def get_model(args, version=None):
"""Build the model."""
print_rank_0('building Bert model ...')
if version is None:
model = BertMixtureModel(num_layers=args.num_layers,
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
embedding_dropout_prob=args.hidden_dropout,
attention_dropout_prob=args.attention_dropout,
output_dropout_prob=args.hidden_dropout,
layernorm_epsilon=args.layernorm_epsilon,
max_sequence_length=args.max_position_embeddings,
checkpoint_activations=args.checkpoint_activations,
checkpoint_num_layers=args.checkpoint_num_layers,
parallel_output=True,
num_experts=args.num_experts,
type_vocab_size=2)
elif version == "v0":
model = BertMixtureModel_v0(num_layers=args.num_layers,
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
embedding_dropout_prob=args.hidden_dropout,
attention_dropout_prob=args.attention_dropout,
output_dropout_prob=args.hidden_dropout,
layernorm_epsilon=args.layernorm_epsilon,
max_sequence_length=args.max_position_embeddings,
checkpoint_activations=args.checkpoint_activations,
checkpoint_num_layers=args.checkpoint_num_layers,
parallel_output=True,
num_experts=args.num_experts,
type_vocab_size=2)
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on model parallel rank {}: {}'.format(
mpu.get_model_parallel_rank(),
sum([p.nelement() for p in model.parameters()])), flush=True)
#To prevent OOM for model sizes that cannot fit in GPU memory in full precision
if args.deepspeed and args.fp16:
model.half()
# GPU allocation.
model.cuda(torch.cuda.current_device())
# Fp16 conversion.
if args.fp16:
model = FP16_Module(model)
# Wrap model for distributed training.
if USE_TORCH_DDP:
i = torch.cuda.current_device()
model = DDP(model, device_ids=[i], output_device=i,
process_group=mpu.get_data_parallel_group())
else:
model = DDP(model)
return model
def get_model(args):
"""Build the model."""
print_rank_0('building GPT3 model ...')
assert args.num_attention_heads % args.model_parallel_size == 0
num_local_heads = args.num_attention_heads // args.model_parallel_size
deepspeed_sparsity_config = None
if DEEPSPEED_WRAP and args.deepspeed:
deepspeed_sparsity_config = get_sparse_attention_config(args, num_local_heads)
if deepspeed_sparsity_config is not None:
print_rank_0(f"Use sparse attention with mode {args.sparse_mode}")
model = GPT3Model(num_layers=args.num_layers,
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
embedding_dropout_prob=args.hidden_dropout,
attention_dropout_prob=args.attention_dropout,
output_dropout_prob=args.hidden_dropout,
max_sequence_length=args.max_position_embeddings,
checkpoint_activations=args.checkpoint_activations,
checkpoint_num_layers=args.checkpoint_num_layers,
parallel_output=True,
deepspeed_sparsity_config=deepspeed_sparsity_config,
sparse_mode=args.sparse_mode)
if args.load_huggingface is not None:
model = load_huggingface_model(model, args.load_huggingface, args.huggingface_double_pos_embeddings)
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on model parallel rank {}: {}'.format(
mpu.get_model_parallel_rank(),
sum([p.nelement() for p in model.parameters()])), flush=True)
# To prevent OOM for model sizes that cannot fit in GPU memory in full precision
if DEEPSPEED_WRAP and args.deepspeed and args.fp16:
model.half()
# GPU allocation.
model.cuda(torch.cuda.current_device())
# Fp16 conversion.
if args.fp16:
model = FP16_Module(model)
# Wrap model for distributed training.
if USE_TORCH_DDP:
i = torch.cuda.current_device()
model = DDP(model, device_ids=[i], output_device=i,
process_group=mpu.get_data_parallel_group())
else:
model = DDP(model)
return model
def DataParallel(module,
device_ids=None,
output_device=None,
dim=0,
chunk_sizes=None):
torch.cuda.set_device(device_ids)
module = module.cuda(device_ids)
if chunk_sizes is None:
return DDP(module, device_ids, output_device, dim)
standard_size = True
for i in range(1, len(chunk_sizes)):
if chunk_sizes[i] != chunk_sizes[0]:
standard_size = False
if standard_size:
return DDP(module, device_ids, output_device, dim)
return _DataParallel(module, device_ids, output_device, dim, chunk_sizes)
def get_model(args):
"""Build the model."""
print_rank_0('building ruGPT2048 model ...')
model = GPT2Model(num_layers=args.num_layers,
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
embedding_dropout_prob=args.hidden_dropout,
attention_dropout_prob=args.attention_dropout,
output_dropout_prob=args.hidden_dropout,
max_sequence_length=args.max_position_embeddings,
checkpoint_activations=args.checkpoint_activations,
checkpoint_num_layers=args.checkpoint_num_layers,
parallel_output=True,
use_sparse=args.use_sparse)
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on model parallel rank {}: {}'.format(
mpu.get_model_parallel_rank(),
sum([p.nelement() for p in model.parameters()])),
flush=True)
# To prevent OOM for model sizes that cannot fit in GPU memory in full precision
if args.deepspeed and args.fp16:
raise NotImplemented("No installed deep speed")
# GPU allocation.
model.cuda(torch.cuda.current_device())
# Fp16 conversion.
if args.fp16:
model = FP16_Module(model)
# Wrap model for distributed training.
if USE_TORCH_DDP:
i = torch.cuda.current_device()
model = DDP(model,
device_ids=[i],
output_device=i,
process_group=mpu.get_data_parallel_group())
else:
model = DDP(model)
return model
def model_builder():
# setup the model
if args.model == 'i3d':
if args.mode == 'flow':
model = InceptionI3d(num_classes=7, in_channels=2)
model.load_state_dict(
{
k: v
for k, v in torch.load('models/flow_imagenet.pt').items()
if k.find('logits') < 0
},
strict=False)
else:
model = InceptionI3d(num_classes=7,
in_channels=3,
dropout_keep_prob=0.5)
model.load_state_dict(
{
k: v
for k, v in torch.load('models/rgb_imagenet.pt').items()
if k.find('logits') < 0
},
strict=False)
elif args.model == 'r2plus1d':
model = R2Plus1DClassifier(num_classes=7)
elif args.model == 'w3d':
model = W3D(num_classes=7)
# model.load_state_dict(torch.load('pev_i3d_best.pt'))
if args.resume is not None:
# Use a local scope to avoid dangling references
def resume():
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
args.resume, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint)
print("=> loaded checkpoint '{}' ".format(args.resume))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
resume()
model = model.cuda()
if args.distributed:
lr = args.lr * args.batch_size * args.world_size / 64.
else:
lr = args.lr * args.batch_size / 56.
