def test_checkpoint_recompute_checkpoint(recompute_checkpoint):
"""
If a checkpoint is saved with `recompute_checkpoint_every_layer`
then we should be able to restore the checkpoint in a new run
that doesn't use `recompute_checkpoint_every_layer` and vice-verse.
"""
args = """
--config unit_test
""".split()
config1 = BertConfig(**(vars(parse_bert_args(args))))
config1.recompute_checkpoint_every_layer = recompute_checkpoint
model1 = PipelinedBertForPretraining(config1).parallelize()
with tempfile.TemporaryDirectory() as dir:
# Save checkpoint
config1.checkpoint_output_dir = dir
save_checkpoint(config1, model1, 0)
# New model with opposite `recompute_checkpoint` to model1
config2 = BertConfig(**(vars(parse_bert_args(args))))
config2.recompute_checkpoint_every_layer = not recompute_checkpoint
model2 = PipelinedBertForPretraining.from_pretrained(os.path.join(dir, "step_0"), config=config2).parallelize()
# Models should now have the same weights
for name, tensor1 in model1.state_dict().items():
tensor2 = model2.state_dict()[name]
assert torch.allclose(tensor1, tensor2)
def test_checkpoint_embedding_serialization(embedding_serialization_factor):
"""
If a checkpoint is saved with embedding_serialization_factor
then we should be able to restore the checkpoint in a new run
where embedding_serialization_factor isn't used.
The reverse should also hold.
"""
args = """
--config unit_test
""".split()
config1 = BertConfig(**(vars(parse_bert_args(args))))
config1.embedding_serialization_factor = embedding_serialization_factor
model1 = PipelinedBertForPretraining(config1).parallelize()
with tempfile.TemporaryDirectory() as dir:
# Save checkpoint
config1.checkpoint_output_dir = dir
save_checkpoint(config1, model1, 0)
# New model with opposite embedding_serialization to model1
config2 = BertConfig(**(vars(parse_bert_args(args))))
config2.embedding_serialization_factor = 5 if embedding_serialization_factor == 1 else 1
model2 = PipelinedBertForPretraining.from_pretrained(os.path.join(dir, "step_0"), config=config2).parallelize()
assert model2.config.embedding_serialization_factor == config2.embedding_serialization_factor
# Models should now have the same weights
for name, tensor1 in model1.state_dict().items():
tensor2 = model2.state_dict()[name]
assert torch.allclose(tensor1, tensor2)
def test_checkpoint_embedding_serialization_qa(embedding_serialization_factor):
"""
If a checkpoint is saved with embedding_serialization_factor
then we should be able to restore the checkpoint in a new run
where embedding_serialization_factor isn't used.
The reverse should also hold.
For PipelinedBertForQuestionAnswering we will need to call `deparallelize`
before checkpointing.
"""
args = """
--config unit_test
""".split()
config = BertConfig(**(vars(parse_bert_args(args))))
config.embedding_serialization_factor = embedding_serialization_factor
model1 = PipelinedBertForQuestionAnswering(config).parallelize()
with tempfile.TemporaryDirectory() as dir:
# Save checkpoint
config.checkpoint_output_dir = dir
model1.deparallelize()
save_checkpoint(config, model1, 0)
# Load the checkpoint, but don't call parallelize
model2 = PipelinedBertForQuestionAnswering.from_pretrained(os.path.join(dir, "step_0"))
# Models should have the same weights
for name, tensor1 in model1.state_dict().items():
tensor2 = model2.state_dict()[name]
assert torch.allclose(tensor1, tensor2)
def test_checkpoint_save_restore(recompute_checkpoint, embedding_serialization_factor):
"""
Test that saving and restoring checkpoints works. Also test checkpointing
with recomputation checkpoints and embedding serialization.
