def save(
output_save_file,
model,
optimizer,
lr_scheduler,
model_config,
num_params,
total_steps,
curr_train_path_index,
args,
partial=True,
translate_to_hf=False,
seq_length=1024,
batch_idx=0,
):
save_fn = save_fp16_optimizer_megatron if args.megatron else save_fp16_optimizer
save_dict = {
"cli_args": args.__dict__,
"num_params": num_params,
"total_steps": total_steps,
"curr_train_path_index": curr_train_path_index,
"model_config": model_config,
"batch_idx": batch_idx,
}
if lr_scheduler is not None:
save_dict["lr_scheduler"] = lr_scheduler.state_dict()
if partial:
save_dict["model"] = model.local_state_dict()
else:
model_state_dict = model.state_dict(gather_to_rank0=True)
if smp.rank() == 0:
save_dict["model"] = (
translate_state_dict_to_hf_gpt2(model_state_dict, seq_length)
if translate_to_hf
else model_state_dict
)
if args.fp16:
if not partial and args.skip_full_optimizer:
print("Skipping saving the final optimizer state")
else:
if args.shard_optimizer_state == 0 or partial:
save_dict["optimizer"] = save_fn(args, model, optimizer, partial=partial)
else:
print("Saving the full optimizer state does not work with shard_optimizer_state > 0! Skipping...")
else:
# fp32
if partial:
save_dict["optimizer"] = optimizer.local_state_dict()
else:
if not args.skip_full_optimizer:
save_dict["optimizer"] = optimizer.state_dict()
else:
print("Skipping saving of full optimizer state")
if (smp.rdp_rank() == 0 and partial) or smp.rank() == 0:
smp.save(save_dict, output_save_file, partial=partial)
print(f"Finished checkpointing after {total_steps} steps: {output_save_file}")
def _check_and_set(self, cls_value, sd_value, name):
"""Auxiliary function for checking the values in the checkpoint and
setting them."""
if self.override_lr_scheduler:
if smp.rank() == 0:
print('Overriding {} value to {}'.format(name, cls_value))
return cls_value
if not self.use_checkpoint_lr_scheduler:
assert cls_value == sd_value, 'AnnealingLR: class input value' \
'and checkpoint values for {} do not match'.format(name)
if smp.rank() == 0:
print(' > using checkpoint value {} for {}'.format(sd_value, name))
return sd_value
def dist_setting(args):
# args.data_parallel = False
print("args.data_parallel : {}".format(args.data_parallel))
print("args.model_parallel : {}".format(args.model_parallel))
print("args.apex : {}".format(args.apex))
args.world_size = 1
args.host_num = args.hosts.index(args.current_host)
if args.data_parallel:
args.world_size = sdp.get_world_size()
args.rank = sdp.get_rank() # total rank in all hosts
args.local_rank = sdp.get_local_rank() # rank per host
elif args.model_parallel:
args.world_size = smp.size()
args.local_rank = smp.local_rank() # rank per host
args.rank = smp.rank()
args.dp_size = smp.dp_size()
args.dp_rank = smp.dp_rank()
print(
"smp.rank() : {}, smp.size() : {}, smp.mp_rank() : {}, smp.local_size() : {}, smp.get_mp_group() : {}, smp.get_dp_group() : {}, smp.local_rank() : {}, smp.dp_size() : {}, smp.dp_rank() : {}"
.format(smp.rank(), smp.size(), smp.mp_rank(), smp.local_size(),
smp.get_mp_group(), smp.get_dp_group(), smp.local_rank(),
smp.dp_size(), smp.dp_rank()))
else:
args.world_size = len(args.hosts) * args.num_gpus
if args.local_rank is not None:
args.rank = args.num_gpus * args.host_num + \
args.local_rank # total rank in all hosts
dist.init_process_group(backend=args.backend,
rank=args.rank,
world_size=args.world_size)
logger.info(
'Initialized the distributed environment: \'{}\' backend on {} nodes. '
.format(args.backend, dist.get_world_size()) +
'Current host rank is {}. Number of gpus: {}'.format(
dist.get_rank(), args.num_gpus))
print("**** [dist_setting] args.rank : {}".format(args.rank))
print("args.world_size : {}".format(args.world_size))
print("Use GPU: {} for training".format(args.local_rank))
args.lr = args.lr * float(args.world_size)
args.batch_size //= args.world_size // args.num_gpus
args.batch_size = max(args.batch_size, 1)
return args
def train(args, model, scaler, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
# SM Distributed: Move input tensors to the GPU ID used by the current process,
# based on the set_device call.
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
# Return value, loss_mb is a StepOutput object
_, loss_mb = train_step(args, model, scaler, data, target)
