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on
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Running
on
Zero
| import os | |
| import socket | |
| import subprocess | |
| from datetime import timedelta | |
| import deepspeed | |
| import torch | |
| import torch.multiprocessing as mp | |
| from torch import distributed as dist | |
| timeout = timedelta(minutes=60) | |
| def _find_free_port(): | |
| # Copied from https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/launch.py # noqa: E501 | |
| sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) | |
| # Binding to port 0 will cause the OS to find an available port for us | |
| sock.bind(('', 0)) | |
| port = sock.getsockname()[1] | |
| sock.close() | |
| # NOTE: there is still a chance the port could be taken by other processes. | |
| return port | |
| def _is_free_port(port): | |
| ips = socket.gethostbyname_ex(socket.gethostname())[-1] | |
| ips.append('localhost') | |
| with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: | |
| return all(s.connect_ex((ip, port)) != 0 for ip in ips) | |
| def init_dist(launcher, backend='nccl', **kwargs): | |
| if mp.get_start_method(allow_none=True) is None: | |
| mp.set_start_method('spawn') | |
| if launcher == 'pytorch': | |
| _init_dist_pytorch(backend, **kwargs) | |
| elif launcher == 'mpi': | |
| _init_dist_mpi(backend, **kwargs) | |
| elif launcher == 'slurm': | |
| _init_dist_slurm(backend, **kwargs) | |
| else: | |
| raise ValueError(f'Invalid launcher type: {launcher}') | |
| def _init_dist_pytorch(backend, **kwargs): | |
| # TODO: use local_rank instead of rank % num_gpus | |
| rank = int(os.environ['RANK']) | |
| num_gpus = torch.cuda.device_count() | |
| torch.cuda.set_device(rank % num_gpus) | |
| # dist.init_process_group(backend=backend, **kwargs) | |
| deepspeed.init_distributed(dist_backend=backend) | |
| def _init_dist_mpi(backend, **kwargs): | |
| local_rank = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK']) | |
| torch.cuda.set_device(local_rank) | |
| if 'MASTER_PORT' not in os.environ: | |
| # 29500 is torch.distributed default port | |
| os.environ['MASTER_PORT'] = '29500' | |
| if 'MASTER_ADDR' not in os.environ: | |
| raise KeyError('The environment variable MASTER_ADDR is not set') | |
| os.environ['WORLD_SIZE'] = os.environ['OMPI_COMM_WORLD_SIZE'] | |
| os.environ['RANK'] = os.environ['OMPI_COMM_WORLD_RANK'] | |
| dist.init_process_group(backend=backend, **kwargs) | |
| def _init_dist_slurm(backend, port=None): | |
| """Initialize slurm distributed training environment. | |
| If argument ``port`` is not specified, then the master port will be system | |
| environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system | |
| environment variable, then a default port ``29500`` will be used. | |
| Args: | |
| backend (str): Backend of torch.distributed. | |
| port (int, optional): Master port. Defaults to None. | |
| """ | |
| proc_id = int(os.environ['SLURM_PROCID']) | |
| ntasks = int(os.environ['SLURM_NTASKS']) | |
| node_list = os.environ['SLURM_NODELIST'] | |
| num_gpus = torch.cuda.device_count() | |
| torch.cuda.set_device(proc_id % num_gpus) | |
| addr = subprocess.getoutput( | |
| f'scontrol show hostname {node_list} | head -n1') | |
| # specify master port | |
| if port is not None: | |
| os.environ['MASTER_PORT'] = str(port) | |
| elif 'MASTER_PORT' in os.environ: | |
| pass # use MASTER_PORT in the environment variable | |
| else: | |
| # if torch.distributed default port(29500) is available | |
| # then use it, else find a free port | |
| if _is_free_port(29500): | |
| os.environ['MASTER_PORT'] = '29500' | |
| else: | |
| os.environ['MASTER_PORT'] = str(_find_free_port()) | |
| # use MASTER_ADDR in the environment variable if it already exists | |
| if 'MASTER_ADDR' not in os.environ: | |
| os.environ['MASTER_ADDR'] = addr | |
| os.environ['WORLD_SIZE'] = str(ntasks) | |
| os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) | |
| os.environ['RANK'] = str(proc_id) | |
| # dist.init_process_group(backend=backend, timeout=timeout) | |
| deepspeed.init_distributed(dist_backend=backend) | |