MLPY/Lib/site-packages/torch/distributed/distributed_c10d.py

"""Distributed Collective Communication (c10d)."""

import itertools
import collections.abc
import contextlib
import hashlib
import io
import logging
import os
import pickle
import sys
import time
import warnings
from collections import namedtuple
from datetime import timedelta
from typing import Any, Callable, Dict, Optional, Tuple, Union, List

import torch
from torch._C._distributed_c10d import (
    AllgatherOptions,
    AllreduceCoalescedOptions,
    AllreduceOptions,
    AllToAllOptions,
    _DistributedBackendOptions,
    BarrierOptions,
    BroadcastOptions,
    GatherOptions,
    PrefixStore,
    ProcessGroup,
    ReduceOp,
    ReduceOptions,
    ReduceScatterOptions,
    ScatterOptions,
    Store,
    DebugLevel,
    get_debug_level,
    Work,
    _register_process_group,
    _resolve_process_group,
    _unregister_all_process_groups,
    _unregister_process_group,
)
from torch._utils_internal import set_pytorch_distributed_envs_from_justknobs
from .constants import default_pg_timeout, default_pg_nccl_timeout
from .c10d_logger import _exception_logger, _time_logger
from .rendezvous import register_rendezvous_handler, rendezvous  # noqa: F401
from ..utils._typing_utils import not_none
DistStoreError = torch._C._DistStoreError

__all__ = [
    'Backend', 'BackendConfig', 'GroupMember', 'P2POp', 'all_gather', 'all_gather_coalesced',
    'all_gather_object', 'all_reduce',
    'all_reduce_coalesced', 'all_to_all',
    'all_to_all_single', 'barrier', 'batch_isend_irecv', 'broadcast',
    'broadcast_object_list', 'destroy_process_group',
    'gather', 'gather_object', 'get_backend_config', 'get_backend', 'get_rank',
    'get_world_size', 'get_pg_count', 'group', 'init_process_group', 'irecv',
    'is_gloo_available', 'is_initialized', 'is_mpi_available', 'is_backend_available',
    'is_nccl_available', 'is_torchelastic_launched', 'is_ucc_available',
    'isend', 'monitored_barrier', 'new_group', 'new_subgroups',
    'new_subgroups_by_enumeration', 'recv', 'reduce',
    'reduce_scatter', 'scatter',
    'scatter_object_list', 'send', 'supports_complex',
    'AllreduceCoalescedOptions', 'AllreduceOptions', 'AllToAllOptions',
    'BarrierOptions', 'BroadcastOptions', 'GatherOptions', 'PrefixStore',
    'ProcessGroup', 'ReduceOp', 'ReduceOptions', 'ReduceScatterOptions',
    'ScatterOptions', 'Store', 'DebugLevel', 'get_debug_level', 'Work',
    'default_pg_timeout', 'get_group_rank', 'get_global_rank', 'get_process_group_ranks',
    'reduce_op', 'all_gather_into_tensor', 'reduce_scatter_tensor',
]

_MPI_AVAILABLE = True
_NCCL_AVAILABLE = True
_GLOO_AVAILABLE = True
_UCC_AVAILABLE = True

_pickler = pickle.Pickler
_unpickler = pickle.Unpickler

# Change __module__ of all imported types from torch._C._distributed_c10d that are public
def _export_c_types() -> None:
    _public_types_to_change_module = [
        AllreduceCoalescedOptions,
        AllreduceOptions,
        AllToAllOptions,
        BarrierOptions,
        BroadcastOptions,
        GatherOptions,
        PrefixStore,
        ProcessGroup,
        ReduceOp,
        ReduceOptions,
        ReduceScatterOptions,
        ScatterOptions,
        Store,
        DebugLevel,
        get_debug_level,
        Work
    ]
    for type in _public_types_to_change_module:
        type.__module__ = "torch.distributed.distributed_c10d"
_export_c_types()

try:
    from torch._C._distributed_c10d import ProcessGroupMPI
    ProcessGroupMPI.__module__ = "torch.distributed.distributed_c10d"
    __all__ += ["ProcessGroupMPI"]
except ImportError:
    _MPI_AVAILABLE = False

try:
    from torch._C._distributed_c10d import ProcessGroupNCCL
    ProcessGroupNCCL.__module__ = "torch.distributed.distributed_c10d"
    __all__ += ["ProcessGroupNCCL"]
except ImportError:
    _NCCL_AVAILABLE = False

try:
    from torch._C._distributed_c10d import ProcessGroupGloo
    from torch._C._distributed_c10d import _ProcessGroupWrapper
    ProcessGroupGloo.__module__ = "torch.distributed.distributed_c10d"
    __all__ += ["ProcessGroupGloo"]
except ImportError:
    _GLOO_AVAILABLE = False

try:
    from torch._C._distributed_c10d import ProcessGroupUCC
    ProcessGroupUCC.__module__ = "torch.distributed.distributed_c10d"
    __all__ += ["ProcessGroupUCC"]
except ImportError:
    _UCC_AVAILABLE = False

logger = logging.getLogger(__name__)

PG_WRAPPER_STORE_PREFIX = "pg_wrapper"


# Some reduce ops are not supported by complex numbers and will result in an error.
# We currently provide complex support to the distributed API by viewing
# complex tensors as real (torch.view_as_real), meaning that calling
# these unsupported ops will return garbage values rather than error out.
# (e.g. max(2+3i, 3+2i) = 3+3i)
# We'd like calls to unsupported ops to error out accordingly,
# rather than returning garbage values.
def supports_complex(reduceOp: ReduceOp) -> bool:
    """Return true if reduce ops is supported. False otherwise."""
    denyList = [
        ReduceOp.MAX,
        ReduceOp.MIN,
        ReduceOp.PRODUCT,
        ReduceOp.BAND,
        ReduceOp.BOR,
        ReduceOp.BXOR,
    ]
    return reduceOp not in denyList


class Backend(str):
    """

    An enum-like class for backends.



    Available backends: GLOO, NCCL, UCC, MPI, and other registered backends.



    The values of this class are lowercase strings, e.g., ``"gloo"``. They can

    be accessed as attributes, e.g., ``Backend.NCCL``.



    This class can be directly called to parse the string, e.g.,

    ``Backend(backend_str)`` will check if ``backend_str`` is valid, and

    return the parsed lowercase string if so. It also accepts uppercase strings,

    e.g., ``Backend("GLOO")`` returns ``"gloo"``.



    .. note:: The entry ``Backend.UNDEFINED`` is present but only used as

              initial value of some fields. Users should neither use it directly

              nor assume its existence.

    """

    UNDEFINED = "undefined"
    GLOO = "gloo"
    NCCL = "nccl"
    UCC = "ucc"
    MPI = "mpi"

    _BackendPlugin = namedtuple("_BackendPlugin", ["creator_fn", "extended_api"])

    _plugins: Dict[str, _BackendPlugin] = {}

    backend_list = [UNDEFINED, GLOO, NCCL, UCC, MPI]

    default_device_backend_map: Dict[str, str] = {
        'cpu' : GLOO,
        'cuda' : NCCL,
    }

    backend_capability: Dict[str, List[str]] = {
        GLOO : ["cpu", "cuda"],
        NCCL : ["cuda"],
        UCC : ["cpu", "cuda"],
        MPI : ["cpu", "cuda"],
    }

    backend_type_map: Dict[str, ProcessGroup.BackendType] = {
        UNDEFINED: ProcessGroup.BackendType.UNDEFINED,
        GLOO : ProcessGroup.BackendType.GLOO,
        NCCL: ProcessGroup.BackendType.NCCL,
        UCC: ProcessGroup.BackendType.UCC,
    }

    def __new__(cls, name: str):
        """Create and return a new instance of the class."""
        if not isinstance(name, str):
            raise ValueError("Backend constructor parameter must be string-ish")
        value = getattr(Backend, name.upper(), Backend.UNDEFINED)

        if value == Backend.UNDEFINED:
            value = name.lower()
        return value

    @classmethod
    def register_backend(cls, name, func, extended_api=False, devices: Optional[Union[str, List[str]]] = None) -> None:
        """

        Register a new backend with the given name and instantiating function.



        This class method is used by 3rd party ``ProcessGroup`` extension to

        register new backends.



        Args:

            name (str): Backend name of the ``ProcessGroup`` extension. It

                        should match the one in ``init_process_group()``.

            func (function): Function handler that instantiates the backend.

                             The function should be implemented in the backend

                             extension and takes four arguments, including

                             ``store``, ``rank``, ``world_size``, and ``timeout``.

            extended_api (bool, optional): Whether the backend supports extended argument structure.

                                           Default: ``False``. If set to ``True``, the backend

                                           will get an instance of ``c10d::DistributedBackendOptions``, and

                                           a process group options object as defined by the backend implementation.

            device (str or list of str, optional): device type this backend

                            supports, e.g. "cpu", "cuda", etc. If `None`,

                            assuming both "cpu" and "cuda"



        .. note:: This support of 3rd party backend is experimental and subject to change.



        """
        # Allow UCC plugin if Pytorch is not built with native support.
        # TODO: remove this exception once UCC plugin is fully deprecated.
        if (name != Backend.UCC or (name == Backend.UCC and is_ucc_available())):
            assert not hasattr(Backend, name.upper()), (
                f"{name.upper()} c10d backend already exist"
            )
        assert name.upper() not in Backend._plugins, (
            f"{name.upper()} c10d backend creator function already exist"
        )

        setattr(Backend, name.upper(), name.lower())
        Backend.backend_list.append(name.lower())
        if devices is not None:
            for device in devices:
                if device != 'cpu' and device != 'cuda':
                    Backend.default_device_backend_map[device] = name.lower()
        Backend.backend_type_map[name.lower()] = ProcessGroup.BackendType.CUSTOM

        # Update device capability matrix in Backend class
        if devices is None:
            # This is more of a backward support for groups like `threaded`:
            # assume default devices "cpu" and "cuda", but warn
            warnings.warn(
                f"Device capability of {name} unspecified, assuming `cpu` and "
                "`cuda`. Please specify it via the `devices` argument of "
                "`register_backend`."
            )
            Backend.backend_capability[name.lower()] = ["cpu", "cuda"]
        elif isinstance(devices, str):
            # Single device string specified. Simply convert to list.
            Backend.backend_capability[name.lower()] = [devices]
        else:
            Backend.backend_capability[name.lower()] = devices

        Backend._plugins[name.upper()] = Backend._BackendPlugin(func, extended_api)

class BackendConfig:
    """Backend configuration class."""

    def __init__(self, backend: Backend):
        """Init."""
        self.device_backend_map: Dict[str, Backend] = {}
        backend = str(backend)

        if backend == Backend.UNDEFINED:
            # default config when backend is not specified
            # supported since PyTorch 2.0
            for device, default_backend in Backend.default_device_backend_map.items():
                if is_backend_available(default_backend):
                    if default_backend == Backend.NCCL and not torch.cuda.is_available():
                        continue
                    self.device_backend_map[device] = Backend(default_backend)
        elif backend.lower() in Backend.backend_list:
            # Cases for when backend is a single string (without device types)
            # e.g. "nccl", "gloo", "ucc", "mpi"
            supported_devices = Backend.backend_capability[backend.lower()]
            backend_val = Backend(backend)
            self.device_backend_map = dict.fromkeys(supported_devices, backend_val)
        elif ":" in backend.lower():
            # Backend specified in "device:backend" format
            # make sure the backend string is in the correct format
            # "{device_type1}:{backend1},{device_type2}:{backend2}"
            # e.g. "cpu:gloo,cuda:nccl"
            backend_str_error_message = f"""The custom backend string argument is invalid: {backend}.

                Custom backend string is an experimental feature where the backend string must be in the format:

                "<device_type1>:<backend1>,<device_type2>:<backend2>...". e.g. 'cpu:gloo,cuda:nccl'"""

            # parse the backend string and populate the device_backend_map
            for device_backend_pair_str in backend.lower().split(","):
                device_backend_pair = device_backend_pair_str.split(":")
                if len(device_backend_pair) != 2:
                    raise ValueError(f"Invalid device:backend pairing: \

                                     {device_backend_pair_str}. {backend_str_error_message}")
                device, backend = device_backend_pair
                if device in self.device_backend_map:
                    raise ValueError(f"Duplicate device type {device} \

                                     in backend string: {backend}. {backend_str_error_message}")
                self.device_backend_map[device] = Backend(backend)
        else:
            # User specified a single backend name whose device capability is
            # unknown, assuming it can support the default devices of PyTorch
            # (cpu and cuda)
            warnings.warn(
                f"Device capability of {backend} unknown, assuming `cpu` and "
                "`cuda`. You can specify it in `device:backend` format in "
                "`init_process_group` call."
            )
            backend_val = Backend(backend)
            self.device_backend_map = {
                "cpu" : backend_val,
                "cuda" : backend_val,
                "xpu" : backend_val,
            }

        logger.info(
            f"Using backend config: {self.device_backend_map}"  # noqa: G004
        )

    def __repr__(self):
        """Return all the device:backend pairs separated by commas."""
        return ",".join(f"{device}:{backend}" for device, backend in self.device_backend_map.items())

    def get_device_backend_map(self) -> Dict[str, Backend]:
        """Return backend map of the device."""
        return self.device_backend_map

class _reduce_op:
    r"""

    Deprecated enum-like class.



    For reduction operations: ``SUM``, ``PRODUCT``, ``MIN``, and ``MAX``.



    :class:`~torch.distributed.ReduceOp` is recommended to use instead.

    """

    def __init__(self):
        # __members__ is a dict storing key-value pairs for enum classes
        for k, v in ReduceOp.RedOpType.__members__.items():
            setattr(self, k, v)
        self.__members__ = ReduceOp.RedOpType.__members__

    def __getattribute__(self, key):
        warnings.warn(
            "torch.distributed.reduce_op is deprecated, please use "
            "torch.distributed.ReduceOp instead"
        )
        return object.__getattribute__(self, key)


reduce_op = _reduce_op()


class P2POp:
    """

    A class to build point-to-point operations for ``batch_isend_irecv``.



