dinov3/distributed/torch_distributed_wrapper.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.

import logging
import os
import random
import socket
import subprocess
from datetime import timedelta
from enum import Enum
from typing import List, Sequence

import torch
import torch.distributed as dist

logger = logging.getLogger("dinov3")

_DEFAULT_PROCESS_GROUP = None
_PROCESS_SUBGROUP = None
_BUILTIN_PRINT = None


def is_distributed_enabled() -> bool:
    """
    Returns:
        True if distributed training is enabled.
    """
    return dist.is_available() and dist.is_initialized()


def get_rank(group=None) -> int:
    """
    Returns:
        The rank of the current process within the specified process group.
    """
    if not is_distributed_enabled():
        return 0
    return dist.get_rank(group=group)


def get_world_size(group=None) -> int:
    """
    Returns:
        The number of processes in the specified process group.
    """
    if not is_distributed_enabled():
        return 1
    return dist.get_world_size(group=group)


def is_main_process(group=None) -> bool:
    """
    Returns:
        True if the current process is the main one in the specified process group.
    """
    return get_rank(group) == 0


def save_in_main_process(*args, **kwargs) -> None:
    """Utility function to save only from the main process."""
    group = kwargs.pop("group", None)
    if not is_main_process(group):
        return
    torch.save(*args, **kwargs)


def _restrict_print_to_main_process() -> None:
    """This function disables printing when not in the main process."""
    import builtins as __builtin__

    global _BUILTIN_PRINT
    _BUILTIN_PRINT = __builtin__.print

    def print(*args, **kwargs):
        force = kwargs.pop("force", False)
        if is_main_process() or force:
            _BUILTIN_PRINT(*args, **kwargs)

    __builtin__.print = print


def _get_master_port(seed: int = 0) -> int:
    MIN_MASTER_PORT, MAX_MASTER_PORT = (20_000, 60_000)

    master_port_str = os.environ.get("MASTER_PORT")
    if master_port_str is None:
        rng = random.Random(seed)
        return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT)

    return int(master_port_str)


def _get_available_port() -> int:
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        # A "" host address means INADDR_ANY i.e. binding to all interfaces.
        # Note this is not compatible with IPv6.
        s.bind(("", 0))
        port = s.getsockname()[1]
        return port


def _parse_slurm_node_list(s: str) -> List[str]:
    return subprocess.check_output(["scontrol", "show", "hostnames", s], text=True).splitlines()


class JobType(Enum):
    TORCHELASTIC = "TorchElastic"
    SLURM = "Slurm"
    MANUAL = "manual"


class TorchDistributedEnvironment:
    """
    Helper class to get (and set) distributed job information from the
    environment. Identifies and supports (in this order):
    - TorchElastic,
    - Slurm,
    - Manual launch (single-node).
    """

    def __init__(self):
        if "TORCHELASTIC_RUN_ID" in os.environ:
            # TorchElastic job created with torchrun
            self.job_id = os.environ["TORCHELASTIC_RUN_ID"]
            self.job_type = JobType.TORCHELASTIC

            self.master_addr = os.environ["MASTER_ADDR"]
            self.master_port = int(os.environ["MASTER_PORT"])
            self.rank = int(os.environ["RANK"])
            self.world_size = int(os.environ["WORLD_SIZE"])
            self.local_rank = int(os.environ["LOCAL_RANK"])
            self.local_world_size = int(os.environ["LOCAL_WORLD_SIZE"])
        elif "SLURM_JOB_ID" in os.environ:
            # Slurm job created with sbatch, submitit, etc...
            self.job_id = int(os.environ["SLURM_JOB_ID"])
            self.job_type = JobType.SLURM

            node_count = int(os.environ["SLURM_JOB_NUM_NODES"])
            nodes = _parse_slurm_node_list(os.environ["SLURM_JOB_NODELIST"])
            assert len(nodes) == node_count

            self.master_addr = nodes[0]
            self.master_port = _get_master_port(seed=self.job_id)
            self.rank = int(os.environ["SLURM_PROCID"])
            self.world_size = int(os.environ["SLURM_NTASKS"])
            self.local_rank = int(os.environ["SLURM_LOCALID"])
            self.local_world_size = self.world_size // node_count
        else:
            # Single node and single job launched manually
            self.job_id = None
            self.job_type = JobType.MANUAL

            self.master_addr = "127.0.0.1"
            self.master_port = _get_available_port()
            self.rank = 0
            self.world_size = 1
            self.local_rank = 0
            self.local_world_size = 1

        assert self.rank < self.world_size
        assert self.local_rank < self.local_world_size

    def export(
        self,
        *,
        overwrite: bool,
        nccl_async_error_handling: bool = False,
    ) -> "TorchDistributedEnvironment":
        # See the "Environment variable initialization" section from
        # https://pytorch.org/docs/stable/distributed.html for the complete list of
        # environment variables required for the env:// initialization method.
        env_vars = {
            "MASTER_ADDR": self.master_addr,
            "MASTER_PORT": str(self.master_port),
            "RANK": str(self.rank),
            "WORLD_SIZE": str(self.world_size),
            "LOCAL_RANK": str(self.local_rank),
            "LOCAL_WORLD_SIZE": str(self.local_world_size),
        }
        if nccl_async_error_handling:
            env_vars.update(
                {
                    "TORCH_NCCL_ASYNC_ERROR_HANDLING": "1",  # "TORCH_" prefix added in PyTorch 2.2
                }
            )

