def _merge_param_group_tp_group(group_idx, param_group):
result_fp32_from_fp16_param_group = []
param_name_group = {}
for i, param in enumerate(param_group):
# for each param, obtain param_name from param using two dicts above for tp_rank 0
param_index = param_id_to_index_tp_group[rank_0][
fp32_from_fp16_paramid_groups_tp_group[rank_0][group_idx][i]]
param_name = param_index_to_name_tp_group[rank_0][param_index]
# obtain distribution axis for the param and check if its distributed
# axis = master_distribution_axis_tp_rank_0[fp32_from_fp16_paramid_groups_tp_group[rank_0][group_idx][i]]
axis = master_distribution_axis_tp_rank_0.get(
fp32_from_fp16_paramid_groups_tp_group[rank_0][group_idx][i],
None)
if axis is not None:
tensors = []
for r in range(smp.tp_size()):
# if distributed, for each rank, obtain param id from index using above two dicts
param_index_r = param_name_to_index_tp_group[r][param_name]
param_id_r = param_index_to_id_tp_group[r][param_index_r]
# search param id in fp32_from_fp16_groups_param_ids and find the index.
group_param_idx = fp32_from_fp16_paramid_groups_tp_group[
r][group_idx].index(param_id_r)
# use the param corresponding to the index from fp32_from_fp16_groups for concatenation along axis
tensors.append(fp32_from_fp16_param_groups_tp_group[r]
[group_idx][group_param_idx])
result_fp32_from_fp16_param_group.append(
torch.cat(tensors, axis))
else:
# if not distributed set tp_rank 0 param as the param
result_fp32_from_fp16_param_group.append(param)
param_name_group[param_name] = i
return result_fp32_from_fp16_param_group, param_name_group
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 save_fp16_optimizer(args, model, optimizer, partial=True):
optimizer_state_dict = {}
loss_scaler = optimizer.loss_scaler
_model = loss_scaler.model
loss_scaler.model = None
_loss_scaler = copy.deepcopy(loss_scaler)
loss_scaler.model = _model
optimizer_state_dict["loss_scaler"] = _loss_scaler
optimizer_state_dict["dynamic_loss_scale"] = optimizer.dynamic_loss_scale
optimizer_state_dict["overflow"] = optimizer.overflow
optimizer_state_dict[
"first_closure_call_this_step"] = optimizer.first_closure_call_this_step
cpu_fp32_from_fp16_groups = [[param.cpu() for param in group]
for group in optimizer.fp32_from_fp16_groups]
if optimizer.master_params_created:
register_optimizer_hooks(model)
if partial:
optimizer_state_dict[
"optimizer_state_dict"] = optimizer.local_state_dict()
if args.shard_optimizer_state:
if smp.rdp_rank() == 0:
print(
"With shard_optimizer_state=True, gather full fp32_from_fp16_groups for the rdp_group on rdp rank 0"
)
gathered_cpu_fp32_from_fp16_groups = [
cpu_fp32_from_fp16_groups
]
for src in range(1, smp.rdp_size()):
gathered_cpu_fp32_from_fp16_groups.append(
smp.recv_from(src, smp.RankType.RDP_RANK))
optimizer_state_dict[
"fp32_from_fp16"] = gathered_cpu_fp32_from_fp16_groups
else:
smp.send(cpu_fp32_from_fp16_groups, 0, smp.RankType.RDP_RANK)
optimizer_state_dict[
"fp32_from_fp16"] = cpu_fp32_from_fp16_groups
else:
optimizer_state_dict["fp32_from_fp16"] = cpu_fp32_from_fp16_groups
if smp.pp_size() > 1:
print(
"WARNING: Ensure that partition decision doesnt change between runs (you can ensure this by setting use_times=False in smp config)."
"If you want to save and load with partition decision changing between runs, use full save and load instead."
)
else:
optimizer_state_dict["optimizer_state_dict"] = optimizer.state_dict()
if smp.tp_size() > 1 and not args.shard_optimizer_state:
tp_merged_fp32_from_fp16_groups, param_name_groups = get_tp_merged_fp32_from_fp16_param_groups(
optimizer, cpu_fp32_from_fp16_groups)
pp_merged_fp32_from_fp16_groups, param_name_groups = get_pp_merged_fp32_from_fp16_param_groups(
optimizer, tp_merged_fp32_from_fp16_groups, param_name_groups)
else:
raise ValueError(
"Loading full optimizer state is not supported, when TP is not enabled or shard_optimizer_state is enabled"
)
optimizer_state_dict[
"fp32_from_fp16"] = pp_merged_fp32_from_fp16_groups
optimizer_state_dict["param_name_groups"] = param_name_groups
return optimizer_state_dict
def hook_fn(model, optimizer):
optimizer.load_state_dict(opt_state_dict["optimizer_state_dict"])
if partial:
if args.shard_optimizer_state:
assert isinstance(
opt_state_dict["fp32_from_fp16"], list
), "Loading with shard_optimizer_state=True must use the checkpoint that was trained with shard_optimizer_state=True!"
optimizer.fp32_from_fp16 = opt_state_dict["fp32_from_fp16"][
smp.rdp_rank()]
else:
optimizer.fp32_from_fp16 = opt_state_dict["fp32_from_fp16"]
for current_group, saved_group in zip(
optimizer.fp32_from_float16_groups,
optimizer.fp32_from_fp16):
for current, saved in zip(current_group, saved_group):
current.data.copy_(saved.data)
else:
optimizer.fp32_from_fp16 = opt_state_dict["fp32_from_fp16"]
param_name_groups = opt_state_dict["param_name_groups"]
param_id_to_index = optimizer._param_id_to_index()
param_index_to_name_tp_group = smp_state.param_index_to_name_tp_group
param_index_to_name = param_index_to_name_tp_group[smp.tp_rank()]
for group_idx, (current_group, saved_group) in enumerate(
zip(optimizer.fp32_from_float16_groups,
optimizer.fp32_from_fp16)):
for current in current_group:
param_id = id(current)
param_index = param_id_to_index[param_id]
param_name = param_index_to_name[param_index]
arr_index = param_name_groups[group_idx][param_name]
saved = saved_group[arr_index]
if optimizer.master_distribution_axis[
param_id] is not None:
axis = optimizer.master_distribution_axis[param_id]
slice_size = saved.size(axis) // smp.tp_size()
saved = torch.narrow(saved.data, axis,
slice_size * smp.tp_rank(),
slice_size).contiguous()
else:
saved = saved.data
current.data.copy_(saved)
optimizer.grad_scaler.load_state_dict(opt_state_dict["grad_scaler"])
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 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