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fix MPI initialization following deepspeed example #1

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fix MPI initialization following deepspeed example #1

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37 changes: 36 additions & 1 deletion examples/pytorch/llama/llama_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,11 +24,44 @@
import torch
import torch.distributed as dist
from transformers import AutoTokenizer
TORCH_DISTRIBUTED_DEFAULT_PORT = 29500

dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.append(dir_path + "/../../..")
from examples.pytorch.llama.utils.llama import Llama

def mpi_discovery(distributed_port=TORCH_DISTRIBUTED_DEFAULT_PORT, verbose=True):
# taken from https://github.com/microsoft/DeepSpeed/blob/4559aa9b02bf9f113951e0092ebecc21debf8e20/deepspeed/comm/comm.py#L630
from mpi4py import MPI
import subprocess
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
world_size = comm.Get_size()

master_addr = None
if rank == 0:
hostname_cmd = ["hostname -I"]
result = subprocess.check_output(hostname_cmd, shell=True)
master_addr = result.decode('utf-8').split()[0]
master_addr = comm.bcast(master_addr, root=0)

# Determine local rank by assuming hostnames are unique
proc_name = MPI.Get_processor_name()
all_procs = comm.allgather(proc_name)
local_rank = sum([i == proc_name for i in all_procs[:rank]])

os.environ['RANK'] = str(rank)
os.environ['WORLD_SIZE'] = str(world_size)
os.environ['LOCAL_RANK'] = str(local_rank)
os.environ['MASTER_ADDR'] = master_addr
os.environ['MASTER_PORT'] = str(distributed_port)

if verbose:
print(
"Discovered MPI settings of world_rank={}, local_rank={}, world_size={}, master_addr={}, master_port={}".
format(os.environ['RANK'], os.environ['LOCAL_RANK'], os.environ['WORLD_SIZE'], os.environ['MASTER_ADDR'],
os.environ['MASTER_PORT']))

def main():
parser = argparse.ArgumentParser()
parser.add_argument('--output_len', type=int, default=32,
Expand Down Expand Up @@ -109,8 +142,10 @@ def main():
print("{}: {}".format(arg, getattr(args, arg)))
print("=========================================\n")

os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", str(TORCH_DISTRIBUTED_DEFAULT_PORT))
mpi_discovery(distributed_port=int(os.environ["MASTER_PORT"]), verbose=True)
if tensor_para_size * pipeline_para_size > 1:
dist.init_process_group(backend=dist.Backend.MPI)
dist.init_process_group(rank=int(os.environ['RANK']), world_size=int(os.environ['WORLD_SIZE']))
rank = dist.get_rank() if dist.is_initialized() else 0
device_count = dist.get_world_size() if dist.is_initialized() else 1
device = rank % device_count
Expand Down