"""
args = """
--config unit_test
""".split()
config = BertConfig(**(vars(parse_bert_args(args))))
config.recompute_checkpoint_every_layer = recompute_checkpoint
config.embedding_serialization_factor = embedding_serialization_factor
model1 = PipelinedBertForPretraining(config).parallelize()
model2 = PipelinedBertForPretraining(config).parallelize()
# The two models should have different initial weights
for name, tensor1 in model1.state_dict().items():
tensor2 = model2.state_dict()[name]
if (tensor1.dtype is not torch.int64) and ("LayerNorm" not in name) and ("bias" not in name):
assert not torch.allclose(tensor1, tensor2)
# Save and restore checkpoint
with tempfile.TemporaryDirectory() as dir:
config.checkpoint_output_dir = dir
# No checkpoints should exist yet
assert not checkpoints_exist(config.checkpoint_output_dir)
save_checkpoint(config, model1, 0)
# Checkpoint should now exist
assert checkpoints_exist(config.checkpoint_output_dir)
# Restore from checkpoint
model2 = PipelinedBertForPretraining.from_pretrained(os.path.join(dir, "step_0"), config=config)
# Models should now have the same weights
for name, tensor1 in model1.state_dict().items():
tensor2 = model2.state_dict()[name]
assert torch.allclose(tensor1, tensor2)
def wgan_gp_run(train_window=52,
test_size=52,
horizon=12,
batch_size=30,
epochs=1,
d_iterations=3,
time_series=True,
BERT=True,
load=None,
cuda_no='cuda:0'):
if torch.cuda.is_available():
device = torch.device(cuda_no)
torch.cuda.set_device(device=device)
FT = torch.cuda.FloatTensor
use_cuda = True
else:
device = torch.device('cpu')
use_cuda = False
FT = torch.FloatTensor
dataloader = get_dataloader(train_window, test_size, batch_size, horizon)
input_size = 1
if time_series:
input_size += 1
if BERT:
input_size += 768
G = LSTMGenerator(input_size=input_size,
hidden_layer_size=300,
num_layers=2,
output_size=1,
horizon=horizon,
device=device)
D = ConvDiscriminator(
input_channels=input_size,
output_channels=1,
)
d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0003)
g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0003)
if use_cuda:
G.cuda(device=device)
D.cuda(device=device)
if not load == None:
load_epoch = load[1]
load_checkpoint(G,
D,
g_optimizer,
d_optimizer,
date=load[0],
epoch=load_epoch,
name=load[2])
else:
load_epoch = 0
if time_series:
cat = cat_with_seq_with_enc if BERT else cat_with_seq_no_enc
else:
cat = cat_no_seq_with_enc if BERT else cat_no_seq_no_enc
for epoch in range(load_epoch + 1, epochs + load_epoch + 1):
i = 0
j = 0
for di in range(d_iterations):
for seq, encoded, labels in dataloader:
if (i == 1) and (di == 0):
print('Currently at Epoch: {}, Discriminator error: {}'.
format(epoch, d_error))
seq = torch.from_numpy(seq).type(FT).unsqueeze(2)
bz = seq.size(0)
G.clear_hidden(bz)
labels = torch.from_numpy(labels).type(FT).unsqueeze(2)
encoded = torch.from_numpy(encoded).type(FT)
with torch.no_grad():
generated_labels = pad(
G(cat(noise((bz, train_window, 1), FT), seq, encoded)),
train_window, horizon)
GP = gradient_penalty(D,
generated_labels,
labels,
encoded,
seq,
Lambda=10,
device=device,
cat=cat)
d_error = train_discriminator(
D, d_optimizer,
cat(labels, seq, encoded).to(device),
cat(generated_labels, seq, encoded).to(device), GP)
del labels, generated_labels, encoded, GP
i += 1
print('Currently at Epoch: {}, Discriminator error: {}'.format(
epoch, d_error))
for seq, encoded, labels in dataloader:
if j == 1:
print('Currently at Epoch: {}, Generator error: {}'.format(
epoch, g_error))
seq = torch.from_numpy(seq).type(FT).unsqueeze(2)
bz = seq.size(0)
G.clear_hidden(bz)
labels = torch.from_numpy(labels).type(FT).unsqueeze(2)
encoded = torch.from_numpy(encoded).type(FT)
generated_labels = pad(
G(cat(noise((bz, train_window, 1), FT), seq, encoded)),
train_window, horizon)
g_error = train_generator(
D, g_optimizer,
cat(generated_labels, seq, encoded).to(device))
del generated_labels, encoded, seq
j += 1
print('Currently at Epoch: {}, Generator error: {}'.format(