    This class builds the type of P2P operation, communication buffer, peer rank,

    Process Group, and tag. Instances of this class will be passed to

    ``batch_isend_irecv`` for point-to-point communications.



    Args:

        op (Callable): A function to send data to or receive data from a peer process.

            The type of ``op`` is either ``torch.distributed.isend`` or

            ``torch.distributed.irecv``.

        tensor (Tensor): Tensor to send or receive.

        peer (int): Destination or source rank.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        tag (int, optional): Tag to match send with recv.

    """

    def __init__(self, op: Callable, tensor: torch.Tensor, peer: int,

                 group: Optional[ProcessGroup] = None, tag: int = 0):
        """Init."""
        self.op = op
        self.tensor = tensor
        self.peer = peer
        self.group = group
        self.tag = tag

    def __new__(cls, op: Callable, tensor: torch.Tensor, peer: int,

                group: Optional[ProcessGroup] = None, tag: int = 0):
        """Create and return a new instance of the class."""
        _check_op(op)
        _check_single_tensor(tensor, "tensor")
        return object.__new__(cls)


class _CollOp:
    """

    A class to capture collective operations.



    Args:

        op (Callable): A collective function, e.g. ``torch.distributed.all_reduce``.

        tensor (Tensor): Tensor to operate on.

        dst_tensor (Tensor, optional): Provided when source and destinaton tensors are not the same.

        redop (ReduceOp, optional): reduce operation.

        root (int, optional): root of broadcast or reduce.

    """

    def __init__(self, op: Callable, tensor: torch.Tensor, dst_tensor: Optional[torch.Tensor] = None,

                 redop: Optional[ReduceOp] = None, root: Optional[int] = None):
        self.op = op
        self.tensor = tensor
        self.dst_tensor = dst_tensor
        self.redop = redop
        self.root = root


# DO NOT USE THESE FIELDS DIRECTLY.
# Use them through the _world object to make sure the _world override mechanism
_pg_map: Dict[ProcessGroup, Tuple[str, Store]] = {}
_pg_names: Dict[ProcessGroup, str] = {}
_pg_group_ranks: Dict[ProcessGroup, Dict[int, int]] = {}
# For a pg, it is a map from ProcessGroup to BackendConfig
_pg_backend_config: Dict[ProcessGroup, str] = {}
_group_count = 0
_tags_to_pg: Dict[str, List[ProcessGroup]] = {}
_pg_to_tag: Dict[ProcessGroup, str] = {}
_backend: Optional[str] = None

class _World:
    """

    Container class for c10d process group state.



    This is used during registration and lookup of PG state.



    .. warning:: This is an experimental API intended to expose the inner workings

       of c10d and is subject to change..

    """

    def __init__(self):
        self._default_pg = None
        self._pg_coalesce_state: Dict[ProcessGroup, List[_CollOp]] = {}
        self._pg_default_device: Dict[ProcessGroup, torch.device] = {}

    @property
    def default_pg(self) -> Optional[ProcessGroup]:
        """

        Process group that includes all ranks of the cluster.



        This default ProcessGroup is used by c10d APIs when a ProcessGroup is needed

        but None is provided.

        """
        return self._default_pg

    @default_pg.setter
    def default_pg(self, value) -> None:
        self._default_pg = value

    @property
    def pg_map(self) -> Dict[ProcessGroup, Tuple[str, Store]]:
        """

        Provide Mapping from ProcessGroup to backend name and store.



        For NCCL and GLOO pg, it is a map from ProcessGroup to (Backend, Store)

        For MPI pg, it is a map from ProcessGroup to (Backend, None)



        TODO don't expose the map, expose fine grained ops

        """
        global _pg_map
        return _pg_map

    @property
    def pg_names(self) -> Dict[ProcessGroup, str]:
        """

        Process group's names, map from ProcessGroup to str.



        TODO don't expose the map, expose fine grained ops

        """
        global _pg_names
        return _pg_names

    @property
    def pg_group_ranks(self) -> Dict[ProcessGroup, Dict[int, int]]:
        """

        Process group's global rank to local rank mapping.



        TODO don't expose the map, expose fine grained ops

        """
        global _pg_group_ranks
        return _pg_group_ranks

    @property
    def pg_backend_config(self) -> Dict[ProcessGroup, str]:
        """

        Process group's backend config.



        TODO don't expose the map, expose fine grained ops

        """
        global _pg_backend_config
        return _pg_backend_config

    @property
    def group_count(self) -> int:
        """

        Process group count for default naming.



        TODO don't expose group_count, use something else instead

        """
        global _group_count
        return _group_count

    @group_count.setter
    def group_count(self, value: int) -> None:
        """Use to compute the name of ProcessGroups when using global synchronization."""
        global _group_count
        _group_count = value

    @property
    def tags_to_pg(self) -> Dict[str, List[ProcessGroup]]:
        global _tags_to_pg
        return _tags_to_pg

    @property
    def pg_to_tag(self) -> Dict[ProcessGroup, str]:
        global _pg_to_tag
        return _pg_to_tag

    @property
    def pg_coalesce_state(self) -> Dict[ProcessGroup, List[_CollOp]]:
        return self._pg_coalesce_state

    @property
    def pg_default_device(self) -> Dict[ProcessGroup, torch.device]:
        return self._pg_default_device

    @property
    def pg_config_info(self) -> List[Dict[str, Any]]:
        """

        Return a list of dict with process groups and backends.



        Along with their unique IDs and configurations (types and ranks).

        """
        config_info: List[Dict[str, Any]] = []
        default_pg_size = _get_group_size(None)
        for pg in self.pg_map.keys():
            ranks = self.pg_group_ranks[pg]
            config_info.append(
                {
                    "pg_name": self.pg_names[pg],
                    "uid": _get_process_group_uid(pg),
                    "backend_config": self.pg_backend_config[pg],
                    "ranks": list(ranks.keys())
                    if len(ranks) != default_pg_size
                    else [],  # 'ranks' is an empty list when all ranks are involved in a pg
                    "group_size": len(ranks),
                    "group_count": self.group_count,
                }
            )
        return config_info


_world = _World()
"""Holds the singleton instance of ``_World`` used by c10. Experimental extension point to override it"""

class _WorldMeta(type):
    """

    Meta class of ``group`` and ``GroupMember``.



    Allows them to have the class property ``WORLD``.

    """

    # Points to the default PG once initialized.
    @property
    def WORLD(cls) -> Optional[ProcessGroup]:
        return _world.default_pg

    @WORLD.setter
    def WORLD(cls, pg: Optional[ProcessGroup]):
        _world.default_pg = pg

class group(metaclass=_WorldMeta):
    """Group class. Placeholder."""

    pass

class GroupMember(metaclass=_WorldMeta):
    """Group member class."""

    NON_GROUP_MEMBER = -100


def _get_default_timeout(backend: Backend) -> timedelta:
    # see note on nccl vs other backend timeout (constants.py)
    if backend == Backend.NCCL:
        if not isinstance(default_pg_nccl_timeout, timedelta):
            # TODO moco benchmark on CPU initializes pgnccl backend today, triggered this assert in CI before it was
            # changed to be a warning.  We should fix the moco model.
            warnings.warn("Attempted to get default timeout for nccl backend, but NCCL support is not compiled")
            return default_pg_timeout
        return default_pg_nccl_timeout
    else:
        return default_pg_timeout

def _check_valid_timeout(timeout: Any) -> None:
    if not isinstance(timeout, timedelta):
        raise TypeError(
            f"Expected timeout argument to be of type datetime.timedelta, got {timeout}"
        )

# Default process group state
_default_pg_init_method: Optional[str] = None

STORE_BASED_BARRIER_PREFIX = "store_based_barrier_key"

def _get_pg_default_device(group: Optional[ProcessGroup] = None) -> torch.device:
    """

    Return the device to use with ``group`` for control flow usage (object collectives, barrier).



    There are selection rules:

        1. If user specifies exactly one backend in ``init_process_group`` call:

            use that backend

        2. Else if user specifies multiple "device:backend" pairs in init_process_group:

            If "cpu" is among those pairs, use "cpu" (because the object is in cpu memory);

            Otherwise, use the first backend (sort of a random pick).



    Args:

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.



    Returns:

        torch.device: The device to use with ``group``.



    """
    group = group or _get_default_group()
    if group in _world.pg_default_device:
        # Previously searched and cached; just return
        return _world.pg_default_device[group]

    if not isinstance(group, ProcessGroup):
        # Provide backward compatibility to cases where `group` passed in is
        # actually a Backend (like `ProcessGroupGloo`) rather than a
        # `ProcessGroup` in PT 2.0 sense
        warnings.warn(
            f"You are using a Backend {type(group)} as a ProcessGroup. "
            "This usage is deprecated since PyTorch 2.0. Please use a public API "
            "of PyTorch Distributed instead."
        )
        # Most users create Gloo with private API for object collectives
        _world.pg_default_device[group] = torch.device("cpu")
        return _world.pg_default_device[group]

    """

    ``group._device_types`` is a property pybind that returns the devices

    ("cpu", "cuda", etc) supported by ``group``. Can be multiple if the

    ``group`` supports multiple devices.

    """
    devices = group._device_types

    if len(devices) == 1:
        # User fixed exactly one backend in `init_process_group`
        _world.pg_default_device[group] = devices[0]
    elif len(devices) == 0:
        # No backend has been registered with this PG (maybe because no
        # collective has been run?) We pick cpu as the default and hopefully
        # this would lazily init Gloo or other available cpu backend.
        _world.pg_default_device[group] = torch.device("cpu")
    elif torch.device("cpu") in devices:
        # There are multiple backends in this PG and cpu is among them.
        # cpu is preferred as the object is in cpu memory. No need for device
        # copy.
        _world.pg_default_device[group] = torch.device("cpu")
    else:
        # No cpu in the backend list. Randomly pick the first backend
        _world.pg_default_device[group] = devices[0]

    logger.info(
        f"Using device {_world.pg_default_device[group]} for object "  # noqa: G004
        "collectives."
    )
    return _world.pg_default_device[group]


@_time_logger
def _store_based_barrier(rank, store, group_name, rendezvous_count, timeout, logging_interval=timedelta(seconds=10)) -> None:
    """

    Store based barrier for synchronizing processes.



    Barrier based on store which is used for synchronizing processes after

    ``init_process_group`` or ``new_group``. Intended to be used only with

    those two methods and is not a generic alternative to ``barrier()``.

    """
    store_key = f"{STORE_BASED_BARRIER_PREFIX}:{group_name}"
    store.add(store_key, 1)
    logger.info("Added key: %s to store for rank: %s", store_key, rank)

    # Now wait for all workers to check in with the store.
    world_size = rendezvous_count
    worker_count = store.add(store_key, 0)

    last_worker_key = f"{store_key}:last_worker"
    if worker_count == world_size:
        store.set(last_worker_key, "1")

    # adjust the timeout to be at least 10secs + 1sec per thousand ranks to reduce the odds of timeout
    # this value was empirically found while scale testing.
    logging_interval = max(logging_interval, timedelta(seconds=10 + world_size / 1000))

    start = time.time()
    while True:
        try:
            # This will throw an exception after the logging_interval in which we print out
            # the status of the group or time out officially, throwing runtime error
            store.wait([last_worker_key], logging_interval)
            break
        except RuntimeError as e:
            worker_count = store.add(store_key, 0)
            # Print status periodically to keep track.
            logger.info(
                "Waiting in store based barrier to initialize process group for "
                "rank: %s, key: %s (world_size=%s, num_workers_joined=%s, timeout=%s)",
                rank, store_key, world_size, worker_count, timeout
            )

            if timedelta(seconds=(time.time() - start)) > timeout:
                raise DistStoreError(  # noqa: TRY200
                    "Timed out initializing process group in store based barrier on "
                    "rank {}, for key: {} (world_size={}, num_workers_joined={}, timeout={})".format(
                        rank, store_key, world_size, worker_count, timeout
                    )
                )

    logger.info(
        "Rank %s: Completed store-based barrier for key:%s with %s nodes.", rank, store_key, world_size
    )


def _rank_not_in_group(group: Optional[ProcessGroup]) -> bool:
    """Check if the current process's rank is not in a given group."""
    if group is None:
        return False
    return group == GroupMember.NON_GROUP_MEMBER


def _warn_not_in_group(op_name) -> None:
    global_rank = -1 if GroupMember.WORLD is None else GroupMember.WORLD.rank()
    warnings.warn(
        f"Running {op_name} on global rank {global_rank} which does not "
        "belong to the given group."
    )


def get_group_rank(group: ProcessGroup, global_rank: int) -> int:
    """

    Translate a global rank into a group rank.



    ``global_rank`` must be part of ``group`` otherwise this raises RuntimeError.



    Args:

        group (ProcessGroup): ProcessGroup to find the relative rank.

        global_rank (int): Global rank to query.



    Returns:

        Group rank of ``global_rank`` relative to ``group``



    N.B. calling this function on the default process group returns identity

    """
    if group is GroupMember.WORLD:
        return global_rank
    if group not in _world.pg_group_ranks:
        raise ValueError(f"Group {group} is not registered, please create group with torch.distributed.new_group API")
    group_ranks = _world.pg_group_ranks[group]
    if global_rank not in group_ranks:
        raise ValueError(f"Global rank {global_rank} is not part of group {group}")

    return group_ranks[global_rank]

def get_global_rank(group: ProcessGroup, group_rank: int) -> int:
    """

    Translate a group rank into a global rank.



    ``group_rank`` must be part of `group` otherwise this raises RuntimeError.



    Args:

        group (ProcessGroup): ProcessGroup to find the global rank from.

        group_rank (int): Group rank to query.