# SM Distributed: Average the loss across microbatches.
loss = loss_mb.reduce_mean()
if args.amp:
scaler.step(optimizer)
scaler.update()
else:
optimizer.step()
if smp.rank() == 0 and batch_idx % args.log_interval == 0:
print("Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
epoch,
batch_idx * len(data),
len(train_loader.dataset),
100.0 * batch_idx / len(train_loader),
loss.item(),
))
if args.dry_run:
break
if args.num_batches and batch_idx + 1 == args.num_batches:
break
def initialize_smp(smp_args, training_args):
smp_config = {
"ddp": smp_args.ddp,
"pipeline_parallel_degree": smp_args.pipeline_parallel_degree,
"microbatches": smp_args.microbatches,
"shard_optimizer_state": smp_args.shard_optimizer_state > 0,
"prescaled_batch": smp_args.prescaled_batch > 0,
"_match_weights": smp_args.match_weights > 0,
"offload_activations": smp_args.offload_activations > 0,
"optimize": smp_args.optimize,
"auto_partition": True,
"default_partition": 0,
"static_mode": smp_args.static_mode > 0,
"fast_mode": smp_args.fast_mode > 0,
}
if smp_args.active_microbatches is not None:
smp_config["active_microbatches"] = smp_args.active_microbatches
smp.init(smp_config)
if smp.rank() == 0:
print("Arguments:", smp_args.__dict__)
print(f"Transformers version: {transformers.__version__}")
print(
f"smdistributed.modelparallel version: {smdistributed.modelparallel.__version__}"
)
print(f"smdistributed config: {smp_config}")
set_seed(training_args.seed)
def should_record():
# only record the ranks that in the tp group that contains global rank 0
if smp.tp_size() > 1:
tp_group = smp.get_tp_group()
return 0 in tp_group
else:
return smp.rank() == 0
def setup_training(args):
assert torch.cuda.is_available()
if args.smp > 0:
# Initialize SMP. The configuration is obtained from the parameters passed to
# the Sagemaker PyTorch estimator.
smp.init()
# SMP: Set the device to the GPU ID used by the current process.
# Input tensors should be transferred to this device.
torch.cuda.set_device(smp.local_rank())
device = torch.device("cuda", smp.local_rank())
args.n_gpu = 1
# if args.local_rank == -1:
# device = torch.device("cuda")
# args.n_gpu = torch.cuda.device_count()
# args.allreduce_post_accumulation = False
# args.allreduce_post_accumulation_fp16 = False
# else:
# torch.cuda.set_device(args.local_rank)
# device = torch.device("cuda", args.local_rank)
# # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
# torch.distributed.init_process_group(backend='nccl', init_method='env://')
# args.n_gpu = 1
if args.gradient_accumulation_steps == 1:
args.allreduce_post_accumulation = False
args.allreduce_post_accumulation_fp16 = False
print(
"device: {} n_gpu: {}, mp_rank: {}, rank: {}, distributed training: {}, 16-bits training: {}"
.format(device, args.n_gpu, smp.mp_rank(), smp.rank(),
bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
if args.train_batch_size % args.gradient_accumulation_steps != 0:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, batch size {} should be divisible"
.format(args.gradient_accumulation_steps, args.train_batch_size))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
if (not args.resume_from_checkpoint and os.path.exists(args.output_dir) and
(os.listdir(args.output_dir)
and any([i.startswith("ckpt")
for i in os.listdir(args.output_dir)]))):
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if (not args.resume_from_checkpoint
or not os.path.exists(args.output_dir)) and is_main_process():
os.makedirs(args.output_dir, exist_ok=True)
return device, args
def delete_oldest_ckpt(args, delete_on_rank0_only=False):
to_delete = smp.rank() == 0 if delete_on_rank0_only else smp.local_rank(
) == 0
if to_delete:
re_pattern = "trained_gpt_nparams-(?P<num_params>\d+)_steps-(?P<total_steps>\d+)\.pt"
# partial
re_pattern += "_(?P<pp_rank>\d+)_(?P<tp_rank>\d+)"
paths_per_step = collections.defaultdict(list)
for p in os.listdir(args.checkpoint_dir):
if re.match(re_pattern, p):
step = int(re.match(re_pattern, p).group("total_steps"))
path = os.path.join(args.checkpoint_dir, p)
paths_per_step[step].append(path)
if paths_per_step:
oldest_step = sorted(paths_per_step.keys())[0]
num_parts = len(paths_per_step[oldest_step])
if len(paths_per_step) >= args.num_kept_checkpoints:
# delete oldest step to save the new one
for p in paths_per_step[oldest_step]:
os.remove(p)
# else We still haven't reached maximum number of checkpoints -- no need to delete older ones
return None
def __init__(self, optimizer, start_lr,
warmup_iter, plateau_iter, total_iters,
decay_style, last_iter, min_lr=0.0,
use_checkpoint_lr_scheduler=True,
override_lr_scheduler=False):
# Class values.
self.optimizer = optimizer
self.start_lr = start_lr
self.min_lr = min_lr
self.warmup_iter = warmup_iter
self.plateau_iter = plateau_iter
self.num_iters = last_iter
self.end_iter = total_iters
assert self.end_iter > 0
self.decay_style = decay_style
self.override_lr_scheduler = override_lr_scheduler
self.use_checkpoint_lr_scheduler = use_checkpoint_lr_scheduler
if self.override_lr_scheduler:
assert not self.use_checkpoint_lr_scheduler, 'both override and '\
'use-checkpoint are set.'
# Set the learning rate
self.step(self.num_iters)
if smp.rank() == 0:
print('Learning rate decay style: {}'.format(self.decay_style))
def get_rank():
if not dist.is_available():
return 0
if not dist.is_initialized():
return 0
if smp.is_initialized():
return smp.rank()
return dist.get_rank()
def is_world_process_zero(self) -> bool:
"""
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be :obj:`True` for one process).
"""
if self.is_model_parallel_enabled:
return smp.rank() == 0 and smp.local_rank() == 0 and smp.mp_rank() == 0 and smp.dp_rank() == 0
else:
return super().is_world_process_zero()
def should_log(self):
"""
Whether or not the current process should produce log.