# lr_sched = optim.lr_scheduler.MultiStepLR(optimizer, [30, 60])
if args.distributed:
model = DDP(model)
else:
model = nn.DataParallel(model)
return model
def get_model(args):
"""Build the model."""
print_rank_0('building BERT model ...')
model = BertModel(args)
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on model parallel rank {}: {}'.format(
mpu.get_model_parallel_rank(),
sum([p.nelement() for p in model.parameters()])),
flush=True)
# GPU allocation.
model.cuda(torch.cuda.current_device())
# Fp16 conversion.
if args.fp16:
model = FP16_Module(model)
if args.fp32_embedding:
model.module.model.bert.embeddings.word_embeddings.float()
model.module.model.bert.embeddings.position_embeddings.float()
model.module.model.bert.embeddings.token_type_embeddings.float()
if args.fp32_tokentypes:
model.module.model.bert.embeddings.token_type_embeddings.float()
if args.fp32_layernorm:
for name, _module in model.named_modules():
if 'LayerNorm' in name:
_module.float()
# Wrap model for distributed training.
if USE_TORCH_DDP:
i = torch.cuda.current_device()
model = DDP(model,
device_ids=[i],
output_device=i,
process_group=mpu.get_data_parallel_group())
else:
model = DDP(model)
return model
def get_model(args, config, do_fp16=False):
"""Build the model."""
print_rank_0('building GPT2 model ...')
model = GPT2Model(**config,
checkpoint_activations=args.checkpoint_activations,
checkpoint_num_layers=args.checkpoint_num_layers,
parallel_output=True)
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on model parallel rank {}: {}'.format(
mpu.get_model_parallel_rank(),
sum([p.nelement() for p in model.parameters()])),
flush=True)
# To prevent OOM for model sizes that cannot fit in GPU memory in full precision
if args.deepspeed and do_fp16:
model.half()
# GPU allocation.
model.cuda(torch.cuda.current_device())
# Fp16 conversion.
if do_fp16:
model = FP16_Module(model)
# Wrap model for distributed training.
if USE_TORCH_DDP:
i = torch.cuda.current_device()
model = DDP(model,
device_ids=[i],
output_device=i,
process_group=mpu.get_data_parallel_group())
else:
model = DDP(model)
return model
def model(self):
if self._wrapped_model is None:
if self.args.distributed_world_size > 1 and not self.args.use_bmuf:
self._wrapped_model = DDP(
module=self._model,
device_ids=[self.args.device_id],
output_device=self.args.device_id,
broadcast_buffers=False,
bucket_cap_mb=self.args.bucket_cap_mb,
check_reduction=True,
find_unused_parameters=self.args.find_unused_parameters,
)
else:
self._wrapped_model = self._model
return self._wrapped_model
def initialize(self, amp_id: int, num_losses: int, use_amp: bool,
amp_opt_level: str, device: torch.device):
self._amp_id = amp_id
self._use_amp = use_amp
if APEX_AVAILABLE and self._use_amp:
self._model, self._optimizer = amp.initialize(
self._model,
self._optimizer,
opt_level=amp_opt_level,
num_losses=num_losses)
if on_multiple_gpus(get_devices()):
self._model = ApexDDP(self._model, delay_allreduce=True)
if not APEX_AVAILABLE and on_multiple_gpus(get_devices()):
self._model = DDP(self._model, device_ids=[device])
def __init__(self, args, loader, is_train):
self.loader = loader
self.dataset = loader.dataset
self.is_train = is_train
self.device = args.device
self.gather = False
self.gpu_gather = args.gpu_gather
self.resume_epoch = 0
self.has_val_score = False
self.exp_dir = args.exp_dir
if self.is_train:
self.last_lr = args.lr
self.lr_update_per_epoch = args.lr_update_per_epoch
self.model = build_model(args, self.dataset)
logging.debug(str(self.model))
logging.debug(
f'Total number of parameters: {sum([p.numel() for p in self.model.parameters()])}'
)
self.rank = dist_get_rank()
if self.rank >= 0:
self.device = torch.cuda.current_device(
) if args.use_cuda else 'cpu'
self.model = self.model.to(self.device)
self.model = DDP(
self.model,
[self.device] if args.use_cuda else None,
find_unused_parameters=True,
)
else:
if args.use_cuda:
if torch.cuda.device_count() > 1:
self.model = DP(self.model)
self.model = self.model.to(self.device)
self.output_device = args.device if args.gpu_gather else 'cpu'
if is_train:
logging.debug(
f'Optimizer: {args.optim} with base learning rate {args.lr:.6g}'
)
self.set_optim(args)
self.set_lr_schedule(args)
self.auto_load(args)
def __init__(
self,
*,
model,
diffusion,
data,
batch_size,
microbatch,
lr,
ema_rate,
log_interval,
save_interval,
resume_checkpoint,
use_fp16=False,
fp16_scale_growth=1e-3,
schedule_sampler=None,
weight_decay=0.0,
lr_anneal_steps=0,
):
self.model = model
self.diffusion = diffusion
self.data = data
self.batch_size = batch_size
self.microbatch = microbatch if microbatch > 0 else batch_size
self.lr = lr
self.ema_rate = (
[ema_rate]
if isinstance(ema_rate, float)
else [float(x) for x in ema_rate.split(",")]
)
self.log_interval = log_interval
self.save_interval = save_interval
self.resume_checkpoint = resume_checkpoint
self.use_fp16 = use_fp16
self.fp16_scale_growth = fp16_scale_growth
self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
self.weight_decay = weight_decay
self.lr_anneal_steps = lr_anneal_steps
self.step = 0
self.resume_step = 0
self.global_batch = self.batch_size * dist.get_world_size()
self.model_params = list(self.model.parameters())
self.master_params = self.model_params
self.lg_loss_scale = INITIAL_LOG_LOSS_SCALE
self.sync_cuda = th.cuda.is_available()
self._load_and_sync_parameters()
if self.use_fp16:
self._setup_fp16()
self.opt = AdamW(self.master_params, lr=self.lr, weight_decay=self.weight_decay)
if self.resume_step:
self._load_optimizer_state()
# Model was resumed, either due to a restart or a checkpoint
# being specified at the command line.
self.ema_params = [
self._load_ema_parameters(rate) for rate in self.ema_rate
]
else:
self.ema_params = [
copy.deepcopy(self.master_params) for _ in range(len(self.ema_rate))
]
if th.cuda.is_available():
self.use_ddp = True
self.ddp_model = DDP(
self.model,
device_ids=[dist_util.dev()],
output_device=dist_util.dev(),
broadcast_buffers=False,
bucket_cap_mb=128,
find_unused_parameters=False,
)
else:
if dist.get_world_size() > 1:
logger.warn(
"Distributed training requires CUDA. "
"Gradients will not be synchronized properly!"