    Returns:

        Global rank of ``group_rank`` relative to ``group``



    N.B. calling this function on the default process group returns identity

    """
    if group is GroupMember.WORLD:
        return group_rank
    if group not in _world.pg_group_ranks:
        raise ValueError(f"Group {group} is not registered, please create group with torch.distributed.new_group API")
    for rank, grp_rank in _world.pg_group_ranks[group].items():
        if grp_rank == group_rank:
            return rank
    raise ValueError(f"Group rank {group_rank} is not part of group {group}")

# TODO: remove this once the ecosystem moves away from it.
def _get_global_rank(group, rank) -> int:
    """Use get_global_rank as this method is deprecated."""
    warnings.warn(
        "torch.distributed.distributed_c10d._get_global_rank is deprecated "
        "please use torch.distributed.distributed_c10d.get_global_rank instead"
    )
    return get_global_rank(group, rank)


def get_process_group_ranks(group: ProcessGroup) -> List[int]:
    """

    Get all ranks associated with ``group``.



    Args:

        group (ProcessGroup): ProcessGroup to get all ranks from.



    Returns:

        List of global ranks ordered by group rank.

    """
    return list(_world.pg_group_ranks[group].keys())

def _get_group_size(group) -> int:
    """Get a given group's world size."""
    if group is GroupMember.WORLD or group is None:
        default_pg = _get_default_group()
        return default_pg.size()
    return group.size()


def _get_group_size_by_name(group_name: str) -> int:
    group = _resolve_process_group(group_name)
    return group.size()


def _resolve_group_name_by_ranks_and_tag(ranks: List[int], tag: str) -> str:
    # TODO(yifu): remove this function once ranks + tag is not a supported
    # identifier for process group for functional collectives.
    group = _find_pg_by_ranks_and_tag(tag, ranks)
    if group is None:
        raise ValueError("")
    return group.group_name


def _check_single_tensor(param, param_name) -> None:
    """Check that the parameter ``param_name`` is a single tensor."""
    if not isinstance(param, torch.Tensor):
        raise TypeError(
            f"""Invalid function argument. Expected parameter `{param_name}` of type torch.Tensor

             but got {type(param)} instead."""
        )


def _check_tensor_list(param, param_name) -> None:
    """Check that the parameter ``param_name`` is a list of tensors."""
    if not isinstance(param, list):
        raise TypeError(
            f"""Invalid function argument. Expected parameter `{param_name}` of type List[torch.Tensor]

             but got {type(param)} instead."""
        )
    elif not all(isinstance(p, torch.Tensor) for p in param):
        raise TypeError(
            f"""Invalid function argument. Expected parameter `{param_name}` of type List[torch.Tensor]

             but got {type(param)} with elements of type {[type(p) for p in param]}."""
        )


def _as_iterable(obj) -> collections.abc.Iterable:
    return obj if isinstance(obj, list) else (obj,)

def _ensure_all_tensors_same_dtype(*tensors) -> None:
    last_dtype = None
    for tensor in itertools.chain.from_iterable(map(_as_iterable, tensors)):
        tensor_dtype = tensor.dtype
        # Mixing complex and its element type is allowed
        if tensor_dtype.is_complex:
            tensor_dtype = torch.float32 if tensor_dtype == torch.complex64 else torch.complex128

        if last_dtype is None:
            last_dtype = tensor_dtype
        else:
            if last_dtype != tensor_dtype:
                raise ValueError(
                    "Invalid usage of tensors with different dtypes"
                    f"Found {last_dtype} and  {tensor.dtype}"
                )


def _check_op(op) -> None:
    """Check that the ``op`` is either isend or irecv."""
    if op not in [isend, irecv]:
        raise ValueError(
            "Invalid ``op``. Expected ``op`` "
            "to be of type ``torch.distributed.isend`` or "
            "``torch.distributed.irecv``."
        )


def _check_p2p_op_list(p2p_op_list) -> None:
    """

    Check that the ``p2p_op_list`` is a list of P2POp instances.



    Also, check that all ops use the same group.

    """
    if not isinstance(p2p_op_list, list) or not all(
        isinstance(p2p_op, P2POp) for p2p_op in p2p_op_list
    ):
        raise ValueError(
            "Invalid ``p2p_op_list``. Each op is expected to "
            "to be of type ``torch.distributed.P2POp``."
        )

    group = p2p_op_list[0].group
    if not all(group == p2p_op.group for p2p_op in p2p_op_list):
        raise ValueError("All ops need to use the same group.")


def is_mpi_available() -> bool:
    """Check if the MPI backend is available."""
    return _MPI_AVAILABLE


def is_nccl_available() -> bool:
    """Check if the NCCL backend is available."""
    return _NCCL_AVAILABLE


def is_gloo_available() -> bool:
    """Check if the Gloo backend is available."""
    return _GLOO_AVAILABLE


def is_ucc_available() -> bool:
    """Check if the UCC backend is available."""
    return _UCC_AVAILABLE


def is_backend_available(backend: str) -> bool:
    """

    Check backend availability.



    Checks if the given backend is available and supports the built-in backends or

    third-party backends through function ``Backend.register_backend``.



    Args:

        backend (str): Backend name.

    Returns:

        bool: Returns true if the backend is available otherwise false.

    """
    # If the backend has an ``is_backend_available`` function, return the result of that function directly
    available_func = getattr(torch.distributed, f"is_{backend.lower()}_available", None)
    if available_func:
        return available_func()

    return backend.lower() in Backend.backend_list


def is_initialized() -> bool:
    """Check if the default process group has been initialized."""
    return GroupMember.WORLD is not None


def is_torchelastic_launched() -> bool:
    """

    Check whether this process was launched with ``torch.distributed.elastic`` (aka torchelastic).



    The existence of ``TORCHELASTIC_RUN_ID`` environment

    variable is used as a proxy to determine whether the current process

    was launched with torchelastic. This is a reasonable proxy since

    ``TORCHELASTIC_RUN_ID`` maps to the rendezvous id which is always a

    non-null value indicating the job id for peer discovery purposes..

    """
    return os.getenv("TORCHELASTIC_RUN_ID") is not None


def _is_barrier_after_init() -> int:
    # Environment variable to control whether process group should perform a
    # barrier after its init. Default value is 0, i.e. no barrier. If you
    # experience issue with this setting, you may set
    # `TORCH_DIST_INIT_BARRIER=1` to add the barrier.
    return int(os.getenv("TORCH_DIST_INIT_BARRIER", "0"))


def _abort_in_destroy_pg() -> bool:
    # Environment variable to control whether to abort the communicators when users call destroy_process_group()
    env = os.getenv("TORCH_NCCL_ABORT_IN_DESTROY_PG", "0")
    return env == "1" or env.lower() == "true"


def _get_default_group() -> ProcessGroup:
    """Get the default process group created by init_process_group."""
    if not is_initialized():
        raise ValueError(
            "Default process group has not been initialized, "
            "please make sure to call init_process_group."
        )
    return not_none(GroupMember.WORLD)


def _get_default_store() -> Store:
    """Get the default store created by init_process_group."""
    if not is_initialized():
        raise ValueError(
            "Default process group has not been initialized, "
            "please make sure to call init_process_group."
        )
    default_pg = _get_default_group()
    _, default_store = _world.pg_map[default_pg]
    return default_store


def _update_default_pg(pg) -> None:
    _world.default_pg = pg
    rank = pg.rank() if pg is not None and pg != GroupMember.NON_GROUP_MEMBER else -1
    torch._C._distributed_c10d._set_global_rank(rank)

def get_backend_config(group: Optional[ProcessGroup] = None) -> str:
    """

    Return the backend configuration of the given process group.



    Args:

        group (ProcessGroup, optional): The process group to work on. The

            default is the general main process group. If another specific group

            is specified, the calling process must be part of :attr:`group`.



    Returns:

        The backend configuration of the given process group as a lower case string.



    """
    if group is None:
        pg = _get_default_group()
    else:
        pg = group
    if _rank_not_in_group(pg):
        raise ValueError("Invalid process group specified")
    backend_config = _world.pg_backend_config.get(pg)
    return str(not_none(backend_config))

def get_backend(group: Optional[ProcessGroup] = None) -> Backend:
    """

    Return the backend of the given process group.



    Args:

        group (ProcessGroup, optional): The process group to work on. The

            default is the general main process group. If another specific group

            is specified, the calling process must be part of :attr:`group`.



    Returns:

        The backend of the given process group as a lower case string.



    """
    if group is None:
        pg = _get_default_group()
    else:
        pg = group
    if _rank_not_in_group(pg):
        raise ValueError("Invalid process group specified")
    pg_store = _world.pg_map[pg] if pg in _world.pg_map else None
    return Backend(not_none(pg_store)[0])

def _get_process_group_uid(pg: ProcessGroup) -> int:
    backend = None
    try:
        backend = pg._get_backend(torch.device("cuda"))
    except RuntimeError:
        pass
    if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
        return backend.uid
    return -1

def _get_pg_config(group: Optional[ProcessGroup] = None) -> Dict[str, Any]:
    """

    Return the pg configuration of the given process group.



    """
    if group is None:
        pg = _get_default_group()
    else:
        pg = group
    return {
        "pg_name": _get_process_group_name(pg),
        "uid": _get_process_group_uid(pg),
        "backend_config": get_backend_config(pg),
        "pg_size": _get_group_size(pg),
        "ranks": get_process_group_ranks(pg),
    }

def _get_all_pg_configs() -> List[Dict[str, Any]]:
    """

    Return the pg configuration of all the process groups.



    """
    config_info: List[Dict[str, Any]] = []
    for pg in _world.pg_map.keys():
        config_info.append(_get_pg_config(pg))
    return config_info

def get_pg_count() -> int:
    """

    Return the number of process groups.



    """
    return _world.group_count

def _set_pg_timeout(timeout: timedelta, group: Optional[ProcessGroup] = None) -> None:
    """

    Set the timeout for the given process group when users want to use a different timeout instead of

    default values.



    Args:

        timeout (timedelta): Timeout for operations executed against the process group which

            users want to set. Default value is 10 minutes for NCCL and 30 minutes for other backends.

            This is the duration after which collectives will be aborted asynchronously and the process will crash.

            This is done since CUDA execution is async and it is no longer safe to continue executing user code since

            failed async NCCL operations might result in subsequent CUDA operations running on corrupted data.

            When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.



        group (ProcessGroup, optional): The process group to work on. The

            default is the general main process group. If another specific group

            is specified, the calling process must be part of :attr:`group`.



    Returns:

        None

    """
    if group is None:
        group = _get_default_group()
    if _rank_not_in_group(group):
        raise ValueError("Invalid process group specified")
    assert isinstance(group, ProcessGroup)
    devices = group._device_types
    backends = set()
    if torch.device("cpu") in devices and is_gloo_available():
        backend = group._get_backend(torch.device("cpu"))
        if isinstance(backend, ProcessGroupGloo):
            backends.add(backend)
    if torch.device("cuda") in devices:
        backend = group._get_backend(torch.device("cuda"))
        if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
            backends.add(backend)  # type: ignore[arg-type]
        elif is_gloo_available() and isinstance(backend, ProcessGroupGloo):
            backends.add(backend)  # type: ignore[arg-type]
    if len(backends) == 0:
        warnings.warn("Set timeout is now only supported for either nccl or gloo.")
    for backend in backends:
        backend._set_default_timeout(timeout)


@_exception_logger
@_time_logger
def init_process_group(

    backend: Optional[str] = None,

    init_method: Optional[str] = None,

    timeout: Optional[timedelta] = None,

    world_size: int = -1,

    rank: int = -1,

    store: Optional[Store] = None,

    group_name: str = "",

    pg_options: Optional[Any] = None,

    device_id: Optional[torch.device] = None,

) -> None:
    """

    Initialize the default distributed process group.



    This will also initialize the distributed package.



    There are 2 main ways to initialize a process group:

        1. Specify ``store``, ``rank``, and ``world_size`` explicitly.

        2. Specify ``init_method`` (a URL string) which indicates where/how

           to discover peers. Optionally specify ``rank`` and ``world_size``,

           or encode all required parameters in the URL and omit them.



    If neither is specified, ``init_method`` is assumed to be "env://".





    Args:

        backend (str or Backend, optional): The backend to use. Depending on

            build-time configurations, valid values include ``mpi``, ``gloo``,

            ``nccl``, and ``ucc``. If the backend is not provided, then both a ``gloo``

            and ``nccl`` backend will be created, see notes below for how multiple

            backends are managed. This field can be given as a lowercase string

            (e.g., ``"gloo"``), which can also be accessed via

            :class:`Backend` attributes (e.g., ``Backend.GLOO``). If using

            multiple processes per machine with ``nccl`` backend, each process

            must have exclusive access to every GPU it uses, as sharing GPUs

            between processes can result in deadlocks. ``ucc`` backend is

            experimental.

        init_method (str, optional): URL specifying how to initialize the

                                     process group. Default is "env://" if no

                                     ``init_method`` or ``store`` is specified.

                                     Mutually exclusive with ``store``.

        world_size (int, optional): Number of processes participating in

                                    the job. Required if ``store`` is specified.

        rank (int, optional): Rank of the current process (it should be a

                              number between 0 and ``world_size``-1).

                              Required if ``store`` is specified.

        store(Store, optional): Key/value store accessible to all workers, used

                                to exchange connection/address information.

                                Mutually exclusive with ``init_method``.

        timeout (timedelta, optional): Timeout for operations executed against

            the process group. Default value is 10 minutes for NCCL and 30 minutes for other backends.

            This is the duration after which collectives will be aborted asynchronously and the process will crash.

            This is done since CUDA execution is async and it is no longer safe to continue executing user code since

            failed async NCCL operations might result in subsequent CUDA operations running on corrupted data.

