def do_train(args):
# Initialize the paddle and paddle fleet execute enviroment
paddle.enable_static()
place = paddle.set_device(args.select_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
lr_scheduler = paddle.optimizer.lr.LambdaDecay(
args.learning_rate,
lambda current_step, num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps if args.max_steps > 0 else
(len(train_data_loader) * args.num_train_epochs): float(
current_step) / float(max(1, num_warmup_steps))
if current_step < num_warmup_steps else max(
0.0,
float(num_training_steps - current_step) / float(
max(1, num_training_steps - num_warmup_steps))))
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 [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
],
multi_precision=args.use_pure_fp16)
if worker_num == 1 and args.use_amp:
custom_black_list = (['lookup_table', 'lookup_table_v2']
if args.use_pure_fp16 else None)
amp_list = paddle.static.amp.AutoMixedPrecisionLists(
custom_white_list=['softmax', 'layer_norm', 'gelu'],
custom_black_list=custom_black_list)
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)
if worker_num > 1:
# 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)
if worker_num == 1:
# Construct the compiled program
main_program = build_compiled_program(main_program, loss)
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_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
for step, batch in enumerate(train_data_loader):
train_reader_cost += time.time() - reader_start
global_step += 1
train_start = time.time()
loss_return = exe.run(main_program,
feed=batch,
fetch_list=[loss])
train_run_cost += time.time() - train_start
total_samples += args.batch_size
# In the new 2.0 api, must call this function to change the learning_rate
lr_scheduler.step()
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],
train_reader_cost / args.logging_steps,
(train_reader_cost + train_run_cost) /
args.logging_steps,
total_samples / args.logging_steps, total_samples /
(train_reader_cost + train_run_cost)))
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
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)
# TODO(fangzeyang): Udpate the save_params to paddle.static
paddle.fluid.io.save_params(exe, output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= args.max_steps:
reader_start = time.time()
del train_data_loader
return
reader_start = time.time()
del train_data_loader
train_data_loader, data_file = dataset_future.result(timeout=None)
epoch += 1
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):
# Initialize the paddle execute enviroment
paddle.enable_static()
place = paddle.set_device(args.select_device)
# Set the random seed
set_seed(args.seed)
# 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
lr_scheduler = paddle.optimizer.lr.LambdaDecay(
args.learning_rate,
lambda current_step, num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps if args.max_steps > 0 else
(len(train_data_loader) * args.num_train_epochs): float(
current_step) / float(max(1, num_warmup_steps))
if current_step < num_warmup_steps else max(
0.0,
float(num_training_steps - current_step) / float(
max(1, num_training_steps - num_warmup_steps))))
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 [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
if args.use_amp:
amp_list = paddle.fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
custom_white_list=['layer_norm', 'softmax', 'gelu'])
optimizer = paddle.fluid.contrib.mixed_precision.decorate(
optimizer,
amp_list,
init_loss_scaling=args.scale_loss,
use_dynamic_loss_scaling=True)
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)
# Construct the compiled program
main_program = build_compiled_program(args, main_program, loss)
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()
random.Random(args.seed + epoch).shuffle(files)
for f_id in range(0, len(files)):
train_data_loader, _ = create_pretraining_dataset(
files[f_id], args.max_predictions_per_seq, args, data_holders)
for step, batch in enumerate(train_data_loader):
global_step += 1
loss_return = exe.run(main_program,\
feed=batch,
fetch_list=[loss])
# In the new 2.0 api, must call this function to change the learning_rate
lr_scheduler.step()
if global_step % args.logging_steps == 0:
time_cost = time.time() - tic_train
print(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s, ips: %.2f sequences/s"
% (global_step, epoch, step, loss_return[0],
args.logging_steps / time_cost,
args.logging_steps * args.batch_size / time_cost))
tic_train = time.time()
if global_step % args.save_steps == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# TODO(fangzeyang): Udpate the save_params to paddle.static
paddle.fluid.io.save_params(exe, output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= args.max_steps:
del train_data_loader
return
del train_data_loader
epoch += 1
def do_train(args):
paddle.set_device("gpu" if args.n_gpu else "cpu")
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()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
model = BertForPretraining(
BertModel(**model_class.pretrained_init_configuration[
args.model_name_or_path]))
criterion = BertPretrainingCriterion(
getattr(model,
BertForPretraining.base_model_prefix).config["vocab_size"])