)
self.use_ddp = False
self.ddp_model = self.model
def main(pargs):
#init distributed training
comm_local_group = comm.init(pargs.wireup_method,
pargs.batchnorm_group_size)
comm_rank = comm.get_rank()
comm_local_rank = comm.get_local_rank()
comm_size = comm.get_size()
comm_local_size = comm.get_local_size()
# set up logging
pargs.logging_frequency = max([pargs.logging_frequency, 1])
log_file = os.path.normpath(
os.path.join(pargs.output_dir, "logs", pargs.run_tag + ".log"))
logger = mll.mlperf_logger(log_file, "deepcam", "Umbrella Corp.")
logger.log_start(key="init_start", sync=True)
logger.log_event(key="cache_clear")
#set seed
seed = pargs.seed
logger.log_event(key="seed", value=seed)
# Some setup
torch.manual_seed(seed)
if torch.cuda.is_available():
device = torch.device("cuda", comm_local_rank)
torch.cuda.manual_seed(seed)
torch.cuda.set_device(device)
torch.backends.cudnn.benchmark = True
else:
device = torch.device("cpu")
#set up directories
root_dir = os.path.join(pargs.data_dir_prefix)
output_dir = pargs.output_dir
plot_dir = os.path.join(output_dir, "plots")
if comm_rank == 0:
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
# logging of rank information
logger.log_event(key="number_of_ranks", value=comm_size)
logger.log_event(key="number_of_nodes",
value=(comm_size // comm_local_size))
logger.log_event(key="accelerators_per_node", value=comm_local_size)
# Logging hyperparameters
logger.log_event(key="global_batch_size",
value=(pargs.local_batch_size * comm_size))
logger.log_event(key="batchnorm_group_size",
value=pargs.batchnorm_group_size)
logger.log_event(key="gradient_accumulation_frequency",
value=pargs.gradient_accumulation_frequency)
logger.log_event(key="checkpoint", value=pargs.checkpoint)
# Define architecture
n_input_channels = len(pargs.channels)
n_output_channels = 3
net = deeplab_xception.DeepLabv3_plus(n_input=n_input_channels,
n_classes=n_output_channels,
os=16,
pretrained=False,
rank=comm_rank,
process_group=comm_local_group)
net.to(device)
#select loss
#some magic numbers
loss_pow = -0.125
class_weights = [
0.986267818390377**loss_pow, 0.0004578708870701058**loss_pow,
0.01327431072255291**loss_pow
]
# extract loss
criterion = losses.CELoss(class_weights).to(device)
criterion = torch.jit.script(criterion)
#select optimizer
optimizer = oh.get_optimizer(pargs, net, logger)
#restart from checkpoint if desired
if pargs.checkpoint is not None:
checkpoint = torch.load(pargs.checkpoint, map_location=device)
start_step = checkpoint['step']
start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
net.load_state_dict(checkpoint['model'])
else:
start_step = 0
start_epoch = 0
#broadcast model and optimizer state
steptens = torch.tensor(np.array([start_step, start_epoch]),
requires_grad=False).to(device)
if dist.is_initialized():
dist.broadcast(steptens, src=0)
#unpack the bcasted tensor
start_step = int(steptens.cpu().numpy()[0])
start_epoch = int(steptens.cpu().numpy()[1])
#select scheduler
scheduler = None
if pargs.lr_schedule:
pargs.lr_schedule["lr_warmup_steps"] = pargs.lr_warmup_steps
pargs.lr_schedule["lr_warmup_factor"] = pargs.lr_warmup_factor
scheduler = oh.get_lr_schedule(pargs.start_lr,
pargs.lr_schedule,
optimizer,
logger,
last_step=start_step)
# print parameters
if comm_rank == 0:
print(net)
print("Total number of elements:",
sum(p.numel() for p in net.parameters() if p.requires_grad))
# get input shapes for the upcoming model preprocessing
# input_shape:
tshape, _ = get_datashapes(pargs, root_dir)
input_shape = tuple([tshape[2], tshape[0], tshape[1]])
#distributed model parameters
bucket_cap_mb = 25
if pargs.batchnorm_group_size > 1:
bucket_cap_mb = 220
# get stream, relevant for graph capture
ddp_net = DDP(net,
device_ids=[device.index],
output_device=device.index,
find_unused_parameters=False,
broadcast_buffers=False,
bucket_cap_mb=bucket_cap_mb,
gradient_as_bucket_view=False)
# get stats handler here
bnstats_handler = bns.BatchNormStatsSynchronize(ddp_net,
reduction="mean",
inplace=True)
# create handles
net_validate = ddp_net
net_train = ddp_net
# Set up the data feeder
train_loader, train_size, validation_loader, validation_size = get_dataloaders(
pargs, root_dir, device, seed, comm_size, comm_rank)
# log size of datasets
logger.log_event(key="train_samples", value=train_size)
val_size = validation_size
logger.log_event(key="eval_samples", value=val_size)
# get start steps
step = start_step
epoch = start_epoch
current_lr = pargs.start_lr if not pargs.lr_schedule else scheduler.get_last_lr(
)[0]
stop_training = False
net_train.train()
# start trining
logger.log_end(key="init_stop", sync=True)
logger.log_start(key="run_start", sync=True)
# training loop
while True:
# start epoch
logger.log_start(key="epoch_start",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
train_loader.sampler.set_epoch(epoch)
# training
step = train_step(pargs, comm_rank, comm_size, device, step, epoch,
net_train, criterion, optimizer, scheduler,
train_loader, logger)
# average BN stats
bnstats_handler.synchronize()
# validation
stop_training = validate(pargs, comm_rank, comm_size, device, step,
epoch, net_validate, criterion,
validation_loader, logger)
# log the epoch
logger.log_end(key="epoch_stop",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
epoch += 1
#save model if desired
if (pargs.save_frequency > 0) and (epoch % pargs.save_frequency == 0):
logger.log_start(key="save_start",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
if comm_rank == 0:
checkpoint = {
'step': step,
'epoch': epoch,
'model': net_train.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(
checkpoint,
os.path.join(
output_dir,
pargs.model_prefix + "_step_" + str(step) + ".cpt"))
logger.log_end(key="save_stop",
metadata={
'epoch_num': epoch + 1,
'step_num': step
},
sync=True)