            When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.



        group_name (str, optional, deprecated): Group name. This argument is ignored

        pg_options (ProcessGroupOptions, optional): process group options

            specifying what additional options need to be passed in during

            the construction of specific process groups. As of now, the only

            options we support is ``ProcessGroupNCCL.Options`` for the ``nccl``

            backend, ``is_high_priority_stream`` can be specified so that

            the nccl backend can pick up high priority cuda streams when

            there're compute kernels waiting.

        device_id (torch.device, optional): a single, specific device

            to "bind" this process to, allowing for backend-specific

            optimizations.  Currently this has two effects, only under

            NCCL: the communicator is immediately formed (calling

            ``ncclCommInit*`` immediately rather than the normal lazy

            call) and sub-groups will use ``ncclCommSplit`` when

            possible to avoid unnecessary overhead of group creation. If you

            want to know NCCL initialization error early, you can also use this

            field.



    .. note:: To enable ``backend == Backend.MPI``, PyTorch needs to be built from source

        on a system that supports MPI.



    .. note:: Support for multiple backends is experimental. Currently when no backend is

        specified, both ``gloo`` and ``nccl`` backends will be created. The ``gloo`` backend

        will be used for collectives with CPU tensors and the ``nccl`` backend will be used

        for collectives with CUDA tensors. A custom backend can be specified by passing in

        a string with format "<device_type>:<backend_name>,<device_type>:<backend_name>", e.g.

        "cpu:gloo,cuda:custom_backend".



    """

    global _world

    global _backend
    global _default_pg_init_method

    if GroupMember.WORLD is not None:
        raise ValueError("trying to initialize the default process group twice!")

    set_pytorch_distributed_envs_from_justknobs()

    assert (store is None) or (
        init_method is None
    ), "Cannot specify both init_method and store."

    if store is not None:
        assert world_size > 0, "world_size must be positive if using store"
        assert rank >= 0, "rank must be non-negative if using store"
    elif init_method is None:
        init_method = "env://"

    if backend:
        backend = Backend(backend)
    else:
        backend = Backend("undefined")

    if timeout is None:
        timeout = _get_default_timeout(backend)

    _check_valid_timeout(timeout)

    """

    Group name is not visible to users unless they access

    internals of c10d. This means we can ignore the value

    they provide as it not exposed in a public way.

    """
    group_name = _process_group_name([], use_hashed_name=False)
    if backend == Backend.MPI:
        if world_size != -1 or rank != -1:
            warnings.warn(
                f"For MPI backend, world_size ({world_size}) and rank ({rank}) "
                "are ignored since they are assigned by the "
                "MPI runtime."
            )

        default_pg, _ = _new_process_group_helper(
            -1, -1, [], backend, None, group_name, timeout=timeout
        )
        _update_default_pg(default_pg)
    else:
        # backward compatible API
        if store is None:
            rendezvous_iterator = rendezvous(
                not_none(init_method), rank, world_size, timeout=timeout
            )
            store, rank, world_size = next(rendezvous_iterator)
            store.set_timeout(timeout)

            # Use a PrefixStore to avoid accidental overrides of keys used by
            # different systems (e.g. RPC) in case the store is multi-tenant.
            store = PrefixStore("default_pg", store)

        default_pg, _ = _new_process_group_helper(
            world_size,
            rank,
            [],
            backend,
            store,
            group_name,
            pg_options=pg_options,
            timeout=timeout,
            device_id=device_id,
        )
        _update_default_pg(default_pg)

    _world.pg_group_ranks[GroupMember.WORLD] = {i: i for i in range(GroupMember.WORLD.size())}  # type: ignore[attr-defined, index]
    _backend = _world.pg_map[not_none(GroupMember.WORLD)][0]
    _default_pg_init_method = init_method

    old_hook = sys.excepthook

    def _distributed_excepthook(*args):
        old_stderr = sys.stderr
        sys.stderr = buf = io.StringIO()
        try:
            old_hook(*args)
        finally:
            sys.stderr = old_stderr
        msg = buf.getvalue()
        prefix = f"[rank{get_rank()}]"
        msg = "\n".join(f"{prefix}: {s}" if s != "" else "" for s in msg.split("\n"))
        sys.stderr.write(msg)
        sys.stderr.flush()

    sys.excepthook = _distributed_excepthook

    if _is_barrier_after_init() == 1:
        # barrier at the end to ensure that once we return from this method, all
        # process groups including global variables (if any) are updated
        # correctly on all ranks.
        # Update 04/2023: for large-scale runs, this barrier (esp. store-based
        # barrier) may be costly and/or unscalable. Also, in a lot of cases,
        # these barriers may be unnecessary, as proven by a green CI after
        # removal. An environment variable `TORCH_DIST_INIT_BARRIER` has been
        # added which enables this barrier only when set to 1.
        logger.info(
            "Performing barrier after ProcessGroup initialization since "
            "TORCH_DIST_INIT_BARRIER = 1"
        )
        if backend == Backend.MPI:
            # MPI backend doesn't use store.
            barrier()
        else:
            # Use store based barrier here since barrier() used a bunch of
            # default devices and messes up NCCL internal state.
            _store_based_barrier(rank, store, group_name, world_size, timeout)

def _get_split_source(pg):
    split_from = None
    if pg.bound_device_id:
        split_from = pg._get_backend(pg.bound_device_id)
    elif pg is _world.default_pg:
        try:
            split_from = pg._get_backend(torch.device("cuda"))
        except RuntimeError:
            # no cuda device associated with this backend
            pass

    if not split_from or not split_from.supports_splitting:
        return None

    # If necessary, find a backend to split from by peeling process
    # group wrappers from our potentially wrapped process group.
    while isinstance(split_from, _ProcessGroupWrapper):
        split_from = split_from.wrapped_pg

    return split_from

def _shutdown_backend(pg):
    """

    Try to shut down the backend of a process group.

    Currently, only ProcessGroupNCCL backend is supported.

    No op for other backends.

    """
    backend = None
    try:
        backend = pg._get_backend(torch.device("cuda"))
    except RuntimeError:
        pass
    if isinstance(backend, ProcessGroupNCCL):
        # explictly call shutdown to ensure that NCCL resources are released
        backend._shutdown()

def _new_process_group_helper(

    group_size,

    group_rank,

    global_ranks_in_group,

    backend,

    store,

    group_name,

    pg_options=None,

    timeout=None,

    pg_tag=None,

    device_id=None,

):
    """

    Create a new distributed process group.



    This function must be called by ALL processes in the global group, even if

    the calling process is not part of the newly created group. In that case,

    this function returns GroupMember.NON_GROUP_MEMBER.



    This function is called with ``global_ranks_in_group == []`` for the default group.

    """
    global _world

    if group_name in _world.pg_names.values():
        raise ValueError(
            "The specified group name has already been "
            "created, please use a different group name"
        )

    if device_id is not None and (device_id.index is None or device_id.type != 'cuda'):
        raise ValueError("init_process_group device_id parameter must be a cuda device with an "
                         "id, e.g. cuda:0, not just cuda or cpu")

    # Note: _new_process_group_helper is only called from init_process_group, which always provides a timeout value
    _check_valid_timeout(timeout)

    if pg_tag not in [None, ""]:
        # creating with the same tag and rank set results in the same underlying PG
        existing_group = _find_pg_by_ranks_and_tag(pg_tag, global_ranks_in_group)
        if existing_group:
            _, prefix_store = _world.pg_map[existing_group]
            return existing_group, prefix_store

    # The list of group ranks is empty if we're creating the default group.
    is_default_group = len(global_ranks_in_group) == 0

    # nccl and potentially other backends allow creation of
    # communicators based on pre-existing ones, which can save
    # initialization time.  Due to lazy initialization of
    # communicators in some backends, we have to be careful and only
    # split when we *know* the backends already are connected _on all
    # ranks_.  We can only know this if the group we are making is the
    # entire world or if we have bound a device id to the world (which
    # causes early connection initialization).
    if (is_initialized() and
            (len(global_ranks_in_group) == _get_default_group().size() or _get_default_group().bound_device_id)):
        split_from = _get_split_source(_get_default_group())
    else:
        split_from = None

    # If this is a subgroup (which means group_ranks is specified),
    # we check if the current process is a member of the new group.
    if not is_default_group:
        global_rank = _get_default_group().rank()
        if global_rank not in global_ranks_in_group:
            # If we are using `ncclCommSplit` (or similar split from
            # other APIs) to create the communicator, we will need to
            # call `ncclCommSplit` on *all* ranks in this new group's
            # parent group, even those not in the new group.  This is
            # a requirement of the NCCL API as otherwise we would get
            # out of sync.
            if split_from:
                split_from.perform_nocolor_split(_get_default_group().bound_device_id)
            return GroupMember.NON_GROUP_MEMBER, None

    prefix_store = PrefixStore(f"{group_name}/", store)
    base_pg_options = ProcessGroup.Options(backend=str(backend))
    base_pg_options._timeout = timeout
    pg: ProcessGroup = ProcessGroup(prefix_store, group_rank, group_size, base_pg_options)
    if device_id:
        pg.bound_device_id = device_id
    backend_config = BackendConfig(backend)
    backend_class: torch._C._distributed_c10d.Backend
    for device, backend_str in backend_config.get_device_backend_map().items():
        # Use the group name as prefix in the default store, such that
        # a single store can be reused by multiple groups.
        backend_prefix_store = PrefixStore(f"{device}/", prefix_store)

        if backend_str == Backend.MPI:
            if not is_mpi_available():
                raise RuntimeError(
                    "Distributed package doesn't have MPI built in."
                    " MPI is only included if you build PyTorch from"
                    " source on a host that has MPI installed."
                )
            backend_class = ProcessGroupMPI.create(global_ranks_in_group)
            backend_type = ProcessGroup.BackendType.MPI
            if not backend_class:
                return GroupMember.NON_GROUP_MEMBER, None
            # create new process group with accurate rank and size
            if pg.rank() == -1 and pg.size() == -1:
                pg = ProcessGroup(backend_prefix_store, backend_class.rank(), backend_class.size(), base_pg_options)
        elif backend_str == Backend.GLOO:
            # TODO: remove this check after lazy initialization is supported
            # if pg_options is not None:
            #     raise RuntimeError("GLOO options not supported")
            backend_class = ProcessGroupGloo(backend_prefix_store, group_rank, group_size, timeout=timeout)
            backend_type = ProcessGroup.BackendType.GLOO
        elif backend_str == Backend.NCCL:
            if not is_nccl_available():
                raise RuntimeError("Distributed package doesn't have NCCL built in")
            if pg_options is not None:
                assert isinstance(
                    pg_options, ProcessGroupNCCL.Options
                ), "Expected pg_options argument to be of type ProcessGroupNCCL.Options"
                if pg_options._timeout != timeout:
                    warnings.warn(
                        "pg_options._timeout was specified, "
                        "but timeout kwarg has a default value that will always override it. "
                    )
            else:
                # default pg_options for NCCL
                pg_options = ProcessGroupNCCL.Options()
                pg_options.is_high_priority_stream = False
            pg_options._timeout = timeout

            if split_from:
                pg_options.split_from = split_from
                pg_options.split_color = _process_group_color(global_ranks_in_group)
            pg_options.global_ranks_in_group = global_ranks_in_group
            backend_class = ProcessGroupNCCL(
                backend_prefix_store, group_rank, group_size, pg_options)
            backend_type = ProcessGroup.BackendType.NCCL
        elif backend_str == Backend.UCC and is_ucc_available():
            # TODO: once UCC plugin is fully deprecated, remove
            # is_ucc_available() from above elif-condition and raise
            # RuntimeError if is_ucc_available() returns false.

            backend_class = ProcessGroupUCC(backend_prefix_store, group_rank, group_size, timeout=timeout)
            backend_type = ProcessGroup.BackendType.UCC
        else:
            assert backend_str.upper() in Backend._plugins, (
                f"Unknown c10d backend type {backend_str.upper()}"
            )

            backend_plugin = Backend._plugins[backend_str.upper()]
            creator_fn = backend_plugin.creator_fn
            extended_api = backend_plugin.extended_api
            backend_type = ProcessGroup.BackendType.CUSTOM

            if not extended_api:
                backend_class = creator_fn(backend_prefix_store, group_rank, group_size, timeout)
            else:
                dist_backend_opts = _DistributedBackendOptions()
                dist_backend_opts.store = backend_prefix_store
                dist_backend_opts.group_rank = group_rank
                dist_backend_opts.group_size = group_size
                dist_backend_opts.timeout = timeout
                dist_backend_opts.group_id = group_name
                dist_backend_opts.global_ranks_in_group = global_ranks_in_group

                backend_class = creator_fn(dist_backend_opts, pg_options)

        # Set sequence numbers for gloo and nccl backends.
        if backend_str == Backend.GLOO:
            assert isinstance(backend_class, ProcessGroupGloo)
            backend_class._set_sequence_number_for_group()
        elif backend_str == Backend.NCCL:
            assert isinstance(backend_class, ProcessGroupNCCL)
            backend_class._set_sequence_number_for_group()

        # If the type is a subclass of ProcessGroup then return this process group immediately
        # TODO: This defaults to the old behavior for PythonProcessGroups which overwrites the
        # ProcessGroup instance
        if issubclass(type(backend_class), ProcessGroup):
            pg = backend_class  # type: ignore[assignment]
            break

        # Process group wrapper initialization for supported PGs when TORCH_DISTRIBUTED_DEBUG is set
        if backend_str in [Backend.GLOO, Backend.NCCL, Backend.UCC]:
            # In debug mode and if GLOO is available, wrap in a wrapper PG that
            # enables enhanced collective checking for debuggability.
            if get_debug_level() == DebugLevel.DETAIL:
                if not _GLOO_AVAILABLE:
                    logger.info(
                        """TORCH_DISTRIBUTED_DEBUG was set to DETAIL, but

                                GLOO is not available. Build with Gloo to

                                create a wrapper process group in debug mode

                                to aid collective desynchronization debugging."""
                    )
                else:
                    backend_class = _create_process_group_wrapper(
                        wrapped_pg=backend_class,
                        store_prefix=group_name,
                        store=backend_prefix_store,
                        rank=group_rank,
                        world_size=group_size,
                        timeout=timeout,
                    )

        # register only a single backend when all get_device_backend_map values are the same
        if len(set(backend_config.get_device_backend_map().values())) == 1:
            for device in backend_config.get_device_backend_map().keys():
                pg._register_backend(torch.device(device), backend_type, backend_class)

            # break out of outer loop to not create any more backends
            break

        pg._register_backend(torch.device(device), backend_type, backend_class)

    if device_id and pg._get_backend(device_id).supports_splitting:
        eager_backend = pg._get_backend(device_id)
        eager_backend.eager_connect_single_device(device_id)

    # update global state
    assert group_name is not None
    _world.pg_map[pg] = (backend, prefix_store)
    _world.pg_names[pg] = group_name
    pg._set_group_name(group_name)
    _register_process_group(group_name, pg)

    _world.pg_backend_config[pg] = str(backend_config)
    # "" is the default tag for user PGs
    if pg_tag in [None, ""]:
        pg_tag = f"ptd:{group_name}"
        _world.tags_to_pg.setdefault("", []).append(pg)
    else:
        pg_tag = f"user:{pg_tag}"

    _world.tags_to_pg.setdefault(pg_tag, []).append(pg)
    _world.pg_to_tag[pg] = pg_tag
    return pg, prefix_store

def destroy_process_group(group: Optional[ProcessGroup] = None):
    """

    Destroy a given process group, and deinitialize the distributed package.