"""
if self.log_on_each_node:
return self.local_process_index == 0
else:
if is_sagemaker_mp_enabled():
return smp.rank() == 0
else:
return self.process_index == 0
def print_num_parameters(model):
seen = set()
num_params = 0
for p in model.parameters():
if p not in seen:
seen.add(p)
num_params += np.prod(p.size())
if smp.rank() == 0:
print(f"# total parameters: {num_params}")
return num_params
def smp_init(args):
args.deepspeed = False
cfg = {
"microbatches": args.num_microbatches,
"placement_strategy": args.placement_strategy,
"pipeline": args.pipeline,
"optimize": args.optimize,
"partitions": args.num_partitions,
"horovod": args.horovod,
"ddp": args.ddp,
}
smp.init(cfg)
args.rank = smp.dp_rank()
args.global_rank = smp.rank()
args.world_size = smp.size()
os.environ['RANK'] = str(args.rank)
os.environ['WORLD_SIZE'] = str(args.world_size)
os.environ['LOCAL_RANK'] = str(smp.local_rank())
# ## SMP_SKIP_GRAPH_VALIDATION=1
os.environ['SMP_SKIP_GRAPH_VALIDATION'] = "0"
# args.bpe_path = "/opt/ml/code/dalle_pytorch/data/bpe_simple_vocab_16e6.txt"
torch.cuda.set_device(smp.local_rank())
args.local_rank = smp.local_rank()
# if args.seed is not None:
# random.seed(args.seed)
# torch.manual_seed(args.seed+args.rank)
# np.random.seed(args.seed)
# torch.cuda.manual_seed_all(args.seed)
# cudnn.deterministic = True
# if cudnn.deterministic:
# warnings.warn('You have chosen to seed training. '
# 'This will turn on the CUDNN deterministic setting, '
# 'which can slow down your training considerably! '
# 'You may see unexpected behavior when restarting '
# 'from checkpoints.')
return args
def memory_status(msg="", reset_max=True, sync=True):
rank = smp.rank()
tp_rank = smp.tp_rank()
pp_rank = smp.pp_rank()
rdp_rank = smp.rdp_rank()
local_rank = smp.local_rank()
if sync:
torch.cuda.synchronize()
if rdp_rank != 0:
return
if py3nvml != None:
py3nvml.nvmlInit()
handle = py3nvml.nvmlDeviceGetHandleByIndex(local_rank)
info = py3nvml.nvmlDeviceGetMemoryInfo(handle)
total_used = info.used / 1024**3
total_used_str = f"Totally used GPU memory: {total_used}"
else:
total_used_str = ""
alloced = torch.cuda.memory_allocated(device=local_rank)
max_alloced = torch.cuda.max_memory_allocated(device=local_rank)
cached = torch.cuda.memory_reserved(device=local_rank)
max_cached = torch.cuda.max_memory_reserved(device=local_rank)
# convert to GB for printing
alloced /= 1024**3
cached /= 1024**3
max_alloced /= 1024**3
max_cached /= 1024**3
print(
f'[{msg}] rank {rank} tp_rank {tp_rank} pp_rank {pp_rank} TORCH {torch.__version__}',
f'device={local_rank} '
f'alloc {alloced:0.4f} max_alloced {max_alloced:0.4f} '
f'cache {cached:0.4f} max_cached {max_cached:0.4f} '
f'{total_used_str}')
if reset_max:
torch.cuda.reset_max_memory_cached()
torch.cuda.reset_max_memory_allocated()
if py3nvml != None:
py3nvml.nvmlShutdown()
def dist_setting(args):
# args.data_parallel = False
print(f"args.data_parallel : {args.data_parallel}, args.model_parallel : {args.model_parallel}, args.apex : {args.apex}")
args.world_size = 1
args.host_num = args.hosts.index(args.current_host)
if args.data_parallel:
sdp, DDP = _sdp_import(args)
args.world_size = sdp.get_world_size()
args.rank = sdp.get_rank() # total rank in all hosts
args.local_rank = sdp.get_local_rank() # rank per host
elif args.model_parallel:
args.world_size = smp.size()
args.world_size = args.num_gpus * len(args.hosts)
args.local_rank = smp.local_rank() # rank per host
args.rank = smp.rank()
args.dp_size = smp.dp_size()
args.dp_rank = smp.dp_rank()
else:
args.world_size = len(args.hosts) * args.num_gpus
if args.local_rank is not None:
args.rank = args.num_gpus * args.host_num + \
args.local_rank # total rank in all hosts
dist.init_process_group(backend=args.backend,
rank=args.rank,
world_size=args.world_size)
logger.info(
'Initialized the distributed environment: \'{}\' backend on {} nodes. '
.format(args.backend, dist.get_world_size()) +
'Current host rank is {}. Number of gpus: {}'.format(
dist.get_rank(), args.num_gpus))
# if not args.model_parallel:
args.lr = args.lr * float(args.world_size)
args.batch_size //= args.world_size // args.num_gpus
args.batch_size = max(args.batch_size, 1)
return args
def memory_status_cpu(msg=""):
import gc
global last_mem_usage
global base_mem_usage
rdp_rank = smp.rdp_rank()
gc.collect()
gc.collect()
gc.collect()
objects = gc.get_objects()
tensors = [
obj for obj in objects
if isinstance(obj, torch.Tensor) and not obj.is_cuda
]
torch_usage = 0
for t in tensors:
torch_usage += t.numel() * dtype_to_bit[t.dtype]
#total_usage = psutil.virtual_memory()[3] # This will get the total usage for all processes
current_usage = process.memory_info().data
total_usage = current_usage - base_mem_usage
