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)
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()
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, attention_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,
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, attention_mask)
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.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()
for step, batch in enumerate(train_data_loader()):
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()
if global_step % args.logging_freq == 0:
if topo.is_last:
loss_return, lr_return = ret
speed = args.logging_freq / (time.time() - tic_train)
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %.9f, speed: %.2f steps/s, ips: %.0f tokens/s, learning rate: %.5e"
% (global_step, epoch, step, loss_return[0], speed,
speed * args.global_batch_size * args.max_seq_len,
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()
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
epoch += 1
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
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)
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()
assert args.dp_degree * args.sharding_degree * args.mp_degree * args.pp_degree == worker_num, \
"The product of degree num should be equal to worker_num."
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):
# shutil.rmtree(log_writer_path)
log_writer = LogWriter(log_writer_path)
# Define the input data in the static mode
base_class, model_class, criterion_class, tokenizer_class = MODEL_CLASSES[
args.model_type]
pretrained_models_list = list(
model_class.pretrained_init_configuration.keys())
# load config in checkpoint
global_step = 0
consumed_samples = 0
checkpoint_dir = os.path.join(args.output_dir, "model_last")
if os.path.exists(checkpoint_dir):
if os.path.isfile(os.path.join(checkpoint_dir, "./config.yml")):
with open(os.path.join(checkpoint_dir, "./config.yml"), "r") as f:
step_config = yaml.load(f, Loader=yaml.FullLoader)
assert step_config[
"global_batch_size"] == args.global_batch_size, "Please ensure checkpoint global batch size is the same. Folder: {}".format(
checkpoint_dir)
consumed_samples = step_config["consumed_samples"]
global_step = step_config["global_step"]
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):
data_holders = create_data_holder(args)
# 0. input_ids,
# 1. segment_ids,
# 2. input_mask,
# 3. masked_lm_positions,
# 4. masked_lm_labels,
# 5. next_sentence_labels
[
input_ids, segment_ids, input_mask, masked_lm_positions,
masked_lm_labels, next_sentence_labels
] = data_holders
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
train_data_loader, valid_data_loader, test_data_loader = create_pretrained_dataset(
args,
data_file,
tokenizer,
data_world_size=topo.data_info.size,
data_world_rank=topo.data_info.rank,
max_seq_len=args.max_seq_len,
places=paddle.static.cuda_places(),
data_holders=data_holders,
current_step=global_step)
fleet.init(is_collective=True)
if args.model_name_or_path in pretrained_models_list:
model_config = model_class.pretrained_init_configuration[
args.model_name_or_path]
if model_config["vocab_size"] % 8 != 0:
model_config["vocab_size"] += 8 - (model_config["vocab_size"] %
8)
model_config["hidden_dropout_prob"] = args.hidden_dropout_prob
model_config[
"attention_probs_dropout_prob"] = args.attention_probs_dropout_prob
model = model_class(base_class(**model_config))
else:
model, _ = model_class.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 model for the gpt pretrain
prediction_scores, seq_relationship_score = model(
input_ids=input_ids,
token_type_ids=segment_ids,
position_ids=None,
attention_mask=input_mask,
masked_positions=masked_lm_positions)
criterion = criterion_class(with_nsp_loss=args.binary_head)
if args.binary_head:
lm_loss, sop_loss = criterion(prediction_scores,
seq_relationship_score,
masked_lm_labels,
next_sentence_labels)
loss = lm_loss + sop_loss
else:
loss = criterion(prediction_scores, seq_relationship_score,
masked_lm_labels)
# Create the learning_rate sheduler and optimizer
if args.decay_steps is None:
args.decay_steps = args.max_steps
# lr_scheduler = CosineAnnealingWithWarmupDecay(
# max_lr=args.max_lr,
# min_lr=args.min_lr,
# warmup_step=args.warmup_rate * args.max_steps,
# decay_step=args.decay_steps, last_epoch=global_step)
lr_scheduler = LinearDecayWithWarmup(args.max_lr,
args.max_steps,
args.warmup_rate,
last_epoch=global_step)
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"])
]
logger.info("Using paddle.optimizer.AdamW.")
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.bert.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.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.")