# are we done?
if (epoch >= pargs.max_epochs) or stop_training:
break
# run done
logger.log_end(key="run_stop", sync=True, metadata={'status': 'success'})
vocab_file=args.vocab_file)
# get the model
model = get_model(args.model)(dataloader.SRC, dataloader.TRG, args)
watcher.info(model)
watcher.info("total trainable parameters: {}".format(
format(count_parameters(model), ',')))
watcher.info("Vocabulary size: {}/{}.".format(len(dataloader.SRC.vocab),
len(dataloader.TRG.vocab)))
# use GPU
if torch.cuda.is_available():
model.cuda()
if args.distributed:
model = DDP(model,
device_ids=[args.device_id],
output_device=args.device_id)
model = setup_pretrained_model(args, model, watcher)
# start running
if args.mode == 'train':
watcher.info('starting training')
train_model(args,
watcher,
model,
dataloader.train,
dataloader.dev,
decoding_path=None)
elif args.mode == 'test':
def main(args):
"""
Main function for training, evaluating, and checkpointing.
Args:
args: `argparse` object.
"""
# Print arguments.
print('\nusing arguments:')
_print_arguments(args)
print()
# Check if GPU is available.
if not args.use_gpu and torch.cuda.is_available():
print('warning: GPU is available but args.use_gpu = False')
print()
local_rank = args.local_rank
# world_size = torch.cuda.device_count() # assume all local GPUs
# Set up distributed process group
rank = setup_dist(local_rank)
# Set up datasets.
train_dataset = QADataset(args, args.train_path)
dev_dataset = QADataset(args, args.dev_path)
# Create vocabulary and tokenizer.
vocabulary = Vocabulary(train_dataset.samples, args.vocab_size)
tokenizer = Tokenizer(vocabulary)
for dataset in (train_dataset, dev_dataset):
dataset.register_tokenizer(tokenizer)
args.vocab_size = len(vocabulary)
args.pad_token_id = tokenizer.pad_token_id
print(f'vocab words = {len(vocabulary)}')
# Print number of samples.
print(f'train samples = {len(train_dataset)}')
print(f'dev samples = {len(dev_dataset)}')
print()
# Select model.
model = _select_model(args)
#model = model.to(rank)
#model = DDP(model, device_ids=[rank], output_device=rank)
num_pretrained = model.load_pretrained_embeddings(
vocabulary, args.embedding_path
)
pct_pretrained = round(num_pretrained / len(vocabulary) * 100., 2)
print(f'using pre-trained embeddings from \'{args.embedding_path}\'')
print(
f'initialized {num_pretrained}/{len(vocabulary)} '
f'embeddings ({pct_pretrained}%)'
)
print()
# device = torch.device(f'cuda:{rank}')
model = model.to(rank)
model = DDP(model, device_ids=[rank], output_device=rank)
# if args.use_gpu:
# model = cuda(args, model)
if args.resume and args.model_path:
map_location = {"cuda:0": "cuda:{}".format(rank)}
model.load_state_dict(torch.load(args.model_path, map_location=map_location))
params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'using model \'{args.model}\' ({params} params)')
print(model)
print()
if args.do_train:
# Track training statistics for checkpointing.
eval_history = []
best_eval_loss = float('inf')
# Begin training.
for epoch in range(1, args.epochs + 1):
# Perform training and evaluation steps.
try:
train_loss = train(args, epoch, model, train_dataset)
except RuntimeError:
print(f'NCCL Wait Timeout, rank: \'{args.local_rank}\' (exit)')
exit(1)
eval_loss = evaluate(args, epoch, model, dev_dataset)
# If the model's evaluation loss yields a global improvement,
# checkpoint the model.
if rank == 0:
eval_history.append(eval_loss < best_eval_loss)
if eval_loss < best_eval_loss:
best_eval_loss = eval_loss
torch.save(model.state_dict(), args.model_path)
print(
f'epoch = {epoch} | '
f'train loss = {train_loss:.6f} | '
f'eval loss = {eval_loss:.6f} | '
f"{'saving model!' if eval_history[-1] else ''}"
)
# If early stopping conditions are met, stop training.
if _early_stop(args, eval_history):
suffix = 's' if args.early_stop > 1 else ''
print(
f'no improvement after {args.early_stop} epoch{suffix}. '
'early stopping...'
)
print()
cleanup_dist()
break
if args.do_test and rank == 0:
# Write predictions to the output file. Use the printed command
# below to obtain official EM/F1 metrics.
write_predictions(args, model, dev_dataset)
eval_cmd = (
'python3 evaluate.py '
f'--dataset_path {args.dev_path} '
f'--output_path {args.output_path}'
)
print()
print(f'predictions written to \'{args.output_path}\'')
print(f'compute EM/F1 with: \'{eval_cmd}\'')
print()
def worker(gpu, npgpus_per_node, args):
START_TIME = time.time()
args.device_id = gpu
args.device = "cuda:{}".format(gpu)
args.local_rank = args.local_rank * npgpus_per_node + gpu
args.world_size = args.world_size * npgpus_per_node
if args.distributed:
torch.distributed.init_process_group(backend='nccl',
init_method=args.init_method,
world_size=args.world_size,
rank=args.local_rank)
torch.cuda.set_device(args.device_id)
# setup watcher settings
watcher = Watcher(
rank=args.local_rank,
log_path=os.path.join(args.workspace_prefix, 'logs',
'log-{}.txt'.format(args.prefix)) if
((args.logfile is None) or (args.logfile == 'none')) else args.logfile)
watcher.info('\n'.join([
'{}:\t{}'.format(a, b)
for a, b in sorted(args.__dict__.items(), key=lambda x: x[0])
]))
watcher.info('Starting with HPARAMS: {}'.format(args.hp_str))
watcher.info("RANK:{}, WORLD_SIZE:{}, DEVICE-ID:{}, MASTER={}".format(
args.local_rank, args.world_size, args.init_method, args.device_id))
# get the dataloader
dataloader = get_dataloader(setup_dataloader(args))(
args, watcher, vocab_file=args.vocab_file)
# get the model
model = get_model(args.model)(dataloader.SRC, dataloader.TRG, args)
watcher.info(model)
watcher.info("total trainable parameters: {}".format(
format(count_parameters(model), ',')))
watcher.info("Vocabulary size: {}/{}.".format(len(dataloader.SRC.vocab),
len(dataloader.TRG.vocab)))
# use GPU
if torch.cuda.is_available():
model.cuda()
if args.distributed:
model = DDP(model,
device_ids=[args.device_id],
output_device=args.device_id)
model = setup_pretrained_model(args, model, watcher)
# start running
if args.mode == 'train':
watcher.info('starting training')
train_model(args,
watcher,
model,
dataloader.train,
dataloader.dev,
decoding_path=None)
elif args.mode == 'test':
if (args.local_rank == 0) and (not os.path.exists(args.decoding_path)):
os.mkdir(args.decoding_path)
watcher.info(
'starting decoding from the pre-trained model, on the test set...')