usage_change = current_usage - last_mem_usage
last_mem_usage = current_usage
torch_usage /= 1024**3
total_usage /= 1024**3
usage_change /= 1024**3
base_usage = base_mem_usage / 1024**3
rank = smp.rank()
tp_rank = smp.tp_rank()
pp_rank = smp.pp_rank()
rdp_rank = smp.rdp_rank()
local_rank = smp.local_rank()
if rdp_rank != 0:
return
print(
f'[{msg}] rank {rank} tp_rank {tp_rank} pp_rank {pp_rank} TORCH {torch.__version__}',
f'device={local_rank} '
f'torch cpu tensor usage {torch_usage:0.4f} cpu mem usage {total_usage:0.4f} change since last measurement {usage_change:0.4f} base cpu mem usage {base_usage:0.4f}'
)
def train_smp(
self,
model,
optimizer,
lr_scheduler,
start_train_path_index,
start_batch_index,
num_params,
total_steps,
args,
prescaled_batch,
):
model.train()
dp_rank = smp.dp_rank() if not prescaled_batch else smp.rdp_rank()
dp_size = smp.dp_size() if not prescaled_batch else smp.rdp_size()
start = time.time()
throughput = None
to_save = {"loss": [], "val_loss": []}
loss_metric = 0
def should_record():
# only record the ranks that in the tp group that contains global rank 0
if smp.tp_size() > 1:
tp_group = smp.get_tp_group()
return 0 in tp_group
else:
return smp.rank() == 0
# Set the same seed for computation
set_seed(args.seed)
sampler = torch.utils.data.DistributedSampler(
self.train_dataset,
shuffle=True,
seed=args.seed,
rank=dp_rank,
num_replicas=dp_size,
drop_last=True,
)
train_dataloader = torch.utils.data.DataLoader(
self.train_dataset,
sampler=sampler,
batch_size=args.per_device_train_batch_size,
collate_fn=self.data_collator,
num_workers=0,
pin_memory=True,
drop_last=True,
)
total_steps = 0
for batch_idx, input_data in enumerate(train_dataloader):
step_start = time.time()
optimizer.zero_grad(set_to_none=True)
input_ids = input_data["input_ids"]
attention_mask = input_data["attention_mask"]
loss_mb = self.train_step(model, optimizer, input_ids,
attention_mask, args)
loss = loss_mb.reduce_mean()
lr_scheduler.step()
total_steps += 1
total_steps += 1
time_elapsed = time.time() - start
step_time = time.time() - step_start
if smp.rank() == 0 and not total_steps % 10:
print(
f"({int(time_elapsed)}s), Batch {total_steps - 1} Loss: {loss.item()}, Speed: {''} samples/sec"
)
if total_steps == args.max_steps:
break
args.is_distributed = len(args.hosts) > 1 and args.backend is not None
args.is_multigpus = args.num_gpus > 1
args.multigpus_distributed = (args.is_distributed or args.is_multigpus)
logger.debug(f"args.image_folder : {args.image_folder}")
args.world_size = 1
args.local_rank = 0
args.rank = 0
if args.model_parallel:
args.world_size = smp.size()
args.local_rank = smp.local_rank() # rank per host
args.rank = smp.rank()
args.dp_size = smp.dp_size()
args.dp_rank = smp.dp_rank()
logger.debug(f"args.world_size : {args.world_size}, args.local_rank : {args.local_rank}, args.rank : {args.rank}, \
args.dp_size : {args.dp_size}, args.dp_rank : {args.dp_rank}")
else:
# initialize deepspeed
print(f"args.deepspeed : {args.deepspeed}")
deepspeed_utils.init_deepspeed(args.deepspeed)
# args.LEARNING_RATE = args.LEARNING_RATE * float(args.world_size)
## SageMaker
try:
if os.environ.get('SM_CHANNEL_TRAINING') is not None:
args.model_dir = os.environ.get('SM_MODEL_DIR')
def main():
if not torch.cuda.is_available():
raise ValueError(
"The script requires CUDA support, but CUDA not available")
use_ddp = True
use_horovod = False
# Fix seeds in order to get the same losses across runs
random.seed(1)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
smp.init()
# SM Distributed: Set the device to the GPU ID used by the current process.
# Input tensors should be transferred to this device.
torch.cuda.set_device(smp.local_rank())
device = torch.device("cuda")
kwargs = {"batch_size": 64}
kwargs.update({"num_workers": 1, "pin_memory": True, "shuffle": False})
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
# SM Distributed: Download only on a single process per instance.
# When this is not present, the file is corrupted by multiple processes trying
# to download and extract at the same time
if smp.local_rank() == 0:
dataset1 = datasets.MNIST("../data",
train=True,
download=True,
transform=transform)
smp.barrier()
dataset1 = datasets.MNIST("../data",
train=True,
download=False,
transform=transform)
if (use_ddp or use_horovod) and smp.dp_size() > 1:
partitions_dict = {
f"{i}": 1 / smp.dp_size()
for i in range(smp.dp_size())
}
dataset1 = SplitDataset(dataset1, partitions=partitions_dict)
dataset1.select(f"{smp.dp_rank()}")
# Download and create dataloaders for train and test dataset
dataset2 = datasets.MNIST("../data", train=False, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1, **kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
model = GroupedNet()
