader