def forward(self, outputs, targets):
outputs_without_aux = {
k: v
for k, v in outputs.items() if k != "aux_outputs"
}
indices = self.matcher(outputs_without_aux, targets)
num_boxes = sum([len(t["labels"]) for t in targets])
num_boxes = torch.as_tensor([num_boxes],
dtype=torch.float,
device=next(iter(outputs.values())).device)
if is_dist_avail_and_initialized():
torch.distributed.all_reduce(num_boxes)
num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item()
losses = {}
for loss in self.losses:
losses.update(
self.get_loss(loss, outputs, targets, indices, num_boxes))
if "aux_outputs" in outputs:
for i, aux_outputs in enumerate(outputs["aux_outputs"]):
indices = self.matcher(aux_outputs, targets)
for loss in self.losses:
if loss == "masks":
continue
kwargs = {}
if loss == "labels":
kwargs = {"log": False}
l_dict = self.get_loss(loss, aux_outputs, targets, indices,
num_boxes, **kwargs)
l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
losses.update(l_dict)
return losses
def take_optimizer_step(args, optimizer, model, overflow_buf, global_step):
global skipped_steps
if args.allreduce_post_accumulation:
# manually allreduce gradients after all accumulation steps
# check for Inf/NaN
# 1. allocate an uninitialized buffer for flattened gradient
loss_scale = _amp_state.loss_scalers[0].loss_scale() if args.fp16 else 1
master_grads = [p.grad for p in amp.master_params(optimizer) if p.grad is not None]
flat_grad_size = sum(p.numel() for p in master_grads)
allreduce_dtype = torch.float16 if args.allreduce_post_accumulation_fp16 else torch.float32
flat_raw = torch.empty(flat_grad_size, device='cuda', dtype=allreduce_dtype)
# 2. combine unflattening and predivision of unscaled 'raw' gradient
allreduced_views = apex_C.unflatten(flat_raw, master_grads)
overflow_buf.zero_()
amp_C.multi_tensor_scale(65536,
overflow_buf,
[master_grads, allreduced_views],
loss_scale / (get_world_size() * args.gradient_accumulation_steps))
# 3. sum gradient across ranks. Because of the predivision, this averages the gradient
torch.distributed.all_reduce(flat_raw)
# 4. combine unscaling and unflattening of allreduced gradient
overflow_buf.zero_()
amp_C.multi_tensor_scale(65536,
overflow_buf,
[allreduced_views, master_grads],
1./loss_scale)
# 5. update loss scale
if args.fp16:
scaler = _amp_state.loss_scalers[0]
old_overflow_buf = scaler._overflow_buf
scaler._overflow_buf = overflow_buf
had_overflow = scaler.update_scale()
scaler._overfloat_buf = old_overflow_buf
else:
had_overflow = 0
# 6. call optimizer step function
if had_overflow == 0:
optimizer.step()
global_step += 1
else:
# Overflow detected, print message and clear gradients
skipped_steps += 1
if is_main_process():
scaler = _amp_state.loss_scalers[0]
dllogger.log(step="PARAMETER", data={"loss_scale": scaler.loss_scale()})
if _amp_state.opt_properties.master_weights:
for param in optimizer._amp_stash.all_fp32_from_fp16_params:
param.grad = None
for param in model.parameters():
param.grad = None
else:
if args.apply_optimizer > 0:
optimizer.step()
# optimizer.zero_grad()
for param in model.parameters():
param.grad = None
global_step += 1
return global_step
def get_dataset_from_features(features, batch_size, drop_remainder=True, ngpu=8, mode="train", v2=False):
"""Input function for training"""
all_input_ids = tf.convert_to_tensor([f.input_ids for f in features], dtype=tf.int64)
all_input_mask = tf.convert_to_tensor([f.attention_mask for f in features], dtype=tf.int64)
all_segment_ids = tf.convert_to_tensor([f.token_type_ids for f in features], dtype=tf.int64)
all_start_pos = tf.convert_to_tensor([f.start_position for f in features], dtype=tf.int64)
all_end_pos = tf.convert_to_tensor([f.end_position for f in features], dtype=tf.int64)
# if v2 else None:
all_cls_index = tf.convert_to_tensor([f.cls_index for f in features], dtype=tf.int64)
all_p_mask = tf.convert_to_tensor([f.p_mask for f in features], dtype=tf.float32)
all_is_impossible = tf.convert_to_tensor([f.is_impossible for f in features], dtype=tf.float32)
dataset = tf.data.Dataset.from_tensor_slices(
(all_input_ids, all_input_mask, all_segment_ids, all_start_pos, all_end_pos)
+ (all_cls_index, all_p_mask, all_is_impossible))
if ngpu > 1:
dataset = dataset.shard(get_world_size(), get_rank())
if mode == "train":
dataset = dataset.shuffle(batch_size * 3)
# dataset = dataset.map(self._preproc_samples,
# num_parallel_calls=multiprocessing.cpu_count()//self._num_gpus)
dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
dataset = dataset.prefetch(batch_size)
return dataset
def take_optimizer_step(args, optimizer, grad_scaler, model, overflow_buf,
global_step):
global skipped_steps
if args.allreduce_post_accumulation:
# manually allreduce gradients after all accumulation steps
# check for Inf/NaN
# 1. allocate an uninitialized buffer for flattened gradient
loss_scale = grad_scaler._get_scale_async() if args.fp16 else 1.
master_grads = [
p.grad for p in model.parameters() if p.grad is not None
]
flat_grad_size = sum(p.numel() for p in master_grads)
allreduce_dtype = torch.float16 if args.allreduce_post_accumulation_fp16 else torch.float32
flat_raw = torch.empty(flat_grad_size,
device='cuda',
dtype=allreduce_dtype)
# 2. combine unflattening and predivision of unscaled 'raw' gradient
allreduced_views = apex_C.unflatten(flat_raw, master_grads)
overflow_buf.zero_()
amp_C.multi_tensor_scale(
65536, overflow_buf, [master_grads, allreduced_views],
loss_scale / (get_world_size() * args.gradient_accumulation_steps))
# 3. sum gradient across ranks. Because of the predivision, this averages the gradient
torch.distributed.all_reduce(flat_raw)
# 4. combine unscaling and unflattening of allreduced gradient
overflow_buf.zero_()
amp_C.multi_tensor_scale(65536, overflow_buf,
[allreduced_views, master_grads], 1.)
# 5. update loss scale
if args.fp16:
had_overflow = overflow_buf.item()
else:
had_overflow = 0
# 6. call optimizer step function
if had_overflow == 0:
global_step += 1
else:
# Overflow detected, print message and clear gradients
skipped_steps += 1
else:
global_step += 1
grad_scaler.step(optimizer)
grad_scaler.update()
optimizer.zero_grad(set_to_none=True)
return global_step
def reduce_loss_dict(loss_dict):
"""
Reduce the loss dictionary from all processes so that process with rank
0 has the averaged results. Returns a dict with the same fields as
loss_dict, after reduction.
"""
world_size = get_world_size()
if world_size < 2:
return loss_dict
with torch.no_grad():
loss_names = []
all_losses = []
for k in sorted(loss_dict.keys()):
loss_names.append(k)
all_losses.append(loss_dict[k])
all_losses = torch.stack(all_losses, dim=0)
dist.reduce(all_losses, dst=0)
if dist.get_rank() == 0:
# only main process gets accumulated, so only divide by
# world_size in this case
all_losses /= world_size
reduced_losses = {k: v for k, v in zip(loss_names, all_losses)}
return reduced_losses
def inference(model, data_loader, dataset_name, device='cuda', output_folder=None,
expected_results=(), expected_results_sigma_tol=4):
device = torch.device(device)
num_devices = get_world_size()
logger = logging.getLogger("RetinaNet.inference")
dataset = data_loader.dataset
logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
total_timer = Timer()
inference_timer = Timer()
total_timer.tic()
predictions = compute_on_dataset(model, data_loader, device, inference_timer)
# wait for all processes to complete before measuring the time
synchronize()
total_time = total_timer.toc()
total_time_str = get_time_str(total_time)
logger.info(
"Total run time: {} ({} s / img per device, on {} devices)".format(
total_time_str, total_time * num_devices / len(dataset), num_devices
)
)
predictions = accumulate_predictions_from_multiple_gpus(predictions)
if not is_main_process():
return
if output_folder:
torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
extra_args = dict(
expected_results=expected_results,
expected_results_sigma_tol=expected_results_sigma_tol,
)
return evaluate(dataset=dataset,
predictions=predictions,
output_folder=output_folder,
**extra_args)
def main(args):
utils.my_init_distributed_mode(args)
print(args)
# if args.distillation_type != 'none' and args.finetune and not args.eval:
# raise NotImplementedError("Finetuning with distillation not yet supported")
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
# if True: # args.distributed:
# num_tasks = utils.get_world_size()
# global_rank = utils.get_rank()
# if args.repeated_aug:
# sampler_train = RASampler(
# dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
# )
# else:
# sampler_train = torch.utils.data.DistributedSampler(
# dataset_train,
# # num_replicas=num_tasks,
# num_replicas=0,
# rank=global_rank, shuffle=True
# )
# if args.dist_eval:
# if len(dataset_val) % num_tasks != 0:
# print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
# 'This will slightly alter validation results as extra duplicate entries are added to achieve '
# 'equal num of samples per-process.')
# sampler_val = torch.utils.data.DistributedSampler(
# dataset_val,
# # num_replicas=num_tasks,
# num_replicas=0,
# rank=global_rank, shuffle=False)
# else:
# sampler_val = torch.utils.data.SequentialSampler(dataset_val)
# else:
# sampler_train = torch.utils.data.RandomSampler(dataset_train)
# sampler_val = torch.utils.data.SequentialSampler(dataset_val)
#
# data_loader_train = torch.utils.data.DataLoader(
# dataset_train, sampler=sampler_train,
# batch_size=args.batch_size,
# num_workers=args.num_workers,
# pin_memory=args.pin_mem,
# drop_last=True,
# )
#
# data_loader_val = torch.utils.data.DataLoader(
# dataset_val, sampler=sampler_val,
# batch_size=int(1.5 * args.batch_size),
# num_workers=args.num_workers,
# pin_memory=args.pin_mem,
# drop_last=False
# )
#
if args.distributed:
if args.cache_mode:
sampler_train = samplers.NodeDistributedSampler(dataset_train)
sampler_val = samplers.NodeDistributedSampler(dataset_val,
shuffle=False)
else:
sampler_train = samplers.DistributedSampler(dataset_train)
sampler_val = samplers.DistributedSampler(dataset_val,
shuffle=False)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
batch_sampler_train = torch.utils.data.BatchSampler(sampler_train,
args.batch_size,
drop_last=True)
data_loader_train = DataLoader(dataset_train,
batch_sampler=batch_sampler_train,
num_workers=args.num_workers,
pin_memory=True)
data_loader_val = DataLoader(dataset_val,
args.batch_size,
sampler=sampler_val,
drop_last=False,
num_workers=args.num_workers,
pin_memory=True)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_fn = Mixup(mixup_alpha=args.mixup,
cutmix_alpha=args.cutmix,
cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob,
mode=args.mixup_mode,
label_smoothing=args.smoothing,
num_classes=args.nb_classes)
print(f"Creating model: {args.model}")
model = create_model(
args.model,
pretrained=False,
num_classes=args.nb_classes,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
drop_block_rate=None,
)
model_without_ddp = model
# # there are bugs
# if args.finetune:
# if args.finetune.startswith('https'):
# checkpoint = torch.hub.load_state_dict_from_url(
# args.finetune, map_location='cpu', check_hash=True)
# else:
# checkpoint = torch.load(args.finetune, map_location='cpu')
#
# checkpoint_model = checkpoint['model']
#
# state_dict = model.state_dict()
# for k in ['head.weight', 'head.bias', 'head_dist.weight', 'head_dist.bias']:
# if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
# print(f"Removing key {k} from pretrained checkpoint")
# del checkpoint_model[k]
#
# _ = model.load_state_dict(checkpoint_model, strict=False)
model.to(device)
model_ema = None
# if args.model_ema:
# # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
# model_ema = ModelEma(
# model,
# decay=args.model_ema_decay,
# device='cpu' if args.model_ema_force_cpu else '',
# resume='')
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size(
) / 512.0
args.lr = linear_scaled_lr
optimizer = create_optimizer(args, model_without_ddp)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
criterion = LabelSmoothingCrossEntropy()
if args.mixup > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
criterion = DistillationLoss(criterion, None, 'none', 0, 0)
output_dir = Path(args.output_dir)
# for finetune
if args.finetune:
if args.finetune.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.finetune,
map_location='cpu',
check_hash=True)
else:
if not os.path.exists(args.finetune):
checkpoint = None
print('NOTICE:' + args.finetune + ' does not exist!')
else:
checkpoint = torch.load(args.finetune, map_location='cpu')
if checkpoint is not None:
if 'model' in checkpoint:
check_model = checkpoint['model']
else:
check_model = checkpoint
missing_keys = model_without_ddp.load_state_dict(
check_model, strict=False).missing_keys
skip_keys = model_without_ddp.no_weight_decay()
# create optimizer manually
param_dicts = [
{
"params": [
p for n, p in model_without_ddp.named_parameters()
if n in missing_keys and n not in skip_keys
],
"lr":
args.lr,
'weight_decay':
args.weight_decay,
},
{
"params": [
p for n, p in model_without_ddp.named_parameters()
if n in missing_keys and n in skip_keys
],
"lr":
args.lr,
'weight_decay':
0,
},
{
"params": [
p for n, p in model_without_ddp.named_parameters()
if n not in missing_keys and n not in skip_keys
],
"lr":
args.lr * args.fine_factor,
'weight_decay':
args.weight_decay,
},
{
"params": [
p for n, p in model_without_ddp.named_parameters()
if n not in missing_keys and n in skip_keys
],
"lr":
args.lr * args.fine_factor,
'weight_decay':
0,
},
]
optimizer = torch.optim.AdamW(param_dicts,
lr=args.lr,
weight_decay=args.weight_decay)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
# if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
# optimizer.load_state_dict(checkpoint['optimizer'])
# lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
# args.start_epoch = checkpoint['epoch'] + 1
# # if args.model_ema:
# # utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
# if 'scaler' in checkpoint:
# loss_scaler.load_state_dict(checkpoint['scaler'])
print('finetune from' + args.finetune)
# for debug
# lr_scheduler.step(10)
# lr_scheduler.step(100)
# lr_scheduler.step(200)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
if not os.path.exists(args.resume):
checkpoint = None
print('NOTICE:' + args.resume + ' does not exist!')
else:
checkpoint = torch.load(args.resume, map_location='cpu')
if checkpoint is not None:
if 'model' in checkpoint:
model_without_ddp.load_state_dict(checkpoint['model'])
else:
model_without_ddp.load_state_dict(checkpoint)
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
# if args.model_ema:
# utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
print('resume from' + args.resume)
if args.eval:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
return
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
max_accuracy = 0.0
max_epoch_dp_warm_up = 100
if 'pvt_tiny' in args.model or 'pvt_small' in args.model:
max_epoch_dp_warm_up = 0
if args.start_epoch < max_epoch_dp_warm_up:
model_without_ddp.reset_drop_path(0.0)
for epoch in range(args.start_epoch, args.epochs):
if args.fp32_resume and epoch > args.start_epoch + 1:
args.fp32_resume = False
loss_scaler._scaler = torch.cuda.amp.GradScaler(
enabled=not args.fp32_resume)
if epoch == max_epoch_dp_warm_up:
model_without_ddp.reset_drop_path(args.drop_path)
if epoch < args.warmup_epochs:
optimizer.param_groups[2]['lr'] = 0
optimizer.param_groups[3]['lr'] = 0
if args.distributed:
# data_loader_train.sampler.set_epoch(epoch)
sampler_train.set_epoch(epoch)
train_stats = my_train_one_epoch(
model,
criterion,
data_loader_train,
optimizer,
device,
epoch,
loss_scaler,
args.clip_grad,
model_ema,
mixup_fn,
# set_training_mode=args.finetune == '', # keep in eval mode during finetuning
fp32=args.fp32_resume)
lr_scheduler.step(epoch)
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
# 'model_ema': get_state_dict(model_ema),
'scaler': loss_scaler.state_dict(),
'args': args,
},
checkpoint_path)
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
**{f'test_{k}': v
for k, v in test_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main():
global timeout_sent
args = parse_arguments()
random.seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
torch.manual_seed(args.seed + args.local_rank)
torch.cuda.manual_seed(args.seed + args.local_rank)
worker_init = WorkerInitObj(args.seed + args.local_rank)
device, args = setup_training(args)
dllogger.log(step="PARAMETER", data={"Config": [str(args)]})
# Prepare optimizer
model, optimizer, lr_scheduler, checkpoint, global_step, criterion = prepare_model_and_optimizer(
args, device)
gradient_accumulation_steps = torch.tensor(
args.gradient_accumulation_steps, dtype=torch.float32).to(device)
world_size = torch.tensor(get_world_size(), dtype=torch.float32).to(device)
if is_main_process():
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
raw_train_start = None
if args.do_train:
if is_main_process():
dllogger.log(step="PARAMETER", data={"train_start": True})
dllogger.log(step="PARAMETER",
data={"batch_size_per_pu": args.train_batch_size})
dllogger.log(step="PARAMETER",
data={"learning_rate": args.learning_rate})
model.train()
most_recent_ckpts_paths = []
average_loss = 0.0 # averaged loss every args.log_freq steps
epoch = 0
training_steps = 0
model_traced = False
if device.type == 'cuda':
pool = ProcessPoolExecutor(1)
# Note: We loop infinitely over epochs, termination is handled via iteration count
while True:
thread = None
restored_data_loader = None
if not args.resume_from_checkpoint or epoch > 0 or (
args.phase2 and global_step < 1) or args.init_checkpoint:
files = [
os.path.join(args.input_dir, f)
for f in os.listdir(args.input_dir)
if os.path.isfile(os.path.join(args.input_dir, f))
and 'training' in f
]
files.sort()
num_files = len(files)
random.Random(args.seed + epoch).shuffle(files)
f_start_id = 0
else:
f_start_id = checkpoint['files'][0]
files = checkpoint['files'][1:]
args.resume_from_checkpoint = False
num_files = len(files)
# may not exist in all checkpoints
epoch = checkpoint.get('epoch', 0)
restored_data_loader = checkpoint.get('data_loader', None)
shared_file_list = {}
if torch.distributed.is_initialized(
) and get_world_size() > num_files:
remainder = get_world_size() % num_files
data_file = files[(f_start_id * get_world_size() + get_rank() +
remainder * f_start_id) % num_files]
else:
data_file = files[(f_start_id * get_world_size() + get_rank())
% num_files]
previous_file = data_file
if restored_data_loader is None:
use_pin_memory = False if args.no_cuda or args.use_habana else True
num_workers = 0 if args.use_habana else 4
train_data = pretraining_dataset(data_file,
args.max_predictions_per_seq)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(
train_data,
sampler=train_sampler,
batch_size=args.train_batch_size * args.n_pu,
num_workers=num_workers,
worker_init_fn=worker_init,
pin_memory=use_pin_memory,
drop_last=True)
# shared_file_list["0"] = (train_dataloader, data_file)
else:
train_dataloader = restored_data_loader
restored_data_loader = None
overflow_buf = None
if args.allreduce_post_accumulation:
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1, len(files)):
if get_world_size() > num_files:
data_file = files[(f_id * get_world_size() + get_rank() +
remainder * f_id) % num_files]
else:
data_file = files[(f_id * get_world_size() + get_rank()) %
num_files]
previous_file = data_file
if device.type == 'cuda':
dataset_future = pool.submit(create_pretraining_dataset,
data_file,
args.max_predictions_per_seq,
shared_file_list, args,
worker_init)
train_iter = tqdm(train_dataloader,
desc="Iteration",
disable=args.disable_progress_bar
) if is_main_process() else train_dataloader
if raw_train_start is None:
raw_train_start = time.time()
for step, batch in enumerate(train_iter):
training_steps += 1
position_ids = compute_position_ids(batch[0])
if torch.distributed.is_initialized():
torch.distributed.barrier()
if args.use_habana:
batch = [t.to(dtype=torch.int32) for t in batch]
position_ids = position_ids.to(dtype=torch.int32)
position_ids = position_ids.to(device)
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch
if args.use_jit_trace:
if model_traced == False:
model = torch.jit.trace(model,
(input_ids, segment_ids,
input_mask, position_ids),
check_trace=False)
model_traced = True
if args.local_rank != -1 and not args.allreduce_post_accumulation:
if args.use_habana:
model = DDP(model)
else:
model = DDP(model,
message_size=250000000,
gradient_predivide_factor=
get_world_size())
if args.local_rank != -1 and not args.allreduce_post_accumulation \
and (training_steps % args.gradient_accumulation_steps != 0):
with model.no_sync():
prediction_scores, seq_relationship_score = model(
input_ids, segment_ids, input_mask,
position_ids)
else:
prediction_scores, seq_relationship_score = model(
input_ids, segment_ids, input_mask,
position_ids)
else:
if args.local_rank != -1 and not args.allreduce_post_accumulation \
and (training_steps % args.gradient_accumulation_steps != 0):
with model.no_sync():
prediction_scores, seq_relationship_score = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
position_ids=position_ids)
else:
prediction_scores, seq_relationship_score = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
position_ids=position_ids)
loss = criterion(prediction_scores, seq_relationship_score,
masked_lm_labels, next_sentence_labels)
if args.n_pu > 1:
loss = loss.mean() # mean() to average on multi-pu.
divisor = args.gradient_accumulation_steps
if args.gradient_accumulation_steps > 1:
if not args.allreduce_post_accumulation:
# this division was merged into predivision
loss = loss / gradient_accumulation_steps
divisor = 1.0
if args.fp16:
with amp.scale_loss(
loss,
optimizer,
delay_overflow_check=args.
allreduce_post_accumulation) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
average_loss += loss.item()
if training_steps % args.gradient_accumulation_steps == 0:
lr_scheduler.step() # learning rate warmup
global_step = take_optimizer_step(
args, optimizer, model, overflow_buf, global_step)
if global_step >= args.steps_this_run or timeout_sent:
train_time_raw = time.time() - raw_train_start
last_num_steps = int(
training_steps /
args.gradient_accumulation_steps) % args.log_freq
last_num_steps = args.log_freq if last_num_steps == 0 else last_num_steps
average_loss = average_loss / (last_num_steps *
divisor)
average_loss = torch.tensor(
average_loss, dtype=torch.float32).to(device)
if (torch.distributed.is_initialized()):
average_loss /= world_size
torch.distributed.all_reduce(average_loss)
final_loss = average_loss.item()
if is_main_process():
dllogger.log(step=(
epoch,
global_step,
),
data={"final_loss": final_loss})
elif training_steps % (
args.log_freq *
args.gradient_accumulation_steps) == 0:
if is_main_process():
dllogger.log(
step=(
epoch,
global_step,
),
data={
"average_loss":
average_loss / (args.log_freq * divisor),
"step_loss":
loss.item() *
args.gradient_accumulation_steps / divisor,
"learning_rate":
optimizer.param_groups[0]['lr']
})
average_loss = 0
if global_step >= args.steps_this_run or training_steps % (
args.num_steps_per_checkpoint * args.
gradient_accumulation_steps) == 0 or timeout_sent:
if is_main_process() and not args.skip_checkpoint:
# Save a trained model
dllogger.log(step="PARAMETER",
data={"checkpoint_step": global_step})
model_to_save = model.module if hasattr(
model, 'module'
) else model # Only save the model it-self
if args.resume_step < 0 or not args.phase2:
output_save_file = os.path.join(
args.output_dir,
"ckpt_{}.pt".format(global_step))
else:
output_save_file = os.path.join(
args.output_dir,
"ckpt_{}.pt".format(global_step +
args.phase1_end_step))
checkpoint_dict = {}
if args.do_train:
if args.use_habana:
config = modeling.BertConfig.from_json_file(
args.config_file)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (
config.vocab_size % 8)
model_copy = modeling.BertForPreTraining(
config)
model_copy.load_state_dict(
model_to_save.state_dict())
param_groups_copy = optimizer.state_dict(
)['param_groups']
state_dict_copy = {}
for st_key, st_val in optimizer.state_dict(
)['state'].items():
st_val_copy = {}
for k, v in st_val.items():
if isinstance(v, torch.Tensor):
st_val_copy[k] = v.to('cpu')
else:
st_val_copy[k] = v
state_dict_copy[
st_key] = st_val_copy
optim_dict = {}
optim_dict['state'] = state_dict_copy
optim_dict[
'param_groups'] = param_groups_copy
checkpoint_dict = {
'model':
model_copy.state_dict(),
'optimizer':
optim_dict,
'files': [f_id] + files,
'epoch':
epoch,
'data_loader':
None if global_step >= args.max_steps
else train_dataloader
}
elif no_cuda:
checkpoint_dict = {
'model':
model_to_save.state_dict(),
'optimizer':
optimizer.state_dict(),
'files': [f_id] + files,
'epoch':
epoch,
'data_loader':
None if global_step >= args.max_steps
else train_dataloader
}
else:
checkpoint_dict = {
'model':
model_to_save.state_dict(),
'optimizer':
optimizer.state_dict(),
'master params':
list(amp.master_params(optimizer)),
'files': [f_id] + files,
'epoch':
epoch,
'data_loader':
None if global_step >= args.max_steps
else train_dataloader
}
torch.save(checkpoint_dict, output_save_file)
most_recent_ckpts_paths.append(
output_save_file)
if len(most_recent_ckpts_paths) > 3:
ckpt_to_be_removed = most_recent_ckpts_paths.pop(
0)
os.remove(ckpt_to_be_removed)
# Exiting the training due to hitting max steps, or being sent a
# timeout from the cluster scheduler
if global_step >= args.steps_this_run or timeout_sent:
del train_dataloader
# thread.join()
return args, final_loss, train_time_raw, global_step
del train_dataloader
# thread.join()
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
if device.type == 'cuda':
train_dataloader, data_file = dataset_future.result(
timeout=None)
else:
train_dataloader, data_file = create_pretraining_dataset(
data_file, args.max_predictions_per_seq,
shared_file_list, args, worker_init)
epoch += 1
def main(args):
utils.init_distributed_mode(args)
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
if args.repeated_aug:
sampler_train = RASampler(dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
else:
sampler_train = torch.utils.data.DistributedSampler(
dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
data_loader_train = torch.utils.data.DataLoader(
dataset_train,
sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
batch_size=int(
1.5 * args.batch_size),
shuffle=False,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_fn = Mixup(mixup_alpha=args.mixup,
cutmix_alpha=args.cutmix,
cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob,
mode=args.mixup_mode,
label_smoothing=args.smoothing,
num_classes=args.nb_classes)
print(f"Creating model: {args.model}")
model = create_model(
args.model,
pretrained=False,
num_classes=args.nb_classes,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
drop_block_rate=args.drop_block,
)
# TODO: finetuning
model.to(device)
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEma(model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume='')
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size(
) / 512.0
args.lr = linear_scaled_lr
optimizer = create_optimizer(args, model)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
criterion = LabelSmoothingCrossEntropy()
if args.mixup > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
output_dir = Path(args.output_dir)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.model_ema:
utils._load_checkpoint_for_ema(model_ema,
checkpoint['model_ema'])
if args.eval:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
return
print("Start training")
start_time = time.time()
max_accuracy = 0.0
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
train_stats = train_one_epoch(model, criterion, data_loader_train,
optimizer, device, epoch, loss_scaler,
args.clip_grad, model_ema, mixup_fn)
lr_scheduler.step(epoch)
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'model_ema': get_state_dict(model_ema),
'args': args,
}, checkpoint_path)
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
**{f'test_{k}': v
for k, v in test_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main():
global timeout_sent
args = parse_arguments()
random.seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
torch.manual_seed(args.seed + args.local_rank)
torch.cuda.manual_seed(args.seed + args.local_rank)
worker_init = WorkerInitObj(args.seed + args.local_rank)
device, args = setup_training(args)
if is_main_process():
dllogger.log(step="PARAMETER", data={"Config": [str(args)]})
# Prepare optimizer
model, optimizer, lr_scheduler, checkpoint, global_step, criterion = prepare_model_and_optimizer(args, device)
if is_main_process():
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
raw_train_start = None
if is_main_process():
dllogger.log(step="PARAMETER", data={"train_start": True})
dllogger.log(step="PARAMETER", data={"batch_size_per_gpu": args.train_batch_size})
dllogger.log(step="PARAMETER", data={"learning_rate": args.learning_rate})
most_recent_ckpts_paths = []
average_loss = 0.0 # averaged loss every args.log_freq steps
epoch = 0
training_steps = 0
test_losses = []
pool = ProcessPoolExecutor(1)
# Note: We loop infinitely over epochs, termination is handled via iteration count
while True:
thread = None
restored_data_loader = None
if not args.resume_from_checkpoint or epoch > 0 or (args.phase2 and global_step < 1) or args.init_checkpoint:
files = [os.path.join(args.input_dir, f) for f in os.listdir(args.input_dir) if
os.path.isfile(os.path.join(args.input_dir, f)) and 'training' in f]
files.sort()
num_files = len(files)
random.Random(args.seed + epoch).shuffle(files)
f_start_id = 0
else:
f_start_id = checkpoint['files'][0]
files = checkpoint['files'][1:]
args.resume_from_checkpoint = False
num_files = len(files)
# may not exist in all checkpoints
epoch = checkpoint.get('epoch', 0)
restored_dataloader = checkpoint.get('data_loader', None)
shared_file_list = {}
if torch.distributed.is_initialized() and get_world_size() > num_files:
remainder = get_world_size() % num_files
data_file = files[(f_start_id*get_world_size()+get_rank() + remainder*f_start_id)%num_files]
else:
data_file = files[(f_start_id*get_world_size()+get_rank())%num_files]
previous_file = data_file
if restored_data_loader is None:
train_data = pretraining_dataset(data_file, args.max_predictions_per_seq)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler,
batch_size=args.train_batch_size * args.n_gpu,
num_workers=4, worker_init_fn=worker_init,
pin_memory=True)
# shared_file_list["0"] = (train_dataloader, data_file)
else:
train_dataloader = restored_data_loader
restored_data_loader = None
overflow_buf = None
if args.allreduce_post_accumulation:
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1 , len(files)):
if get_world_size() > num_files:
data_file = files[(f_id*get_world_size()+get_rank() + remainder*f_id)%num_files]
else:
data_file = files[(f_id*get_world_size()+get_rank())%num_files]
previous_file = data_file
dataset_future = pool.submit(create_pretraining_dataset, data_file, args.max_predictions_per_seq, shared_file_list, args, worker_init)
train_iter = tqdm(train_dataloader, desc="Iteration", disable=args.disable_progress_bar) if is_main_process() else train_dataloader
if raw_train_start is None:
raw_train_start = time.time()
for step, batch in enumerate(train_iter):
training_steps += 1
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch
if args.do_train:
from smdistributed.modelparallel.test.torch.utils import verify, dump_model
model.train()
if args.smp > 0:
loss_mbs = smp_step(args, device, input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels, model, optimizer, criterion, step)
loss = loss_mbs.reduce_mean()
if smp.rank() == 0:
print("Loss:", loss.item())
else:
loss = train_step(args, device, input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels, model, optimizer, criterion, step)
divisor=1
average_loss += loss.item()
if training_steps % args.gradient_accumulation_steps == 0:
lr_scheduler.step() # learning rate warmup
global_step = take_optimizer_step(args, optimizer, model, overflow_buf, global_step)
if global_step >= args.steps_this_run or timeout_sent:
train_time_raw = time.time() - raw_train_start
last_num_steps = int(training_steps / args.gradient_accumulation_steps) % args.log_freq
last_num_steps = args.log_freq if last_num_steps == 0 else last_num_steps
average_loss = torch.tensor(average_loss, dtype=torch.float32).cuda()
average_loss = average_loss / (last_num_steps * divisor)
if (torch.distributed.is_initialized()):
average_loss /= get_world_size()
torch.distributed.all_reduce(average_loss)
final_loss = loss.item()
if is_main_process():
dllogger.log(step=(epoch, global_step, ), data={"final_loss": final_loss})
elif training_steps % (args.log_freq * args.gradient_accumulation_steps) == 0:
if is_main_process():
dllogger.log(step=(epoch, global_step, ), data={"average_loss": average_loss / (args.log_freq * divisor),
"step_loss": loss.item() * args.gradient_accumulation_steps / divisor,
"learning_rate": optimizer.param_groups[0]['lr']})
average_loss = 0
if global_step >= args.steps_this_run or training_steps % (
args.num_steps_per_checkpoint * args.gradient_accumulation_steps) == 0 or timeout_sent:
if smp.dp_rank() == 0 and not args.skip_checkpoint:
# Save a trained model
dllogger.log(step="PARAMETER", data={"checkpoint_step": global_step})
# model_to_save = model.module if hasattr(model,
# 'module') else model # Only save the model it-self
if args.resume_step < 0 or not args.phase2:
output_save_file = os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step))
else:
output_save_file = os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step + args.phase1_end_step))
if args.do_train:
save_dict = {
'model': model.local_state_dict(),
'optimizer': optimizer.local_state_dict(),
'files': [f_id] + files,
'epoch': epoch,
'data_loader': None if global_step >= args.steps_this_run else train_dataloader}
if args.fp16:
save_dict['master params'] = list(amp.master_params(optimizer))
# SMP: Checkpoint mp_rank specific state
smp.save(save_dict, output_save_file, partial=True)
most_recent_ckpts_paths.append(output_save_file)
if len(most_recent_ckpts_paths) > 3 and (args.smp == 0 or smp.dp_rank() == 0):
ckpt_to_be_removed = most_recent_ckpts_paths.pop(0)
os.remove(ckpt_to_be_removed+f"_{smp.mp_rank()}")
# Exiting the training due to hitting max steps, or being sent a
# timeout from the cluster scheduler
if global_step >= args.steps_this_run or timeout_sent:
del train_dataloader
# thread.join()
if smp.dp_rank() == 0 and args.save_full:
output_save_file = os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step))
save_dict = {
'model': model.local_state_dict(),
'optimizer': optimizer.local_state_dict(),
'files': [f_id] + files,
'epoch': epoch,
'data_loader': None if global_step >= args.steps_this_run else train_dataloader}
if args.fp16:
save_dict['master params'] = list(amp.master_params(optimizer))
# SMP: Save a single checkpoint containing entire model parameters
smp.save(save_dict, output_save_file, partial=False)
smp.barrier()
return args, final_loss, train_time_raw, global_step
else:
model.eval()
with torch.no_grad():
loss = test_step(args, device, input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels, model, criterion, step)
print(f"global_step {global_step} Test Loss:", loss)
test_losses.append(loss)
global_step += 1
if global_step >= args.steps_this_run:
return sum(test_losses) / len(test_losses)
del train_dataloader
# thread.join()
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(timeout=None)
epoch += 1
def prepare_model_and_optimizer(args, device):
# Prepare model
config = modeling.BertConfig.from_json_file(args.config_file)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
modeling.ACT2FN["bias_gelu"] = modeling.bias_gelu_training
model = modeling.BertForPreTraining(config)
checkpoint = None
if not args.resume_from_checkpoint:
global_step = 0
else:
if args.resume_step == -1 and not args.init_checkpoint:
model_names = [
f for f in os.listdir(args.output_dir) if f.endswith(".pt")
]
args.resume_step = max([
int(x.split('.pt')[0].split('_')[1].strip())
for x in model_names
])
global_step = args.resume_step if not args.init_checkpoint else 0
if not args.init_checkpoint:
checkpoint = torch.load(os.path.join(
args.output_dir, "ckpt_{}.pt".format(global_step)),
map_location="cpu")
else:
checkpoint = torch.load(args.init_checkpoint, map_location="cpu")
model.load_state_dict(checkpoint['model'], strict=False)
if args.phase2 and not args.init_checkpoint:
global_step -= args.phase1_end_step
if is_main_process():
print("resume step from ", args.resume_step)
model.to(device)
# BERT modeling uses weight sharing between word embedding and prediction decoder.
# So make sure the storage is pointing properly even after model is moved to device.
if args.use_habana:
model.cls.predictions.decoder.weight = model.bert.embeddings.word_embeddings.weight
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta', 'LayerNorm']
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.use_habana:
if args.use_fused_lamb:
try:
from hb_custom import FusedLamb
except ImportError:
raise ImportError("Please install hbopt.")
optimizer = FusedLamb(optimizer_grouped_parameters,
lr=args.learning_rate)
else:
optimizer = NVLAMB(optimizer_grouped_parameters,
lr=args.learning_rate)
else:
if torch.cuda.is_available():
optimizer = FusedLAMB(optimizer_grouped_parameters,
lr=args.learning_rate)
else:
optimizer = NVLAMB(optimizer_grouped_parameters,
lr=args.learning_rate)
lr_scheduler = PolyWarmUpScheduler(optimizer,
warmup=args.warmup_proportion,
total_steps=args.max_steps)
if args.fp16:
if args.loss_scale == 0:
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
loss_scale="dynamic",
cast_model_outputs=torch.float16)
else:
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
loss_scale=args.loss_scale,
cast_model_outputs=torch.float16)
amp._amp_state.loss_scalers[0]._loss_scale = args.init_loss_scale
model.checkpoint_activations(args.checkpoint_activations)
if args.resume_from_checkpoint:
if args.phase2 or args.init_checkpoint:
keys = list(checkpoint['optimizer']['state'].keys())
#Override hyperparameters from previous checkpoint
for key in keys:
checkpoint['optimizer']['state'][key]['step'] = global_step
for iter, item in enumerate(
checkpoint['optimizer']['param_groups']):
checkpoint['optimizer']['param_groups'][iter][
'step'] = global_step
checkpoint['optimizer']['param_groups'][iter][
't_total'] = args.max_steps
checkpoint['optimizer']['param_groups'][iter][
'warmup'] = args.warmup_proportion
checkpoint['optimizer']['param_groups'][iter][
'lr'] = args.learning_rate
optimizer.load_state_dict(checkpoint['optimizer']) # , strict=False)
# Restore AMP master parameters
if args.fp16:
optimizer._lazy_init_maybe_master_weights()
optimizer._amp_stash.lazy_init_called = True
optimizer.load_state_dict(checkpoint['optimizer'])
for param, saved_param in zip(amp.master_params(optimizer),
checkpoint['master params']):
param.data.copy_(saved_param.data)
if args.local_rank != -1:
if not args.allreduce_post_accumulation:
if not args.use_jit_trace:
if args.use_habana:
model = DDP(model)
else:
model = DDP(model,
message_size=250000000,
gradient_predivide_factor=get_world_size())
else:
flat_dist_call([param.data for param in model.parameters()],
torch.distributed.broadcast, (0, ))
elif args.n_pu > 1:
model = torch.nn.DataParallel(model)
criterion = BertPretrainingCriterion(config.vocab_size)
return model, optimizer, lr_scheduler, checkpoint, global_step, criterion
def main():
global timeout_sent
args = parse_arguments()
random.seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
torch.manual_seed(args.seed + args.local_rank)
torch.cuda.manual_seed(args.seed + args.local_rank)
worker_init = WorkerInitObj(args.seed + args.local_rank)
device, args = setup_training(args)
dllogger.log(step="PARAMETER", data={"Config": [str(args)]})
# Prepare optimizer
model, optimizer, lr_scheduler, checkpoint, global_step, criterion = prepare_model_and_optimizer(args, device)
if args.disable_weight_tying:
# Sanity Check that new param is in optimizer
print ("SANITY CHECK OPTIMIZER: ", id(model.module.cls.predictions.decoder.weight) in [id(g) for g in optimizer.param_groups[0]['params']])
assert id(model.module.cls.predictions.decoder.weight) in [id(g) for g in optimizer.param_groups[0]['params']]
print (f"SAVING EVERY {args.num_steps_per_checkpoint} STEPS!")
if is_main_process():
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
raw_train_start = None
if args.do_train:
if is_main_process():
dllogger.log(step="PARAMETER", data={"train_start": True})
dllogger.log(step="PARAMETER", data={"batch_size_per_gpu": args.train_batch_size})
dllogger.log(step="PARAMETER", data={"learning_rate": args.learning_rate})
model.train()
most_recent_ckpts_paths = []
average_loss = 0.0 # averaged loss every args.log_freq steps
epoch = 0
training_steps = 0
pool = ProcessPoolExecutor(1)
# Note: We loop infinitely over epochs, termination is handled via iteration count
while True:
thread = None
restored_data_loader = None
if not args.resume_from_checkpoint or epoch > 0 or (args.phase2 and global_step < 1) or args.init_checkpoint:
files = [os.path.join(args.input_dir, f) for f in os.listdir(args.input_dir) if
os.path.isfile(os.path.join(args.input_dir, f)) and ('training' in f or 'train' in f)]
files.sort()
num_files = len(files)
random.Random(args.seed + epoch).shuffle(files)
f_start_id = 0
else:
f_start_id = checkpoint['files'][0]
files = checkpoint['files'][1:]
args.resume_from_checkpoint = False
num_files = len(files)
# may not exist in all checkpoints
epoch = checkpoint.get('epoch', 0)
restored_data_loader = checkpoint.get('data_loader', None)
shared_file_list = {}
if torch.distributed.is_initialized() and get_world_size() > num_files:
remainder = get_world_size() % num_files
data_file = files[(f_start_id*get_world_size()+get_rank() + remainder*f_start_id)%num_files]
else:
data_file = files[(f_start_id*get_world_size()+get_rank())%num_files]
previous_file = data_file
if restored_data_loader is None:
train_data = pretraining_dataset(data_file, args.max_predictions_per_seq)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler,
batch_size=args.train_batch_size * args.n_gpu,
num_workers=4, worker_init_fn=worker_init,
pin_memory=True)
# shared_file_list["0"] = (train_dataloader, data_file)
else:
train_dataloader = restored_data_loader
restored_data_loader = None
overflow_buf = None
if args.allreduce_post_accumulation:
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1 , len(files)):
if get_world_size() > num_files:
data_file = files[(f_id*get_world_size()+get_rank() + remainder*f_id)%num_files]
else:
data_file = files[(f_id*get_world_size()+get_rank())%num_files]
previous_file = data_file
dataset_future = pool.submit(create_pretraining_dataset, data_file, args.max_predictions_per_seq, shared_file_list, args, worker_init)
train_iter = tqdm(train_dataloader, desc="Iteration", disable=args.disable_progress_bar) if is_main_process() else train_dataloader
if raw_train_start is None:
raw_train_start = time.time()
for step, batch in enumerate(train_iter):
training_steps += 1
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch
prediction_scores, seq_relationship_score = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
loss, mlm_loss, ns_loss = criterion(prediction_scores, seq_relationship_score, masked_lm_labels, next_sentence_labels)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
mlm_loss = mlm_loss.detach().mean()
ns_loss = ns_loss.detach().mean()
divisor = args.gradient_accumulation_steps
if args.gradient_accumulation_steps > 1:
if not args.allreduce_post_accumulation:
# this division was merged into predivision
loss = loss / args.gradient_accumulation_steps
mlm_loss = mlm_loss.detach() / args.gradient_accumulation_steps
ns_loss = ns_loss.detach() / args.gradient_accumulation_steps
divisor = 1.0
if args.fp16:
with amp.scale_loss(loss, optimizer, delay_overflow_check=args.allreduce_post_accumulation) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
average_loss += loss.item()
if training_steps % args.gradient_accumulation_steps == 0:
lr_scheduler.step() # learning rate warmup
global_step = take_optimizer_step(args, optimizer, model, overflow_buf, global_step)
if global_step >= args.steps_this_run or timeout_sent:
train_time_raw = time.time() - raw_train_start
last_num_steps = int(training_steps / args.gradient_accumulation_steps) % args.log_freq
last_num_steps = args.log_freq if last_num_steps == 0 else last_num_steps
average_loss = torch.tensor(average_loss, dtype=torch.float32).cuda()
average_loss = average_loss / (last_num_steps * divisor)
if (torch.distributed.is_initialized()):
average_loss /= get_world_size()
torch.distributed.all_reduce(average_loss)
final_loss = average_loss.item()
if is_main_process():
dllogger.log(step=(epoch, global_step, ), data={"final_loss": final_loss})
elif training_steps % (args.log_freq * args.gradient_accumulation_steps) == 0:
if is_main_process():
dllogger.log(step=(epoch, global_step, ), data={"average_loss": average_loss / (args.log_freq * divisor),
"step_loss": loss.item() * args.gradient_accumulation_steps / divisor,
"learning_rate": optimizer.param_groups[0]['lr'],
"mlm_loss" : mlm_loss.item(),
"ns_loss" : ns_loss.item()})
average_loss = 0
if global_step >= args.steps_this_run or training_steps % (
args.num_steps_per_checkpoint * args.gradient_accumulation_steps) == 0 or timeout_sent:
if is_main_process() and not args.skip_checkpoint:
# Save a trained model
dllogger.log(step="PARAMETER", data={"checkpoint_step": global_step})
model_to_save = model.module if hasattr(model,
'module') else model # Only save the model it-self
if args.resume_step < 0 or not args.phase2:
output_save_file = os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step))
else:
output_save_file = os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step + args.phase1_end_step))
if args.do_train:
torch.save({'model': model_to_save.state_dict(),
'optimizer': optimizer.state_dict(),
'master params': list(amp.master_params(optimizer)),
'files': [f_id] + files,
'epoch': epoch,
'data_loader': None if global_step >= args.max_steps else train_dataloader}, output_save_file)
most_recent_ckpts_paths.append(output_save_file)
# Exiting the training due to hitting max steps, or being sent a
# timeout from the cluster scheduler
if global_step >= args.steps_this_run or timeout_sent:
del train_dataloader
# thread.join()
return args, final_loss, train_time_raw, global_step
del train_dataloader
# thread.join()
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(timeout=None)
epoch += 1
def main(e2e_start_time):
# Parse essential argumentss
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", required=True)
parser.add_argument("--model_size",
default="base",
type=str,
help="base or large")
parser.add_argument("--pretrain_tfrecords", type=str)
parser.add_argument("--phase2", action='store_true')
parser.add_argument("--fp16_compression", action='store_true')
parser.add_argument("--amp",
action='store_true',
help="Whether to use fp16.")
parser.add_argument("--xla",
action='store_true',
help="Whether to use xla.")
parser.add_argument("--seed", default=42, type=int)
parser.add_argument("--num_train_steps", type=int)
parser.add_argument("--num_warmup_steps", type=int)
parser.add_argument("--learning_rate", type=float)
parser.add_argument("--train_batch_size", type=int)
parser.add_argument("--max_seq_length", type=int)
parser.add_argument("--mask_prob", type=float)
parser.add_argument("--disc_weight", type=float)
parser.add_argument("--generator_hidden_size", type=float)
parser.add_argument("--log_freq",
type=int,
default=10,
help="Training metrics logging frequency")
parser.add_argument("--save_checkpoints_steps", type=int)
parser.add_argument("--keep_checkpoint_max", type=int)
parser.add_argument("--restore_checkpoint", default=None, type=str)
parser.add_argument("--load_weights", action='store_true')
parser.add_argument("--weights_dir")
parser.add_argument("--optimizer",
default="adam",
type=str,
help="adam or lamb")
parser.add_argument(
"--skip_adaptive",
action='store_true',
help="Whether to apply adaptive LR on LayerNorm and biases")
parser.add_argument("--gradient_accumulation_steps",
type=int,
default=1,
help="Number of Gradient Accumulation steps")
parser.add_argument("--lr_decay_power",
type=float,
default=0.5,
help="LR decay power")
parser.add_argument("--opt_beta_1",
type=float,
default=0.878,
help="Optimizer beta1")
parser.add_argument("--opt_beta_2",
type=float,
default=0.974,
help="Optimizer beta2")
parser.add_argument("--end_lr", type=float, default=0.0, help="Ending LR")
parser.add_argument("--log_dir",
type=str,
default=None,
help="Path to store logs")
parser.add_argument("--results_dir",
type=str,
default=None,
help="Path to store all model results")
parser.add_argument("--skip_checkpoint",
action='store_true',
default=False,
help="Path to store logs")
parser.add_argument(
'--json-summary',
type=str,
default=None,
help=
'If provided, the json summary will be written to the specified file.')
args = parser.parse_args()
config = PretrainingConfig(**args.__dict__)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
# Set up tensorflow
hvd.init()
args.log_dir = config.log_dir
# DLLogger
setup_logger(args)
set_affinity(hvd.local_rank())
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()],
'GPU')
tf.config.optimizer.set_jit(config.xla)
#tf.config.optimizer.set_experimental_options({"auto_mixed_precision": config.amp})
if config.amp:
policy = tf.keras.mixed_precision.experimental.Policy(
"mixed_float16", loss_scale="dynamic")
tf.keras.mixed_precision.experimental.set_policy(policy)
print('Compute dtype: %s' %
policy.compute_dtype) # Compute dtype: float16
print('Variable dtype: %s' %
policy.variable_dtype) # Variable dtype: float32
#tf.random.set_seed(config.seed)
# Set up config cont'
if config.load_weights and config.restore_checkpoint:
raise ValueError(
"`load_weights` and `restore_checkpoint` should not be on at the same time."
)
if config.phase2 and not config.restore_checkpoint:
raise ValueError(
"`phase2` cannot be used without `restore_checkpoint`.")
utils.heading("Config:")
log_config(config)
# Save pretrain configs
pretrain_config_json = os.path.join(config.checkpoints_dir,
'pretrain_config.json')
if is_main_process():
utils.write_json(config.__dict__, pretrain_config_json)
log("Configuration saved in {}".format(pretrain_config_json))
# Set up model
model = PretrainingModel(config)
# Set up metrics
metrics = dict()
metrics["train_perf"] = tf.keras.metrics.Mean(name="train_perf")
metrics["total_loss"] = tf.keras.metrics.Mean(name="total_loss")
metrics["masked_lm_accuracy"] = tf.keras.metrics.Accuracy(
name="masked_lm_accuracy")
metrics["masked_lm_loss"] = tf.keras.metrics.Mean(name="masked_lm_loss")
if config.electra_objective:
metrics["sampled_masked_lm_accuracy"] = tf.keras.metrics.Accuracy(
name="sampled_masked_lm_accuracy")
if config.disc_weight > 0:
metrics["disc_loss"] = tf.keras.metrics.Mean(name="disc_loss")
metrics["disc_auc"] = tf.keras.metrics.AUC(name="disc_auc")
metrics["disc_accuracy"] = tf.keras.metrics.Accuracy(
name="disc_accuracy")
metrics["disc_precision"] = tf.keras.metrics.Accuracy(
name="disc_precision")
metrics["disc_recall"] = tf.keras.metrics.Accuracy(
name="disc_recall")
# Set up tensorboard
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = os.path.join(
config.log_dir, current_time,
'train_' + str(get_rank()) + '_of_' + str(get_world_size()))
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
# Set up dataset
dataset = pretrain_utils.get_dataset(config,
config.train_batch_size,
world_size=get_world_size(),
rank=get_rank())
train_iterator = iter(dataset)
# Set up optimizer
optimizer = create_optimizer(init_lr=config.learning_rate,
num_train_steps=config.num_train_steps,
num_warmup_steps=config.num_warmup_steps,
weight_decay_rate=config.weight_decay_rate,
optimizer=config.optimizer,
skip_adaptive=config.skip_adaptive,
power=config.lr_decay_power,
beta_1=config.opt_beta_1,
beta_2=config.opt_beta_2,
end_lr=config.end_lr)
accumulator = GradientAccumulator()
if config.amp:
optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
optimizer, "dynamic")
# Set up model checkpoint
checkpoint = tf.train.Checkpoint(step=tf.Variable(0),
phase2=tf.Variable(False),
optimizer=optimizer,
model=model)
manager = tf.train.CheckpointManager(
checkpoint,
config.checkpoints_dir,
max_to_keep=config.keep_checkpoint_max)
if config.restore_checkpoint and config.restore_checkpoint != "latest":
checkpoint.restore(config.restore_checkpoint)
log(" ** Restored model checkpoint from {}".format(
config.restore_checkpoint))
elif config.restore_checkpoint and config.restore_checkpoint == "latest" and manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
log(" ** Restored model checkpoint from {}".format(
manager.latest_checkpoint))
elif config.load_weights:
model.generator(model.generator.dummy_inputs)
model.discriminator(model.discriminator.dummy_inputs)
model.generator.load_weights(
os.path.join(config.weights_dir, 'generator', 'tf_model.h5'))
model.discriminator.load_weights(
os.path.join(config.weights_dir, 'discriminator', 'tf_model.h5'))
else:
log(" ** Initializing from scratch.")
restore_iterator = bool(
config.restore_checkpoint) and config.restore_checkpoint == "latest"
# Initialize global step for phase2
if config.phase2 and not bool(checkpoint.phase2):
optimizer.iterations.assign(0)
checkpoint.step.assign(0)
checkpoint.phase2.assign(True)
restore_iterator = False
if bool(checkpoint.phase2):
manager = tf.train.CheckpointManager(
checkpoint,
config.checkpoints_dir,
checkpoint_name='ckpt-p2',
max_to_keep=config.keep_checkpoint_max)
# Set up iterator checkpoint
iter_checkpoint = tf.train.Checkpoint(train_iterator=train_iterator,
world_size=tf.Variable(
get_world_size()),
rank=tf.Variable(get_rank()))
iter_manager = tf.train.CheckpointManager(
iter_checkpoint,
os.path.join(config.checkpoints_dir,
'iter_ckpt_rank_' + '{:02}'.format(get_rank())),
checkpoint_name='iter_ckpt_rank_' + '{:02}'.format(get_rank()),
max_to_keep=config.keep_checkpoint_max)
if restore_iterator and iter_manager.latest_checkpoint:
ckpt_world_size = tf.train.load_variable(
iter_manager.latest_checkpoint,
'world_size/.ATTRIBUTES/VARIABLE_VALUE')
if ckpt_world_size == get_world_size():
iter_checkpoint.restore(iter_manager.latest_checkpoint)
log(" ** Restored iterator checkpoint from {}".format(
iter_manager.latest_checkpoint),
all_rank=True)
utils.heading("Running training")
accumulator.reset()
train_start, start_step = time.time(), int(checkpoint.step) - 1
local_step = 0
saved_ckpt = False
while int(checkpoint.step) <= config.num_train_steps:
saved_ckpt = False
step = int(checkpoint.step)
features = next(train_iterator)
iter_start = time.time()
# if step == 200: tf.profiler.experimental.start(logdir=train_log_dir)
total_loss, eval_fn_inputs = train_one_step(
config,
model,
optimizer,
features,
accumulator,
local_step == 1,
take_step=local_step % args.gradient_accumulation_steps == 0)
# if step == 300: tf.profiler.experimental.stop()
metrics["train_perf"].update_state(config.train_batch_size *
get_world_size() /
(time.time() - iter_start))
metrics["total_loss"].update_state(values=total_loss)
metric_fn(config, metrics, eval_fn_inputs)
if (step % args.log_freq
== 0) and (local_step % args.gradient_accumulation_steps == 0):
log_info_dict = {
k: float(v.result().numpy() *
100) if "accuracy" in k else float(v.result().numpy())
for k, v in metrics.items()
}
dllogger.log(step=(step, ), data=log_info_dict, verbosity=0)
log('Step:{step:6d}, Loss:{total_loss:10.6f}, Gen_loss:{masked_lm_loss:10.6f}, Disc_loss:{disc_loss:10.6f}, Gen_acc:{masked_lm_accuracy:6.2f}, '
'Disc_acc:{disc_accuracy:6.2f}, Perf:{train_perf:4.0f}, Loss Scaler: {loss_scale}, Elapsed: {elapsed}, ETA: {eta}, '
.format(step=step,
**log_info_dict,
loss_scale=optimizer.loss_scale if config.amp else 1,
elapsed=utils.get_readable_time(time.time() -
train_start),
eta=utils.get_readable_time(
(time.time() - train_start) / (step - start_step) *
(config.num_train_steps - step))),
all_rank=True)
with train_summary_writer.as_default():
for key, m in metrics.items():
tf.summary.scalar(key, m.result(), step=step)
if int(checkpoint.step) < config.num_train_steps:
for m in metrics.values():
m.reset_states()
#Print allreduced metrics on the last step
if int(checkpoint.step) == config.num_train_steps and (
local_step % args.gradient_accumulation_steps == 0):
log_info_dict = {
k: float(hvd.allreduce(v.result()).numpy() * 100) if "accuracy"
in k else float(hvd.allreduce(v.result()).numpy())
for k, v in metrics.items()
}
log_info_dict["training_sequences_per_second"] = log_info_dict[
"train_perf"]
log_info_dict["final_loss"] = log_info_dict["total_loss"]
log_info_dict["e2e_train_time"] = time.time() - e2e_start_time
dllogger.log(step=(), data=log_info_dict, verbosity=0)
log('<FINAL STEP METRICS> Step:{step:6d}, Loss:{total_loss:10.6f}, Gen_loss:{masked_lm_loss:10.6f}, Disc_loss:{disc_loss:10.6f}, Gen_acc:{masked_lm_accuracy:6.2f}, '
'Disc_acc:{disc_accuracy:6.2f}, Perf:{train_perf:4.0f},'.
format(step=step, **log_info_dict),
all_rank=False)
if local_step % args.gradient_accumulation_steps == 0:
checkpoint.step.assign(int(optimizer.iterations))
local_step += 1
if not config.skip_checkpoint and (
local_step %
(config.save_checkpoints_steps * args.gradient_accumulation_steps)
== 0):
saved_ckpt = True
if is_main_process():
save_path = manager.save(checkpoint_number=step)
log(" ** Saved model checkpoint for step {}: {}".format(
step, save_path))
iter_save_path = iter_manager.save(checkpoint_number=step)
log(" ** Saved iterator checkpoint for step {}: {}".format(
step, iter_save_path),
all_rank=True)
step = (int(checkpoint.step) - 1)
dllogger.flush()
if not config.skip_checkpoint and not saved_ckpt:
if is_main_process():
save_path = manager.save(checkpoint_number=step)
log(" ** Saved model checkpoint for step {}: {}".format(
step, save_path))
iter_save_path = iter_manager.save(checkpoint_number=step)
log(" ** Saved iterator checkpoint for step {}: {}".format(
step, iter_save_path),
all_rank=True)
return args
def main():
global timeout_sent
args = parse_arguments()
random.seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
torch.manual_seed(args.seed + args.local_rank)
torch.cuda.manual_seed(args.seed + args.local_rank)
device, args = setup_training(args)
dllogger.log(step="PARAMETER", data={"Config": [str(args)]})
# Prepare optimizer
model, optimizer, grad_scaler, lr_scheduler, checkpoint, global_step, criterion, epoch = prepare_model_and_optimizer(args, device, sequence_output_is_dense=not args.no_dense_sequence_output)
# Prepare the data loader.
if is_main_process():
tic = time.perf_counter()
train_dataloader = lddl.torch.get_bert_pretrain_data_loader(
args.input_dir,
local_rank=max(args.local_rank, 0),
vocab_file=args.vocab_file,
data_loader_kwargs={
'batch_size': args.train_batch_size * args.n_gpu,
'num_workers': args.num_workers,
'pin_memory': True,
},
base_seed=args.seed,
log_dir=None if args.output_dir is None else os.path.join(args.output_dir, 'lddl_log'),
log_level=logging.WARNING,
start_epoch=epoch,
)
if is_main_process():
print('get_bert_pretrain_data_loader took {} s!'.format(time.perf_counter() - tic))
if is_main_process():
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
dllogger.log(step="PARAMETER", data={"train_start": True})
dllogger.log(step="PARAMETER", data={"batch_size_per_gpu": args.train_batch_size})
dllogger.log(step="PARAMETER", data={"learning_rate": args.learning_rate})
model.train()
most_recent_ckpts_paths = []
stats = SyncFreeStats()
# Host Only Stats
stats.add_stat('model_step')
# Device/Host Sync-ed Stats
stats.add_stat('optimizer_step', dtype=torch.int32, device_func=(lambda: optimizer.param_groups[0]['step']))
stats.add_stat('average_loss', dtype=torch.float32, device_tensor=torch.zeros(1, dtype=torch.float32, device=device))
stats.add_stat('learning_rate', dtype=torch.float32, device_func=(lambda: optimizer.param_groups[0]['lr']))
if grad_scaler.is_enabled():
# This stat only indicates a skipped step occurred. It does not accumulate the number of skipped steps
stats.add_stat('skip_optimizer_step', dtype=torch.float32, device_func=(lambda: grad_scaler._found_inf_per_device(optimizer)[device]))
stats.add_stat('skipped_optimizer_steps', dtype=torch.float32, device_tensor=torch.zeros(1, dtype=torch.float32, device=device),
device_func=(lambda x: x.add_(grad_scaler._found_inf_per_device(optimizer)[device])))
else:
stats.add_stat('skip_optimizer_step', dtype=torch.float32)
stats.add_stat('skipped_optimizer_steps', dtype=torch.float32)
static_gpu_batch = None
full_cudagraph = None
grad_accum_cudagraph = None
if args.cuda_graphs:
static_gpu_batch = {
'input_ids': torch.ones(args.train_batch_size, args.max_seq_length, dtype=torch.int64, device=device),
'token_type_ids': torch.ones(args.train_batch_size, args.max_seq_length, dtype=torch.int64, device=device),
'attention_mask': torch.ones(args.train_batch_size, args.max_seq_length, dtype=torch.int64, device=device),
'labels': torch.ones(args.train_batch_size, args.max_seq_length, dtype=torch.int64, device=device),
'next_sentence_labels': torch.ones(args.train_batch_size, dtype=torch.int64, device=device),
}
side_stream = torch.cuda.Stream()
# Warmup Steps - includes jitting fusions
side_stream = torch.cuda.Stream()
side_stream.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(side_stream):
for _ in range(11):
take_training_step(args, grad_scaler, model, criterion, static_gpu_batch, stats)
take_optimizer_step(args, lr_scheduler, optimizer, grad_scaler, device, stats)
torch.cuda.current_stream().wait_stream(side_stream)
# Capture Graph
full_cudagraph = torch.cuda.CUDAGraph()
with torch.cuda.graph(full_cudagraph):
take_training_step(args, grad_scaler, model, criterion, static_gpu_batch, stats)
take_optimizer_step(args, lr_scheduler, optimizer, grad_scaler, device, stats)
# Warmup Steps - includes jitting fusions
side_stream.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(side_stream):
for _ in range(3):
with model.no_sync():
take_training_step(args, grad_scaler, model, criterion, static_gpu_batch, stats)
torch.cuda.current_stream().wait_stream(side_stream)
# Capture Graph
grad_accum_cudagraph = torch.cuda.CUDAGraph()
with torch.cuda.graph(grad_accum_cudagraph):
with model.no_sync():
take_training_step(args, grad_scaler, model, criterion, static_gpu_batch, stats)
train_iter = tqdm(
train_dataloader,
desc="Iteration",
disable=args.disable_progress_bar,
total=len(train_dataloader),
) if is_main_process() else train_dataloader
raw_train_start = None
# avoid nvfuser compilation times in measuring perf with phase2 binning
# ideally skip > 3 * num_bins fwd+bwd iterations to start measuring perf
skip_fwd_bwd_for_perf = 4
if args.phase2: #we use 8 bins with phase2
skip_fwd_bwd_for_perf = 50
while True:
for step, batch in enumerate(train_iter):
# The first training step is 1 and not 0 when gradient accumulating
# in order to avoid an optimizer step on the very first step
stats.host_stat('model_step').add_(1)
grad_accumulation_step = (stats.host_stat_value('model_step') % args.gradient_accumulation_steps) != 0
if raw_train_start is None and step == skip_fwd_bwd_for_perf:
raw_train_start = time.time()
# Execute Model Step
if args.cuda_graphs:
for k in batch.keys():
static_gpu_batch[k].copy_(batch[k], non_blocking=True)
if grad_accumulation_step:
grad_accum_cudagraph.replay()
else:
full_cudagraph.replay()
else:
batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
if args.allreduce_post_accumulation and grad_accumulation_step:
with model.no_sync():
take_training_step(args, grad_scaler, model, criterion, batch, stats)
else:
take_training_step(args, grad_scaler, model, criterion, batch, stats)
if not grad_accumulation_step:
take_optimizer_step(args, lr_scheduler, optimizer, grad_scaler, device, stats)
# Log Optimizer Step
if (not grad_accumulation_step) or timeout_sent:
static_optimizer_step = stats.host_stat_value('model_step') // args.gradient_accumulation_steps
dynamic_optimizer_step = static_optimizer_step - int(stats.host_stat_value('skipped_optimizer_steps'))
no_log_steps = static_optimizer_step % args.log_freq
# Log Final Step (MAYBE)
# Since the stats are asynchronously pushed from the GPU to CPU, they are not always reliable
# Therefore, a synchronization is required to guarantee you see the intended value.
# Without a synchronization, it is possible for some GPUs to go through the exit conditional
# and others to not because they accidentally see a different value for `skipped_optimizer_steps`.
# In order to remove most device syncs, synchronizations only begin in the last few steps
# where the skipped step count matters.
if static_optimizer_step >= args.steps_this_run or timeout_sent:
torch.cuda.synchronize()
dynamic_optimizer_step = static_optimizer_step - int(stats.host_stat_value('skipped_optimizer_steps'))
if dynamic_optimizer_step >= args.steps_this_run or timeout_sent:
train_time_raw = time.time() - raw_train_start
last_num_steps = args.log_freq if no_log_steps == 0 else no_log_steps
stats.device_stat('average_loss').div_(last_num_steps * args.gradient_accumulation_steps)
if (torch.distributed.is_initialized()):
stats.device_stat('average_loss').div_(get_world_size())
torch.distributed.all_reduce(stats.device_stat('average_loss'))
# We block on this copy to insure the final value
stats.host_stat('average_loss').copy_(stats.device_stat('average_loss'))
if is_main_process():
dllogger.log(step=(epoch, dynamic_optimizer_step,), data={"final_loss": stats.host_stat_value('average_loss')})
checkpoint_step(args, epoch, dynamic_optimizer_step, model, optimizer, grad_scaler, most_recent_ckpts_paths)
return args, train_time_raw, stats, skip_fwd_bwd_for_perf
if no_log_steps == 0:
if is_main_process():
dllogger.log(step=(epoch, dynamic_optimizer_step,),
data={"average_loss": stats.host_stat_value('average_loss') / (args.log_freq * args.gradient_accumulation_steps),
"learning_rate": stats.host_stat_value('learning_rate'),
"skipped_steps": int(stats.host_stat_value('skipped_optimizer_steps'))})
if stats.host_stat_value('skip_optimizer_step') > 0.:
dllogger.log(step="PARAMETER", data={"loss_scale": grad_scaler._get_scale_async().item()})
stats.device_stat('average_loss').zero_()
if not args.skip_checkpoint and (dynamic_optimizer_step % args.num_steps_per_checkpoint == 0):
checkpoint_step(args, epoch, dynamic_optimizer_step, model, optimizer, grad_scaler, most_recent_ckpts_paths)
epoch += 1
def eval_linear(args):
utils.init_distributed_mode(args)
print("git:\n {}\n".format(utils.get_sha()))
print("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items())))
cudnn.benchmark = True
# ============ preparing data ... ============
train_transform = pth_transforms.Compose([
pth_transforms.RandomResizedCrop(224),
pth_transforms.RandomHorizontalFlip(),
pth_transforms.ToTensor(),
pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
val_transform = pth_transforms.Compose([
pth_transforms.Resize(256, interpolation=3),
pth_transforms.CenterCrop(224),
pth_transforms.ToTensor(),
pth_transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
dataset_train = datasets.ImageFolder(os.path.join(args.data_path, "train"), transform=train_transform)
dataset_val = datasets.ImageFolder(os.path.join(args.data_path, "val"), transform=val_transform)
sampler = torch.utils.data.distributed.DistributedSampler(dataset_train)
train_loader = torch.utils.data.DataLoader(
dataset_train,
sampler=sampler,
batch_size=args.batch_size_per_gpu,
num_workers=args.num_workers,
pin_memory=True,
)
val_loader = torch.utils.data.DataLoader(
dataset_val,
batch_size=args.batch_size_per_gpu,
num_workers=args.num_workers,
pin_memory=True,
)
print(f"Data loaded with {len(dataset_train)} train and {len(dataset_val)} val imgs.")
# ============ building network ... ============
model = vits.__dict__[args.arch](patch_size=args.patch_size, num_classes=0)
model.cuda()
model.eval()
print(f"Model {args.arch} {args.patch_size}x{args.patch_size} built.")
# load weights to evaluate
utils.load_pretrained_weights(model, args.pretrained_weights, args.checkpoint_key, args.arch, args.patch_size)
linear_classifier = LinearClassifier(model.embed_dim * (args.n_last_blocks + int(args.avgpool_patchtokens)), num_labels=args.num_labels)
linear_classifier = linear_classifier.cuda()
linear_classifier = nn.parallel.DistributedDataParallel(linear_classifier, device_ids=[args.gpu])
# set optimizer
optimizer = torch.optim.SGD(
linear_classifier.parameters(),
args.lr * (args.batch_size_per_gpu * utils.get_world_size()) / 256., # linear scaling rule
momentum=0.9,
weight_decay=0, # we do not apply weight decay
)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs, eta_min=0)
# Optionally resume from a checkpoint
to_restore = {"epoch": 0, "best_acc": 0.}
utils.restart_from_checkpoint(
os.path.join(args.output_dir, "checkpoint.pth.tar"),
run_variables=to_restore,
state_dict=linear_classifier,
optimizer=optimizer,
scheduler=scheduler,
)
start_epoch = to_restore["epoch"]
best_acc = to_restore["best_acc"]
for epoch in range(start_epoch, args.epochs):
train_loader.sampler.set_epoch(epoch)
train_stats = train(model, linear_classifier, optimizer, train_loader, epoch, args.n_last_blocks, args.avgpool_patchtokens)
scheduler.step()
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch}
if epoch % args.val_freq == 0 or epoch == args.epochs - 1:
test_stats = validate_network(val_loader, model, linear_classifier, args.n_last_blocks, args.avgpool_patchtokens)
print(f"Accuracy at epoch {epoch} of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
best_acc = max(best_acc, test_stats["acc1"])
print(f'Max accuracy so far: {best_acc:.2f}%')
log_stats = {**{k: v for k, v in log_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()}}
if utils.is_main_process():
with (Path(args.output_dir) / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
save_dict = {
"epoch": epoch + 1,
"state_dict": linear_classifier.state_dict(),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"best_acc": best_acc,
}
torch.save(save_dict, os.path.join(args.output_dir, "checkpoint.pth.tar"))
print("Training of the supervised linear classifier on frozen features completed.\n"
"Top-1 test accuracy: {acc:.1f}".format(acc=best_acc))
def main():
global timeout_sent
args = parse_arguments()
random.seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
torch.manual_seed(args.seed + args.local_rank)
torch.cuda.manual_seed(args.seed + args.local_rank)
worker_init = WorkerInitObj(args.seed + args.local_rank)
device, args = setup_training(args)
dllogger.log(step="PARAMETER", data={"Config": [str(args)]})
# Prepare optimizer
model, optimizer, lr_scheduler, checkpoint, global_step, criterion = prepare_model_and_optimizer(args, device)
if is_main_process():
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
raw_train_start = None
if args.do_train:
if is_main_process():
dllogger.log(step="PARAMETER", data={"train_start": True})
dllogger.log(step="PARAMETER", data={"batch_size_per_gpu": args.train_batch_size})
dllogger.log(step="PARAMETER", data={"learning_rate": args.learning_rate})
model.train()
most_recent_ckpts_paths = []
average_loss = 0.0 # averaged loss every args.log_freq steps
ave_mask_acc=0.0
ave_cgp_acc=0.0
epoch = 0
training_steps = 0
pool = ProcessPoolExecutor(1)
# Note: We loop infinitely over epochs, termination is handled via iteration count
while True:
thread = None
restored_data_loader = None
if not args.resume_from_checkpoint or epoch > 0 or (args.phase2 and global_step < 1) or args.init_checkpoint:
files = [i for i in range(256)]
files.sort()
num_files = len(files)
random.Random(args.seed + epoch).shuffle(files)
f_start_id = 0
else:
f_start_id = checkpoint['files'][0]
files = checkpoint['files'][1:]
args.resume_from_checkpoint = False
num_files = len(files)
# may not exist in all checkpoints
epoch = checkpoint.get('epoch', 0)
restored_dataloader = checkpoint.get('data_loader', None)
shared_file_list = {}
if torch.distributed.is_initialized() and get_world_size() > num_files:
remainder = get_world_size() % num_files
data_file = files[(f_start_id*get_world_size()+get_rank() + remainder*f_start_id)%num_files]
else:
data_file = files[(f_start_id*get_world_size()+get_rank())%num_files]
previous_file = data_file
if restored_data_loader is None:
train_data = PretrainDataset("data/" + args.dataset,rank=data_file)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoaderMasking(train_data, sampler=train_sampler,
batch_size=args.train_batch_size * args.n_gpu,
num_workers=4, worker_init_fn=worker_init,
pin_memory=True)
# shared_file_list["0"] = (train_dataloader, data_file)
else:
train_dataloader = restored_data_loader
restored_data_loader = None
overflow_buf = None
if args.allreduce_post_accumulation:
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1 , len(files)):
if get_world_size() > num_files:
data_file = files[(f_id*get_world_size()+get_rank() + remainder*f_id)%num_files]
else:
data_file = files[(f_id*get_world_size()+get_rank())%num_files]
previous_file = data_file
dataset_future = pool.submit(create_pretraining_dataset, data_file, args, worker_init)
train_iter = tqdm(train_dataloader, desc="Iteration", disable=args.disable_progress_bar) if is_main_process() else train_dataloader
if raw_train_start is None:
raw_train_start = time.time()
for step, batch in enumerate(train_iter):
training_steps += 1
batch = batch.to(device)
pred_node, pred_graph = model(batch)
loss = criterion(pred_node, pred_graph,batch.mask_node_label,batch.ngp_y)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
divisor = args.gradient_accumulation_steps
if args.gradient_accumulation_steps > 1:
if not args.allreduce_post_accumulation:
# this division was merged into predivision
loss = loss / args.gradient_accumulation_steps
divisor = 1.0
if args.fp16:
with amp.scale_loss(loss, optimizer, delay_overflow_check=args.allreduce_post_accumulation) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
average_loss += loss.item()
acc_node = compute_accuracy(pred_node, batch.mask_node_label)
acc_cgp = compute_accuracy(pred_graph, batch.ngp_y)
ave_mask_acc+=acc_node
ave_cgp_acc+=acc_cgp
if training_steps % args.gradient_accumulation_steps == 0:
lr_scheduler.step() # learning rate warmup
global_step = take_optimizer_step(args, optimizer, model, overflow_buf, global_step)
if global_step >= args.steps_this_run or timeout_sent:
train_time_raw = time.time() - raw_train_start
last_num_steps = int(training_steps / args.gradient_accumulation_steps) % args.log_freq
last_num_steps = args.log_freq if last_num_steps == 0 else last_num_steps
average_loss = torch.tensor(average_loss, dtype=torch.float32).cuda()
average_loss = average_loss / (last_num_steps * divisor)
ave_mask_acc = torch.tensor(ave_mask_acc, dtype=torch.float32).cuda()
ave_mask_acc = ave_mask_acc / (last_num_steps * divisor)
ave_cgp_acc = torch.tensor(ave_cgp_acc, dtype=torch.float32).cuda()
ave_cgp_acc = ave_cgp_acc / (last_num_steps * divisor)
if (torch.distributed.is_initialized()):
average_loss /= get_world_size()
torch.distributed.all_reduce(average_loss)
ave_mask_acc/=get_world_size()
torch.distributed.all_reduce(ave_mask_acc)
ave_cgp_acc /= get_world_size()
torch.distributed.all_reduce(ave_cgp_acc)
final_loss = average_loss.item()
final_maskacc = ave_mask_acc.item()
final_cgpacc = ave_mask_acc.item()
if is_main_process():
dllogger.log(step=(epoch, global_step, ), data={"final_loss": final_loss,"final_maskacc":final_maskacc,
"final_cgpacc":final_cgpacc})
elif training_steps % (args.log_freq * args.gradient_accumulation_steps) == 0:
if is_main_process():
dllogger.log(step=(epoch, global_step, ), data={"average_loss": average_loss / (args.log_freq * divisor),
"average_mask_acc":ave_mask_acc / (args.log_freq * divisor),
"average_cgp_acc": ave_cgp_acc / (args.log_freq * divisor),
"step_loss": loss.item() * args.gradient_accumulation_steps / divisor,
"learning_rate": optimizer.param_groups[0]['lr']})
average_loss = 0
ave_mask_acc = 0
ave_cgp_acc = 0
if global_step >= args.steps_this_run or training_steps % (
args.num_steps_per_checkpoint * args.gradient_accumulation_steps) == 0 or timeout_sent:
if is_main_process() and not args.skip_checkpoint:
# Save a trained model
dllogger.log(step="PARAMETER", data={"checkpoint_step": global_step})
model_to_save = model.module if hasattr(model,
'module') else model # Only save the model it-self
if args.resume_step < 0 or not args.phase2:
output_save_file = os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step))
else:
output_save_file = os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step + args.phase1_end_step))
if args.do_train:
torch.save({'model': model_to_save.state_dict(),
'gnn':model_to_save.gnn.state_dict(),
'linear_atom':model_to_save.linear_pred_atoms.state_dict(),
'optimizer': optimizer.state_dict(),
'master params': list(amp.master_params(optimizer)),
'files': [f_id] + files,
'epoch': epoch,
'data_loader': None if global_step >= args.max_steps else train_dataloader
}, output_save_file)
most_recent_ckpts_paths.append(output_save_file)
if len(most_recent_ckpts_paths) > 3:
ckpt_to_be_removed = most_recent_ckpts_paths.pop(0)
os.remove(ckpt_to_be_removed)
# Exiting the training due to hitting max steps, or being sent a
# timeout from the cluster scheduler
if global_step >= args.steps_this_run or timeout_sent:
del train_dataloader
# thread.join()
return args, final_loss, final_maskacc, final_cgpacc, train_time_raw, global_step
del train_dataloader
# thread.join()
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(timeout=None)
epoch += 1
def main(args):
args.fp16 = args.fp16 or args.amp
if args.server_ip and args.server_port:
# Distant debugging - see
# https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
logger.info("Waiting for debugger attach")
ptvsd.enable_attach(
address=(args.server_ip, args.server_port),
redirect_output=True,
)
ptvsd.wait_for_attach()
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.
if not torch.distributed.is_initialized():
torch.distributed.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 not args.do_train and not args.do_eval and not args.do_predict:
raise ValueError("At least one of `do_train`, `do_eval` or "
"`do_predict` must be True.")
if is_main_process():
if (os.path.exists(args.output_dir) and os.listdir(args.output_dir)
and args.do_train):
logger.warning("Output directory ({}) already exists and is not "
"empty.".format(args.output_dir))
mkdir_by_main_process(args.output_dir)
if is_main_process():
dllogger.init(backends=[
dllogger.JSONStreamBackend(
verbosity=dllogger.Verbosity.VERBOSE,
filename=os.path.join(args.output_dir, 'dllogger.json'),
),
dllogger.StdOutBackend(
verbosity=dllogger.Verbosity.VERBOSE,
step_format=format_step,
),
])
else:
dllogger.init(backends=[])
dllogger.log(step="PARAMETER", data={"Config": [str(args)]})
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, "
"should be >= 1".format(
args.gradient_accumulation_steps))
if args.gradient_accumulation_steps > args.train_batch_size:
raise ValueError("gradient_accumulation_steps ({}) cannot be larger "
"train_batch_size ({}) - there cannot be a fraction "
"of one sample.".format(
args.gradient_accumulation_steps,
args.train_batch_size,
))
args.train_batch_size = (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)
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
processor = PROCESSORS[args.task_name]()
num_labels = len(processor.get_labels())
#tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
tokenizer = BertTokenizer(
args.vocab_file,
do_lower_case=args.do_lower_case,
max_len=512,
) # for bert large
num_train_optimization_steps = None
if args.do_train:
train_features = get_train_features(
args.data_dir,
args.bert_model,
args.max_seq_length,
args.do_lower_case,
args.local_rank,
args.train_batch_size,
args.gradient_accumulation_steps,
args.num_train_epochs,
tokenizer,
processor,
)
num_train_optimization_steps = int(
len(train_features) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = (num_train_optimization_steps //
torch.distributed.get_world_size())
# Prepare model
config = modeling.BertConfig.from_json_file(args.config_file)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
# modeling.ACT2FN["bias_gelu"] = modeling.bias_gelu_training
model = modeling.BertForSequenceClassification(
config,
num_labels=num_labels,
)
logger.info("USING CHECKPOINT from {}".format(args.init_checkpoint))
checkpoint = torch.load(args.init_checkpoint, map_location='cpu')
checkpoint = checkpoint["model"] if "model" in checkpoint.keys(
) else checkpoint
model.load_state_dict(checkpoint, strict=False)
logger.info("USED CHECKPOINT from {}".format(args.init_checkpoint))
dllogger.log(
step="PARAMETER",
data={
"num_parameters":
sum([p.numel() for p in model.parameters() if p.requires_grad]),
},
)
model.to(device)
# Prepare optimizer
model, optimizer, scheduler = init_optimizer_and_amp(
model,
args.learning_rate,
args.loss_scale,
args.warmup_proportion,
num_train_optimization_steps,
args.fp16,
)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("Please install apex from "
"https://www.github.com/nvidia/apex to use "
"distributed and fp16 training.")
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
loss_fct = torch.nn.CrossEntropyLoss()
results = {}
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_features))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
train_data = gen_tensor_dataset(train_features)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(
train_data,
sampler=train_sampler,
batch_size=args.train_batch_size,
)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
latency_train = 0.0
nb_tr_examples = 0
model.train()
tic_train = time.perf_counter()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
if args.max_steps > 0 and global_step > args.max_steps:
break
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
logits = model(input_ids, segment_ids, input_mask)
loss = loss_fct(
logits.view(-1, num_labels),
label_ids.view(-1),
)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up for BERT
# which FusedAdam doesn't do
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
latency_train = time.perf_counter() - tic_train
tr_loss = tr_loss / nb_tr_steps
results.update({
'global_step':
global_step,
'train:loss':
tr_loss,
'train:latency':
latency_train,
'train:num_samples_per_gpu':
nb_tr_examples,
'train:num_steps':
nb_tr_steps,
'train:throughput':
get_world_size() * nb_tr_examples / latency_train,
})
if is_main_process() and not args.skip_checkpoint:
model_to_save = model.module if hasattr(model, 'module') else model
torch.save(
{"model": model_to_save.state_dict()},
os.path.join(args.output_dir, modeling.WEIGHTS_NAME),
)
with open(
os.path.join(args.output_dir, modeling.CONFIG_NAME),
'w',
) as f:
f.write(model_to_save.config.to_json_string())
if (args.do_eval or args.do_predict) and is_main_process():
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features, label_map = convert_examples_to_features(
eval_examples,
processor.get_labels(),
args.max_seq_length,
tokenizer,
)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_data = gen_tensor_dataset(eval_features)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(
eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size,
)
model.eval()
preds = None
out_label_ids = None
eval_loss = 0
nb_eval_steps, nb_eval_examples = 0, 0
cuda_events = [(torch.cuda.Event(enable_timing=True),
torch.cuda.Event(enable_timing=True))
for _ in range(len(eval_dataloader))]
for i, (input_ids, input_mask, segment_ids, label_ids) in tqdm(
enumerate(eval_dataloader),
desc="Evaluating",
):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
cuda_events[i][0].record()
logits = model(input_ids, segment_ids, input_mask)
cuda_events[i][1].record()
if args.do_eval:
eval_loss += loss_fct(
logits.view(-1, num_labels),
label_ids.view(-1),
).mean().item()
nb_eval_steps += 1
nb_eval_examples += input_ids.size(0)
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = label_ids.detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids,
label_ids.detach().cpu().numpy(),
axis=0,
)
torch.cuda.synchronize()
eval_latencies = [
event_start.elapsed_time(event_end)
for event_start, event_end in cuda_events
]
eval_latencies = list(sorted(eval_latencies))
def infer_latency_sli(threshold):
index = int(len(eval_latencies) * threshold) - 1
index = min(max(index, 0), len(eval_latencies) - 1)
return eval_latencies[index]
eval_throughput = (args.eval_batch_size /
(np.mean(eval_latencies) / 1000))
results.update({
'eval:num_samples_per_gpu': nb_eval_examples,
'eval:num_steps': nb_eval_steps,
'infer:latency(ms):50%': infer_latency_sli(0.5),
'infer:latency(ms):90%': infer_latency_sli(0.9),
'infer:latency(ms):95%': infer_latency_sli(0.95),
'infer:latency(ms):99%': infer_latency_sli(0.99),
'infer:latency(ms):100%': infer_latency_sli(1.0),
'infer:latency(ms):avg': np.mean(eval_latencies),
'infer:latency(ms):std': np.std(eval_latencies),
'infer:latency(ms):sum': np.sum(eval_latencies),
'infer:throughput(samples/s):avg': eval_throughput,
})
preds = np.argmax(preds, axis=1)
if args.do_predict:
dump_predictions(
os.path.join(args.output_dir, 'predictions.json'),
label_map,
preds,
eval_examples,
)
if args.do_eval:
results['eval:loss'] = eval_loss / nb_eval_steps
eval_result = compute_metrics(args.task_name, preds, out_label_ids)
results.update(eval_result)
if is_main_process():
logger.info("***** Results *****")
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
with open(os.path.join(args.output_dir, "results.txt"), "w") as writer:
json.dump(results, writer)
dllogger_queries_from_results = {
'exact_match': 'acc',
'F1': 'f1',
'e2e_train_time': 'train:latency',
'training_sequences_per_second': 'train:throughput',
'e2e_inference_time':
('infer:latency(ms):sum', lambda x: x / 1000),
'inference_sequences_per_second':
'infer:throughput(samples/s):avg',
}
for key, query in dllogger_queries_from_results.items():
results_key, convert = (query if isinstance(query, tuple) else
(query, lambda x: x))
if results_key not in results:
continue
dllogger.log(
step=tuple(),
data={key: convert(results[results_key])},
)
dllogger.flush()
return results
def prepare_model_and_optimizer(args, device):
# Prepare model
# model = Pretrain_model(args.num_layer, args.emb_dim, args.heads, args.num_message_passing, args.dropout_ratio)
model = Pretrain_MolGNet(args.num_layer, args.emb_dim, args.heads, args.num_message_passing, args.dropout_ratio)
checkpoint = None
if not args.resume_from_checkpoint:
global_step = 0
else:
if args.resume_step == -1 and not args.init_checkpoint:
model_names = [f for f in os.listdir(args.output_dir) if f.endswith(".pt")]
args.resume_step = max([int(x.split('.pt')[0].split('_')[1].strip()) for x in model_names])
global_step = args.resume_step if not args.init_checkpoint else 0
if not args.init_checkpoint:
checkpoint = torch.load(os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step)), map_location="cpu")
else:
checkpoint = torch.load(args.init_checkpoint, map_location="cpu")
model.load_state_dict(checkpoint['model'], strict=False)
if args.phase2 and not args.init_checkpoint:
global_step -= args.phase1_end_step
if is_main_process():
print("resume step from ", args.resume_step)
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta', 'LayerNorm']
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}]
optimizer = FusedLAMB(optimizer_grouped_parameters,
lr=args.learning_rate)
lr_scheduler = PolyWarmUpScheduler(optimizer,
warmup=args.warmup_proportion,
total_steps=args.max_steps)
if args.fp16:
if args.loss_scale == 0:
model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale="dynamic", cast_model_outputs=torch.float16)
else:
model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale=args.loss_scale, cast_model_outputs=torch.float16)
amp._amp_state.loss_scalers[0]._loss_scale = args.init_loss_scale
if args.resume_from_checkpoint:
if args.phase2 or args.init_checkpoint:
keys = list(checkpoint['optimizer']['state'].keys())
#Override hyperparameters from previous checkpoint
for key in keys:
checkpoint['optimizer']['state'][key]['step'] = global_step
for iter, item in enumerate(checkpoint['optimizer']['param_groups']):
checkpoint['optimizer']['param_groups'][iter]['step'] = global_step
checkpoint['optimizer']['param_groups'][iter]['t_total'] = args.max_steps
checkpoint['optimizer']['param_groups'][iter]['warmup'] = args.warmup_proportion
checkpoint['optimizer']['param_groups'][iter]['lr'] = args.learning_rate
optimizer.load_state_dict(checkpoint['optimizer']) # , strict=False)
# Restore AMP master parameters
if args.fp16:
optimizer._lazy_init_maybe_master_weights()
optimizer._amp_stash.lazy_init_called = True
optimizer.load_state_dict(checkpoint['optimizer'])
for param, saved_param in zip(amp.master_params(optimizer), checkpoint['master params']):
param.data.copy_(saved_param.data)
if args.local_rank != -1:
if not args.allreduce_post_accumulation:
model = DDP(model, message_size=250000000, gradient_predivide_factor=get_world_size())
else:
flat_dist_call([param.data for param in model.parameters()], torch.distributed.broadcast, (0,) )
elif args.n_gpu > 1:
model = torch.nn.DataParallel(model)
criterion = PretrainingCriterion()
return model, optimizer, lr_scheduler, checkpoint, global_step, criterion
def main(args):
utils.init_distributed_mode(args)
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print(
'Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val,
num_replicas=num_tasks,
rank=global_rank,
shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
data_loader_train = torch.utils.data.DataLoader(
dataset_train,
sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
sampler=sampler_val,
batch_size=int(
1.0 * args.batch_size),
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_fn = Mixup(mixup_alpha=args.mixup,
cutmix_alpha=args.cutmix,
cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob,
mode=args.mixup_mode,
label_smoothing=args.smoothing,
num_classes=args.nb_classes)
print(f"Creating model: {args.model}")
# model = create_model(
# args.model,
# pretrained=False,
# num_classes=args.nb_classes,
# drop_rate=args.drop,
# drop_path_rate=args.drop_path,
# drop_block_rate=None,
# )
model = getattr(SwinTransformer, args.model)(num_classes=args.nb_classes,
drop_path_rate=args.drop_path)
model.to(device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size(
) / 512.0
args.lr = linear_scaled_lr
linear_scaled_warmup_lr = args.warmup_lr * args.batch_size * utils.get_world_size(
) / 512.0
args.warmup_lr = linear_scaled_warmup_lr
optimizer = create_optimizer(args, model_without_ddp)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
# criterion = LabelSmoothingCrossEntropy()
if args.mixup > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
output_dir = Path(args.output_dir)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
if args.eval:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
return
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
max_accuracy = 0.0
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
lr_scheduler.step(epoch + 1)
train_stats = train_one_epoch(
model,
criterion,
data_loader_train,
optimizer,
device,
epoch,
loss_scaler,
args.clip_grad,
mixup_fn,
set_training_mode=True # keep in eval mode during finetuning
)
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'scaler': loss_scaler.state_dict(),
'args': args,
}, checkpoint_path)
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
**{f'test_{k}': v
for k, v in test_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main(args):
utils.init_distributed_mode(args)
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
model = get_model(args)
patch_size = model.patch_embed.patch_size
print("Patch size = %s" % str(patch_size))
args.window_size = (args.input_size // patch_size[0], args.input_size // patch_size[1])
args.patch_size = patch_size
# get dataset
dataset_train = build_beit_pretraining_dataset(args)
# prepare discrete vae
d_vae = utils.create_d_vae(
weight_path=args.discrete_vae_weight_path, d_vae_type=args.discrete_vae_type,
device=device, image_size=args.second_input_size)
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_rank = global_rank
num_training_steps_per_epoch = len(dataset_train) // args.batch_size // num_tasks
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=sampler_rank, shuffle=True
)
print("Sampler_train = %s" % str(sampler_train))
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
if global_rank == 0 and args.log_dir is not None:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
else:
log_writer = None
data_loader_train = torch.utils.data.DataLoader(
dataset_train, sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
model.to(device)
model_without_ddp = model
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print("Model = %s" % str(model_without_ddp))
print('number of params:', n_parameters)
total_batch_size = args.batch_size * utils.get_world_size()
print("LR = %.8f" % args.lr)
print("Batch size = %d" % total_batch_size)
print("Number of training steps = %d" % num_training_steps_per_epoch)
print("Number of training examples per epoch = %d" % (total_batch_size * num_training_steps_per_epoch))
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
optimizer = create_optimizer(
args, model_without_ddp)
loss_scaler = NativeScaler()
print("Use step level LR & WD scheduler!")
lr_schedule_values = utils.cosine_scheduler(
args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
)
if args.weight_decay_end is None:
args.weight_decay_end = args.weight_decay
wd_schedule_values = utils.cosine_scheduler(
args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch)
print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values)))
utils.auto_load_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
if log_writer is not None:
log_writer.set_step(epoch * num_training_steps_per_epoch)
train_stats = train_one_epoch(
model, d_vae, data_loader_train,
optimizer, device, epoch, loss_scaler,
args.clip_grad, log_writer=log_writer,
start_steps=epoch * num_training_steps_per_epoch,
lr_schedule_values=lr_schedule_values,
wd_schedule_values=wd_schedule_values,
)
if args.output_dir:
if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
utils.save_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch)
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch, 'n_parameters': n_parameters}
if args.output_dir and utils.is_main_process():
if log_writer is not None:
log_writer.flush()
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
del train_dataloader
# thread.join()
return args, final_loss, train_time_raw, global_step
del train_dataloader
# thread.join()
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(timeout=None)
epoch += 1
if __name__ == "__main__":
now = time.time()
args, final_loss, train_time_raw, global_step = main()
gpu_count = args.n_gpu
global_step += args.phase1_end_step if (args.phase2 and args.resume_step > 0) else 0
if args.resume_step == -1:
args.resume_step = 0
if torch.distributed.is_initialized():
gpu_count = get_world_size()
if is_main_process():
e2e_time = time.time() - now
training_perf = args.train_batch_size * args.gradient_accumulation_steps * gpu_count\
* (global_step - args.resume_step + skipped_steps) / train_time_raw
dllogger.log(step=tuple(), data={"e2e_train_time": e2e_time, "training_sequences_per_second": training_perf,
"final_loss": final_loss, "raw_train_time": train_time_raw })
dllogger.flush()
def main():
# Parse essential args
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir",
required=True,
help="Location of data files (model weights, etc).")
parser.add_argument("--model_name",
required=True,
help="The name of the model being fine-tuned.")
parser.add_argument("--pretrain_tfrecords", type=str)
parser.add_argument("--seed", type=int)
parser.add_argument("--num_train_steps", type=int)
parser.add_argument("--num_warmup_steps", type=int)
parser.add_argument("--learning_rate", type=float)
parser.add_argument("--train_batch_size", type=int)
parser.add_argument("--max_seq_length", type=int)
parser.add_argument("--mask_prob", type=float)
parser.add_argument("--disc_weight", type=float)
parser.add_argument("--generator_hidden_size", type=float)
parser.add_argument("--save_checkpoints_steps", type=int)
parser.add_argument("--keep_checkpoint_max", type=int)
parser.add_argument("--restore_checkpoint", action='store_true')
parser.add_argument("--optimizer",
default="adam",
type=str,
help="adam or lamb")
args = parser.parse_args()
config = PretrainingConfig(**args.__dict__)
# Set up tensorflow
hvd.init()
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()],
'GPU')
tf.config.optimizer.set_jit(config.xla)
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": config.amp})
tf.random.set_seed(config.seed)
# Set up config
if config.do_train == config.do_eval:
raise ValueError(
"Exactly one of `do_train` or `do_eval` must be True.")
if config.debug and config.do_train:
utils.rmkdir(config.model_dir)
utils.heading("Config:")
log_config(config)
# Save pretrain configs
pretrain_config_json = os.path.join(config.checkpoints_dir,
'pretrain_config.json')
if is_main_process():
utils.write_json(config.__dict__, pretrain_config_json)
log("Configuration saved in {}".format(pretrain_config_json))
# Set up model
model = PretrainingModel(config)
# Set up metrics
perf_metrics = dict()
perf_metrics["train_perf"] = tf.keras.metrics.Mean(name="train_perf")
eval_metrics = dict()
eval_metrics["total_loss"] = tf.keras.metrics.Mean(name="total_loss")
eval_metrics["masked_lm_accuracy"] = tf.keras.metrics.Accuracy(
name="masked_lm_accuracy")
eval_metrics["masked_lm_loss"] = tf.keras.metrics.Mean(
name="masked_lm_loss")
if config.electra_objective:
eval_metrics["sampled_masked_lm_accuracy"] = tf.keras.metrics.Accuracy(
name="sampled_masked_lm_accuracy")
if config.disc_weight > 0:
eval_metrics["disc_loss"] = tf.keras.metrics.Mean(name="disc_loss")
eval_metrics["disc_auc"] = tf.keras.metrics.AUC(name="disc_auc")
eval_metrics["disc_accuracy"] = tf.keras.metrics.Accuracy(
name="disc_accuracy")
eval_metrics["disc_precision"] = tf.keras.metrics.Accuracy(
name="disc_precision")
eval_metrics["disc_recall"] = tf.keras.metrics.Accuracy(
name="disc_recall")
# Set up tensorboard
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = os.path.join(
config.log_dir, current_time,
'train_' + str(get_rank()) + '_of_' + str(get_world_size()))
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
# Set up dataset
dataset = pretrain_utils.get_dataset(config,
config.train_batch_size,
world_size=get_world_size(),
rank=get_rank())
train_iterator = iter(dataset)
# Set up optimizer
optimizer = create_optimizer(init_lr=config.learning_rate,
num_train_steps=config.num_train_steps,
num_warmup_steps=config.num_warmup_steps,
weight_decay_rate=config.weight_decay_rate,
optimizer=config.optimizer)
if config.amp:
optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
optimizer, "dynamic")
if config.do_train:
# Set up checkpoint manager
checkpoint = tf.train.Checkpoint(step=tf.Variable(1),
optimizer=optimizer,
model=model)
manager = tf.train.CheckpointManager(
checkpoint,
config.checkpoints_dir,
max_to_keep=config.keep_checkpoint_max)
iter_checkpoint = tf.train.Checkpoint(train_iterator=train_iterator)
iter_manager = tf.train.CheckpointManager(
iter_checkpoint,
os.path.join(config.checkpoints_dir,
'iter_ckpt_rank_' + '{:02}'.format(get_rank())),
checkpoint_name='iter_ckpt_rank_' + '{:02}'.format(get_rank()),
max_to_keep=config.keep_checkpoint_max)
if config.restore_checkpoint and manager.latest_checkpoint:
checkpoint.restore(manager.latest_checkpoint)
log(" ** Restored model checkpoint from {}".format(
manager.latest_checkpoint))
if iter_manager.latest_checkpoint:
iter_checkpoint.restore(iter_manager.latest_checkpoint)
log(" ** Restored iterator checkpoint from {}".format(
iter_manager.latest_checkpoint),
all_rank=True)
else:
log(" ** Initializing from scratch.")
utils.heading("Running training")
train_start, start_step = time.time(), int(checkpoint.step) - 1
while int(checkpoint.step) <= config.num_train_steps:
step = int(checkpoint.step)
features = next(train_iterator)
iter_start = time.time()
# if step == 200: tf.profiler.experimental.start(logdir=train_log_dir)
total_loss, eval_fn_inputs = train_one_step(
config, model, optimizer, features, step <= 1)
# if step == 300: tf.profiler.experimental.stop()
perf_metrics["train_perf"].update_state(config.train_batch_size *
get_world_size() /
(time.time() - iter_start))
eval_metrics["total_loss"].update_state(values=total_loss)
metric_fn(config, eval_metrics, eval_fn_inputs)
if step % 100 == 0:
log('Step:{:6d}, Loss:{:10.6f}, Gen_loss:{:10.6f}, Disc_loss:{:10.6f}, Gen_acc:{:6.2f}, '
'Disc_acc:{:6.2f}, Perf:{:4.0f}, Elapsed: {}, ETA: {}, '.
format(
step, total_loss,
eval_metrics["masked_lm_loss"].result().numpy(),
eval_metrics["disc_loss"].result().numpy(),
eval_metrics["masked_lm_accuracy"].result().numpy() *
100,
eval_metrics["disc_accuracy"].result().numpy() * 100,
perf_metrics["train_perf"].result().numpy(),
utils.get_readable_time(time.time() - train_start),
utils.get_readable_time(
(time.time() - train_start) / (step - start_step) *
(config.num_train_steps - step))),
all_rank=True)
with train_summary_writer.as_default():
for key, m in eval_metrics.items():
tf.summary.scalar(key, m.result(), step=step)
for m in eval_metrics.values():
m.reset_states()
checkpoint.step.assign_add(1)
if step % config.save_checkpoints_steps == 0:
if is_main_process():
save_path = manager.save()
log(" ** Saved model checkpoint for step {}: {}".format(
step, save_path))
iter_save_path = iter_manager.save()
log(" ** Saved iterator checkpoint for step {}: {}".format(
step, iter_save_path),
all_rank=True)
if config.do_eval:
pass
def prepare_model_and_optimizer(args, device):
# Prepare model
config = modeling.BertConfig.from_json_file(args.config_file)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
modeling.ACT2FN["bias_gelu"] = modeling.bias_gelu_training
model = modeling.BertForPreTraining(config)
if args.disable_weight_tying:
import torch.nn as nn
print ("WARNING!!!!!!! Disabling weight tying for this run")
print ("BEFORE ", model.cls.predictions.decoder.weight is model.bert.embeddings.word_embeddings.weight)
model.cls.predictions.decoder.weight = torch.nn.Parameter(model.cls.predictions.decoder.weight.clone().detach())
print ("AFTER ", model.cls.predictions.decoder.weight is model.bert.embeddings.word_embeddings.weight)
assert (model.cls.predictions.decoder.weight is model.bert.embeddings.word_embeddings.weight) == False
checkpoint = None
if not args.resume_from_checkpoint:
global_step = 0
else:
if args.resume_step == -1 and not args.init_checkpoint:
model_names = [f for f in os.listdir(args.output_dir) if f.endswith(".pt")]
args.resume_step = max([int(x.split('.pt')[0].split('_')[1].strip()) for x in model_names])
global_step = args.resume_step if not args.init_checkpoint else 0
if not args.init_checkpoint:
checkpoint = torch.load(os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step)), map_location="cpu")
else:
checkpoint = torch.load(args.init_checkpoint, map_location="cpu")
model.load_state_dict(checkpoint['model'], strict=False)
if args.phase2 and not args.init_checkpoint:
global_step -= args.phase1_end_step
if is_main_process():
print("resume step from ", args.resume_step)
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta', 'LayerNorm']
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}]
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate)
lr_scheduler = PolyWarmUpScheduler(optimizer,
warmup=args.warmup_proportion,
total_steps=args.max_steps,
degree=1)
if args.fp16:
if args.loss_scale == 0:
model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale="dynamic", cast_model_outputs=torch.float16)
else:
model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale=args.loss_scale, cast_model_outputs=torch.float16)
amp._amp_state.loss_scalers[0]._loss_scale = args.init_loss_scale
model.checkpoint_activations(args.checkpoint_activations)
if args.resume_from_checkpoint:
if args.phase2 or args.init_checkpoint:
keys = list(checkpoint['optimizer']['state'].keys())
#Override hyperparameters from previous checkpoint
for key in keys:
checkpoint['optimizer']['state'][key]['step'] = global_step
for iter, item in enumerate(checkpoint['optimizer']['param_groups']):
checkpoint['optimizer']['param_groups'][iter]['step'] = global_step
checkpoint['optimizer']['param_groups'][iter]['t_total'] = args.max_steps
checkpoint['optimizer']['param_groups'][iter]['warmup'] = args.warmup_proportion
checkpoint['optimizer']['param_groups'][iter]['lr'] = args.learning_rate
optimizer.load_state_dict(checkpoint['optimizer']) # , strict=False)
# Restore AMP master parameters
if args.fp16:
optimizer._lazy_init_maybe_master_weights()
optimizer._amp_stash.lazy_init_called = True
optimizer.load_state_dict(checkpoint['optimizer'])
for param, saved_param in zip(amp.master_params(optimizer), checkpoint['master params']):
param.data.copy_(saved_param.data)
if args.local_rank != -1:
if not args.allreduce_post_accumulation:
model = DDP(model, message_size=250000000, gradient_predivide_factor=get_world_size())
else:
flat_dist_call([param.data for param in model.parameters()], torch.distributed.broadcast, (0,) )
elif args.n_gpu > 1:
model = torch.nn.DataParallel(model)
criterion = BertPretrainingCriterion(config.vocab_size)
if args.disable_weight_tying:
# Sanity Check that new param is in optimizer
print ("SANITY CHECK OPTIMIZER: ", id(model.module.cls.predictions.decoder.weight) in [id(g) for g in optimizer.param_groups[0]['params']])
assert id(model.module.cls.predictions.decoder.weight) in [id(g) for g in optimizer.param_groups[0]['params']]
return model, optimizer, lr_scheduler, checkpoint, global_step, criterion
def train_dino(args):
utils.init_distributed_mode(args)
utils.fix_random_seeds(args.seed)
print("git:\n {}\n".format(utils.get_sha()))
print("\n".join("%s: %s" % (k, str(v))
for k, v in sorted(dict(vars(args)).items())))
cudnn.benchmark = True
# ============ preparing data ... ============
transform = DataAugmentationDINO(
args.global_crops_scale,
args.local_crops_scale,
args.local_crops_number,
)
#dataset = datasets.ImageFolder(args.data_path, transform=transform)
from sen12ms import get_transform
dataset = AllSen12MSDataset(args.data_path,
"train",
transform=transform,
tansform_coord=None,
classes=None,
seasons=None,
split_by_region=True,
download=False)
sampler = torch.utils.data.DistributedSampler(dataset, shuffle=True)
data_loader = torch.utils.data.DataLoader(
dataset,
sampler=sampler,
batch_size=args.batch_size_per_gpu,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
)
print(f"Data loaded: there are {len(dataset)} images.")
# ============ building student and teacher networks ... ============
# if the network is a vision transformer (i.e. deit_tiny, deit_small, vit_base)
if args.arch in vits.__dict__.keys():
student = vits.__dict__[args.arch](
patch_size=args.patch_size,
drop_path_rate=0.1, # stochastic depth
)
teacher = vits.__dict__[args.arch](patch_size=args.patch_size)
embed_dim = student.embed_dim
student = utils.replace_input_layer(student, inchannels=13)
teacher = utils.replace_input_layer(teacher, inchannels=13)
# otherwise, we check if the architecture is in torchvision models
elif args.arch in torchvision_models.__dict__.keys():
student = torchvision_models.__dict__[args.arch]()
teacher = torchvision_models.__dict__[args.arch]()
embed_dim = student.fc.weight.shape[1]
else:
print(f"Unknow architecture: {args.arch}")
# multi-crop wrapper handles forward with inputs of different resolutions
student = utils.MultiCropWrapper(
student,
DINOHead(
embed_dim,
args.out_dim,
use_bn=args.use_bn_in_head,
norm_last_layer=args.norm_last_layer,
))
teacher = utils.MultiCropWrapper(
teacher,
DINOHead(embed_dim, args.out_dim, args.use_bn_in_head),
)
# move networks to gpu
student, teacher = student.cuda(), teacher.cuda()
# synchronize batch norms (if any)
if utils.has_batchnorms(student):
student = nn.SyncBatchNorm.convert_sync_batchnorm(student)
teacher = nn.SyncBatchNorm.convert_sync_batchnorm(teacher)
# we need DDP wrapper to have synchro batch norms working...
teacher = nn.parallel.DistributedDataParallel(teacher,
device_ids=[args.gpu])
teacher_without_ddp = teacher.module
else:
# teacher_without_ddp and teacher are the same thing
teacher_without_ddp = teacher
student = nn.parallel.DistributedDataParallel(student,
device_ids=[args.gpu])
# teacher and student start with the same weights
teacher_without_ddp.load_state_dict(student.module.state_dict())
# there is no backpropagation through the teacher, so no need for gradients
for p in teacher.parameters():
p.requires_grad = False
print(f"Student and Teacher are built: they are both {args.arch} network.")
# ============ preparing loss ... ============
dino_loss = DINOLoss(
args.out_dim,
args.local_crops_number +
2, # total number of crops = 2 global crops + local_crops_number
args.warmup_teacher_temp,
args.teacher_temp,
args.warmup_teacher_temp_epochs,
args.epochs,
).cuda()
# ============ preparing optimizer ... ============
params_groups = utils.get_params_groups(student)
if args.optimizer == "adamw":
optimizer = torch.optim.AdamW(params_groups) # to use with ViTs
elif args.optimizer == "sgd":
optimizer = torch.optim.SGD(params_groups, lr=0,
momentum=0.9) # lr is set by scheduler
elif args.optimizer == "lars":
optimizer = utils.LARS(
params_groups) # to use with convnet and large batches
# for mixed precision training
fp16_scaler = None
if args.use_fp16:
fp16_scaler = torch.cuda.amp.GradScaler()
# ============ init schedulers ... ============
lr_schedule = utils.cosine_scheduler(
args.lr * (args.batch_size_per_gpu * utils.get_world_size()) /
256., # linear scaling rule
args.min_lr,
args.epochs,
len(data_loader),
warmup_epochs=args.warmup_epochs,
)
wd_schedule = utils.cosine_scheduler(
args.weight_decay,
args.weight_decay_end,
args.epochs,
len(data_loader),
)
# momentum parameter is increased to 1. during training with a cosine schedule
momentum_schedule = utils.cosine_scheduler(args.momentum_teacher, 1,
args.epochs, len(data_loader))
print(f"Loss, optimizer and schedulers ready.")
# ============ optionally resume training ... ============
to_restore = {"epoch": 0}
utils.restart_from_checkpoint(
os.path.join(args.output_dir, "checkpoint.pth"),
run_variables=to_restore,
student=student,
teacher=teacher,
optimizer=optimizer,
fp16_scaler=fp16_scaler,
dino_loss=dino_loss,
)
start_epoch = to_restore["epoch"]
start_time = time.time()
print("Starting DINO training !")
for epoch in range(start_epoch, args.epochs):
data_loader.sampler.set_epoch(epoch)
# ============ training one epoch of DINO ... ============
train_stats = train_one_epoch(student, teacher, teacher_without_ddp,
dino_loss, data_loader, optimizer,
lr_schedule, wd_schedule,
momentum_schedule, epoch, fp16_scaler,
args)
# ============ writing logs ... ============
save_dict = {
'student': student.state_dict(),
'teacher': teacher.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch + 1,
'args': args,
'dino_loss': dino_loss.state_dict(),
}
if fp16_scaler is not None:
save_dict['fp16_scaler'] = fp16_scaler.state_dict()
utils.save_on_master(save_dict,
os.path.join(args.output_dir, 'checkpoint.pth'))
if args.saveckp_freq and epoch % args.saveckp_freq == 0:
utils.save_on_master(
save_dict,
os.path.join(args.output_dir, f'checkpoint{epoch:04}.pth'))
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()}, 'epoch': epoch
}
if utils.is_main_process():
with (Path(args.output_dir) / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main(args):
utils.init_distributed_mode(args)
update_config_from_file(args.cfg)
print(args)
args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
if args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
if args.repeated_aug:
sampler_train = RASampler(dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
else:
sampler_train = torch.utils.data.DistributedSampler(
dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print(
'Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val,
num_replicas=num_tasks,
rank=global_rank,
shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
sampler_train = torch.utils.data.RandomSampler(dataset_train)
data_loader_train = torch.utils.data.DataLoader(
dataset_train,
sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
batch_size=int(
2 * args.batch_size),
sampler=sampler_val,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_fn = Mixup(mixup_alpha=args.mixup,
cutmix_alpha=args.cutmix,
cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob,
mode=args.mixup_mode,
label_smoothing=args.smoothing,
num_classes=args.nb_classes)
print(f"Creating SuperVisionTransformer")
print(cfg)
model = Vision_TransformerSuper(
img_size=args.input_size,
patch_size=args.patch_size,
embed_dim=cfg.SUPERNET.EMBED_DIM,
depth=cfg.SUPERNET.DEPTH,
num_heads=cfg.SUPERNET.NUM_HEADS,
mlp_ratio=cfg.SUPERNET.MLP_RATIO,
qkv_bias=True,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
gp=args.gp,
num_classes=args.nb_classes,
max_relative_position=args.max_relative_position,
relative_position=args.relative_position,
change_qkv=args.change_qkv,
abs_pos=not args.no_abs_pos)
choices = {
'num_heads': cfg.SEARCH_SPACE.NUM_HEADS,
'mlp_ratio': cfg.SEARCH_SPACE.MLP_RATIO,
'embed_dim': cfg.SEARCH_SPACE.EMBED_DIM,
'depth': cfg.SEARCH_SPACE.DEPTH
}
model.to(device)
if args.teacher_model:
teacher_model = create_model(
args.teacher_model,
pretrained=True,
num_classes=args.nb_classes,
)
teacher_model.to(device)
teacher_loss = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
teacher_model = None
teacher_loss = None
model_ema = None
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size(
) / 512.0
args.lr = linear_scaled_lr
optimizer = create_optimizer(args, model_without_ddp)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
# criterion = LabelSmoothingCrossEntropy()
if args.mixup > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
output_dir = Path(args.output_dir)
if not output_dir.exists():
output_dir.mkdir(parents=True)
# save config for later experiments
with open(output_dir / "config.yaml", 'w') as f:
f.write(args_text)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
if args.model_ema:
utils._load_checkpoint_for_ema(model_ema,
checkpoint['model_ema'])
retrain_config = None
if args.mode == 'retrain' and "RETRAIN" in cfg:
retrain_config = {
'layer_num': cfg.RETRAIN.DEPTH,
'embed_dim': [cfg.RETRAIN.EMBED_DIM] * cfg.RETRAIN.DEPTH,
'num_heads': cfg.RETRAIN.NUM_HEADS,
'mlp_ratio': cfg.RETRAIN.MLP_RATIO
}
if args.eval:
print(retrain_config)
test_stats = evaluate(data_loader_val,
model,
device,
mode=args.mode,
retrain_config=retrain_config)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
return
print("Start training")
start_time = time.time()
max_accuracy = 0.0
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
train_stats = train_one_epoch(
model,
criterion,
data_loader_train,
optimizer,
device,
epoch,
loss_scaler,
args.clip_grad,
model_ema,
mixup_fn,
amp=args.amp,
teacher_model=teacher_model,
teach_loss=teacher_loss,
choices=choices,
mode=args.mode,
retrain_config=retrain_config,
)
lr_scheduler.step(epoch)
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
# 'model_ema': get_state_dict(model_ema),
'scaler': loss_scaler.state_dict(),
'args': args,
},
checkpoint_path)
test_stats = evaluate(data_loader_val,
model,
device,
amp=args.amp,
choices=choices,
mode=args.mode,
retrain_config=retrain_config)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
**{f'test_{k}': v
for k, v in test_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main(args):
utils.init_distributed_mode(args)
# disable any harsh augmentation in case of Self-supervise training
if args.training_mode == 'SSL':
print("NOTE: Smoothing, Mixup, CutMix, and AutoAugment will be disabled in case of Self-supervise training")
args.smoothing = args.reprob = args.reprob = args.recount = args.mixup = args.cutmix = 0.0
args.aa = ''
if args.SiT_LinearEvaluation == 1:
print("Warning: Linear Evaluation should be set to 0 during SSL training - changing SiT_LinearEvaluation to 0")
args.SiT_LinearEvaluation = 0
utils.print_args(args)
device = torch.device(args.device)
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
print("Loading dataset ....")