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
# If use defalut last_epoch, lr of the first iteration is 0.
# Use `last_epoch = 0` to be consistent with nv bert.
lr_scheduler = paddle.optimizer.lr.LambdaDecay(
args.learning_rate,
lambda current_step, num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps if args.max_steps > 0 else
(len(train_data_loader) * args.num_train_epochs): float(
current_step) / float(max(1, num_warmup_steps))
if current_step < num_warmup_steps else max(
0.0,
float(num_training_steps - current_step) / float(
max(1, num_training_steps - num_warmup_steps))),
last_epoch=0)
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 [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
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 "training" 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)
for step, batch in enumerate(train_data_loader):
global_step += 1
(input_ids, segment_ids, input_mask, masked_lm_positions,
masked_lm_labels, next_sentence_labels,
masked_lm_scale) = batch
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 global_step % args.logging_steps == 0:
if (not args.n_gpu > 1
) or paddle.distributed.get_rank() == 0:
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, epoch, step, loss,
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_gradients()
if global_step % args.save_steps == 0:
if (not args.n_gpu > 1
) or 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
del train_data_loader
train_data_loader, data_file = dataset_future.result(timeout=None)
def do_train(args):
# Initialize the paddle and paddle fleet execute enviroment
paddle.enable_static()
place = paddle.set_device(args.select_device)
fleet.init(is_collective=True)
# paddle.distributed.init_parallel_env()
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)
worker_init = WorkerInitObj(args.seed)
tracker = get_rng_state_tracker()
tracker.add('global_seed', args.seed)
tracker.add('local_seed', args.seed + worker_index + 2021)
# 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)
config['num_partitions'] = args.num_partitions
model = BertForPretraining(BertModel(**config), args.num_partitions)
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
lr_scheduler = paddle.optimizer.lr.LambdaDecay(
args.learning_rate,
lambda current_step, num_warmup_steps=args.warmup_steps,
num_training_steps=args.max_steps if args.max_steps > 0 else
(len(train_data_loader) * args.num_train_epochs): float(
current_step) / float(max(1, num_warmup_steps))
if current_step < num_warmup_steps else max(
0.0,
float(num_training_steps - current_step) / float(
max(1, num_training_steps - num_warmup_steps))))
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 [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
# if worker_num == 1 and args.use_amp:
# amp_list = paddle.fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
# custom_white_list=['softmax', 'layer_norm', 'gelu'])
# optimizer = paddle.fluid.contrib.mixed_precision.decorate(
# optimizer,
# amp_list,
# init_loss_scaling=args.scale_loss,
# use_dynamic_loss_scaling=True)
if fleet.worker_num() > 1:
# 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 worker_num == 1:
# # Construct the compiled program
# main_program = build_compiled_program(main_program, loss)
main_program._graph = None
if fleet.worker_index() == 0:
with open('startup_%d' % fleet.worker_num(), 'w') as f:
f.writelines(str(startup_program))
with open('main_%d' % fleet.worker_num(), 'w') as f:
f.writelines(str(main_program))
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, 1, 0)
#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, 1, 0)
# 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())
for step, batch in enumerate(train_data_loader):
global_step += 1
if step == 10 and worker_index == 0:
profiler.start_profiler("All")
if step == 20 and worker_index == 0:
profiler.stop_profiler("total", "/tmp/profile")
loss_return = exe.run(main_program,
feed=batch,
fetch_list=[loss])
# In the new 2.0 api, must call this function to change the learning_rate
lr_scheduler.step()
if global_step % args.logging_steps == 0:
time_cost = time.time() - tic_train
print(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s, ips: %.2f sequences/s"
% (global_step, epoch, step, loss_return[0],
args.logging_steps / time_cost,
args.logging_steps * args.batch_size / time_cost))
tic_train = time.time()
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)
# TODO(fangzeyang): Udpate the save_params to paddle.static
paddle.fluid.io.save_params(exe, output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= args.max_steps:
del train_data_loader
return
del train_data_loader
train_data_loader, data_file = dataset_future.result(timeout=None)
epoch += 1