assert args.load_from is not None, 'must decode from a pre-trained model.'
with torch.no_grad():
test_set = dataloader.test if args.decode_test else dataloader.dev
name = '{}.b={}_a={}.txt'.format(
args.test_set if args.decode_test else args.dev_set,
args.beam_size, args.alpha)
args.decoding_path += '/{}'.format(name)
for set_i in test_set:
valid_model(args,
watcher,
model,
set_i,
print_out=True,
decoding_path=args.decoding_path,
dataflow=['src', 'trg'])
elif args.mode == 'valid_ppl':
watcher.info(
'starting to evaluate the model from the pre-trained model, on the test set...'
)
assert args.load_from is not None, 'must decode from a pre-trained model.'
with torch.no_grad():
test_set = dataloader.test if args.decode_test else dataloader.dev
for set_i in test_set:
if args.sweep_target_tokens is not None:
target_tokens = [
'<{}>'.format(a)
for a in args.sweep_target_tokens.split(',')
]
else:
target_tokens = [set_i.init_tokens['trg']]
for trg_tok in target_tokens:
set_i.init_tokens['trg'] = trg_tok
watcher.info("{} -> {}".format(set_i.task,
set_i.init_tokens))
output_file = open(
args.decoding_path + '/{}->{}.txt'.format(
set_i.task, set_i.init_tokens['trg'][1:-1]), 'w')
outputs = valid_model_ppl(args,
watcher,
model,
set_i,
dataflow=['src', 'trg'],
lm_only=args.lm_only)
if args.local_rank == 0:
for s, t, ppl in zip(
*
[outputs['src'], outputs['trg'], outputs['loss']]):
line = '{}\t{}\t{}'.format(ppl, s, t)
print(line, file=output_file, flush=True)
print('write done.')
watcher.info("all done. Total clock time = {}".format(
str(datetime.timedelta(seconds=(time.time() - START_TIME)))))
def _test_fsdp_parity(
self,
model_class: Type[FSDPTestModel],
fsdp_init_mode: FSDPInitMode,
cuda_init_mode: CUDAInitMode,
ref_init_fn: Optional[Callable] = None,
num_iters: int = 2,
save_model: bool = True,
cpu_offload: CPUOffload = CPUOffload(),
backward_prefetch: Optional[BackwardPrefetch] = None,
forward_prefetch: bool = False,
sharding_strategy: Optional[ShardingStrategy] = None,
mixed_precision: Optional[MixedPrecision] = None,
enable_sharded_grad_scaler: bool = False,
use_pure_fp16: bool = False,
norm_type: Optional[Union[float, int]] = None,
init_kwargs: Optional[Dict[str, Any]] = None,
**fsdp_kwargs,
):
"""
Tests FSDP training against a reference, which defaults to DDP but
may be customized with ``ref_init_fn``.
Args:
model_class (Type[FSDPTestModel]): A model class that inherits from
``FSDPTestModel``, which defines the expected interface.
fsdp_init_mode (FSDPInitMode): The mode to initialize the
FSDP-wrapped model. This should not be ``NO_FSDP``.
ref_init_fn (Optional[Callable]): A callable to invoke that wraps a
non-wrapped model to construct the reference model, where this
wrapper should provide data parallel semantics. If ``None``,
then the callable defaults to the DDP constructor.
"""
assert fsdp_init_mode != FSDPInitMode.NO_FSDP, "Expects an FSDP init mode that wraps with FSDP"
if init_kwargs is None:
init_kwargs = {}
lr = 1e-2
rank = self.process_group.rank()
# Establish reference behavior with DDP
model = model_class.init(
self.process_group,
FSDPInitMode.NO_FSDP,
CUDAInitMode.CUDA_BEFORE,
deterministic=True,
**init_kwargs,
)
if ref_init_fn is None:
ref_model = DDP(model, device_ids=[rank], output_device=rank)
else:
ref_model = ref_init_fn(model)
if use_pure_fp16:
ref_model = ref_model.half()
ref_loss = self._train_for_several_steps(
ref_model,
num_iters,
autocast=mixed_precision is not None,
lr=lr,
fsdp_cpu_offload=cpu_offload,
mixed_precision=mixed_precision,
norm_type=norm_type,
enable_sharded_grad_scaler=enable_sharded_grad_scaler,
use_pure_fp16=use_pure_fp16,
)
ddp_params = list(ref_model.parameters())
# Check against FSDP behavior
fsdp_kwargs.update({
"cpu_offload": cpu_offload,
"backward_prefetch": backward_prefetch,
"forward_prefetch": forward_prefetch,
"sharding_strategy": sharding_strategy,
"mixed_precision": mixed_precision,
})
try:
fsdp_model = model_class.init(
self.process_group,
fsdp_init_mode,
cuda_init_mode,
fsdp_kwargs,
deterministic=True,
**init_kwargs,
)
except Exception as e:
raise ValueError(
f"Initializing {model_class} raised error {str(e)}")
if not isinstance(fsdp_model, FSDP):
# Enforce that we wrap with top-level FSDP since we are comparing
# assuming a data parallel reference and some test models may not
# do so in their `init()` method
fsdp_model = FSDP(fsdp_model, self.process_group, **fsdp_kwargs)
if use_pure_fp16:
# Change the model parameter dtype after FSDP initialization
fsdp_model = fsdp_model.half()
if cuda_init_mode == CUDAInitMode.CUDA_AFTER:
fsdp_model = fsdp_model.cuda()
offload_params = cpu_offload is not None and cpu_offload.offload_params
# Offloading parameters with `CUDA_AFTER` should raise an error during
# lazy initialization due to the parameter devices not being CPU;
# otherwise, all parameter devices should be CPU
expects_device_error = offload_params and cuda_init_mode == CUDAInitMode.CUDA_AFTER
expects_cpu_device = offload_params and cuda_init_mode != CUDAInitMode.CUDA_AFTER
if expects_cpu_device:
cpu_device = torch.device("cpu")
for param in fsdp_model.parameters():
self.assertEqual(param.device, cpu_device)
context = (self.assertRaisesRegex(AssertionError,
"Expected param to be on CPU")
if expects_device_error else suppress())
with context:
fsdp_loss = self._train_for_several_steps(
fsdp_model,
num_iters,
autocast=False,
lr=lr,
fsdp_cpu_offload=cpu_offload,
save_model=save_model,
mixed_precision=mixed_precision,
norm_type=norm_type,
enable_sharded_grad_scaler=enable_sharded_grad_scaler,
use_pure_fp16=use_pure_fp16,
)
# No need to check for parameter and loss parity if expecting an error
if expects_device_error:
return
# Check parameter devices are CPU if offloading to CPU before calling
# `get_full_params()`, which will cast the parameters to FP32
if offload_params:
for param in fsdp_model.parameters():
self.assertEqual(param.device, cpu_device)
fsdp_loss = fsdp_loss.cuda()
fsdp_unsharded_params = get_full_params(fsdp_model)
torch.testing.assert_allclose(ref_loss, fsdp_loss)
# Do not check for parameter parity if using mixed precision since (1)
# the DDP parameters are in FP16 (from `half()`) while the FSDP
# parameters are in FP32 (from `summon_full_params()`) and (2) DDP runs
# the optimizer in FP16 while FSDP runs it in FP32
if mixed_precision is not None:
self.assertEqual(
ddp_params,
fsdp_unsharded_params,
exact_device=True,
msg="FSDP did not match DDP",
)
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--src_file",
default=None,
type=str,
help="The input data file name.")