# SMP handles the transfer of parameters to the right device
# and the user doesn't need to call 'model.to' explicitly.
# model.to(device)
optimizer = optim.Adadelta(model.parameters(), lr=4.0)
# SM Distributed: Use the DistributedModel container to provide the model
# to be partitioned across different ranks. For the rest of the script,
# the returned DistributedModel object should be used in place of
# the model provided for DistributedModel class instantiation.
model = smp.DistributedModel(model)
scaler = smp.amp.GradScaler()
optimizer = smp.DistributedOptimizer(optimizer)
scheduler = StepLR(optimizer, step_size=1, gamma=0.7)
for epoch in range(1, 2):
train(model, scaler, device, train_loader, optimizer, epoch)
test_loss = test(model, device, test_loader)
scheduler.step()
if smp.rank() == 0:
if os.path.exists("/opt/ml/local_checkpoints"):
print("-INFO- PATH DO EXIST")
else:
os.makedirs("/opt/ml/local_checkpoints")
print("-INFO- PATH DO NOT EXIST")
# Waiting the save checkpoint to be finished before run another allgather_object
smp.barrier()
if smp.dp_rank() == 0:
model_dict = model.local_state_dict()
opt_dict = optimizer.local_state_dict()
smp.save(
{
"model_state_dict": model_dict,
"optimizer_state_dict": opt_dict
},
f"/opt/ml/local_checkpoints/pt_mnist_checkpoint.pt",
partial=True,
)
smp.barrier()
if smp.local_rank() == 0:
print("Start syncing")
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="/opt/ml/local_checkpoints",
s3_path=full_s3_path)
print("Finished syncing")
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
def train(
model,
optimizer,
lr_scheduler,
model_config,
start_train_path_index,
start_batch_index,
num_params,
total_steps,
args,
):
if args.enable_memory_profiling > 0:
memory_status_cpu(msg="before train step")
model.train()
if args.parallel_proc_data_processing:
pool = ProcessPoolExecutor(1)
dp_rank = smp.dp_rank() if not args.prescaled_batch else smp.rdp_rank()
dp_size = smp.dp_size() if not args.prescaled_batch else smp.rdp_size()
data_type = "BERT" if args.use_bert_data else "GPT"
if args.use_bert_data:
train_paths = sorted([
os.path.join(args.training_dir, p)
for p in os.listdir(args.training_dir)
if os.path.isfile(os.path.join(args.training_dir, p))
and "training" in p
])
else:
if args.zipped_data > 0:
file_extension = ".json.gz"
else:
file_extension = ".json"
train_paths = sorted([
os.path.join(args.training_dir, p)
for p in os.listdir(args.training_dir)
if p.endswith(file_extension)
])
train_dataloader = create_pretraining_dataloader(
[train_paths[start_train_path_index]],
args.train_batch_size,
args.max_context_width,
seed=args.seed,
dp_rank=dp_rank,
dp_size=dp_size,
shuffle=args.same_seed < 1,
zipped=args.zipped_data > 0,
use_last_file_only=args.fast_validation > 0,
data_type=data_type,
)
if args.validation_freq is not None:
# load all validation examples
if smp.rank() == 0:
print("Creating val dataloader")
if args.use_bert_data:
val_paths = sorted([
os.path.join(args.test_dir, p)
for p in os.listdir(args.test_dir)
if os.path.isfile(os.path.join(args.test_dir, p))
and "testing" in p
])
else:
if args.zipped_data > 0:
file_extension = ".json.gz"
else:
file_extension = ".json"
val_paths = sorted([
os.path.join(args.test_dir, p)
for p in os.listdir(args.test_dir)
if p.endswith(file_extension)
])
val_dataloader = create_pretraining_dataloader(
val_paths,
args.val_batch_size,
args.max_context_width,
seed=args.seed,
dp_rank=dp_rank,
dp_size=dp_size,
shuffle=True,
zipped=args.zipped_data > 0,
use_last_file_only=args.fast_validation > 0,
data_type=data_type,
)
if smp.rank() == 0:
print("Created val dataloader")
start = time.time()
throughput = None
to_save = {"loss": [], "val_loss": []}
loss_metric = 0
def should_record():
# only record the ranks that in the tp group that contains global rank 0
if smp.tp_size() > 1:
tp_group = smp.get_tp_group()
return 0 in tp_group
else:
return smp.rank() == 0
# Set the same seed for computation
set_seed(args.seed)
for index in range(start_train_path_index, args.epochs * len(train_paths)):
next_train_path_index = (index + 1) % len(train_paths)
curr_train_path_index = index % len(train_paths)
if total_steps >= args.max_steps:
break
if args.parallel_proc_data_processing:
dataset_future = pool.submit(
create_pretraining_dataloader,
[train_paths[next_train_path_index]],
args.train_batch_size,
args.max_context_width,
seed=args.seed,
dp_rank=dp_rank,
dp_size=dp_size,
shuffle=args.same_seed < 1,
zipped=args.zipped_data > 0,
use_last_file_only=args.fast_validation > 0,
data_type=data_type,
)
if smp.rank() == 0:
if args.use_bert_data:
print(
f"Reading data from training path {train_dataloader.dataset.input_file}"
)
else:
print(
f"Reading data from training path {train_dataloader.dataset.input_paths}"
)
for batch_idx, input_data in enumerate(train_dataloader):
if batch_idx < start_batch_index:
if smp.rank() == 0:
print(
f"Resuming from saved batch index {start_batch_index}, skipping batch {batch_idx}..."