# load checkpoint vars
if os.path.exists(checkpoint_dir):
if os.path.isfile(os.path.join(checkpoint_dir, "./config.yml")):
paddle.static.load(main_program,
os.path.join(checkpoint_dir, "static_vars"),
exe)
fetch_loss_vars = collections.OrderedDict()
fetch_other_vars = collections.OrderedDict()
fetch_loss_vars["loss"] = loss
if args.binary_head:
fetch_loss_vars["lm_loss"] = lm_loss
fetch_loss_vars["sop_loss"] = sop_loss
fetch_other_vars["learning_rate"] = main_program.global_block(
).vars["learning_rate_0"]
additional_vars = collections.OrderedDict()
if args.use_amp:
for key in ["loss_scaling", "num_good_steps", "num_bad_steps"]:
additional_vars[key] = main_program.global_block().vars[key + "_0"]
tic_train = time.time()
while True:
fetchs = []
fetchs_keys = []
if topo.is_last:
fetchs = list(fetch_loss_vars.values()) + list(
fetch_other_vars.values()) + list(additional_vars.values())
fetchs_keys = list(fetch_loss_vars.keys()) + list(
fetch_other_vars.keys()) + list(additional_vars.keys())
# 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()
for step, batch in enumerate(train_data_loader()):
ret = exe.run(main_program,
feed=batch,
fetch_list=fetchs,
use_program_cache=True)
# Skip for accumulate_steps in global step
if (step + 1) % args.accumulate_steps != 0:
continue
global_step += 1
# In the new 2.0 api, must call this function to change the learning_rate
lr_scheduler.step()
if global_step % args.logging_freq == 0:
if topo.is_last:
res = collections.defaultdict(float)
for k, v in zip(fetchs_keys, ret):
res[k] = v[0]
speed = args.logging_freq / (time.time() - tic_train)
loss_info = "loss: %.6f, lm_loss: %.6f, sop_loss: %.6f"
loss_info = ", ".join([
"{}: {:.6f}".format(k, res[k])
for k in fetch_loss_vars.keys()
])
common_loginfo = "global step %d, %s, speed: %.2f steps/s, ips: %.2f seqs/s, learning rate: %.5e" % (
global_step, loss_info, speed,
speed * args.global_batch_size, res["learning_rate"])
additional_loginfo = ", ".join([
"{}: {}".format(k, res[k])
for k in additional_vars.keys()
])
if additional_loginfo:
common_loginfo += ", " + additional_loginfo
logger.info(common_loginfo)
for k, v in res.items():
log_writer.add_scalar(k, v, global_step)
tic_train = time.time()
#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 = collections.OrderedDict()
if topo.is_last:
eval_fetch["loss"] = loss
if args.binary_head:
eval_fetch["lm_loss"] = lm_loss
eval_fetch["sop_loss"] = sop_loss
run_evaluate(valid_data_loader, exe, test_program,
args.eval_iters, log_writer, global_step, args,
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.checkpoint_steps == 0:
output_dir = os.path.join(args.output_dir, "model_last")
if worker_index == 0:
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir_bak = os.path.join(args.output_dir,
"model_last_bak")
if os.path.exists(output_dir):
if os.path.exists(output_dir_bak):
shutil.rmtree(output_dir_bak)
shutil.move(output_dir, output_dir_bak)
os.mkdir(output_dir)
step_config = {
"model_name": args.model_name_or_path,
"global_step": global_step,
"global_batch_size": args.global_batch_size,
"consumed_samples":
global_step * args.global_batch_size,
}
with open(os.path.join(output_dir, "config.yml"),
"w") as f:
yaml.dump(step_config,
f,
encoding='utf-8',
allow_unicode=True)
fleet.barrier_worker()
logger.debug("saving models to {}".format(output_dir))
if args.sharding_degree <= 1:
# Save on the first worker by default.
if worker_index == 0:
paddle.static.save(
main_program,
os.path.join(output_dir, "static_vars"))
else:
# Use save_persistables in sharding, but more slower
save_persistables(exe,
os.path.join(output_dir, "static_vars"),
main_program)
if global_step >= args.max_steps:
eval_fetch = collections.OrderedDict()
if topo.is_last:
eval_fetch["loss"] = loss
if args.binary_head:
eval_fetch["lm_loss"] = lm_loss
eval_fetch["sop_loss"] = sop_loss
run_evaluate(test_data_loader, exe, test_program,
args.test_iters, log_writer, global_step, args,
topo.is_last, eval_fetch, "test")
del train_data_loader
return
def do_generation(args):
# Initialize the paddle and paddle fleet execute environment
paddle.enable_static()
assert args.dp_degree == 1, "Data parallel is not supported in inference"
assert args.sharding_degree == 1, "Sharding parallel is temporarily not supported in inference"
assert args.pp_degree == 1, "Pipeline parallel will be supported later"
if args.mp_degree == 1:
args.mp_degree = paddle.distributed.get_world_size()
else:
assert args.mp_degree == paddle.distributed.get_world_size(), \
"If mp_degree is specified, the size must be the same as world_size"
strategy = fleet.DistributedStrategy()
strategy.tensor_parallel = True
strategy.tensor_parallel_configs = {
"tensor_parallel_degree": args.mp_degree
}
fleet.init(is_collective=True, strategy=strategy)