def setUp(self):
self.config['vocab_size'] = 1024
self.criterion = BertPretrainingCriterion(**self.config)
self.np_criterion = NpBertPretrainingCriterion(**self.config)
def do_train(args):
# Initialize the paddle and paddle fleet execute enviroment
paddle.enable_static()
place = paddle.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0)))
fleet.init(is_collective=True)
# Create the random seed for the worker
set_seed(args.seed)
worker_init = WorkerInitObj(args.seed + fleet.worker_index())
# Define the input data in the static mode
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)
model = BertForPretraining(
BertModel(**model_class.pretrained_init_configuration[
args.model_name_or_path]))
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)
num_training_steps = args.max_steps if args.max_steps > 0 else len(
train_data_loader) * args.num_train_epochs
# Define the dynamic learing_reate scheduler and optimizer
lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps,
args.warmup_steps)
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 [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
# Use the fleet api to compile the distributed optimizer
strategy = fleet.DistributedStrategy()
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
optimizer.minimize(loss)
# Define the Executor for running the static model
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_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(paddle.static.default_main_program(),
reset_state_dict)
pool = ThreadPoolExecutor(1)
global_step = 0
tic_train = time.time()
worker_num = fleet.worker_num()
worker_index = fleet.worker_index()
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())
for step, batch in enumerate(train_data_loader):
global_step += 1
loss_return = exe.run(paddle.static.default_main_program(),\
feed=batch,
fetch_list=[loss])
# In the new 2.0 api, must call this function to change the learning_rate
lr_scheduler.step()
if global_step % args.logging_steps == 0:
time_cost = time.time() - tic_train
print(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s, ips :%.2f sequences/s"
% (global_step, epoch, step, loss_return[0],
args.logging_steps / time_cost,
args.logging_steps * args.batch_size / time_cost))
tic_train = time.time()
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)
# TODO(fangzeyang): Udpate the save_params to paddle.static
paddle.fluid.io.save_params(exe, output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= args.max_steps:
del train_data_loader
return
del train_data_loader
train_data_loader, data_file = dataset_future.result(timeout=None)
epoch += 1
def do_train(args):
# Initialize the paddle execute enviroment
paddle.enable_static()
place = paddle.set_device(args.device)
# Set the random seed
set_seed(args.seed)
# 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=False)
if args.use_amp:
custom_black_list = (['lookup_table', 'lookup_table_v2']
if args.use_pure_fp16 else None)
amp_list = paddle.static.amp.AutoMixedPrecisionLists(
custom_white_list=['layer_norm', 'softmax', 'gelu'],
custom_black_list=custom_black_list)
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(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)
# Construct the compiled program
main_program = build_compiled_program(args, main_program, loss)
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()
random.Random(args.seed + epoch).shuffle(files)
for f_id in range(0, len(files)):
train_data_loader, _ = create_pretraining_dataset(
files[f_id], args.max_predictions_per_seq, args, data_holders)
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
for step, batch in enumerate(train_data_loader):
train_reader_cost += time.time() - reader_start
global_step += 1
train_start = time.time()
loss_return = exe.run(main_program,\
feed=batch,
fetch_list=[loss])
train_run_cost += time.time() - train_start
total_samples += args.batch_size
# In the new 2.0 api, must call this function to change the learning_rate
lr_scheduler.step()
if global_step % args.logging_steps == 0:
print(
"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_return[0],
train_reader_cost / args.logging_steps,
(train_reader_cost + train_run_cost) /
args.logging_steps,
total_samples / args.logging_steps, total_samples /
(train_reader_cost + train_run_cost)))
train_reader_cost = 0.0
train_run_cost = 0.0
total_samples = 0
if global_step % args.save_steps == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# TODO(fangzeyang): Udpate the save_params to paddle.static
paddle.fluid.io.save_params(exe, output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= args.max_steps:
reader_start = time.time()
del train_data_loader
return
reader_start = time.time()
del train_data_loader
epoch += 1