)
if start_batch_index == len(train_dataloader):
# If saving at the last batch of the file, read from the next file
start_batch_index = 0
break
continue
else:
start_batch_index = 0
if args.use_bert_data:
input_ids, _, attention_mask, _, _ = input_data
else:
input_ids, attention_mask = input_data
if total_steps >= args.max_steps:
break
step_start = time.time()
if args.smp_version < 110:
optimizer.zero_grad(set_grads_to_None=True)
else:
optimizer.zero_grad(set_to_none=True)
if args.logits_output:
train_output = train_step(model, optimizer, input_ids,
attention_mask, args)
loss_mb = train_output["loss"]
logits_mb = train_output["logits"]
if smp.tp_size() > 1:
logits = torch.cat(tuple(logits_mb.outputs), dim=1)
else:
logits = torch.cat(tuple(logits_mb.outputs), dim=0)
else:
# Return value, loss_mb is a StepOutput object
loss_mb = train_step(model, optimizer, input_ids,
attention_mask, args)
# smdistributed: Average the loss across microbatches.
loss = loss_mb.reduce_mean()
if not args.validation_freq:
loss_metric = loss.item()
if args.enable_memory_profiling > 0:
memory_status_cpu("After_train_step_cpu")
memory_status(msg="After_train_step")
if args.clean_cache > 0:
# empty the cache to avoid OOM
torch.cuda.empty_cache()
if args.fp16:
if args.smp_version < 110:
optimizer.update_master_grads()
optimizer.clip_master_grads(args.grad_clip)
optimizer.step()
if not (args.fp16 and optimizer.overflow):
lr_scheduler.step()
if args.enable_memory_profiling > 0:
memory_status(msg="After_opt_step")
total_steps += 1
time_elapsed = time.time() - start
step_time = time.time() - step_start
sample_processed = input_ids.shape[0] * dp_size
throughput = sample_processed / step_time
if smp.rank() == 0 and not total_steps % args.logging_freq:
print(
f"({int(time_elapsed)}s), Batch {total_steps - 1} Loss: {loss.item()}, Speed: {throughput} samples/sec"
)
# evaluate on validation
if args.validation_freq and not (total_steps %
args.validation_freq):
cur_state = np.random.get_state()
model = model.eval()
val_loss, val_ppl = eval_model(model, val_dataloader,
args.validation_batches,
args.use_bert_data)
if is_main_process(smp.rank()):
print(
f"({int(time.time()-start)}s) Batch {total_steps - 1} Validation loss: {val_loss}"
)
print(
f"({int(time.time()-start)}s) Batch {total_steps - 1} Validation perplexity: {val_ppl}"
)
loss_metric = val_loss
if args.logits_output:
to_save["val_loss"].append(val_loss)
model = model.train()
if args.preserve_np_state > 0:
np.random.set_state(cur_state)
# checkpoint
if not (total_steps % args.checkpoint_freq):
base_path = f"trained_gpt_nparams-{num_params}_steps-{total_steps}.pt"
out_path = os.path.join(args.checkpoint_dir, base_path)
total_ckpts = total_steps // args.checkpoint_freq
delete_oldest_ckpt(args, delete_on_rank0_only=args.use_fsx > 0)
save(
out_path,
model,
optimizer,
lr_scheduler,
model_config,
num_params,
total_steps,
curr_train_path_index,
args,
partial=True,
batch_idx=batch_idx + 1,
)
if args.logits_output:
to_save["loss"].append(loss.item())
if total_steps >= args.max_steps:
if should_record() and args.logits_output:
to_save["logits"] = logits.detach().cpu()
output_file = f"rank_{smp.rank()}_" + args.logits_output
torch.save(to_save, os.path.join(args.model_dir, output_file))
print(
f"logits and loss saved at {os.path.join(args.model_dir, output_file)}"
)
break
del train_dataloader
if args.parallel_proc_data_processing:
s = time.time()
train_dataloader = dataset_future.result(timeout=None)
wait_time = time.time() - s
if wait_time > 1:
# TODO if this happens, we should try num_workers>1 in dataloader
print(
f"[{smp.rank()}] Waited {wait_time} for data loader to be ready. Please check if dataloader performance can be improved to avoid these waits."
)
else:
train_dataloader = create_pretraining_dataloader(
[train_paths[next_train_path_index]],
args.train_batch_size,
args.max_context_width,
seed=args.seed,
dp_rank=dp_rank,
dp_size=dp_size,
shuffle=args.same_seed < 1,
zipped=args.zipped_data > 0,
use_last_file_only=args.fast_validation > 0,
data_type=data_type,
)
return total_steps, throughput, loss_metric
def main():
args = parse_args()
if args.shard_optimizer_state > 0 and not args.skip_full_optimizer:
raise ValueError(
"If shard_optimizer_state is enabled, skip_full_optimizer must also be enabled. Full optimizer saving is currently not supported under optimizer state sharding."