# temp use dynamic init, use HybridParallelInferenceHelper in future?
paddle.distributed.init_parallel_env()
# 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))
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
pretrained_models_list = list(
model_class.pretrained_init_configuration.keys())
data_file = get_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'):
feeds = create_data_holder(args)
tokenizer = tokenizer_class.from_pretrained(
args.model_name_or_path)
eos_id = tokenizer.eos_token_id
_, _, 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=feeds,
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_config["fuse"] = args.fuse
model = GPTForGeneration(
GPTModel(**model_config),
max_length=args.max_dec_len,
decoding_strategy=args.decoding_strategy,
temperature=args.temperature,
top_k=args.topk,
top_p=args.topp,
eos_id=eos_id,
fuse=args.fuse)
else:
logger.error("No checkpoint load.")
model.eval()
ins = {v.name: v for v in feeds}
preds = model(ins)
# Define the Executor for running the static model
exe = paddle.static.Executor(place)
exe.run(startup_program)
main_program = main_program.clone(for_test=True)
model_urls = model.pretrained_resource_files_map['model_state']
model_path = args.model_name_or_path
if model_path in pretrained_models_list and model_path in model_urls:
flag_loaded = False
from paddle.utils.download import get_weights_path_from_url
dygraph_path = get_weights_path_from_url(model_urls[model_path])
if os.path.exists(dygraph_path):
if args.sharding_degree > 1:
logger.warning("Sharding should init with static vars")
else:
logger.info("Loading parameters from %s" % dygraph_path)
init_static_with_params(
model,
paddle.load(
dygraph_path, return_numpy=True),
topo,
main_program)
flag_loaded = True
if not flag_loaded:
logger.error("No checkpoint load.")
global_step = 0
epoch = 0
fetchs = [preds]
### check resutls
text = [
"Question: Where is the capital of China? Answer:",
"Question:Who is the CEO of Apple? Answer:"
]
inputs = tokenizer(
text,
padding=True,
return_attention_mask=True,
return_position_ids=True)
ids = np.array(inputs["input_ids"]).reshape(len(text), -1).astype('int64')
position_ids = np.array(inputs["position_ids"]).reshape(len(text),
-1).astype('int64')
attention_mask = np.array(inputs["attention_mask"]).reshape(
len(text), -1).astype('float32')
t_ids = paddle.fluid.core.Tensor()
t_ids.set(ids, place)
t_mask = paddle.fluid.core.Tensor()
t_mask.set(attention_mask, place)
t_pos = paddle.fluid.core.Tensor()
t_pos.set(position_ids, place)
feed_data = {'src_ids': t_ids, 'pos_ids': t_pos, 'input_mask': t_mask}
ret = exe.run(main_program, feed=feed_data, fetch_list=fetchs)
ret = np.array(ret[0])
for i in range(ret.shape[0]):
o = [int(x) for x in ret[i]]
ret_str = tokenizer.convert_ids_to_string(o)
ret_str = text[i] + ret_str
logger.info(ret_str)
##################
for step, batch in enumerate(test_data_loader()):
ret = exe.run(main_program, feed=batch, fetch_list=fetchs)
if step == 5:
break
if args.save_inference_model_then_exist:
save_inference_model_dir = 'inference_model_pp{pp_degree}mp{mp_degree}'.format(
pp_degree=args.pp_degree, mp_degree=args.mp_degree)
inference_save_path = os.path.join(save_inference_model_dir,
'rank_' + str(fleet.worker_index()),
'step_' + str(0))
print("saving inference models to {}".format(inference_save_path))
feed_names = [v.name for v in feeds]
fetchs_names = [v.name for v in fetchs]
print('feeds: ', feed_names, 'fetches: ', fetchs_names)
paddle.static.save_inference_model(
inference_save_path, feeds, fetchs, exe, program=main_program)