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
if args.repeated_aug:
sampler_train = RASampler(dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True)
else:
sampler_train = torch.utils.data.DistributedSampler(dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
data_loader_train = torch.utils.data.DataLoader(dataset_train, sampler=sampler_train,
batch_size=args.batch_size, num_workers=args.num_workers,
pin_memory=args.pin_mem, drop_last=True, collate_fn=collate_fn)
data_loader_val = torch.utils.data.DataLoader(dataset_val, sampler=sampler_val,
batch_size=int(1.5 * args.batch_size), num_workers=args.num_workers,
pin_memory=args.pin_mem, drop_last=False, collate_fn=collate_fn)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_fn = Mixup(
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
label_smoothing=args.smoothing, num_classes=args.nb_classes)
print(f"Creating model: {args.model}")
model = create_model(
args.model, pretrained=False, num_classes=args.nb_classes,
drop_rate=args.drop, drop_path_rate=args.drop_path, representation_size=args.representation_size,
drop_block_rate=None, training_mode=args.training_mode)
if args.finetune:
checkpoint = torch.load(args.finetune, map_location='cpu')
checkpoint_model = checkpoint['model']
state_dict = model.state_dict()
for k in ['rot_head.weight', 'rot_head.bias', 'contrastive_head.weight', 'contrastive_head.bias']:
if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
print(f"Removing key {k} from pretrained checkpoint")
del checkpoint_model[k]
# interpolate position embedding
pos_embed_checkpoint = checkpoint_model['pos_embed']
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = model.patch_embed.num_patches
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
new_size = int(num_patches ** 0.5)
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model['pos_embed'] = new_pos_embed
model.load_state_dict(checkpoint_model, strict=False)
model.to(device)
# Freeze the backbone in case of linear evaluation
if args.SiT_LinearEvaluation == 1:
requires_grad(model, False)
model.rot_head.weight.requires_grad = True
model.rot_head.bias.requires_grad = True
model.contrastive_head.weight.requires_grad = True
model.contrastive_head.bias.requires_grad = True
if args.representation_size is not None:
model.pre_logits_rot.fc.weight.requires_grad = True
model.pre_logits_rot.fc.bias.requires_grad = True
model.pre_logits_contrastive.fc.weight.requires_grad = True
model.pre_logits_contrastive.fc.bias.requires_grad = True
model_ema = None
if args.model_ema:
model_ema = ModelEma(model, decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '', resume='')
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('number of params:', n_parameters)
linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size() / 512.0
args.lr = linear_scaled_lr
optimizer = create_optimizer(args, model_without_ddp)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
if args.training_mode == 'SSL':
criterion = MTL_loss(args.device, args.batch_size)
elif args.training_mode == 'finetune' and args.mixup > 0.:
criterion = SoftTargetCrossEntropy()
else:
criterion = torch.nn.CrossEntropyLoss()
output_dir = Path(args.output_dir)
if args.resume:
checkpoint = torch.load(args.resume, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.model_ema:
utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
if args.eval:
test_stats = evaluate_SSL(data_loader_val, model, device)
print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
return
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
max_accuracy = 0.0
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
if args.training_mode == 'SSL':
train_stats = train_SSL(
model, criterion, data_loader_train, optimizer, device, epoch, loss_scaler,
args.clip_grad, model_ema, mixup_fn)
else:
train_stats = train_finetune(
model, criterion, data_loader_train, optimizer, device, epoch, loss_scaler,
args.clip_grad, model_ema, mixup_fn)
lr_scheduler.step(epoch)
if epoch%args.validate_every == 0:
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
for checkpoint_path in checkpoint_paths:
utils.save_on_master({
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'model_ema': get_state_dict(model_ema),
'scaler': loss_scaler.state_dict(),
'args': args,
}, checkpoint_path)
if args.training_mode == 'SSL':
test_stats = evaluate_SSL(data_loader_val, model, device, epoch, args.output_dir)
else:
test_stats = evaluate_finetune(data_loader_val, model, device)
print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()},
'epoch': epoch,
'n_parameters': n_parameters}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main():
args = parse_args()
hvd.init()
set_affinity(hvd.local_rank())
if is_main_process():
log("Running total processes: {}".format(get_world_size()))
log("Starting process: {}".format(get_rank()))
if is_main_process():
dllogger.init(backends=[dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
filename=args.json_summary),
dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
else:
dllogger.init(backends=[])
tf.random.set_seed(args.seed)
dllogger.log(step="PARAMETER", data={"SEED": args.seed})
# script parameters
BATCH_SIZE = args.train_batch_size
EVAL_BATCH_SIZE = args.predict_batch_size
USE_XLA = args.xla
USE_AMP = args.amp
EPOCHS = args.num_train_epochs
if not args.do_train:
EPOCHS = args.num_train_epochs = 1
log("Since running inference only, setting args.num_train_epochs to 1")
if not os.path.exists(args.output_dir) and is_main_process():
os.makedirs(args.output_dir)
# TensorFlow configuration
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')
tf.config.optimizer.set_jit(USE_XLA)
#tf.config.optimizer.set_experimental_options({"auto_mixed_precision": USE_AMP})
if args.amp:
policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16", loss_scale="dynamic")
tf.keras.mixed_precision.experimental.set_policy(policy)
print('Compute dtype: %s' % policy.compute_dtype) # Compute dtype: float16
print('Variable dtype: %s' % policy.variable_dtype) # Variable dtype: float32
if is_main_process():
log("***** Loading tokenizer and model *****")
# Load tokenizer and model from pretrained model/vocabulary. Specify the number of labels to classify (2+: classification, 1: regression)
electra_model = args.electra_model
config = ElectraConfig.from_pretrained(electra_model, cache_dir=args.cache_dir)
config.update({"amp": args.amp})
if args.vocab_file is None:
tokenizer = ElectraTokenizer.from_pretrained(electra_model, cache_dir=args.cache_dir)
else:
tokenizer = ElectraTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case)
model = TFElectraForQuestionAnswering.from_pretrained(electra_model, config=config, cache_dir=args.cache_dir, args=args)
if is_main_process():
log("***** Loading dataset *****")
# Load data
processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
train_examples = processor.get_train_examples(args.data_dir) if args.do_train else None
dev_examples = processor.get_dev_examples(args.data_dir) if args.do_predict else None
if is_main_process():
log("***** Loading features *****")
# Load cached features
squad_version = '2.0' if args.version_2_with_negative else '1.1'
if args.cache_dir is None:
args.cache_dir = args.data_dir
cached_train_features_file = args.cache_dir.rstrip('/') + '/' + 'TF2_train-v{4}.json_{1}_{2}_{3}'.format(
electra_model.split("/")[1], str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length), squad_version)
cached_dev_features_file = args.cache_dir.rstrip('/') + '/' + 'TF2_dev-v{4}.json_{1}_{2}_{3}'.format(
electra_model.split("/")[1], str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length), squad_version)
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader) if args.do_train else []
with open(cached_dev_features_file, "rb") as reader:
dev_features = pickle.load(reader) if args.do_predict else []
except:
train_features = ( # TODO: (yy) do on rank 0?
squad_convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True,
return_dataset="",
)
if args.do_train
else []
)
dev_features = (
squad_convert_examples_to_features(
examples=dev_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False,
return_dataset="",
)
if args.do_predict
else []
)
# Dump Cached features
if not args.skip_cache and is_main_process():
if args.do_train:
log("***** Building Cache Files: {} *****".format(cached_train_features_file))
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
if args.do_predict:
log("***** Building Cache Files: {} *****".format(cached_dev_features_file))
with open(cached_dev_features_file, "wb") as writer:
pickle.dump(dev_features, writer)
len_train_features = len(train_features)
total_train_steps = int((len_train_features * EPOCHS / BATCH_SIZE) / get_world_size()) + 1
train_steps_per_epoch = int((len_train_features / BATCH_SIZE) / get_world_size()) + 1
len_dev_features = len(dev_features)
total_dev_steps = int((len_dev_features / EVAL_BATCH_SIZE)) + 1
train_dataset = get_dataset_from_features(train_features, BATCH_SIZE,
v2=args.version_2_with_negative) if args.do_train else []
dev_dataset = get_dataset_from_features(dev_features, EVAL_BATCH_SIZE, drop_remainder=False, ngpu=1, mode="dev",
v2=args.version_2_with_negative) if args.do_predict else []
opt = create_optimizer(init_lr=args.learning_rate, num_train_steps=total_train_steps,
num_warmup_steps=int(args.warmup_proportion * total_train_steps),
weight_decay_rate=args.weight_decay_rate,
layerwise_lr_decay=args.layerwise_lr_decay,
n_transformer_layers=model.num_hidden_layers)
if USE_AMP:
# loss scaling is currently required when using mixed precision
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, "dynamic")
# Define loss function
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss_class = tf.keras.losses.BinaryCrossentropy(
from_logits=True,
name='binary_crossentropy'
)
metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
model.compile(optimizer=opt, loss=loss, metrics=[metric])
train_loss_results = []
if args.do_train and is_main_process():
log("***** Running training *****")
log(" Num examples = ", len_train_features)
log(" Num Epochs = ", args.num_train_epochs)
log(" Instantaneous batch size per GPU = ", args.train_batch_size)
log(
" Total train batch size (w. parallel, distributed & accumulation) = ",
args.train_batch_size
* get_world_size(),
)
log(" Total optimization steps =", total_train_steps)
total_train_time = 0
latency = []
for epoch in range(EPOCHS):
if args.do_train:
epoch_loss_avg = tf.keras.metrics.Mean()
epoch_perf_avg = tf.keras.metrics.Mean()
epoch_start = time.time()
epoch_iterator = tqdm(train_dataset, total=train_steps_per_epoch, desc="Iteration", mininterval=5,
disable=not is_main_process())
for iter, inputs in enumerate(epoch_iterator):
# breaking criterion if max_steps if > 1
if args.max_steps > 0 and (epoch * train_steps_per_epoch + iter) > args.max_steps:
break
iter_start = time.time()
# Optimize the model
loss_value = train_step(model, inputs, loss, USE_AMP, opt, (iter == 0 and epoch == 0),
v2=args.version_2_with_negative, loss_class=loss_class, fp16=USE_AMP)
epoch_perf_avg.update_state(1. * BATCH_SIZE / (time.time() - iter_start))
if iter % args.log_freq == 0:
if is_main_process():
log("\nEpoch: {:03d}, Step:{:6d}, Loss:{:12.8f}, Perf:{:5.0f}, loss_scale:{}, opt_step:{}".format(epoch, iter, loss_value,
epoch_perf_avg.result() * get_world_size(), opt.loss_scale if config.amp else 1,
int(opt.iterations)))
dllogger.log(step=(epoch, iter,), data={"step_loss": float(loss_value.numpy()),
"train_perf": float( epoch_perf_avg.result().numpy() * get_world_size())})
# Track progress
epoch_loss_avg.update_state(loss_value) # Add current batch loss
# End epoch
train_loss_results.append(epoch_loss_avg.result())
total_train_time += float(time.time() - epoch_start)
# Summarize and save checkpoint at the end of each epoch
if is_main_process():
dllogger.log(step=tuple(), data={"e2e_train_time": total_train_time,
"training_sequences_per_second": float(
epoch_perf_avg.result().numpy() * get_world_size()),
"final_loss": float(epoch_loss_avg.result().numpy())})
if not args.skip_checkpoint:
if args.ci:
checkpoint_name = "{}/electra_base_qa_v2_{}_epoch_{}_ckpt".format(args.output_dir, args.version_2_with_negative, epoch + 1)
else:
checkpoint_name = "checkpoints/electra_base_qa_v2_{}_epoch_{}_ckpt".format(args.version_2_with_negative, epoch + 1)
if is_main_process():
model.save_weights(checkpoint_name)
if args.do_predict and (args.evaluate_during_training or epoch == args.num_train_epochs - 1):
if not args.do_train:
log("***** Loading checkpoint: {} *****".format(args.init_checkpoint))
model.load_weights(args.init_checkpoint).expect_partial()
current_feature_id = 0
all_results = []
if is_main_process():
log("***** Running evaluation *****")
log(" Num Batches = ", total_dev_steps)
log(" Batch size = ", args.predict_batch_size)
raw_infer_start = time.time()
if is_main_process():
infer_perf_avg = tf.keras.metrics.Mean()
dev_iterator = tqdm(dev_dataset, total=total_dev_steps, desc="Iteration", mininterval=5,
disable=not is_main_process())
for input_ids, input_mask, segment_ids, start_positions, end_positions, cls_index, p_mask, is_impossible in dev_iterator:
# training=False is needed only if there are layers with different
# behavior during training versus inference (e.g. Dropout).
iter_start = time.time()
if not args.joint_head:
batch_start_logits, batch_end_logits = infer_step(model, input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
)[:2]
#Synchronize with GPU to compute time
_ = batch_start_logits.numpy()
else:
outputs = infer_step(model, input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
cls_index=cls_index,
p_mask=p_mask,
)
#Synchronize with GPU to compute time
_ = outputs[0].numpy()
infer_time = (time.time() - iter_start)
infer_perf_avg.update_state(1. * EVAL_BATCH_SIZE / infer_time)
latency.append(infer_time)
for iter_ in range(input_ids.shape[0]):
if not args.joint_head:
start_logits = batch_start_logits[iter_].numpy().tolist()
end_logits = batch_end_logits[iter_].numpy().tolist()
dev_feature = dev_features[current_feature_id]
current_feature_id += 1
unique_id = int(dev_feature.unique_id)
all_results.append(RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
else:
dev_feature = dev_features[current_feature_id]
current_feature_id += 1
unique_id = int(dev_feature.unique_id)
output = [output[iter_].numpy().tolist() for output in outputs]
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
all_results.append(result)
# Compute and save predictions
answers, nbest_answers = get_answers(dev_examples, dev_features, all_results, args)
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
e2e_infer_time = time.time() - raw_infer_start
# if args.version_2_with_negative:
# output_null_log_odds_file = os.path.join(args.output_dir, "null_odds.json")
# else:
# output_null_log_odds_file = None
with open(output_prediction_file, "w") as f:
f.write(json.dumps(answers, indent=4) + "\n")
with open(output_nbest_file, "w") as f:
f.write(json.dumps(nbest_answers, indent=4) + "\n")
if args.do_eval:
if args.version_2_with_negative:
dev_file = "dev-v2.0.json"
else:
dev_file = "dev-v1.1.json"
eval_out = subprocess.check_output([sys.executable, args.eval_script,
args.data_dir + "/" + dev_file, output_prediction_file])
log(eval_out.decode('UTF-8'))
scores = str(eval_out).strip()
exact_match = float(scores.split(":")[1].split(",")[0])
if args.version_2_with_negative:
f1 = float(scores.split(":")[2].split(",")[0])
else:
f1 = float(scores.split(":")[2].split("}")[0])
log("Epoch: {:03d} Results: {}".format(epoch, eval_out.decode('UTF-8')))
log("**EVAL SUMMARY** - Epoch: {:03d}, EM: {:6.3f}, F1: {:6.3f}, Infer_Perf: {:4.0f} seq/s"
.format(epoch, exact_match, f1, infer_perf_avg.result()))
latency_all = sorted(latency)[:-2]
log(
"**LATENCY SUMMARY** - Epoch: {:03d}, Ave: {:6.3f} ms, 90%: {:6.3f} ms, 95%: {:6.3f} ms, 99%: {:6.3f} ms"
.format(epoch, sum(latency_all) / len(latency_all) * 1000,
sum(latency_all[:int(len(latency_all) * 0.9)]) / int(len(latency_all) * 0.9) * 1000,
sum(latency_all[:int(len(latency_all) * 0.95)]) / int(len(latency_all) * 0.95) * 1000,
sum(latency_all[:int(len(latency_all) * 0.99)]) / int(len(latency_all) * 0.99) * 1000,
))
dllogger.log(step=tuple(),
data={"inference_sequences_per_second": float(infer_perf_avg.result().numpy()),
"e2e_inference_time": e2e_infer_time})
if is_main_process() and args.do_train and args.do_eval:
log(
"**RESULTS SUMMARY** - EM: {:6.3f}, F1: {:6.3f}, Train_Time: {:4.0f} s, Train_Perf: {:4.0f} seq/s, Infer_Perf: {:4.0f} seq/s"
.format(exact_match, f1, total_train_time, epoch_perf_avg.result() * get_world_size(),
infer_perf_avg.result()))
dllogger.log(step=tuple(), data={"exact_match": exact_match, "F1": f1})
def main(args, ds_init):
utils.init_distributed_mode(args)
if ds_init is not None:
utils.create_ds_config(args)
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
if args.disable_eval_during_finetuning:
dataset_val = None
else:
dataset_val, _ = build_dataset(is_train=False, args=args)
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
print("Sampler_train = %s" % str(sampler_train))
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print(
'Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val,
num_replicas=num_tasks,
rank=global_rank,
shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
if global_rank == 0 and args.log_dir is not None:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
else:
log_writer = None
data_loader_train = torch.utils.data.DataLoader(
dataset_train,
sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
if dataset_val is not None:
data_loader_val = torch.utils.data.DataLoader(
dataset_val,
sampler=sampler_val,
batch_size=int(1.5 * args.batch_size),
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False)
else:
data_loader_val = None
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
print("Mixup is activated!")