parser.add_argument("--tgt_file",
default=None,
type=str,
help="The output data file name.")
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument("--config_path",
default=None,
type=str,
help="Bert config file path.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--log_dir",
default='',
type=str,
required=True,
help="The output directory where the log will be written.")
parser.add_argument("--model_recover_path",
default=None,
type=str,
required=True,
help="The file of fine-tuned pretraining model.")
parser.add_argument("--optim_recover_path",
default=None,
type=str,
help="The file of pretraining optimizer.")
# Other parameters
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=64,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--label_smoothing",
default=0,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="The weight decay rate for Adam.")
parser.add_argument("--finetune_decay",
action='store_true',
help="Weight decay to the original weights.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--hidden_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for hidden states.")
parser.add_argument("--attention_probs_dropout_prob",
default=0.1,
type=float,
help="Dropout rate for attention probabilities.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--fp32_embedding',
action='store_true',
help=
"Whether to use 32-bit float precision instead of 16-bit for embeddings"
)
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--amp',
action='store_true',
help="Whether to use amp for fp16")
parser.add_argument(
'--from_scratch',
action='store_true',
help=
"Initialize parameters with random values (i.e., training from scratch)."
)
parser.add_argument('--new_segment_ids',
action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument('--new_pos_ids',
action='store_true',
help="Use new position ids for LMs.")
parser.add_argument('--tokenized_input',
action='store_true',
help="Whether the input is tokenized.")
parser.add_argument('--max_len_a',
type=int,
default=0,
help="Truncate_config: maximum length of segment A.")
parser.add_argument('--max_len_b',
type=int,
default=0,
help="Truncate_config: maximum length of segment B.")
parser.add_argument(
'--trunc_seg',
default='',
help="Truncate_config: first truncate segment A/B (option: a, b).")
parser.add_argument(
'--always_truncate_tail',
action='store_true',
help="Truncate_config: Whether we should always truncate tail.")
parser.add_argument(
"--mask_prob",
default=0.15,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument(
"--mask_prob_eos",
default=0,
type=float,
help=
"Number of prediction is sometimes less than max_pred when sequence is short."
)
parser.add_argument('--max_pred',
type=int,
default=20,
help="Max tokens of prediction.")
parser.add_argument("--num_workers",
default=0,
type=int,
help="Number of workers for the data loader.")
parser.add_argument('--mask_source_words',
action='store_true',
help="Whether to mask source words for training")
parser.add_argument('--skipgram_prb',
type=float,
default=0.0,
help='prob of ngram mask')
parser.add_argument('--skipgram_size',
type=int,
default=1,
help='the max size of ngram mask')
parser.add_argument('--mask_whole_word',
action='store_true',
help="Whether masking a whole word.")
parser.add_argument('--do_l2r_training',
action='store_true',
help="Whether to do left to right training")
parser.add_argument(
'--has_sentence_oracle',
action='store_true',
help="Whether to have sentence level oracle for training. "
"Only useful for summary generation")
parser.add_argument('--max_position_embeddings',
type=int,
default=None,
help="max position embeddings")
parser.add_argument('--relax_projection',
action='store_true',
help="Use different projection layers for tasks.")
parser.add_argument('--ffn_type',
default=0,
type=int,
help="0: default mlp; 1: W((Wx+b) elem_prod x);")
parser.add_argument('--num_qkv',
default=0,
type=int,
help="Number of different <Q,K,V>.")
parser.add_argument('--seg_emb',
action='store_true',
help="Using segment embedding for self-attention.")
parser.add_argument(
'--s2s_special_token',
action='store_true',
help="New special tokens ([S2S_SEP]/[S2S_CLS]) of S2S.")
parser.add_argument('--s2s_add_segment',
action='store_true',
help="Additional segmental for the encoder of S2S.")
parser.add_argument(
'--s2s_share_segment',
action='store_true',
help=
"Sharing segment embeddings for the encoder of S2S (used with --s2s_add_segment)."
)
parser.add_argument('--pos_shift',
action='store_true',
help="Using position shift for fine-tuning.")