)
if args.partition_assignment != "" and args.manual_partition == 0:
print("[Warning] partition_assignment is set, enable manual_partition")
args.manual_partition = 1
# any value here is overriden by the config set in notebook when launching the sagemaker job
smp_config = {
"ddp": True,
"tensor_parallel_degree": args.tensor_parallel_degree,
"pipeline_parallel_degree": args.pipeline_parallel_degree,
"microbatches": args.microbatches,
# if activation_checkpointing true checkpoints transformer layers below
"checkpoint_attentions":
False if args.activation_checkpointing else True,
"shard_optimizer_state": args.shard_optimizer_state > 0,
"prescaled_batch": args.prescaled_batch > 0,
"offload_activations": args.offload_activations > 0,
"optimize": args.optimize,
"auto_partition": False if args.manual_partition else True,
"default_partition": 0,
"static_mode": args.static_mode > 0,
"fast_mode": args.fast_mode > 0,
}
if args.smp_version < 110:
smp_config["fp16_params"] = args.fp16 > 0
else:
smp_config["fp16"] = args.fp16 > 0
smp_config["delayed_parameter_initialization"] = args.delayed_param > 0
smp_config["placement_strategy"] = args.placement_strategy
smp_config[
"activation_loading_horizon"] = args.activation_loading_horizon
smp_config["skip_tracing"] = args.skip_tracing > 0
if args.active_microbatches is not None:
smp_config["active_microbatches"] = args.active_microbatches
smp.init(smp_config)
if smp.rank() == 0:
print("Arguments:", args.__dict__)
print(f"Transformers version: {transformers.__version__}")
print(
f"smdistributed.modelparallel version: {smdistributed.modelparallel.__version__}"
)
print(f"smdistributed config: {smp_config}")
if args.save_final_full_model and smp.rank() == 0:
print(
f"[Warning] Note that save_final_full_model only saves the final model at the end of all steps. It does not save optimizer state. Optimizer state is only saved with partial models which are saved at checkpointing_freq during training. If you want to restart training you need partial checkpoints."
)
if args.partition_assignment != "":
partition_assignment = args.partition_assignment.split(",")
assert (
len(partition_assignment) == smp.pp_size()
), f"partition_assignment must have the same size as pipeline parallel degree, but getting {len(partition_assignment)} vs {smp.pp_size()}"
if smp.rank() == 0 or (smp.local_rank() == 0 and args.use_fsx == 0):
for path in [args.model_dir, args.checkpoint_dir]:
if not os.path.exists(path):
os.makedirs(path, exist_ok=True)
model_config = GPT2Config(
vocab_size=args.vocab_size,
n_positions=args.max_context_width,
n_embd=args.hidden_width,
n_layer=args.num_layers,
n_head=args.num_heads,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=args.resid_pdrop,
embd_pdrop=args.embd_pdrop,
attn_pdrop=args.attn_pdrop,
layer_norm_epsilon=1e-05,
initializer_range=0.02,
summary_type="cls_index",
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=args.summary_first_pdrop,
# gradient_checkpointing=args.gradient_checkpointing > 0,
use_cache=False,
bos_token_id=50256,
eos_token_id=50256,
return_dict=True,
)
# the following improves start-up time by skipping proper initialization
# of weights in the original model. this is not a problem because DistributedModel
# will override those weights anyway when tensor_parallel_degree > 1.
if smp.tp_size() > 1:
from transformers.modeling_utils import PreTrainedModel
PreTrainedModel.init_weights = lambda x: None
set_seed(args.seed)
if args.enable_memory_profiling > 0:
memory_status_cpu(msg="before model creation")
if args.smp_version < 110:
if args.fp16:
torch.set_default_dtype(torch.float16)
with smp.tensor_parallelism(
enabled=smp.tp_size() > 1,
attention_in_fp32=args.attention_in_fp32 > 0):
with smp.delay_param_initialization(
enabled=(smp.tp_size() > 1 and args.delayed_param > 0)):
model = AutoModelForCausalLM.from_config(model_config)
else:
with smp.model_creation(
tensor_parallelism=smp.tp_size() > 1,
attention_in_fp32=args.attention_in_fp32 > 0,
query_key_layer_scaling=args.query_key_layer_scaling > 0,
fused_softmax=args.fused_softmax > 0,
fused_bias_gelu=args.fused_bias_gelu > 0,
dtype=torch.float16
if args.fp16 else torch.get_default_dtype(),
):
model = AutoModelForCausalLM.from_config(model_config)
if args.smp_version < 110 and args.fp16:
model = FP16_Module(model)
if args.enable_memory_profiling > 0:
memory_status_cpu(msg="after model creation")
num_params = sum([np.prod(p.size()) for p in model.parameters()])
if smp.rank() == 0:
print(f"# total parameters: {num_params}")