        if not overwrite:
            for k, v in env_vars.items():
                # Only check for difference with preset environment variables
                if k not in os.environ:
                    continue
                if os.environ[k] == v:
                    continue
                raise RuntimeError(f"Cannot export environment variables as {k} is already set")

        os.environ.update(env_vars)
        return self

    @property
    def is_main_process(self) -> bool:
        return self.rank == 0

    def __str__(self):
        return (
            f"{self.job_type.value} job "
            + (f"({self.job_id}) " if self.job_id else "")
            + f"using {self.master_addr}:{self.master_port} "  # noqa: E231
            f"(rank={self.rank}, world size={self.world_size})"
        )

    def __repr__(self):
        return (
            f"{self.__class__.__name__}("
            f"master_addr={self.master_addr},"  # noqa: E231
            f"master_port={self.master_port},"  # noqa: E231
            f"rank={self.rank},"  # noqa: E231
            f"world_size={self.world_size},"  # noqa: E231
            f"local_rank={self.local_rank},"  # noqa: E231
            f"local_world_size={self.local_world_size}"
            ")"
        )


def enable_distributed(
    *,
    set_cuda_current_device: bool = True,
    overwrite: bool = False,
    nccl_async_error_handling: bool = False,
    restrict_print_to_main_process: bool = True,
    timeout: timedelta | None = None,
):
    """Enable distributed mode.

    Args:
        set_cuda_current_device: If True, call torch.cuda.set_device() to set the
            current PyTorch CUDA device to the one matching the local rank.
        overwrite: If True, overwrites already set variables. Else fails.
        nccl_async_error_handling: Enables NCCL asynchronous error handling. As a
            side effect, this enables timing out PyTorch distributed operations
            after a default 30 minutes delay).
        restrict_print_to_main_process: If True, the print function of non-main processes
            (ie rank>0) is disabled. Use print(..., force=True) to print anyway.
            If False, nothing is changed and all processes can print as usual.
        timeout: Timeout for operations executed against the process group.
            Default value is 10 minutes for NCCL and 30 minutes for other backends.
    """
    global _DEFAULT_PROCESS_GROUP

    if _DEFAULT_PROCESS_GROUP is not None:
        raise RuntimeError("Distributed mode has already been enabled")

    torch_env = TorchDistributedEnvironment()
    logger.info(f"PyTorch distributed environment: {torch_env}")
    torch_env.export(
        overwrite=overwrite,
        nccl_async_error_handling=nccl_async_error_handling,
    )

    if set_cuda_current_device:
        torch.cuda.set_device(torch_env.local_rank)

    dist.init_process_group(backend="nccl", timeout=timeout)
    dist.barrier()

    if restrict_print_to_main_process:
        _restrict_print_to_main_process()

    # Finalize setup
    _DEFAULT_PROCESS_GROUP = torch.distributed.group.WORLD


def get_default_process_group():
    return _DEFAULT_PROCESS_GROUP


def disable_distributed() -> None:
    global _BUILTIN_PRINT
    if _BUILTIN_PRINT is not None:
        import builtins as __builtin__

        __builtin__.print = _BUILTIN_PRINT

    global _PROCESS_SUBGROUP
    # checking here because get_process_subgroup can return _DEFAULT_PROCESS_GROUP
    if _PROCESS_SUBGROUP is not None:
        torch.distributed.destroy_process_group(_PROCESS_SUBGROUP)
        _PROCESS_SUBGROUP = None

    global _DEFAULT_PROCESS_GROUP
    if _DEFAULT_PROCESS_GROUP is not None:  # not initialized
        torch.distributed.destroy_process_group(_DEFAULT_PROCESS_GROUP)
        _DEFAULT_PROCESS_GROUP = None


def new_subgroups(all_subgroup_ranks: Sequence[Sequence[int]]):
    """Create new process subgroups according to the provided specification.

    Args:
       all_subgroup_ranks: a sequence of rank sequences (first rank, ..., last rank),
           one for each process subgroup. Example: ((0, 1), (2, 3), (4, 5, 6, 7)).

    Note:
       This is similar to the (non-documented) new_subgroups_by_enumeration().
       This should be called once (and not sequentially) to create all subgroups.
    """
    all_ranks = tuple(rank for subgroup_ranks in all_subgroup_ranks for rank in subgroup_ranks)
    rank = get_rank()
    assert len(all_ranks) == len(set(all_ranks))
    assert rank in all_ranks

    global _PROCESS_SUBGROUP
    assert _PROCESS_SUBGROUP is None

    for subgroup_ranks in all_subgroup_ranks:
        subgroup = torch.distributed.new_group(subgroup_ranks)
        if rank in subgroup_ranks:
            _PROCESS_SUBGROUP = subgroup


def get_process_subgroup():
    """
    Returns:
        The process subgroup of this rank (or None).
    """
    return _PROCESS_SUBGROUP or _DEFAULT_PROCESS_GROUP


def get_subgroup_rank() -> int:
    """
    Returns:
        The rank of the current process within its process subgroup.
    """
    return get_rank(group=get_process_subgroup())


def get_subgroup_size() -> int:
    """
    Returns:
        The number of processes in the process subgroup
    """
    return get_world_size(group=get_process_subgroup())


def is_subgroup_main_process() -> bool:
    """
    Returns:
        True if the current process is the main one within its process subgroup.
    """
    return get_rank(group=get_process_subgroup()) == 0