epoch, g_error))
if (epoch) % 5000 == 0:
save_checkpoint(
G, D, g_optimizer, d_optimizer, epoch,
'tw-{}_hz-{}_bs-{}_di-{}_t-{}_B-{}_id-{}_hlr_snp'.format(
train_window, horizon, batch_size, d_iterations,
time_series, BERT, D.identifier))
def do_train(args):
paddle.set_device(args.device)
strategy = fleet.DistributedStrategy()
strategy.hybrid_configs = {
"dp_degree": args.dp_degree,
"mp_degree": args.mp_degree,
"pp_degree": args.pp_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
}
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()
local_rank = int(os.getenv("PADDLE_RANK_IN_NODE", 0))
# seed control in hybrid parallel
set_hyrbid_parallel_seed(args.seed, dp_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
# MOE config
initialize_model_and_expert_group(hcg)
model_config['expert_mode'] = args.expert_mode
model_config['hcg'] = hcg
model_config['num_experts'] = args.num_experts
model_config['top_k'] = args.top_k
if args.expert_mode:
model_config['gate'] = args.gate
if args.pp_degree == 1:
model_config["recompute_interval"] = 1 if args.use_recompute else 0
model_config["recompute_partition"] = args.recompute_partition
model_config["recompute_offload"] = args.recompute_offload
if args.use_recompute and args.recompute_partition:
raise Exception(
"when use_recompute is True, recompute_partition must be False in MoE."
)
model = GPTForPretraining(GPTModel(**model_config))
else:
model_config['topology'] = hcg.topology()
model_config["recompute_interval"] = 1 if args.use_recompute else 0
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)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
if args.use_pure_fp16:
scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
scaler = fleet.distributed_scaler(scaler)
scaler._unscale = MethodType(unscale_method, scaler)
model = paddle.amp.decorate(models=model,
optimizers=None,
level='O2',
save_dtype='float32')
opt_fused_tensors, decay_fused_tensors, reduce_fused_tensors, gate_fused_tensors, \
expert_fusion_names = parameters_classify(model)
decay_params = [p.name for p in decay_fused_tensors]
clip = None
if args.grad_clip > 0:
is_expert_param_fun = lambda param: param.name in expert_fusion_names
clip = moe.ClipGradByGlobalNorm(clip_norm=args.grad_clip, \
is_expert_param_func = is_expert_param_fun, \
moe_group = hcg.get_expert_parallel_group())
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=opt_fused_tensors,
weight_decay=args.weight_decay,
grad_clip=clip,
apply_decay_param_fun=lambda x: x in decay_params, #decay_params,
multi_precision=args.use_pure_fp16)
if paddle.distributed.get_world_size() > 1 and args.resume_dir is None:
print(">> initialize....")
initialize_mp_dp_parameters(model, hcg)
#in order to restore reader.
pass_num = 0
file_id = 0
start_epoch = 0
args.resume_dir = None if len(args.resume_dir) <= 0 else args.resume_dir
if args.resume_dir is not None:
global_step, loss_scale, data_meta = load_checkpoint(
args, model, optimizer, lr_scheduler, tokenizer, dp_rank, mp_rank,
pp_rank)
pass_num = data_meta["pass_num"]
file_id = data_meta["file_id"]
start_epoch = data_meta["start_epoch"]
if args.use_pure_fp16:
scaler = paddle.amp.GradScaler(
init_loss_scaling=loss_scale if args.
resume_dir is not None else args.scale_loss)
scaler = fleet.distributed_scaler(scaler)
scaler._unscale = MethodType(unscale_method, scaler)
model, optimizer = paddle.amp.decorate(models=model,
optimizers=optimizer,
level='O2',
save_dtype='float32')
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 if args.resume_dir is None else global_step
timers = get_timers()
tic_train = time.time()
for epoch in range(start_epoch, 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 "npz_" not in str(f))
]
files.sort()
num_files = len(files)
for f_id in range(file_id, 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=args.dp_degree,
data_world_rank=dp_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()
for step, batch in enumerate(train_data_loader()):