args = parser.parse_args()
assert Path(
args.model_recover_path).exists(), "--model_recover_path doesn't exist"
args.output_dir = args.output_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
args.log_dir = args.log_dir.replace('[PT_OUTPUT_DIR]',
os.getenv('PT_OUTPUT_DIR', ''))
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
json.dump(args.__dict__,
open(os.path.join(args.output_dir, 'opt.json'), 'w'),
sort_keys=True,
indent=2)
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
dist.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
if args.max_position_embeddings:
tokenizer.max_len = args.max_position_embeddings
data_tokenizer = WhitespaceTokenizer(
) if args.tokenized_input else tokenizer
if args.local_rank == 0:
dist.barrier()
if args.do_train:
print("Loading Train Dataset", args.data_dir)
bi_uni_pipeline = [
seq2seq_loader.Preprocess4Seq2seq(
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb,
skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word,
mode="s2s",
has_oracle=args.has_sentence_oracle,
num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)
]
file_oracle = None
if args.has_sentence_oracle:
file_oracle = os.path.join(args.data_dir, 'train.oracle')
fn_src = os.path.join(args.data_dir,
args.src_file if args.src_file else 'train.src')
fn_tgt = os.path.join(args.data_dir,
args.tgt_file if args.tgt_file else 'train.tgt')
train_dataset = seq2seq_loader.Seq2SeqDataset(
fn_src,
fn_tgt,
args.train_batch_size,
data_tokenizer,
args.max_seq_length,
file_oracle=file_oracle,
bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset, replacement=False)
_batch_size = args.train_batch_size
else:
train_sampler = DistributedSampler(train_dataset)
_batch_size = args.train_batch_size // dist.get_world_size()
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=_batch_size,
sampler=train_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
# note: args.train_batch_size has been changed to (/= args.gradient_accumulation_steps)
# t_total = int(math.ceil(len(train_dataset.ex_list) / args.train_batch_size)
t_total = int(
len(train_dataloader) * args.num_train_epochs /
args.gradient_accumulation_steps)
amp_handle = None
if args.fp16 and args.amp:
from apex import amp
amp_handle = amp.init(enable_caching=True)
logger.info("enable fp16 with amp")
# Prepare model
recover_step = _get_max_epoch_model(args.output_dir)
cls_num_labels = 2
type_vocab_size = 6 + \
(1 if args.s2s_add_segment else 0) if args.new_segment_ids else 2
num_sentlvl_labels = 2 if args.has_sentence_oracle else 0
relax_projection = 4 if args.relax_projection else 0
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
if (recover_step is None) and (args.model_recover_path is None):
# if _state_dict == {}, the parameters are randomly initialized
# if _state_dict == None, the parameters are initialized with bert-init
_state_dict = {} if args.from_scratch else None
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=_state_dict,
num_labels=cls_num_labels,
num_rel=0,
type_vocab_size=type_vocab_size,
config_path=args.config_path,
task_idx=3,
num_sentlvl_labels=num_sentlvl_labels,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
relax_projection=relax_projection,
new_pos_ids=args.new_pos_ids,
ffn_type=args.ffn_type,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb)
global_step = 0
else:
if recover_step:
logger.info("***** Recover model: %d *****", recover_step)
model_recover = torch.load(os.path.join(
args.output_dir, "model.{0}.bin".format(recover_step)),
map_location='cpu')
# recover_step == number of epochs
global_step = math.floor(recover_step * t_total /
args.num_train_epochs)
elif args.model_recover_path:
logger.info("***** Recover model: %s *****",
args.model_recover_path)
model_recover = torch.load(args.model_recover_path,
map_location='cpu')
global_step = 0
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=model_recover,
num_labels=cls_num_labels,
num_rel=0,
type_vocab_size=type_vocab_size,
config_path=args.config_path,
task_idx=3,
num_sentlvl_labels=num_sentlvl_labels,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
relax_projection=relax_projection,
new_pos_ids=args.new_pos_ids,
ffn_type=args.ffn_type,
hidden_dropout_prob=args.hidden_dropout_prob,
attention_probs_dropout_prob=args.attention_probs_dropout_prob,
num_qkv=args.num_qkv,
seg_emb=args.seg_emb)
if args.local_rank == 0:
dist.barrier()
if args.fp16:
model.half()
if args.fp32_embedding:
model.bert.embeddings.word_embeddings.float()
model.bert.embeddings.position_embeddings.float()
model.bert.embeddings.token_type_embeddings.float()
model.to(device)
if args.local_rank != -1:
try:
from torch.nn.parallel.distributed import DistributedDataParallel as DDP
except ImportError:
raise ImportError("DistributedDataParallel")
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
#model = torch.nn.DataParallel(model)
model = DataParallelImbalance(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
#from apex.optimizers.fp16_optimizer import FP16_Optimizer
from pytorch_pretrained_bert.optimization_fp16 import FP16_Optimizer_State
from apex.optimizers.fused_adam import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False)
if args.loss_scale == 0:
optimizer = FP16_Optimizer_State(optimizer,
dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer_State(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
if recover_step:
logger.info("***** Recover optimizer: %d *****", recover_step)
optim_recover = torch.load(os.path.join(
args.output_dir, "optim.{0}.bin".format(recover_step)),
map_location='cpu')
if hasattr(optim_recover, 'state_dict'):
optim_recover = optim_recover.state_dict()
optimizer.load_state_dict(optim_recover)
if args.loss_scale == 0:
logger.info("***** Recover optimizer: dynamic_loss_scale *****")
optimizer.dynamic_loss_scale = True
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", t_total)
model.train()
if recover_step:
start_epoch = recover_step + 1
else:
start_epoch = 1
for i_epoch in trange(start_epoch,
int(args.num_train_epochs) + 1,
desc="Epoch",
disable=args.local_rank not in (-1, 0)):
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
iter_bar = tqdm(train_dataloader,
desc='Iter (loss=X.XXX)',
disable=args.local_rank not in (-1, 0))
for step, batch in enumerate(iter_bar):
batch = [
t.to(device) if t is not None else None for t in batch
]
if args.has_sentence_oracle:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, oracle_pos, oracle_weights, oracle_labels = batch
else:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx = batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
loss_tuple = model(input_ids,
segment_ids,
input_mask,
lm_label_ids,
is_next,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels,
mask_qkv=mask_qkv)
masked_lm_loss, next_sentence_loss = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
# loss = loss.mean()
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
loss = masked_lm_loss + next_sentence_loss
# logging for each step (i.e., before normalization by args.gradient_accumulation_steps)
iter_bar.set_description('Iter (loss=%5.3f)' % loss.item())
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
if amp_handle:
amp_handle._clear_cache()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
lr_this_step = args.learning_rate * \
warmup_linear(global_step/t_total,
args.warmup_proportion)
if args.fp16:
# modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
if (args.local_rank == -1 or torch.distributed.get_rank() == 0):
logger.info(
"** ** * Saving fine-tuned model and optimizer ** ** * ")
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(
args.output_dir, "model.{0}.bin".format(i_epoch))
torch.save(model_to_save.state_dict(), output_model_file)
output_optim_file = os.path.join(
args.output_dir, "optim.{0}.bin".format(i_epoch))
torch.save(optimizer.state_dict(), output_optim_file)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
watcher.info("total trainable parameters: {}".format(
format(count_parameters(model), ',')))
watcher.info("Vocabulary size: {}/{}.".format(len(dataloader.SRC.vocab),
len(dataloader.TRG.vocab)))
# use GPU
if torch.cuda.is_available():
model.cuda()
if args.distributed:
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank)
# load pre-trained parameters
if args.load_from != 'none':
with torch.cuda.device(args.local_rank):
pretrained_dict = torch.load(
os.path.join(args.workspace_prefix, 'models',
args.load_from + '.pt'),
map_location=lambda storage, loc: storage.cuda())
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in model_dict
}
model_dict.update(pretrained_dict)
def main():
args = create_argparser().parse_args()
dist_util.setup_dist()
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_classifier_and_diffusion(
**args_to_dict(args,
classifier_and_diffusion_defaults().keys()))
model.to(dist_util.dev())
if args.noised:
schedule_sampler = create_named_schedule_sampler(
args.schedule_sampler, diffusion)
resume_step = 0
if args.resume_checkpoint:
resume_step = parse_resume_step_from_filename(args.resume_checkpoint)
if dist.get_rank() == 0:
logger.log(
f"loading model from checkpoint: {args.resume_checkpoint}... at {resume_step} step"
)
model.load_state_dict(
dist_util.load_state_dict(args.resume_checkpoint,
map_location=dist_util.dev()))