mixup_fn = Mixup(mixup_alpha=args.mixup,
cutmix_alpha=args.cutmix,
cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob,
mode=args.mixup_mode,
label_smoothing=args.smoothing,
num_classes=args.nb_classes)
model = create_model(
args.model,
pretrained=False,
num_classes=args.nb_classes,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
attn_drop_rate=args.attn_drop_rate,
drop_block_rate=None,
use_mean_pooling=args.use_mean_pooling,
init_scale=args.init_scale,
use_rel_pos_bias=args.rel_pos_bias,
use_abs_pos_emb=args.abs_pos_emb,
init_values=args.layer_scale_init_value,
)
patch_size = model.patch_embed.patch_size
print("Patch size = %s" % str(patch_size))
args.window_size = (args.input_size // patch_size[0],
args.input_size // patch_size[1])
args.patch_size = patch_size
if args.finetune:
if args.finetune.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.finetune,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.finetune, map_location='cpu')
print("Load ckpt from %s" % args.finetune)
checkpoint_model = None
for model_key in args.model_key.split('|'):
if model_key in checkpoint:
checkpoint_model = checkpoint[model_key]
print("Load state_dict by model_key = %s" % model_key)
break
if checkpoint_model is None:
checkpoint_model = checkpoint
state_dict = model.state_dict()
for k in ['head.weight', 'head.bias']:
if k in checkpoint_model and checkpoint_model[
k].shape != state_dict[k].shape:
print(f"Removing key {k} from pretrained checkpoint")
del checkpoint_model[k]
if model.use_rel_pos_bias and "rel_pos_bias.relative_position_bias_table" in checkpoint_model:
print(
"Expand the shared relative position embedding to each transformer block. "
)
num_layers = model.get_num_layers()
rel_pos_bias = checkpoint_model[
"rel_pos_bias.relative_position_bias_table"]
for i in range(num_layers):
checkpoint_model["blocks.%d.attn.relative_position_bias_table"
% i] = rel_pos_bias.clone()
checkpoint_model.pop("rel_pos_bias.relative_position_bias_table")
all_keys = list(checkpoint_model.keys())
for key in all_keys:
if "relative_position_index" in key:
checkpoint_model.pop(key)
if "relative_position_bias_table" in key:
rel_pos_bias = checkpoint_model[key]
src_num_pos, num_attn_heads = rel_pos_bias.size()
dst_num_pos, _ = model.state_dict()[key].size()
dst_patch_shape = model.patch_embed.patch_shape
if dst_patch_shape[0] != dst_patch_shape[1]:
raise NotImplementedError()
num_extra_tokens = dst_num_pos - (
dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1)
src_size = int((src_num_pos - num_extra_tokens)**0.5)
dst_size = int((dst_num_pos - num_extra_tokens)**0.5)
if src_size != dst_size:
print("Position interpolate for %s from %dx%d to %dx%d" %
(key, src_size, src_size, dst_size, dst_size))
extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
def geometric_progression(a, r, n):
return a * (1.0 - r**n) / (1.0 - r)
left, right = 1.01, 1.5
while right - left > 1e-6:
q = (left + right) / 2.0
gp = geometric_progression(1, q, src_size // 2)
if gp > dst_size // 2:
right = q
else:
left = q
# if q > 1.090307:
# q = 1.090307
dis = []
cur = 1
for i in range(src_size // 2):
dis.append(cur)
cur += q**(i + 1)
r_ids = [-_ for _ in reversed(dis)]
x = r_ids + [0] + dis
y = r_ids + [0] + dis
t = dst_size // 2.0
dx = np.arange(-t, t + 0.1, 1.0)
dy = np.arange(-t, t + 0.1, 1.0)
print("Original positions = %s" % str(x))
print("Target positions = %s" % str(dx))
all_rel_pos_bias = []
for i in range(num_attn_heads):
z = rel_pos_bias[:, i].view(src_size,
src_size).float().numpy()
f = interpolate.interp2d(x, y, z, kind='cubic')
all_rel_pos_bias.append(
torch.Tensor(f(dx, dy)).contiguous().view(
-1, 1).to(rel_pos_bias.device))
rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens),
dim=0)
checkpoint_model[key] = new_rel_pos_bias
# interpolate position embedding
if 'pos_embed' in checkpoint_model:
pos_embed_checkpoint = checkpoint_model['pos_embed']
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = model.patch_embed.num_patches
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
# height (== width) for the checkpoint position embedding
orig_size = int(
(pos_embed_checkpoint.shape[-2] - num_extra_tokens)**0.5)
# height (== width) for the new position embedding
new_size = int(num_patches**0.5)
# class_token and dist_token are kept unchanged
if orig_size != new_size:
print("Position interpolate from %dx%d to %dx%d" %
(orig_size, orig_size, new_size, new_size))
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size,
embedding_size).permute(
0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens,
size=(new_size, new_size),
mode='bicubic',
align_corners=False)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model['pos_embed'] = new_pos_embed
utils.load_state_dict(model,
checkpoint_model,
prefix=args.model_prefix)
# model.load_state_dict(checkpoint_model, strict=False)
model.to(device)
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEma(model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume='')
print("Using EMA with decay = %.8f" % args.model_ema_decay)
model_without_ddp = model
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Model = %s" % str(model_without_ddp))
print('number of params:', n_parameters)
total_batch_size = args.batch_size * args.update_freq * utils.get_world_size(
)
num_training_steps_per_epoch = len(dataset_train) // total_batch_size
print("LR = %.8f" % args.lr)
print("Batch size = %d" % total_batch_size)
print("Update frequent = %d" % args.update_freq)
print("Number of training examples = %d" % len(dataset_train))
print("Number of training training per epoch = %d" %
num_training_steps_per_epoch)
num_layers = model_without_ddp.get_num_layers()
if args.layer_decay < 1.0:
assigner = LayerDecayValueAssigner(
list(args.layer_decay**(num_layers + 1 - i)
for i in range(num_layers + 2)))
else:
assigner = None
if assigner is not None:
print("Assigned values = %s" % str(assigner.values))
skip_weight_decay_list = model.no_weight_decay()
if args.disable_weight_decay_on_rel_pos_bias:
for i in range(num_layers):
skip_weight_decay_list.add(
"blocks.%d.attn.relative_position_bias_table" % i)
if args.enable_deepspeed:
loss_scaler = None
optimizer_params = get_parameter_groups(
model, args.weight_decay, skip_weight_decay_list,
assigner.get_layer_id if assigner is not None else None,
assigner.get_scale if assigner is not None else None)
model, optimizer, _, _ = ds_init(
args=args,
model=model,
model_parameters=optimizer_params,
dist_init_required=not args.distributed,
)
print("model.gradient_accumulation_steps() = %d" %
model.gradient_accumulation_steps())
assert model.gradient_accumulation_steps() == args.update_freq
else:
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
optimizer = create_optimizer(args,
model_without_ddp,
skip_list=skip_weight_decay_list,
get_num_layer=assigner.get_layer_id
if assigner is not None else None,
get_layer_scale=assigner.get_scale
if assigner is not None else None)
loss_scaler = NativeScaler()
print("Use step level LR scheduler!")
lr_schedule_values = utils.cosine_scheduler(
args.lr,
args.min_lr,
args.epochs,
num_training_steps_per_epoch,
warmup_epochs=args.warmup_epochs,
warmup_steps=args.warmup_steps,
)
if args.weight_decay_end is None:
args.weight_decay_end = args.weight_decay
wd_schedule_values = utils.cosine_scheduler(args.weight_decay,
args.weight_decay_end,
args.epochs,
num_training_steps_per_epoch)
print("Max WD = %.7f, Min WD = %.7f" %
(max(wd_schedule_values), min(wd_schedule_values)))
if mixup_fn is not None:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing > 0.:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
print("criterion = %s" % str(criterion))
utils.auto_load_model(args=args,
model=model,
model_without_ddp=model_without_ddp,
optimizer=optimizer,
loss_scaler=loss_scaler,
model_ema=model_ema)
if args.eval:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
exit(0)
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
max_accuracy = 0.0
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
if log_writer is not None:
log_writer.set_step(epoch * num_training_steps_per_epoch *
args.update_freq)
train_stats = train_one_epoch(
model,
criterion,
data_loader_train,
optimizer,
device,
epoch,
loss_scaler,
args.clip_grad,
model_ema,
mixup_fn,
log_writer=log_writer,
start_steps=epoch * num_training_steps_per_epoch,
lr_schedule_values=lr_schedule_values,
wd_schedule_values=wd_schedule_values,
num_training_steps_per_epoch=num_training_steps_per_epoch,
update_freq=args.update_freq,
)
if args.output_dir and args.save_ckpt:
if (epoch +
1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
utils.save_model(args=args,
model=model,
model_without_ddp=model_without_ddp,
optimizer=optimizer,
loss_scaler=loss_scaler,
epoch=epoch,
model_ema=model_ema)
if data_loader_val is not None:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
if max_accuracy < test_stats["acc1"]:
max_accuracy = test_stats["acc1"]
if args.output_dir and args.save_ckpt:
utils.save_model(args=args,
model=model,
model_without_ddp=model_without_ddp,
optimizer=optimizer,
loss_scaler=loss_scaler,
epoch="best",
model_ema=model_ema)
print(f'Max accuracy: {max_accuracy:.2f}%')
if log_writer is not None:
log_writer.update(test_acc1=test_stats['acc1'],
head="perf",
step=epoch)
log_writer.update(test_acc5=test_stats['acc5'],
head="perf",
step=epoch)
log_writer.update(test_loss=test_stats['loss'],
head="perf",
step=epoch)
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
**{f'test_{k}': v
for k, v in test_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
else:
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
# **{f'test_{k}': v for k, v in test_stats.items()},
'epoch': epoch,
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
if log_writer is not None:
log_writer.flush()
with open(os.path.join(args.output_dir, "log.txt"),
mode="a",
encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main(args):
utils.init_distributed_mode(args)
print(args)
if args.distillation_type != 'none' and args.finetune and not args.eval:
raise NotImplementedError(
"Finetuning with distillation not yet supported")
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
# random.seed(seed)
cudnn.benchmark = True
dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
dataset_val, _ = build_dataset(is_train=False, args=args)
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
if args.repeated_aug:
sampler_train = RASampler(dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
else:
sampler_train = torch.utils.data.DistributedSampler(
dataset_train,
num_replicas=num_tasks,
rank=global_rank,
shuffle=True)
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print(
'Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val,
num_replicas=num_tasks,
rank=global_rank,
shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
data_loader_train = torch.utils.data.DataLoader(
dataset_train,
sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
data_loader_val = torch.utils.data.DataLoader(dataset_val,
sampler=sampler_val,
batch_size=int(
1.5 * args.batch_size),
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
mixup_fn = Mixup(mixup_alpha=args.mixup,
cutmix_alpha=args.cutmix,
cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob,
switch_prob=args.mixup_switch_prob,
mode=args.mixup_mode,
label_smoothing=args.smoothing,
num_classes=args.nb_classes)
print(f"Creating model: {args.model}")
model = create_model(
args.model,
pretrained=False,
num_classes=args.nb_classes,
drop_rate=args.drop,
drop_path_rate=args.drop_path,
drop_block_rate=None,
)
if args.finetune:
if args.finetune.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.finetune,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.finetune, map_location='cpu')
checkpoint_model = checkpoint['model']
state_dict = model.state_dict()
for k in [
'head.weight', 'head.bias', 'head_dist.weight',
'head_dist.bias'
]:
if k in checkpoint_model and checkpoint_model[
k].shape != state_dict[k].shape:
print(f"Removing key {k} from pretrained checkpoint")
del checkpoint_model[k]
# interpolate position embedding
pos_embed_checkpoint = checkpoint_model['pos_embed']
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = model.patch_embed.num_patches
num_extra_tokens = model.pos_embed.shape[-2] - num_patches
# height (== width) for the checkpoint position embedding
orig_size = int(
(pos_embed_checkpoint.shape[-2] - num_extra_tokens)**0.5)
# height (== width) for the new position embedding
new_size = int(num_patches**0.5)
# class_token and dist_token are kept unchanged
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size,
embedding_size).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(pos_tokens,
size=(new_size, new_size),
mode='bicubic',
align_corners=False)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
checkpoint_model['pos_embed'] = new_pos_embed
model.load_state_dict(checkpoint_model, strict=False)
model.to(device)
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEma(model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume='')
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size(
) / 512.0
args.lr = linear_scaled_lr
optimizer = create_optimizer(args, model_without_ddp)
loss_scaler = NativeScaler()
lr_scheduler, _ = create_scheduler(args, optimizer)
criterion = LabelSmoothingCrossEntropy()
if args.mixup > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
teacher_model = None
if args.distillation_type != 'none':
assert args.teacher_path, 'need to specify teacher-path when using distillation'
print(f"Creating teacher model: {args.teacher_model}")
teacher_model = create_model(
args.teacher_model,
pretrained=False,
num_classes=args.nb_classes,
global_pool='avg',
)
if args.teacher_path.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.teacher_path,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.teacher_path, map_location='cpu')
teacher_model.load_state_dict(checkpoint['model'])
teacher_model.to(device)
teacher_model.eval()
# wrap the criterion in our custom DistillationLoss, which
# just dispatches to the original criterion if args.distillation_type is 'none'
criterion = DistillationLoss(criterion, teacher_model,
args.distillation_type,
args.distillation_alpha,
args.distillation_tau)
output_dir = Path(args.output_dir)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.model_ema:
utils._load_checkpoint_for_ema(model_ema,
checkpoint['model_ema'])
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
if args.eval:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
return
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
max_accuracy = 0.0
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
train_stats = train_one_epoch(
model,
criterion,
data_loader_train,
optimizer,
device,
epoch,
loss_scaler,
args.clip_grad,
model_ema,
mixup_fn,
set_training_mode=args.finetune ==
'' # keep in eval mode during finetuning
)
lr_scheduler.step(epoch)
if args.output_dir:
checkpoint_paths = [output_dir / ('checkpoint_%04d.pth' % (epoch))]
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'model_ema': get_state_dict(model_ema),
'scaler': loss_scaler.state_dict(),
'args': args,
}, checkpoint_path)
if not args.train_without_eval:
test_stats = evaluate(data_loader_val, model, device)
print(
f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
)
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()},
**{f'test_{k}': v
for k, v in test_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
else:
log_stats = {
**{f'train_{k}': v
for k, v in train_stats.items()}, 'epoch': epoch,
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main():
global timeout_sent
args = parse_arguments()
random.seed(args.seed + args.local_rank)
np.random.seed(args.seed + args.local_rank)
torch.manual_seed(args.seed + args.local_rank)
torch.cuda.manual_seed(args.seed + args.local_rank)
worker_init = WorkerInitObj(args.seed + args.local_rank)
device, args = setup_training(args)
# Prepare optimizer
(
model,
optimizer,
lr_scheduler,
checkpoint,
global_step,
criterion,
) = prepare_model_and_optimizer(args, device)
raw_train_start = None
most_recent_ckpts_paths = []
average_loss = 0.0 # averaged loss every args.log_freq steps
epoch = 0
training_steps = 0
test_losses = []
pool = ProcessPoolExecutor(1)
# Note: We loop infinitely over epochs, termination is handled via iteration count
while True:
thread = None
restored_data_loader = None
if (not args.resume_from_checkpoint or epoch > 0
or (args.phase2 and global_step < 1) or args.init_checkpoint):
files = [
os.path.join(args.input_dir, f)
for f in os.listdir(args.input_dir)
if os.path.isfile(os.path.join(args.input_dir, f))
and "training" in f
]
files.sort()
num_files = len(files)
random.Random(args.seed + epoch).shuffle(files)
f_start_id = 0
else:
f_start_id = checkpoint["files"][0]
files = checkpoint["files"][1:]
args.resume_from_checkpoint = False
num_files = len(files)
# may not exist in all checkpoints
epoch = checkpoint.get("epoch", 0)
restored_dataloader = checkpoint.get("data_loader", None)
shared_file_list = {}
if smp.is_initialized():
dpsize = smp.dp_size()
dprank = smp.dp_rank()
elif torch.distributed.is_initialized():
dpsize = get_world_size()
dprank = get_rank()
else:
dpsize = 1
dprank = 0
dparallel = dpsize > 1
if dparallel and dpsize > num_files:
remainder = dpsize % num_files
data_file = files[(f_start_id * dpsize + dprank +
remainder * f_start_id) % num_files]
else:
data_file = files[(f_start_id * dpsize + dprank) % num_files]
previous_file = data_file
if restored_data_loader is None:
train_data = pretraining_dataset(data_file,
args.max_predictions_per_seq)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(
train_data,
sampler=train_sampler,
batch_size=args.train_batch_size * args.n_gpu,
num_workers=4,
worker_init_fn=worker_init,
pin_memory=True,
drop_last=True,
)
# shared_file_list["0"] = (train_dataloader, data_file)
else:
train_dataloader = restored_data_loader
restored_data_loader = None
overflow_buf = None
if args.allreduce_post_accumulation:
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1, len(files)):
if get_world_size() > num_files:
data_file = files[(f_id * get_world_size() + get_rank() +
remainder * f_id) % num_files]
else:
data_file = files[(f_id * get_world_size() + get_rank()) %
num_files]
previous_file = data_file
dataset_future = pool.submit(
create_pretraining_dataset,
data_file,
args.max_predictions_per_seq,
shared_file_list,
args,
worker_init,
)
train_iter = (tqdm(train_dataloader,
desc="Iteration",
disable=args.disable_progress_bar)
if is_main_process() else train_dataloader)
if raw_train_start is None:
raw_train_start = time.time()
for step, batch in enumerate(train_iter):
training_steps += 1
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch
if args.do_train:
from smdistributed.modelparallel.test.torch.utils import dump_model, verify
model.train()
if args.smp > 0:
loss_mbs = smp_step(
args,
device,
input_ids,
segment_ids,
input_mask,
masked_lm_labels,
next_sentence_labels,
model,
optimizer,
criterion,
step,
)
loss = loss_mbs.reduce_mean()
if smp.rank() == 0:
print("Loss:", loss.item())
else:
loss = train_step(
args,
device,
input_ids,
segment_ids,
input_mask,
masked_lm_labels,
next_sentence_labels,
model,
optimizer,
criterion,
step,
)
divisor = 1
average_loss += loss.item()
if training_steps % args.gradient_accumulation_steps == 0:
lr_scheduler.step() # learning rate warmup
global_step = take_optimizer_step(
args, optimizer, model, overflow_buf, global_step)
if global_step >= args.steps_this_run or timeout_sent:
train_time_raw = time.time() - raw_train_start
last_num_steps = (int(
training_steps / args.gradient_accumulation_steps)
% args.log_freq)
last_num_steps = args.log_freq if last_num_steps == 0 else last_num_steps
average_loss = torch.tensor(
average_loss, dtype=torch.float32).cuda()
average_loss = average_loss / (last_num_steps *
divisor)
if torch.distributed.is_initialized():
average_loss /= get_world_size()
torch.distributed.all_reduce(average_loss)
final_loss = loss.item()
elif training_steps % (
args.log_freq *
args.gradient_accumulation_steps) == 0:
average_loss = 0
if (global_step >= args.steps_this_run or training_steps %
(args.num_steps_per_checkpoint *
args.gradient_accumulation_steps) == 0
or timeout_sent):
if smp.dp_rank() == 0 and not args.skip_checkpoint:
if args.resume_step < 0 or not args.phase2:
output_save_file = os.path.join(
args.output_dir,
"ckpt_{}.pt".format(global_step))
else:
output_save_file = os.path.join(
args.output_dir,
"ckpt_{}.pt".format(global_step +
args.phase1_end_step),
)
if args.do_train:
save_dict = {
"model":
model.local_state_dict(),
"optimizer":
optimizer.local_state_dict(),
"files": [f_id] + files,
"epoch":
epoch,
"data_loader":
None if global_step >= args.steps_this_run
else train_dataloader,
}
if args.fp16:
save_dict["master params"] = list(
amp.master_params(optimizer))
# SMP: Checkpoint mp_rank specific state
smp.save(save_dict,
output_save_file,
partial=True)
most_recent_ckpts_paths.append(
output_save_file)
if len(most_recent_ckpts_paths) > 3 and (
args.smp == 0 or smp.dp_rank() == 0):
ckpt_to_be_removed = most_recent_ckpts_paths.pop(
0)
os.remove(ckpt_to_be_removed +
f"_{smp.mp_rank()}")
# Exiting the training due to hitting max steps, or being sent a
# timeout from the cluster scheduler
if global_step >= args.steps_this_run or timeout_sent:
del train_dataloader
# thread.join()
if smp.dp_rank() == 0 and args.save_full:
output_save_file = os.path.join(
args.output_dir,
"ckpt_{}.pt".format(global_step))
save_dict = {
"model":
model.local_state_dict(),
"optimizer":
optimizer.local_state_dict(),
"files": [f_id] + files,
"epoch":
epoch,
"data_loader":
None if global_step >= args.steps_this_run
else train_dataloader,
}
if args.fp16:
save_dict["master params"] = list(
amp.master_params(optimizer))
# SMP: Save a single checkpoint containing entire model parameters
smp.save(save_dict,
output_save_file,
partial=False)
smp.barrier()
if smp.local_rank() == 0:
print(f"Start syncing model checkpoints to s3")
base_s3_path = os.path.dirname(
os.path.dirname(
os.getenv("SM_MODULE_DIR", "")))
curr_host = os.getenv("SM_CURRENT_HOST")
full_s3_path = f"{base_s3_path}/checkpoints/{curr_host}/"
sync_local_checkpoints_to_s3(
local_path=args.output_dir,
s3_path=full_s3_path)
print(
f"Finished syncing model checkpoints to s3"
)
return args, final_loss, train_time_raw, global_step
else:
model.eval()
with torch.no_grad():
loss = test_step(
args,
device,
input_ids,
segment_ids,
input_mask,
masked_lm_labels,
next_sentence_labels,
model,
criterion,
step,
)
print(f"global_step {global_step} Test Loss:", loss)
test_losses.append(loss)
global_step += 1
if global_step >= args.steps_this_run:
return sum(test_losses) / len(test_losses)
del train_dataloader
# thread.join()
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(timeout=None)
epoch += 1