# to remove the train data that has been studyed.
if step < global_step - pass_num: continue
global_step += 1
tokens, loss_mask, labels = batch
loss_mask.stop_gradient = True
labels.stop_gradient = True
loss = 0.0
for i in range(accumulate_steps):
start_index = i * args.micro_batch_size
end_index = start_index + args.micro_batch_size
timers('forward-compute').start()
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, :])
loss_mbs = criterion(
preds, labels[start_index:end_index, :],
loss_mask[start_index:end_index, :])
timers('forward-compute').stop()
if args.gate != "naive" and args.balance_loss_weight:
aux_loss_list = [
l.moe_mlp.gate.get_loss(clear=False)
for l in model.gpt.decoder.layers
if hasattr(l.moe_mlp, "gate")
]
bal_loss = paddle.concat(aux_loss_list)
if bal_loss.dtype == paddle.float16:
bal_loss = paddle.cast(bal_loss,
dtype=paddle.float32)
bal_loss = bal_loss.mean()
loss_mbs += bal_loss * args.balance_loss_weight
loss_mbs = loss_mbs / accumulate_steps
timers('backward-compute').start()
if args.use_pure_fp16:
scaler.scale(loss_mbs).backward()
else:
loss_mbs.backward()
timers('backward-compute').stop()
loss = loss + loss_mbs
timers('backward-params-all-reduce').start()
all_reduce_parameters(gate_fused_tensors,
hcg.get_expert_parallel_group())
all_reduce_parameters(reduce_fused_tensors,
hcg.get_data_parallel_group())
timers('backward-params-all-reduce').stop()
if args.use_pure_fp16:
scaler.minimize(optimizer, loss)
else:
optimizer.step()
learning_rate = optimizer.get_lr()
if lr_scheduler is not None:
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.logging_freq == 0:
avg_loss = loss.numpy()
speed = args.logging_freq / (time.time() - tic_train)
if args.gate != "naive" and args.balance_loss_weight:
bal_loss = bal_loss.numpy()
avg_loss -= bal_loss
else:
bal_loss = -1
logger.info(
"global step %d, epoch: %d, batch: %d, loss: %.9f, bal_loss: %.9f, speed: %.2f step/s, ips: %.0f tokens/s, learning rate: %.5e"
% (global_step, epoch, step, avg_loss, bal_loss, speed,
speed * default_global_tokens_num, learning_rate))
log_writer.add_scalar("loss", float(loss), global_step)
log_writer.add_scalar("learning_rate", learning_rate,
global_step)
tic_train = time.time()
timer_log(args.logging_freq)
if (global_step % args.save_steps == 0
or global_step >= args.max_steps):
loss_scale = scaler._scale if args.use_pure_fp16 else None
save_checkpoint(args, global_step, model, optimizer,
lr_scheduler, tokenizer, loss_scale,
dp_rank, mp_rank, pp_rank, pass_num,
file_id, epoch)
print(
"save checkpoint for step_{} successfully...loss_scale = {}"
.format(global_step, loss_scale))
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")
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
# to record sum of the length of train_data_loader that has been read.
pass_num += len(train_data_loader())
del train_data_loader
start_compile = time.perf_counter()
datum = get_generated_datum(config)
poptorch_model.compile(*datum)
duration_compilation = time.perf_counter() - start_compile
logger(f"Compiled/Loaded model in {duration_compilation} secs")
logger("-----------------------------------------------------------")
# Save model and end here if compile only mode is enabled
if config.compile_only:
logger(
"Model successfully compiled. Exiting now as '--compile-only' argument was passed."
)
sys.exit(0)
# Checkpoint model at start of run
save_checkpoint(config, model, steps_finished, optimizer)
# Training loop
logger("--------------------- Training Started --------------------")
factor = config.gradient_accumulation * config.batches_per_step
start_train = time.perf_counter()
train_iterator = tqdm(
range(steps_finished, config.training_steps),
desc="Training",
disable=config.disable_progress_bar
or (config.use_popdist and not (config.popdist_rank == 0)))
for step in train_iterator:
start_step = time.perf_counter()
outputs = poptorch_model(*next(loader))
scheduler.step()
poptorch_model.setOptimizer(optimizer)