# Needed for creating correct EMAs and fp16 parameters.
dist_util.sync_params(model.parameters())
mp_trainer = MixedPrecisionTrainer(model=model,
use_fp16=args.classifier_use_fp16,
initial_lg_loss_scale=16.0)
model = DDP(
model,
device_ids=[dist_util.dev()],
output_device=dist_util.dev(),
broadcast_buffers=False,
bucket_cap_mb=128,
find_unused_parameters=False,
)
logger.log("creating data loader...")
data = load_data(
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=True,
random_crop=True,
)
if args.val_data_dir:
val_data = load_data(
data_dir=args.val_data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=True,
)
else:
val_data = None
logger.log(f"creating optimizer...")
opt = AdamW(mp_trainer.master_params,
lr=args.lr,
weight_decay=args.weight_decay)
if args.resume_checkpoint:
opt_checkpoint = bf.join(bf.dirname(args.resume_checkpoint),
f"opt{resume_step:06}.pt")
logger.log(
f"loading optimizer state from checkpoint: {opt_checkpoint}")
opt.load_state_dict(
dist_util.load_state_dict(opt_checkpoint,
map_location=dist_util.dev()))
logger.log("training classifier model...")
def forward_backward_log(data_loader, prefix="train"):
batch, extra = next(data_loader)
labels = extra["y"].to(dist_util.dev())
batch = batch.to(dist_util.dev())
# Noisy images
if args.noised:
t, _ = schedule_sampler.sample(batch.shape[0], dist_util.dev())
batch = diffusion.q_sample(batch, t)
else:
t = th.zeros(batch.shape[0], dtype=th.long, device=dist_util.dev())
for i, (sub_batch, sub_labels, sub_t) in enumerate(
split_microbatches(args.microbatch, batch, labels, t)):
logits = model(sub_batch, timesteps=sub_t)
loss = F.cross_entropy(logits, sub_labels, reduction="none")
losses = {}
losses[f"{prefix}_loss"] = loss.detach()
losses[f"{prefix}_acc@1"] = compute_top_k(logits,
sub_labels,
k=1,
reduction="none")
losses[f"{prefix}_acc@5"] = compute_top_k(logits,
sub_labels,
k=5,
reduction="none")
log_loss_dict(diffusion, sub_t, losses)
del losses
loss = loss.mean()
if loss.requires_grad:
if i == 0:
mp_trainer.zero_grad()
mp_trainer.backward(loss * len(sub_batch) / len(batch))
for step in range(args.iterations - resume_step):
logger.logkv("step", step + resume_step)
logger.logkv(
"samples",
(step + resume_step + 1) * args.batch_size * dist.get_world_size(),
)
if args.anneal_lr:
set_annealed_lr(opt, args.lr,
(step + resume_step) / args.iterations)
forward_backward_log(data)
mp_trainer.optimize(opt)
if val_data is not None and not step % args.eval_interval:
with th.no_grad():
with model.no_sync():
model.eval()
forward_backward_log(val_data, prefix="val")
model.train()
if not step % args.log_interval:
logger.dumpkvs()
if (step and dist.get_rank() == 0
and not (step + resume_step) % args.save_interval):
logger.log("saving model...")
save_model(mp_trainer, opt, step + resume_step)
if dist.get_rank() == 0:
logger.log("saving model...")
save_model(mp_trainer, opt, step + resume_step)
dist.barrier()
def get_model(args):
"""Build the model."""
print_rank_0('building GPT3 model ...')
print ("asserting we have a correct number of attention heads...")
assert args.num_attention_heads % args.model_parallel_size == 0
num_local_heads = args.num_attention_heads // args.model_parallel_size
deepspeed_sparsity_config = None
if DEEPSPEED_WRAP and args.deepspeed:
print ("we're using deepspeed, and so we're getting a sparse attention config")
deepspeed_sparsity_config = get_sparse_attention_config(args, num_local_heads)
if deepspeed_sparsity_config is not None:
print_rank_0(f"Using sparse attention with mode {args.sparse_mode}")
print ("Calling GPT3Model constructor...")
model = GPT3Model(num_layers=args.num_layers,
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
embedding_dropout_prob=args.hidden_dropout,
attention_dropout_prob=args.attention_dropout,
output_dropout_prob=args.hidden_dropout,
max_sequence_length=args.max_position_embeddings,
checkpoint_activations=args.checkpoint_activations,
checkpoint_num_layers=args.checkpoint_num_layers,
parallel_output=True,
deepspeed_sparsity_config=deepspeed_sparsity_config,
sparse_mode=args.sparse_mode)
if args.load_huggingface is not None:
print ("Loading huggingface model...")
model = load_huggingface_model(model, args.load_huggingface, args.huggingface_double_pos_embeddings)
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on model parallel rank {}: {}'.format(
mpu.get_model_parallel_rank(),
sum([p.nelement() for p in model.parameters()])), flush=True)
# To prevent OOM for model sizes that cannot fit in GPU memory in full precision
if DEEPSPEED_WRAP and args.deepspeed and args.fp16:
print ("We've had deepspeed AND fp16, so we're halfing the model...")
model.half()
# GPU allocation.
print (f"placing the model on device {torch.cuda.current_device()}")
model.cuda(torch.cuda.current_device())
# Fp16 conversion.
if args.fp16:
print ("we've halfed the model before, but now we're wrapping it into a fp16_module. For...some reason...")
model = FP16_Module(model)
# Wrap model for distributed training.
print ("Setting up distributed training...")
if USE_TORCH_DDP:
i = torch.cuda.current_device()
print (f"Using classic pytorch DDP with device {i}")
model = DDP(model, device_ids=[i], output_device=i,
process_group=mpu.get_data_parallel_group())
else:
print ("Using sberbank magic DDP")
model = DDP(model)
# input ("ready to return model")
print ("ready to return model")
return model