    Args:

        group (ProcessGroup, optional): The process group to be destroyed, if

                                        group.WORLD is given, all process

                                        groups including the default one will

                                        be destroyed.

    """
    global _world

    if group == GroupMember.NON_GROUP_MEMBER:
        return

    if group is None:
        pg = GroupMember.WORLD
    else:
        pg = group

    assert pg is not None
    if _world.pg_map.get(pg, None) is None:
        raise ValueError("Invalid process group specified")

    # When users register Python onCompletion hooks, those hooks will run on a
    # different thread than the main thread. Today, the ProcessGroup dtor does
    # wait for that thread. However, the dtor might finish after the Python
    # Interpreter exits. After that grabbing the GIL for the Python hook will crash.
    # We can either revive the interpreter when running hooks or keep the main one
    # alive until all works and hooks are done. The current implementation does the
    # latter. Therefore, we explicitly call _wait_for_pending_works() here to wait
    # for the pending hooks to finish.
    if pg.name().lower() == "nccl" and pg._has_hooks():
        pg._wait_for_pending_works()

    if group is None or group == GroupMember.WORLD:
        if _abort_in_destroy_pg():
            # shutdown all backends in the order of pg names. shutting down in order because
            # ncclCommAbort() was a 'collective' call in some versions of NCCL.
            for pg_to_shutdown in sorted(_world.pg_names, key=lambda x: _world.pg_names[x], reverse=True):
                _shutdown_backend(pg_to_shutdown)

        _update_default_pg(None)
        _world.pg_map.clear()
        _world.pg_names.clear()
        _world.pg_group_ranks.clear()
        _world.pg_backend_config.clear()
        _world.pg_to_tag.clear()
        _world.tags_to_pg.clear()
        _world.pg_coalesce_state.clear()
        _world.pg_default_device.clear()
        _unregister_all_process_groups()

        # when process group doesn't have an explicit name (only WORLD (default)
        # process group can have an explicit name), we use global _world.group_count
        # to generate the name. We need to reset the counter on destruction to
        # allow consistent value to be generated when we re-create process
        # groups after some trainers recover from failure
        #
        # We only reset this when WORLD is being destroyed because if this
        # process group is in good state, we aren't dealing with failures.
        _world.group_count = 0
    else:
        if _abort_in_destroy_pg():
            _shutdown_backend(pg)
        del _world.pg_map[pg]
        del _world.pg_names[pg]
        del _world.pg_group_ranks[pg]
        del _world.pg_backend_config[pg]
        if pg in _world.pg_default_device:
            del _world.pg_default_device[pg]
        if pg in _world.pg_coalesce_state.keys():
            warnings.warn(
                "Some coalesced collectives haven't been launched when "
                "ProcessGroup is destroyed. They will be cleaned."
            )
            del _world.pg_coalesce_state[pg]

        tag = _world.pg_to_tag.get(pg)
        del _world.pg_to_tag[pg]
        if tag is not None:
            try:
                _world.tags_to_pg[tag].remove(pg)
                if tag.startswith("ptd:"):
                    _world.tags_to_pg[""].remove(pg)
            except Exception:
                pass
        _unregister_process_group(pg.group_name)


def get_rank(group: Optional[ProcessGroup] = None) -> int:
    """

    Return the rank of the current process in the provided ``group``, default otherwise.



    Rank is a unique identifier assigned to each process within a distributed

    process group. They are always consecutive integers ranging from 0 to

    ``world_size``.



    Args:

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.



    Returns:

        The rank of the process group

        -1, if not part of the group



    """
    if _rank_not_in_group(group):
        return -1

    default_pg = _get_default_group()
    if group is None or group is GroupMember.WORLD:
        return default_pg.rank()

    return get_group_rank(group, default_pg.rank())


def get_world_size(group: Optional[ProcessGroup] = None) -> int:
    """

    Return the number of processes in the current process group.



    Args:

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.



    Returns:

        The world size of the process group

        -1, if not part of the group



    """
    if _rank_not_in_group(group):
        return -1

    return _get_group_size(group)


def isend(tensor: torch.Tensor, dst: int, group: Optional[ProcessGroup] = None, tag: int = 0) -> Optional[Work]:
    """

    Send a tensor asynchronously.



    .. warning::

        Modifying ``tensor`` before the request completes causes undefined

        behavior.



    .. warning::

        ``tag`` is not supported with the NCCL backend.



    Args:

        tensor (Tensor): Tensor to send.

        dst (int): Destination rank on global process group (regardless of ``group`` argument)

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        tag (int, optional): Tag to match send with remote recv



    Returns:

        A distributed request object.

        None, if not part of the group



    """
    _check_single_tensor(tensor, "tensor")
    if _rank_not_in_group(group):
        _warn_not_in_group("isend")
        return None

    if tensor.is_complex():
        tensor = torch.view_as_real(tensor)

    if group is None or group is GroupMember.WORLD:
        pg = _get_default_group()
    else:
        pg = group
        dst = get_group_rank(pg, dst)

    return pg.send([tensor], dst, tag)

def irecv(tensor: torch.Tensor, src: Optional[int] = None, group: Optional[ProcessGroup] = None, tag: int = 0) -> Optional[Work]:
    """

    Receives a tensor asynchronously.



    .. warning::

        ``tag`` is not supported with the NCCL backend.



    Args:

        tensor (Tensor): Tensor to fill with received data.

        src (int, optional): Source rank on global process group (regardless of ``group`` argument).

            Will receive from any process if unspecified.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        tag (int, optional): Tag to match recv with remote send



    Returns:

        A distributed request object.

        None, if not part of the group



    """
    _check_single_tensor(tensor, "tensor")
    if _rank_not_in_group(group):
        _warn_not_in_group("irecv")
        return None

    if tensor.is_complex():
        tensor = torch.view_as_real(tensor)

    if group is None or group is GroupMember.WORLD:
        pg = _get_default_group()
    else:
        pg = group

    if src is None:
        return pg.recv_anysource([tensor], tag)
    else:
        if pg is GroupMember.WORLD:
            return pg.recv([tensor], src, tag)
        else:
            group_src_rank = get_group_rank(pg, src)
            return pg.recv([tensor], group_src_rank, tag)

@_exception_logger
def send(tensor: torch.Tensor, dst: int, group: Optional[ProcessGroup] = None, tag: int = 0) -> None:
    """

    Send a tensor synchronously.



    Args:

        tensor (Tensor): Tensor to send.

        dst (int): Destination rank on global process group (regardless of ``group`` argument).

            Destination rank should not be the same as the rank of the current process.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        tag (int, optional): Tag to match send with remote recv



    """
    if get_rank() == dst:
        raise ValueError(
            "Invalid destination rank: destination rank should not be the same as "
            "the rank of the current process."
        )

    _check_single_tensor(tensor, "tensor")
    if _rank_not_in_group(group):
        _warn_not_in_group("send")
        return None

    if tensor.is_complex():
        tensor = torch.view_as_real(tensor)

    if group is None or group is GroupMember.WORLD:
        default_pg = _get_default_group()
        default_pg.send([tensor], dst, tag).wait()
    else:
        group_dst_rank = get_group_rank(group, dst)
        group.send([tensor], group_dst_rank, tag).wait()

@_exception_logger
def recv(tensor: torch.Tensor, src: Optional[int] = None, group: Optional[ProcessGroup] = None, tag: int = 0) -> int:
    """

    Receives a tensor synchronously.



    Args:

        tensor (Tensor): Tensor to fill with received data.

        src (int, optional): Source rank on global process group (regardless of ``group`` argument).

            Will receive from any process if unspecified.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        tag (int, optional): Tag to match recv with remote send



    Returns:

        Sender rank

        -1, if not part of the group



    """
    _check_single_tensor(tensor, "tensor")
    if _rank_not_in_group(group):
        _warn_not_in_group("recv")
        return -1

    if tensor.is_complex():
        tensor = torch.view_as_real(tensor)

    if group is None:
        pg = _get_default_group()
    else:
        pg = group

    if src is None:
        work = pg.recv_anysource([tensor], tag)
        work.wait()
        src_rank = work._source_rank()
        if group is None or group is GroupMember.WORLD:
            return src_rank
        else:
            return get_global_rank(pg, src_rank)
    else:
        if group is None or group is GroupMember.WORLD:
            pg.recv([tensor], src, tag).wait()
        else:
            group_src_rank = get_group_rank(pg, src)
            pg.recv([tensor], group_src_rank, tag).wait()
        return src


class _IllegalWork(Work):
    def __getattribute__(self, name):
        if name in ["is_success", "exception", "wait", "source_rank", "_source_rank", "result", "synchronize"]:
            raise ValueError(f"Illegal to call {name} on IllegalWork object")


class _CoalescingManager:
    def __init__(self):
        self.works: List[Work] = []

    def append(self, work: Work):
        if work:
            self.works.append(work)

    def wait(self):
        for work in self.works:
            work.wait()


@contextlib.contextmanager
def _coalescing_manager(

    group: Optional[ProcessGroup] = None,

    device: Optional[torch.device] = None,

    async_ops: Optional[bool] = False,

):
    """

    Context manager used to coalesce collectives or P2P operations when possible.



    Args:

        group (`ProcessGroup`, optional): The process group to work on. If None,

            the default process group will be used.

        device (`torch.device`, optional): Default is None, set to a device if

            there isn't a `**_coalesced` implementation by the backend.

        async_ops (`bool`, optional): whether the coalesced ops are async ops.



    Examples:

        >>> # xdoctest: +SKIP("no rank")

        >>> # Synchronous ops

        >>> with _coalescing_manager():

        >>>     for i in range(num_colls):

        >>>         dist.all_reduce(tensors[i])

        >>> # Asynchronous ops

        >>> with _coalescing_manager(async_ops=True) as cm:

        >>>     for i in range(num_colls):

        >>>         dist.all_reduce(tensors[i])

        >>> cm.wait()



    .. warning::

       :func:`_coalescing_manager` currently do not support coalescing

       all-reduces with different reduce operators, e.g.  `ReduceOp.SUM` mixed

       with `ReduceOp.PRODUCT`.

    """
    group = group or _get_default_group()
    op_list = _world.pg_coalesce_state.setdefault(group, [])
    if op_list:
        raise ValueError("ProcessGroup has non-empty op list at the start of coalescing")
    if device:
        group._start_coalescing(device)
    cm = _CoalescingManager()
    yield cm
    op_list = _world.pg_coalesce_state.pop(group)
    if op_list:
        # Collectives supporting "Fast Path" coalescing are captured.
        # See implementation in corresponding collective APIs.
        # Currently supported:
        # - coalesced `all_reduce`
        # - coalesced `all_gather_into_tensor`
        # - coalesced `reduce_scatter_tensor`
        op0 = op_list[0].op
        if op0 == all_reduce:
            tensors = []
            for op in op_list:
                tensors.append(op.tensor)
            all_reduce_opts = AllreduceCoalescedOptions()
            all_reduce_opts.reduceOp = not_none(op_list[0].redop)
            work = group.allreduce_coalesced(tensors, all_reduce_opts)
        elif op0 == all_gather_into_tensor:
            inputs = []
            outputs = []
            for op in op_list:
                inputs.append(op.tensor)
                outputs.append(not_none(op.dst_tensor))
            work = group.allgather_into_tensor_coalesced(outputs, inputs)
        elif op0 == reduce_scatter_tensor:
            inputs = []
            outputs = []
            for op in op_list:
                inputs.append(op.tensor)
                outputs.append(not_none(op.dst_tensor))
            reduce_opts = ReduceScatterOptions()
            reduce_opts.reduceOp = not_none(op_list[0].redop)
            work = group.reduce_scatter_tensor_coalesced(outputs, inputs, reduce_opts)
        else:
            raise AssertionError(
                f"Coalescing manager does not support fast-path coalescing of {op0}, "
                f"yet {op0} is still recorded in op list. This is an internal error of c10d."
            )

    if device:
        # Old style of letting each coll inside the context manager to call into C++ counterpart via python binding
        work = group._end_coalescing(device)

    if async_ops:
        cm.append(work)  # type: ignore[possibly-undefined]
    else:
        work.wait()  # type: ignore[possibly-undefined]


def batch_isend_irecv(p2p_op_list):
    """

    Send or Receive a batch of tensors asynchronously and return a list of requests.



    Process each of the operations in ``p2p_op_list`` and return the corresponding

    requests. NCCL, Gloo, and UCC backend are currently supported.