# smdistributed: Set the device to the GPU ID used by the current process.
# Input tensors should be transferred to this device.
torch.cuda.set_device(smp.local_rank())
device = torch.device("cuda")
if not args.same_seed:
# Set seed by tp_rank to prevent weights from being the same on different tp_ranks
set_seed(args.seed + smp.tp_rank())
# smdistributed: Use the DistributedModel container to provide the model
# to be partitioned across different ranks. For the rest of the script,
# the returned DistributedModel object should be used in place of
# the model provided for DistributedModel class instantiation.
if args.smp_version < 110 and args.fp16:
torch.set_default_dtype(torch.float16)
if args.enable_memory_profiling > 0:
memory_status_cpu(msg="before dist model creation")
model = smp.DistributedModel(model, trace_device="gpu")
if args.enable_memory_profiling > 0:
memory_status_cpu(msg="after dist model creation")
if args.smp_version < 110:
if smp.tp_size() > 1:
transformer_layers = model.module.module.module.transformer.seq_layers
else:
transformer_layers = model.module.module.module.transformer.h
else:
m = model.get_module()
if smp.tp_size() > 1:
transformer_layers = m.transformer.seq_layers
else:
transformer_layers = m.transformer.h
if args.manual_partition:
print(f"Manual partition enabled")
if args.partition_assignment != "":
get_num_layers = lambda x: int(partition_assignment[x])
total_layers = sum(
[get_num_layers(pp_rank) for pp_rank in range(smp.pp_size())])
assert (
total_layers == args.num_layers
), f"partition_assignment must have the same total transformer layers as model, but getting {total_layers} vs {args.num_layers}"
else:
# evenly distribute layers across all partitions
div, rem = divmod(args.num_layers, smp.pp_size())
get_num_layers = lambda x: (div + 1
if x >= smp.pp_size() - rem else div)
assignments = []
# (TODO) This is required for 175B otherwise a hang for partition "8,17,17,18,18,18"
# Need further investigation
# for pp_rank in reversed(range(smp.pp_size())):
for pp_rank in range(smp.pp_size()):
nl = get_num_layers(pp_rank)
print(f"{nl} layers assigned to partition {pp_rank}")
assignments += [pp_rank for _ in range(nl)]
for i, c in enumerate(transformer_layers.children()):
smp.set_partition(c, assignments[i])
if args.smp_version < 110:
iter_model = model
# Build parameter groups (weight decay and non-decay).
while isinstance(iter_model, (DistributedDataParallel, FP16_Module)):
iter_model = iter_model.module
else:
iter_model = m
param_groups = get_param_groups_by_weight_decay(iter_model)
if args.use_adamw > 0:
optimizer = optim.AdamW(param_groups,
betas=(args.beta1, args.beta2),
lr=args.lr,
weight_decay=args.weight_decay)
else:
optimizer = optim.Adam(param_groups,
betas=(args.beta1, args.beta2),
lr=args.lr,
weight_decay=args.weight_decay)
if args.activation_checkpointing:
kwargs = {}
if isinstance(transformer_layers, nn.Sequential):
kwargs["pack_args_as_tuple"] = True
kwargs["strategy"] = args.activation_strategy
smp.set_activation_checkpointing(transformer_layers, **kwargs)
if args.smp_version < 110:
optimizer = FP16_Optimizer(
model,
optimizer,
static_loss_scale=None,
dynamic_loss_scale=True,
use_smp=True,
dynamic_loss_args={
"scale_window": 1000,
"min_scale": 1,
"delayed_shift": 2
},
params_have_main_grad=False,
shard_optimizer_state=args.shard_optimizer_state > 0,
)
optimizer = smp.DistributedOptimizer(optimizer)
model.register_post_step_hook(
lambda model, optimizer: optimizer.init_master_params())
else:
optimizer = smp.DistributedOptimizer(
optimizer,
static_loss_scale=None,
dynamic_loss_scale=True,
dynamic_loss_args={
"scale_window": 1000,
"min_scale": 1,
"delayed_shift": 2
},
)
lr_scheduler = get_learning_rate_scheduler(optimizer, args)
if args.enable_memory_profiling > 0:
model.register_post_partition_hook(
lambda model, optimizer: memory_status(msg="After_partition"))
# load after wrapping model and optimizer with smp Distributed...
if args.load_full or args.load_partial:
if args.load_partial and args.load_full:
print(
"Since both --load_partial and --load_full set, will try to load from full checkpoint."
"If the intention is to load from partial checkpoint, please don't set --load_full"
)
partial = not args.load_full
path = args.checkpoint_dir if partial else args.model_dir
translate_from_hf = not partial
model, optimizer, total_steps, start_train_path_index, start_batch_index = load_model_and_optimizer(
path,
model,
optimizer,
lr_scheduler,
partial,
args,
translate_from_hf=translate_from_hf,
seq_length=args.max_context_width,
load_model=True,
load_optimizer=args.load_partial > 0,
num_params=num_params,
)
else:
total_steps = 0
start_train_path_index = 0
start_batch_index = 0
start = time.time()
total_steps, throughput, loss = train(
model,
optimizer,
lr_scheduler,
model_config,
start_train_path_index,
start_batch_index,
num_params,
total_steps,
args,
)
time_to_train = time.time() - start
if args.ci:
print(f"[SMP_METRIC]__GPT2__Time_to_train__{time_to_train}")
print(f"[SMP_METRIC]__GPT2__samples/second__{throughput}")
print(f"[SMP_METRIC]__GPT2__Loss__{loss}")
if not args.load_partial and not args.load_full:
assert time_to_train < args.time_to_train
assert throughput > args.throughput
if args.loss:
assert loss < args.loss
if args.save_final_full_model:
# saves full model at the end
base_path = f"trained_gpt_nparams-{num_params}_steps-{total_steps}.pt"
out_path = os.path.join(args.model_dir, base_path)
if smp.rdp_rank() == 0:
save(
out_path,
model,
optimizer,
lr_scheduler,
model_config,
num_params,
total_steps,
-1,
args,
partial=False,
translate_to_hf=smp.tp_size() > 1,
seq_length=args.max_context_width,
)
smp.barrier()
if smp.rank() == 0:
print("SMP training finished successfully")
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