models/CLAP/training/distributed.py

import os

import torch
import socket

try:
    import horovod.torch as hvd
except ImportError:
    hvd = None


def is_global_master(args):
    return args.rank == 0


def is_local_master(args):
    return args.local_rank == 0


def is_master(args, local=False):
    return is_local_master(args) if local else is_global_master(args)


def is_using_horovod():
    # NOTE w/ horovod run, OMPI vars should be set, but w/ SLURM PMI vars will be set
    # Differentiating between horovod and DDP use via SLURM may not be possible, so horovod arg still required...
    ompi_vars = ["OMPI_COMM_WORLD_RANK", "OMPI_COMM_WORLD_SIZE"]
    pmi_vars = ["PMI_RANK", "PMI_SIZE"]
    if all([var in os.environ for var in ompi_vars]) or all(
        [var in os.environ for var in pmi_vars]
    ):
        return True
    else:
        return False


def is_using_distributed():
    if "WORLD_SIZE" in os.environ:
        return int(os.environ["WORLD_SIZE"]) > 1
    if "SLURM_NTASKS" in os.environ:
        return int(os.environ["SLURM_NTASKS"]) > 1
    return False


def world_info_from_env():
    local_rank = 0
    for v in (
        "SLURM_LOCALID",
        "MPI_LOCALRANKID",
        "OMPI_COMM_WORLD_LOCAL_RANK",
        "LOCAL_RANK",
    ):
        if v in os.environ:
            local_rank = int(os.environ[v])
            break
    global_rank = 0
    for v in ("SLURM_PROCID", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "RANK"):
        if v in os.environ:
            global_rank = int(os.environ[v])
            break
    world_size = 1
    for v in ("SLURM_NTASKS", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "WORLD_SIZE"):
        if v in os.environ:
            world_size = int(os.environ[v])
            break

    return local_rank, global_rank, world_size


def init_distributed_device(args):
    # Distributed training = training on more than one GPU.
    # Works in both single and multi-node scenarios.
    args.distributed = False
    args.world_size = 1
    args.rank = 0  # global rank
    args.local_rank = 0
    if args.horovod:
        assert hvd is not None, "Horovod is not installed"
        hvd.init()
        world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"])
        world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"])
        local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"])
        args.local_rank = local_rank
        args.rank = world_rank
        args.world_size = world_size
        # args.local_rank = int(hvd.local_rank())
        # args.rank = hvd.rank()
        # args.world_size = hvd.size()
        args.distributed = True
        os.environ["LOCAL_RANK"] = str(args.local_rank)
        os.environ["RANK"] = str(args.rank)
        os.environ["WORLD_SIZE"] = str(args.world_size)
        print(
            f"Distributed training: local_rank={args.local_rank}, "
            f"rank={args.rank}, world_size={args.world_size}, "
            f"hostname={socket.gethostname()}, pid={os.getpid()}"
        )
    elif is_using_distributed():
        if "SLURM_PROCID" in os.environ:
            # DDP via SLURM
            args.local_rank, args.rank, args.world_size = world_info_from_env()
            # SLURM var -> torch.distributed vars in case needed
            os.environ["LOCAL_RANK"] = str(args.local_rank)
            os.environ["RANK"] = str(args.rank)
            os.environ["WORLD_SIZE"] = str(args.world_size)
            torch.distributed.init_process_group(
                backend=args.dist_backend,
                init_method=args.dist_url,
                world_size=args.world_size,
                rank=args.rank,
            )
        elif "OMPI_COMM_WORLD_SIZE" in os.environ:  # using Summit cluster
            world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"])
            world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"])
            local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"])
            args.local_rank = local_rank
            args.rank = world_rank
            args.world_size = world_size
            torch.distributed.init_process_group(
                backend=args.dist_backend,
                init_method=args.dist_url,
                world_size=args.world_size,
                rank=args.rank,
            )
        else:
            # DDP via torchrun, torch.distributed.launch
            args.local_rank, _, _ = world_info_from_env()
            torch.distributed.init_process_group(
                backend=args.dist_backend, init_method=args.dist_url
            )
            args.world_size = torch.distributed.get_world_size()
            args.rank = torch.distributed.get_rank()
        args.distributed = True
        print(
            f"Distributed training: local_rank={args.local_rank}, "
            f"rank={args.rank}, world_size={args.world_size}, "
            f"hostname={socket.gethostname()}, pid={os.getpid()}"
        )

    if torch.cuda.is_available():
        if args.distributed and not args.no_set_device_rank:
            device = "cuda:%d" % args.local_rank
        else:
            device = "cuda:0"
        torch.cuda.set_device(device)
    else:
        device = "cpu"
    args.device = device
    device = torch.device(device)
    return device