    Args:

        p2p_op_list: A list of point-to-point operations(type of each operator is

            ``torch.distributed.P2POp``). The order of the isend/irecv in the list

            matters and it needs to match with corresponding isend/irecv on the

            remote end.



    Returns:

        A list of distributed request objects returned by calling the corresponding

        op in the op_list.



    Examples:

        >>> # xdoctest: +SKIP("no rank")

        >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank

        >>> recv_tensor = torch.randn(2, dtype=torch.float32)

        >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1)%world_size)

        >>> recv_op = dist.P2POp(dist.irecv, recv_tensor, (rank - 1 + world_size)%world_size)

        >>> reqs = batch_isend_irecv([send_op, recv_op])

        >>> for req in reqs:

        >>>     req.wait()

        >>> recv_tensor

        tensor([2, 3])     # Rank 0

        tensor([0, 1])     # Rank 1



    .. note:: Note that when this API is used with the NCCL PG backend, users must set

        the current GPU device with `torch.cuda.set_device`, otherwise it will

        lead to unexpected hang issues.



        In addition, if this API is the first collective call in the ``group``

        passed to ``dist.P2POp``, all ranks of the ``group`` must participate in

        this API call; otherwise, the behavior is undefined. If this API call is

        not the first collective call in the ``group``, batched P2P operations

        involving only a subset of ranks of the ``group`` are allowed.

    """
    _check_p2p_op_list(p2p_op_list)
    group = p2p_op_list[0].group
    device = p2p_op_list[0].tensor.device
    if device.type == "cuda":
        # NCCL style coalescing
        with _coalescing_manager(group, device, async_ops=True) as cm:
            for p2p_op in p2p_op_list:
                p2p_op.op(p2p_op.tensor, p2p_op.peer, p2p_op.group, p2p_op.tag)
        return cm.works
    else:
        # Backward support for Gloo
        reqs = []
        for p2p_op in p2p_op_list:
            work = p2p_op.op(p2p_op.tensor, p2p_op.peer, p2p_op.group, p2p_op.tag)
            if work:
                reqs.append(work)
        return reqs


@_exception_logger
def broadcast(tensor, src, group=None, async_op=False):
    """

    Broadcasts the tensor to the whole group.



    ``tensor`` must have the same number of elements in all processes

    participating in the collective.



    Args:

        tensor (Tensor): Data to be sent if ``src`` is the rank of current

            process, and tensor to be used to save received data otherwise.

        src (int): Source rank on global process group (regardless of ``group`` argument).

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    """
    _check_single_tensor(tensor, "tensor")
    if _rank_not_in_group(group):
        _warn_not_in_group("broadcast")
        return

    opts = BroadcastOptions()
    opts.rootRank = src
    opts.rootTensor = 0
    opts.asyncOp = async_op

    if group is None or group is GroupMember.WORLD:
        default_pg = _get_default_group()
        work = default_pg.broadcast([tensor], opts)
    else:
        group_src_rank = get_group_rank(group, src)
        opts.rootRank = group_src_rank
        work = group.broadcast([tensor], opts)
    if async_op:
        return work
    else:
        work.wait()

@_exception_logger
def all_reduce(tensor, op=ReduceOp.SUM, group=None, async_op=False):
    """

    Reduces the tensor data across all machines in a way that all get the final result.



    After the call ``tensor`` is going to be bitwise identical in all processes.



    Complex tensors are supported.



    Args:

        tensor (Tensor): Input and output of the collective. The function

            operates in-place.

        op (optional): One of the values from

            ``torch.distributed.ReduceOp``

            enum.  Specifies an operation used for element-wise reductions.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    Examples:

        >>> # xdoctest: +SKIP("no rank")

        >>> # All tensors below are of torch.int64 type.

        >>> # We have 2 process groups, 2 ranks.

        >>> device = torch.device(f'cuda:{rank}')

        >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank

        >>> tensor

        tensor([1, 2], device='cuda:0') # Rank 0

        tensor([3, 4], device='cuda:1') # Rank 1

        >>> dist.all_reduce(tensor, op=ReduceOp.SUM)

        >>> tensor

        tensor([4, 6], device='cuda:0') # Rank 0

        tensor([4, 6], device='cuda:1') # Rank 1



        >>> # All tensors below are of torch.cfloat type.

        >>> # We have 2 process groups, 2 ranks.

        >>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat, device=device) + 2 * rank * (1+1j)

        >>> tensor

        tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0

        tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1

        >>> dist.all_reduce(tensor, op=ReduceOp.SUM)

        >>> tensor

        tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0

        tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1



    """
    _check_single_tensor(tensor, "tensor")
    if _rank_not_in_group(group):
        _warn_not_in_group("all_reduce")
        return

    if tensor.is_complex():
        if not supports_complex(op):
            raise ValueError(f"all_reduce does not support {op} on complex tensors")
        tensor = torch.view_as_real(tensor)

    opts = AllreduceOptions()
    opts.reduceOp = op
    if group is None:
        group = _get_default_group()

    if group in _world.pg_coalesce_state.keys():
        # We are in coalescing context, do not issue single operation, just append a collective representation
        coll = _CollOp(all_reduce, tensor, None, op, None)
        _world.pg_coalesce_state[group].append(coll)
        if async_op:
            return _IllegalWork()
        else:
            return None

    work = group.allreduce([tensor], opts)

    if async_op:
        return work
    else:
        work.wait()

@_exception_logger
def all_reduce_coalesced(tensors, op=ReduceOp.SUM, group=None, async_op=False):
    """

    WARNING: at this time individual shape checking is not implemented across nodes.



    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the

    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the allreduce

    operation will proceed without complaint and return erroneous outputs. This lack

    of shape checking results in significant performance improvements but users of this

    function should take extra care to ensure that each node passes in tensors whose

    shapes match across nodes.



    Reduces each tensor in tensors (residing on the same device) across all machines

    in such a way that all get the final result.



    After the call each tensor in tensors is going to bitwise identical

    in all processes.



    Complex tensors are supported.



    Args:

        tensors (Union[List[Tensor], Tensor]): Input and output of the collective.

            The function operates in-place.

        op (Optional[ReduceOp]): One of the values from

            ``torch.distributed.ReduceOp`` enum. Specifies an operation used for

            element-wise reductions.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (Optional[bool]): Whether this op should be an async op.



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group.



    """
    warnings.warn(
        "torch.distributed.all_reduce_coalesced will be deprecated. If you must "
        "use it, please revisit our documentation later at "
        "https://pytorch.org/docs/master/distributed.html#collective-functions"
    )
    if isinstance(tensors, torch.Tensor):
        tensors = [tensors]
    _check_tensor_list(tensors, "tensor")
    _ensure_all_tensors_same_dtype(tensors)
    if _rank_not_in_group(group):
        _warn_not_in_group("all_reduce_coalesced")
        return

    if any(t.is_complex() for t in tensors) and not supports_complex(op):
        raise ValueError(f"all_reduce does not support {op} on complex tensors")

    tensors = [t if not t.is_complex() else torch.view_as_real(t) for t in tensors]

    opts = AllreduceCoalescedOptions()
    opts.reduceOp = op
    if group is None:
        default_pg = _get_default_group()
        work = default_pg.allreduce_coalesced(tensors, opts)
    else:
        work = group.allreduce_coalesced(tensors, opts)

    if async_op:
        return work.get_future()
    else:
        work.wait()

@_exception_logger
def reduce(tensor, dst, op=ReduceOp.SUM, group=None, async_op=False):
    """

    Reduces the tensor data across all machines.



    Only the process with rank ``dst`` is going to receive the final result.



    Args:

        tensor (Tensor): Input and output of the collective. The function

            operates in-place.

        dst (int): Destination rank on global process group (regardless of ``group`` argument)

        op (optional): One of the values from

            ``torch.distributed.ReduceOp``

            enum.  Specifies an operation used for element-wise reductions.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    """
    _check_single_tensor(tensor, "tensor")
    if _rank_not_in_group(group):
        _warn_not_in_group("reduce")
        return

    opts = ReduceOptions()
    opts.reduceOp = op
    opts.rootRank = dst

    if group is None or group is GroupMember.WORLD:
        default_pg = _get_default_group()
        work = default_pg.reduce([tensor], opts)
    else:
        group_dst_rank = get_group_rank(group, dst)
        opts.rootRank = group_dst_rank
        work = group.reduce([tensor], opts)

    if async_op:
        return work
    else:
        work.wait()

def _object_to_tensor(obj, device, group):
    f = io.BytesIO()
    _pickler(f).dump(obj)
    byte_storage = torch.ByteStorage._from_buffer(f.getvalue())  # type: ignore[attr-defined]
    # Do not replace `torch.ByteTensor` or `torch.LongTensor` with torch.tensor and specifying dtype.
    # Otherwise, it will casue 100X slowdown.
    # See: https://github.com/pytorch/pytorch/issues/65696
    byte_tensor = torch.ByteTensor(byte_storage).to(device)
    if get_debug_level() == DebugLevel.DETAIL and is_nccl_available():
        backend = get_backend(group)
        if backend == Backend.NCCL:
            hash = torch._C._distributed_c10d._hash_tensors([byte_tensor])
            logger.warning(f"_object_to_tensor size: {byte_tensor.numel()} hash value: {hash}")  # noqa: G004
    local_size = torch.LongTensor([byte_tensor.numel()]).to(device)
    return byte_tensor, local_size


def _tensor_to_object(tensor, tensor_size, group):
    if get_debug_level() == DebugLevel.DETAIL and is_nccl_available():
        backend = get_backend(group)
        if backend == Backend.NCCL:
            hash = torch._C._distributed_c10d._hash_tensors([tensor])
            logger.warning(f"_tensor_to_object size: {tensor.numel()} hash value: {hash}")  # noqa: G004
    tensor = tensor.cpu()
    buf = tensor.numpy().tobytes()[:tensor_size]
    return _unpickler(io.BytesIO(buf)).load()


@_exception_logger
def all_gather_object(object_list, obj, group=None):
    """

    Gathers picklable objects from the whole group into a list.



    Similar to :func:`all_gather`, but Python objects can be passed in.

    Note that the object must be picklable in order to be gathered.



    Args:

        object_list (list[Any]): Output list. It should be correctly sized as the

            size of the group for this collective and will contain the output.

        obj (Any): Pickable Python object to be broadcast from current process.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used. Default is ``None``.



    Returns:

        None. If the calling rank is part of this group, the output of the

        collective will be populated into the input ``object_list``. If the

        calling rank is not part of the group, the passed in ``object_list`` will

        be unmodified.



    .. note:: Note that this API differs slightly from the :func:`all_gather`

        collective since it does not provide an ``async_op`` handle and thus

        will be a blocking call.



    .. note:: For NCCL-based processed groups, internal tensor representations

        of objects must be moved to the GPU device before communication takes

        place. In this case, the device used is given by

        ``torch.cuda.current_device()`` and it is the user's responsiblity to

        ensure that this is set so that each rank has an individual GPU, via

        ``torch.cuda.set_device()``.



    .. warning::

        :func:`all_gather_object` uses ``pickle`` module implicitly, which is

        known to be insecure. It is possible to construct malicious pickle data

        which will execute arbitrary code during unpickling. Only call this

        function with data you trust.



    .. warning::

        Calling :func:`all_gather_object` with GPU tensors is not well supported

        and inefficient as it incurs GPU -> CPU transfer since tensors would be

        pickled. Please consider using :func:`all_gather` instead.



    Example::

        >>> # xdoctest: +SKIP("need process group init")

        >>> # Note: Process group initialization omitted on each rank.

        >>> import torch.distributed as dist

        >>> # Assumes world_size of 3.

        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object

        >>> output = [None for _ in gather_objects]

        >>> dist.all_gather_object(output, gather_objects[dist.get_rank()])

        >>> output

        ['foo', 12, {1: 2}]

    """
    if _rank_not_in_group(group):
        _warn_not_in_group("all_gather_object")
        return

    current_device = _get_pg_default_device(group)
    input_tensor, local_size = _object_to_tensor(obj, current_device, group)

    # Gather all local sizes. This is so that we can find the max size, and index
    # until the correct size when deserializing the tensors.
    group_size = get_world_size(group=group)
    object_sizes_tensor = torch.zeros(
        group_size, dtype=torch.long, device=current_device
    )
    object_size_list = [
        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
    ]
    # Allgather tensor sizes
    all_gather(object_size_list, local_size, group=group)
    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
    # Resize tensor to max size across all ranks.
    input_tensor.resize_(max_object_size)
    coalesced_output_tensor = torch.empty(
        max_object_size * group_size, dtype=torch.uint8, device=current_device
    )
    # Output tensors are nonoverlapping views of coalesced_output_tensor
    output_tensors = [
        coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
        for i in range(group_size)
    ]
    all_gather(output_tensors, input_tensor, group=group)
    # Deserialize outputs back to object.
    for i, tensor in enumerate(output_tensors):
        tensor = tensor.type(torch.uint8)
        tensor_size = object_size_list[i]
        object_list[i] = _tensor_to_object(tensor, tensor_size, group)


@_exception_logger
def gather_object(obj, object_gather_list=None, dst=0, group=None):
    """

    Gathers picklable objects from the whole group in a single process.



    Similar to :func:`gather`, but Python objects can be passed in. Note that the

    object must be picklable in order to be gathered.



    Args:

        obj (Any): Input object. Must be picklable.

        object_gather_list (list[Any]): Output list. On the ``dst`` rank, it

            should be correctly sized as the size of the group for this

            collective and will contain the output. Must be ``None`` on non-dst

            ranks. (default is ``None``)

        dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0)

        group: (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used. Default is ``None``.



    Returns:

        None. On the ``dst`` rank, ``object_gather_list`` will contain the

        output of the collective.



    .. note:: Note that this API differs slightly from the gather collective

        since it does not provide an async_op handle and thus will be a blocking

        call.



