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updates across examples to use deepspeed.init_distributed() #71

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Merged
jeffra merged 1 commit into from
Dec 17, 2020
Merged

updates across examples to use deepspeed.init_distributed() #71

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2 changes: 1 addition & 1 deletion BingBertSquad/nvidia_run_squad_deepspeed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -741,7 +741,7 @@ def set_optimizer_params_grad(named_params_optimizer,
def main():
parser = get_argument_parser()

torch.distributed.init_process_group(backend='nccl')
deepspeed.init_distributed(dist_backend='nccl')

# Include DeepSpeed configuration arguments
parser = deepspeed.add_config_arguments(parser)
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26 changes: 15 additions & 11 deletions Megatron-LM/pretrain_gpt2.py
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Expand Up @@ -549,20 +549,24 @@ def set_deepspeed_activation_checkpointing(args):
def initialize_distributed(args):
"""Initialize torch.distributed."""

# Manually set the device ids.
device = args.rank % torch.cuda.device_count()
if args.deepspeed:
deepspeed.init_distributed(dist_backend=args.distributed_backend)
else:
# Manually set the device ids.
device = args.rank % torch.cuda.device_count()
# Call the init process
init_method = 'tcp://'
master_ip = os.getenv('MASTER_ADDR', 'localhost')
master_port = os.getenv('MASTER_PORT', '6000')
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(
backend=args.distributed_backend,
world_size=args.world_size, rank=args.rank,
init_method=init_method)

if args.local_rank is not None:
device = args.local_rank
torch.cuda.set_device(device)
# Call the init process
init_method = 'tcp://'
master_ip = os.getenv('MASTER_ADDR', 'localhost')
master_port = os.getenv('MASTER_PORT', '6000')
init_method += master_ip + ':' + master_port
torch.distributed.init_process_group(
backend=args.distributed_backend,
world_size=args.world_size, rank=args.rank,
init_method=init_method)

# Set the model-parallel / data-parallel communicators.
mpu.initialize_model_parallel(args.model_parallel_size)
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2 changes: 1 addition & 1 deletion bing_bert/deepspeed_train.py
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Expand Up @@ -390,7 +390,7 @@ def prepare_optimizer_parameters(args, model):

def prepare_model_optimizer(args):
# Initialize torch distributed
# torch.distributed.init_process_group(backend="nccl")
deepspeed.init_distributed(dist_backend='nccl')

# Loading Model
model = BertMultiTask(args)
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2 changes: 1 addition & 1 deletion pipeline_parallelism/train.py
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Expand Up @@ -149,7 +149,7 @@ def train_pipe(args, part='parameters'):
args = get_args()

torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend=args.backend)
deepspeed.init_distributed(dist_backend=args.backend)

if args.pipeline_parallel_size == 0:
train_base(args)
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