    .. note:: For NCCL-based processed groups, internal tensor representations

        of objects must be moved to the GPU device before communication takes

        place. In this case, the device used is given by

        ``torch.cuda.current_device()`` and it is the user's responsiblity to

        ensure that this is set so that each rank has an individual GPU, via

        ``torch.cuda.set_device()``.



    .. warning::

        :func:`gather_object` uses ``pickle`` module implicitly, which is

        known to be insecure. It is possible to construct malicious pickle data

        which will execute arbitrary code during unpickling. Only call this

        function with data you trust.



    .. warning::

        Calling :func:`gather_object` with GPU tensors is not well supported

        and inefficient as it incurs GPU -> CPU transfer since tensors would be

        pickled. Please consider using :func:`gather` instead.



    Example::

        >>> # xdoctest: +SKIP("need process group init")

        >>> # Note: Process group initialization omitted on each rank.

        >>> import torch.distributed as dist

        >>> # Assumes world_size of 3.

        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object

        >>> output = [None for _ in gather_objects]

        >>> dist.gather_object(

        ...     gather_objects[dist.get_rank()],

        ...     output if dist.get_rank() == 0 else None,

        ...     dst=0

        ... )

        >>> # On rank 0

        >>> output

        ['foo', 12, {1: 2}]

    """
    if _rank_not_in_group(group):
        _warn_not_in_group("gather_object")
        return

    # Ensure object_gather_list is specified appropriately.
    my_rank = get_rank()
    _validate_output_list_for_rank(my_rank, dst, object_gather_list)
    current_device = _get_pg_default_device(group)
    input_tensor, local_size = _object_to_tensor(obj, current_device, group)

    # Gather all local sizes. This is so that we can find the max size, and index
    # until the correct size when deserializing the tensors.
    group_size = get_world_size(group=group)
    object_sizes_tensor = torch.zeros(
        group_size, dtype=torch.long, device=current_device
    )
    object_size_list = [
        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
    ]
    # Allgather tensor sizes. An all-gather is needed here despite this being a
    # gather, since each rank needs to broadcast a tensor of the same (maximal)
    # size.
    all_gather(object_size_list, local_size, group=group)
    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
    # Resize tensor to max size across all ranks.
    input_tensor.resize_(max_object_size)
    # Avoid populating output tensors if the result won't be gathered on this rank.
    if my_rank == dst:
        coalesced_output_tensor = torch.empty(
            max_object_size * group_size, dtype=torch.uint8, device=current_device
        )
        # Output tensors are nonoverlapping views of coalesced_output_tensor
        output_tensors = [
            coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
            for i in range(group_size)
        ]
    # All ranks call gather with equal-sized tensors.
    gather(
        input_tensor,
        gather_list=output_tensors if my_rank == dst else None,  # type: ignore[possibly-undefined]
        dst=dst,
        group=group,
    )
    if my_rank != dst:
        return
    for i, tensor in enumerate(output_tensors):
        tensor = tensor.type(torch.uint8)
        tensor_size = object_size_list[i]
        object_gather_list[i] = _tensor_to_object(tensor, tensor_size, group)


@_exception_logger
def broadcast_object_list(object_list, src=0, group=None, device=None):
    """

    Broadcasts picklable objects in ``object_list`` to the whole group.



    Similar to :func:`broadcast`, but Python objects can be passed in.

    Note that all objects in ``object_list`` must be picklable in order to be

    broadcasted.



    Args:

        object_list (List[Any]): List of input objects to broadcast.

            Each object must be picklable. Only objects on the ``src`` rank will

            be broadcast, but each rank must provide lists of equal sizes.

        src (int): Source rank from which to broadcast ``object_list``.

            Source rank is based on global process group (regardless of ``group`` argument)

        group: (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used. Default is ``None``.

        device (``torch.device``, optional): If not None, the objects are

            serialized and converted to tensors which are moved to the

            ``device`` before broadcasting. Default is ``None``.



    Returns:

        ``None``. If rank is part of the group, ``object_list`` will contain the

        broadcasted objects from ``src`` rank.



    .. note:: For NCCL-based process groups, internal tensor representations

        of objects must be moved to the GPU device before communication takes

        place. In this case, the device used is given by

        ``torch.cuda.current_device()`` and it is the user's responsibility to

        ensure that this is set so that each rank has an individual GPU, via

        ``torch.cuda.set_device()``.



    .. note:: Note that this API differs slightly from the :func:`all_gather`

        collective since it does not provide an ``async_op`` handle and thus

        will be a blocking call.



    .. warning::

        :func:`broadcast_object_list` uses ``pickle`` module implicitly, which

        is known to be insecure. It is possible to construct malicious pickle

        data which will execute arbitrary code during unpickling. Only call this

        function with data you trust.



    .. warning::

        Calling :func:`broadcast_object_list` with GPU tensors is not well supported

        and inefficient as it incurs GPU -> CPU transfer since tensors would be

        pickled. Please consider using :func:`broadcast` instead.



    Example::

        >>> # xdoctest: +SKIP("need process group init")

        >>> # Note: Process group initialization omitted on each rank.

        >>> import torch.distributed as dist

        >>> if dist.get_rank() == 0:

        >>>     # Assumes world_size of 3.

        >>>     objects = ["foo", 12, {1: 2}] # any picklable object

        >>> else:

        >>>     objects = [None, None, None]

        >>> # Assumes backend is not NCCL

        >>> device = torch.device("cpu")

        >>> dist.broadcast_object_list(objects, src=0, device=device)

        >>> objects

        ['foo', 12, {1: 2}]

    """
    if _rank_not_in_group(group):
        _warn_not_in_group("broadcast_object_list")
        return

    # Current device selection.
    # To preserve backwards compatibility, ``device`` is default to ``None``
    # in which case we run current logic of device selection, i.e.
    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
    # case it is not ``None`` we move the size and object tensors to be
    # broadcasted to this device.
    current_device = device or _get_pg_default_device(group)
    my_rank = get_rank()
    # Serialize object_list elements to tensors on src rank.
    if my_rank == src:
        tensor_list, size_list = zip(*[_object_to_tensor(obj, current_device, group) for obj in object_list])
        object_sizes_tensor = torch.cat(size_list)
    else:
        object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long, device=current_device)

    # Broadcast object sizes
    broadcast(object_sizes_tensor, src=src, group=group)

    # Concatenate and broadcast serialized object tensors
    # Note: torch.cat will do an extra memory copy to the current device, if the tensor_list
    # has only one element, we can skip the copy.
    if my_rank == src:
        if len(tensor_list) == 1:  # type: ignore[possibly-undefined]
            object_tensor = tensor_list[0]
        else:
            object_tensor = torch.cat(tensor_list)
    else:
        object_tensor = torch.empty(  # type: ignore[call-overload]
            torch.sum(object_sizes_tensor).item(),  # type: ignore[arg-type]
            dtype=torch.uint8,
            device=current_device
        )

    broadcast(object_tensor, src=src, group=group)
    # Deserialize objects using their stored sizes.
    offset = 0
    if my_rank != src:
        for i, obj_size in enumerate(object_sizes_tensor):
            obj_view = object_tensor[offset : offset + obj_size]
            obj_view = obj_view.type(torch.uint8)
            offset += obj_size
            object_list[i] = _tensor_to_object(obj_view, obj_size, group)


@_exception_logger
def scatter_object_list(

    scatter_object_output_list, scatter_object_input_list, src=0, group=None

):
    """

    Scatters picklable objects in ``scatter_object_input_list`` to the whole group.



    Similar to :func:`scatter`, but Python objects can be passed in. On

    each rank, the scattered object will be stored as the first element of

    ``scatter_object_output_list``. Note that all objects in

    ``scatter_object_input_list`` must be picklable in order to be scattered.



    Args:

        scatter_object_output_list (List[Any]): Non-empty list whose first

            element will store the object scattered to this rank.

        scatter_object_input_list (List[Any]): List of input objects to scatter.

            Each object must be picklable. Only objects on the ``src`` rank will

            be scattered, and the argument can be ``None`` for non-src ranks.

        src (int): Source rank from which to scatter ``scatter_object_input_list``.

            Source rank is based on global process group (regardless of ``group`` argument).

        group: (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used. Default is ``None``.



    Returns:

        ``None``. If rank is part of the group, ``scatter_object_output_list``

        will have its first element set to the scattered object for this rank.



    .. note:: Note that this API differs slightly from the scatter collective

        since it does not provide an ``async_op`` handle and thus will be a

        blocking call.



    .. warning::

        :func:`scatter_object_list` uses ``pickle`` module implicitly, which

        is known to be insecure. It is possible to construct malicious pickle

        data which will execute arbitrary code during unpickling. Only call this

        function with data you trust.



    .. warning::

        Calling :func:`scatter_object_list` with GPU tensors is not well supported

        and inefficient as it incurs GPU -> CPU transfer since tensors would be

        pickled. Please consider using :func:`scatter` instead.



    Example::

        >>> # xdoctest: +SKIP("need process group init")

        >>> # Note: Process group initialization omitted on each rank.

        >>> import torch.distributed as dist

        >>> if dist.get_rank() == 0:

        >>>     # Assumes world_size of 3.

        >>>     objects = ["foo", 12, {1: 2}] # any picklable object

        >>> else:

        >>>     # Can be any list on non-src ranks, elements are not used.

        >>>     objects = [None, None, None]

        >>> output_list = [None]

        >>> dist.scatter_object_list(output_list, objects, src=0)

        >>> # Rank i gets objects[i]. For example, on rank 2:

        >>> output_list

        [{1: 2}]

    """
    if _rank_not_in_group(group):
        _warn_not_in_group("scatter_object_list")
        return

    if (
        not isinstance(scatter_object_output_list, list)
        or len(scatter_object_output_list) < 1
    ):
        raise ValueError(
            "Expected argument scatter_object_output_list to be a list of size at least 1."
        )

    my_rank = get_rank()
    pg_device = _get_pg_default_device(group)
    if my_rank == src:
        tensor_list, tensor_sizes = zip(
            *[_object_to_tensor(obj, pg_device, group) for obj in scatter_object_input_list]
        )
        tensor_list, tensor_sizes = list(tensor_list), list(tensor_sizes)

    # Src rank broadcasts the maximum tensor size. This is because all ranks are
    # expected to call into scatter() with equal-sized tensors.
    if my_rank == src:
        max_tensor_size = max(tensor_sizes)  # type: ignore[possibly-undefined]
        for tensor in tensor_list:  # type: ignore[possibly-undefined]
            tensor.resize_(max_tensor_size)
    else:
        max_tensor_size = torch.tensor([0], dtype=torch.long, device=pg_device)
    broadcast(max_tensor_size, src=src, group=group)

    # Scatter actual serialized objects
    output_tensor = torch.empty(max_tensor_size.item(), dtype=torch.uint8, device=pg_device)
    scatter(
        output_tensor,
        scatter_list=None if my_rank != src else tensor_list,  # type: ignore[possibly-undefined]
        src=src,
        group=group,
    )

    # Scatter per-object sizes to trim tensors when deserializing back to object
    obj_tensor_size = torch.tensor([0], dtype=torch.long, device=pg_device)
    scatter(
        obj_tensor_size,
        scatter_list=None if my_rank != src else tensor_sizes,  # type: ignore[possibly-undefined]
        src=src,
        group=group,
    )

    # Deserialize back to object
    scatter_object_output_list[0] = _tensor_to_object(output_tensor, obj_tensor_size, group)


@_exception_logger
def all_gather(tensor_list, tensor, group=None, async_op=False):
    """

    Gathers tensors from the whole group in a list.



    Complex tensors are supported.



    Args:

        tensor_list (list[Tensor]): Output list. It should contain

            correctly-sized tensors to be used for output of the collective.

        tensor (Tensor): Tensor to be broadcast from current process.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    Examples:

        >>> # xdoctest: +SKIP("need process group init")

        >>> # All tensors below are of torch.int64 dtype.

        >>> # We have 2 process groups, 2 ranks.

        >>> device = torch.device(f'cuda:{rank}')

        >>> tensor_list = [torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)]

        >>> tensor_list

        [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0

        [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:1')] # Rank 1

        >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank

        >>> tensor

        tensor([1, 2], device='cuda:0') # Rank 0

        tensor([3, 4], device='cuda:1') # Rank 1

        >>> dist.all_gather(tensor_list, tensor)

        >>> tensor_list

        [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0

        [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1



        >>> # All tensors below are of torch.cfloat dtype.

        >>> # We have 2 process groups, 2 ranks.

        >>> tensor_list = [torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)]

        >>> tensor_list

        [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0

        [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1

        >>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat, device=device) + 2 * rank * (1+1j)

        >>> tensor

        tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0

        tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1

        >>> dist.all_gather(tensor_list, tensor)

        >>> tensor_list

        [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0

        [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1



    """
    _check_tensor_list(tensor_list, "tensor_list")
    _check_single_tensor(tensor, "tensor")
    _ensure_all_tensors_same_dtype(tensor_list, tensor)
    if _rank_not_in_group(group):
        _warn_not_in_group("all_gather")
        return

    tensor_list = [
        t if not t.is_complex() else torch.view_as_real(t) for t in tensor_list
    ]
    tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)

    if group is None:
        default_pg = _get_default_group()
        work = default_pg.allgather([tensor_list], [tensor])
    else:
        work = group.allgather([tensor_list], [tensor])

    if async_op:
        return work
    else:
        work.wait()


@_exception_logger
def all_gather_into_tensor(output_tensor, input_tensor, group=None, async_op=False):
    """

    Gather tensors from all ranks and put them in a single output tensor.



    Args:

        output_tensor (Tensor): Output tensor to accommodate tensor elements

            from all ranks. It must be correctly sized to have one of the

            following forms:

            (i) a concatenation of all the input tensors along the primary

            dimension; for definition of "concatenation", see ``torch.cat()``;

            (ii) a stack of all the input tensors along the primary dimension;

            for definition of "stack", see ``torch.stack()``.

            Examples below may better explain the supported output forms.

        input_tensor (Tensor): Tensor to be gathered from current rank.

            Different from the ``all_gather`` API, the input tensors in this

            API must have the same size across all ranks.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    Examples:

        >>> # xdoctest: +SKIP("need process group init")

        >>> # All tensors below are of torch.int64 dtype and on CUDA devices.

        >>> # We have two ranks.

        >>> device = torch.device(f'cuda:{rank}')

        >>> tensor_in = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank

        >>> tensor_in

        tensor([1, 2], device='cuda:0') # Rank 0

        tensor([3, 4], device='cuda:1') # Rank 1

        >>> # Output in concatenation form

        >>> tensor_out = torch.zeros(world_size * 2, dtype=torch.int64, device=device)

        >>> dist.all_gather_into_tensor(tensor_out, tensor_in)

        >>> tensor_out

        tensor([1, 2, 3, 4], device='cuda:0') # Rank 0

        tensor([1, 2, 3, 4], device='cuda:1') # Rank 1

        >>> # Output in stack form

        >>> tensor_out2 = torch.zeros(world_size, 2, dtype=torch.int64, device=device)

        >>> dist.all_gather_into_tensor(tensor_out2, tensor_in)

        >>> tensor_out2

        tensor([[1, 2],

                [3, 4]], device='cuda:0') # Rank 0

        tensor([[1, 2],

                [3, 4]], device='cuda:1') # Rank 1



    .. warning::

        The Gloo backend does not support this API.



    """
    _check_single_tensor(input_tensor, "input_tensor")
    _check_single_tensor(output_tensor, "output_tensor")
    if _rank_not_in_group(group):
        _warn_not_in_group("all_gather_into_tensor")
        return

    output_tensor = (
        output_tensor
        if not output_tensor.is_complex()
        else torch.view_as_real(output_tensor)
    )
    input_tensor = (
        input_tensor
        if not input_tensor.is_complex()
        else torch.view_as_real(input_tensor)
    )

    opts = AllgatherOptions()
    opts.asyncOp = async_op

    group = group or _get_default_group()

    if group in _world.pg_coalesce_state.keys():
        # We are in coalescing context, do not issue single operation, just append a collective representation
        coll = _CollOp(all_gather_into_tensor, input_tensor, output_tensor)
        _world.pg_coalesce_state[group].append(coll)
        if async_op:
            return _IllegalWork()
        else:
            return None

    work = group._allgather_base(output_tensor, input_tensor, opts)

    if async_op:
        return work
    else:
        work.wait()


@_exception_logger
def _all_gather_base(output_tensor, input_tensor, group=None, async_op=False):
    """

    Single tensor all gather. Gathers a single tensor from all ranks, and puts them in a single output tensor.



    Args:

        output_tensor (Tensor): Output tensor. It should contain

            correctly-sized tensors to be used for output of the collective.

        input_tensor (Tensor): Tensor to be broadcast from current process.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    .. warning::

        `_all_gather_base` is a private function. Users should use

        `all_gather_into_tensor` instead.



    """
    warnings.warn(
        "torch.distributed._all_gather_base is a private function and will be "
        "deprecated. Please use torch.distributed.all_gather_into_tensor "
        "instead."
    )
    return all_gather_into_tensor(output_tensor, input_tensor, group, async_op)


@_exception_logger
def all_gather_coalesced(

    output_tensor_lists, input_tensor_list, group=None, async_op=False

):
    """

    Gathers input tensors from the whole group in a list in a coalesced manner.



    Complex tensors are supported.



    Args:

        output_tensor_lists (list[list[Tensor]]): Output list. It should contain

            correctly-sized tensors to be used for output of the collective.

        input_tensor_list (list[Tensor]): Tensors to be broadcast from

            current process. At least one tensor has to be non empty.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op.



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    Example:

        we have 2 process groups, 2 ranks.

        rank 0 passes:

            input_tensor_list = [[[1, 1], [1, 1]], [2], [3, 3]]

            output_tensor_lists =

               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],

                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]

        rank 1 passes:

            input_tensor_list = [[[3, 3], [3, 3]], [5], [1, 1]]

            output_tensor_lists =

               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],

                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]

        both rank 0 and 1 get:

            output_tensor_lists =

               [[[1, 1], [1, 1]], [2], [3, 3]],

                [[3, 3], [3, 3]], [5], [1, 1]]].



    WARNING: at this time individual shape checking is not implemented across nodes.

    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the

    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the

    all_gather_coalesced operation will proceed without complaint and return

    erroneous outputs. This lack of shape checking results in significant

    performance improvements but users of this function should take extra care

    to ensure that each node passes in tensors whose shapes match across nodes.

    """
    warnings.warn(
        "torch.distributed.all_gather_coalesced will be deprecated. If you must "
        "use it, please revisit our documentation later at "
        "https://pytorch.org/docs/master/distributed.html#collective-functions"
    )
    # We only check basic compatibility with C++ params here, C++ code will
    # do shape and type checking.
    if _rank_not_in_group(group):
        _warn_not_in_group("all_gather_coalesced")
        return
    _check_tensor_list(input_tensor_list, "input_tensor_list")
    _ensure_all_tensors_same_dtype(input_tensor_list)
    if not isinstance(output_tensor_lists, list):
        raise TypeError(
            "Invalid function argument: output_tensor_lists should be a list"
        )
    for output_tensor_list in output_tensor_lists:
        _check_tensor_list(output_tensor_list, "output_tensor_lists")
        _ensure_all_tensors_same_dtype(output_tensor_list)

    output_tensor_lists = [
        [t if not t.is_complex() else torch.view_as_real(t) for t in l]
        for l in output_tensor_lists
    ]
    input_tensor_list = [
        t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list
    ]

    if group is None:
        default_pg = _get_default_group()
        work = default_pg.allgather_coalesced(output_tensor_lists, input_tensor_list)
    else:
        work = group.allgather_coalesced(output_tensor_lists, input_tensor_list)

    if async_op:
        return work.get_future()
    else:
        work.wait()


def _validate_output_list_for_rank(my_rank, dst, gather_list):
    if dst == my_rank:
        if not gather_list:
            raise ValueError(
                "Argument ``gather_list`` must be specified on destination rank."
            )
    elif gather_list:
        raise ValueError(
            "Argument ``gather_list`` must NOT be specified "
            "on non-destination ranks."
        )


@_exception_logger
def gather(tensor, gather_list=None, dst=0, group=None, async_op=False):
    """

    Gathers a list of tensors in a single process.



    Args:

        tensor (Tensor): Input tensor.

        gather_list (list[Tensor], optional): List of appropriately-sized

            tensors to use for gathered data (default is None, must be specified

            on the destination rank)

        dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). (default is 0)

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    """
    _check_single_tensor(tensor, "tensor")

    # Parameter ``gather_list`` may be left unspecified on non-dst ranks.
    if gather_list:
        _check_tensor_list(gather_list, "gather_list")
    else:
        gather_list = []
    _ensure_all_tensors_same_dtype(tensor, gather_list)

    if _rank_not_in_group(group):
        _warn_not_in_group("gather")
        return

    my_rank = get_rank()
    _validate_output_list_for_rank(my_rank, dst, gather_list)
    output_tensors = [gather_list] if dst == my_rank else []
    input_tensors = [tensor]

    opts = GatherOptions()
    opts.rootRank = dst

    if group is None or group is GroupMember.WORLD:
        default_pg = _get_default_group()
        work = default_pg.gather(output_tensors, input_tensors, opts)
    else:
        group_dst_rank = get_group_rank(group, dst)
        opts.rootRank = group_dst_rank
        work = group.gather(output_tensors, input_tensors, opts)

    if async_op:
        return work
    else:
        work.wait()


@_exception_logger
def scatter(tensor, scatter_list=None, src=0, group=None, async_op=False):
    """

    Scatters a list of tensors to all processes in a group.



    Each process will receive exactly one tensor and store its data in the

    ``tensor`` argument.



    Complex tensors are supported.



    Args:

        tensor (Tensor): Output tensor.

        scatter_list (list[Tensor]): List of tensors to scatter (default is

            None, must be specified on the source rank)

        src (int): Source rank on global process group (regardless of ``group`` argument).

            Default is 0

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    .. note:: Note that all Tensors in scatter_list must have the same size.



    Example::

        >>> # xdoctest: +SKIP("need process group init")

        >>> # Note: Process group initialization omitted on each rank.

        >>> import torch.distributed as dist

        >>> tensor_size = 2

        >>> t_ones = torch.ones(tensor_size)

        >>> t_fives = torch.ones(tensor_size) * 5

        >>> output_tensor = torch.zeros(tensor_size)

        >>> if dist.get_rank() == 0:

        >>>     # Assumes world_size of 2.

        >>>     # Only tensors, all of which must be the same size.

        >>>     scatter_list = [t_ones, t_fives]

        >>> else:

        >>>     scatter_list = None

        >>> dist.scatter(output_tensor, scatter_list, src=0)

        >>> # Rank i gets scatter_list[i]. For example, on rank 1:

        >>> output_tensor

        tensor([5., 5.])



    """
    _check_single_tensor(tensor, "tensor")

    # Parameter ``scatter_list`` may be left unspecified on non-src ranks.
    if scatter_list:
        _check_tensor_list(scatter_list, "scatter_list")
    else:
        scatter_list = []
    _ensure_all_tensors_same_dtype(tensor, scatter_list)

    if _rank_not_in_group(group):
        _warn_not_in_group("scatter")
        return
    scatter_list = [
        t if not t.is_complex() else torch.view_as_real(t) for t in scatter_list
    ]
    tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)

    my_rank = get_rank()
    if src == my_rank:
        if not scatter_list:
            raise ValueError(
                "Argument ``scatter_list`` must be specified on source rank."
            )
        input_tensors = [scatter_list]
        output_tensors = [tensor]
    else:
        if scatter_list:
            raise ValueError(
                "Argument ``scatter_list`` must NOT be specified "
                "on non-source ranks."
            )
        input_tensors = []
        output_tensors = [tensor]

    opts = ScatterOptions()
    opts.rootRank = src
    opts.asyncOp = async_op

    if group is None or group is GroupMember.WORLD:
        default_pg = _get_default_group()
        work = default_pg.scatter(output_tensors, input_tensors, opts)
    else:
        group_src_rank = get_group_rank(group, src)
        opts.rootRank = group_src_rank
        work = group.scatter(output_tensors, input_tensors, opts)

    if async_op:
        return work
    else:
        work.wait()


@_exception_logger
def reduce_scatter(output, input_list, op=ReduceOp.SUM, group=None, async_op=False):
    """

    Reduces, then scatters a list of tensors to all processes in a group.



    Args:

        output (Tensor): Output tensor.

        input_list (list[Tensor]): List of tensors to reduce and scatter.

        op (optional): One of the values from

            ``torch.distributed.ReduceOp``

            enum.  Specifies an operation used for element-wise reductions.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op.



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group.



    """
    _check_single_tensor(output, "output")
    _check_tensor_list(input_list, "input_list")
    _ensure_all_tensors_same_dtype(output, input_list)
    if _rank_not_in_group(group):
        _warn_not_in_group("reduce_scatter")
        return

    opts = ReduceScatterOptions()
    opts.reduceOp = op

    if group is None:
        default_pg = _get_default_group()
        work = default_pg.reduce_scatter([output], [input_list], opts)
    else:
        work = group.reduce_scatter([output], [input_list], opts)

    if async_op:
        return work
    else:
        work.wait()


@_exception_logger
def reduce_scatter_tensor(output, input, op=ReduceOp.SUM, group=None, async_op=False):
    """

    Reduces, then scatters a tensor to all ranks in a group.



    Args:

        output (Tensor): Output tensor. It should have the same size across all

            ranks.

        input (Tensor): Input tensor to be reduced and scattered. Its size

            should be output tensor size times the world size. The input tensor

            can have one of the following shapes:

            (i) a concatenation of the output tensors along the primary

            dimension, or

            (ii) a stack of the output tensors along the primary dimension.

            For definition of "concatenation", see ``torch.cat()``.

            For definition of "stack", see ``torch.stack()``.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op.



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group.



    Examples:

        >>> # xdoctest: +SKIP("need process group init")

        >>> # All tensors below are of torch.int64 dtype and on CUDA devices.

        >>> # We have two ranks.

        >>> device = torch.device(f'cuda:{rank}')

        >>> tensor_out = torch.zeros(2, dtype=torch.int64, device=device)

        >>> # Input in concatenation form

        >>> tensor_in = torch.arange(world_size * 2, dtype=torch.int64, device=device)

        >>> tensor_in

        tensor([0, 1, 2, 3], device='cuda:0') # Rank 0

        tensor([0, 1, 2, 3], device='cuda:1') # Rank 1

        >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)

        >>> tensor_out

        tensor([0, 2], device='cuda:0') # Rank 0

        tensor([4, 6], device='cuda:1') # Rank 1

        >>> # Input in stack form

        >>> tensor_in = torch.reshape(tensor_in, (world_size, 2))

        >>> tensor_in

        tensor([[0, 1],

                [2, 3]], device='cuda:0') # Rank 0

        tensor([[0, 1],

                [2, 3]], device='cuda:1') # Rank 1

        >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)

        >>> tensor_out

        tensor([0, 2], device='cuda:0') # Rank 0

        tensor([4, 6], device='cuda:1') # Rank 1



    .. warning::

        The Gloo backend does not support this API.



    """
    _check_single_tensor(output, "output")
    _check_single_tensor(input, "input")

    if _rank_not_in_group(group):
        _warn_not_in_group("reduce_scatter_tensor")
        return

    opts = ReduceScatterOptions()
    opts.reduceOp = op
    opts.asyncOp = async_op

    group = group or _get_default_group()

    # Check if we are in coalescing context
    # If we are, do not issue single operation, just append a collective representation
    if group in _world.pg_coalesce_state.keys():
        coll = _CollOp(reduce_scatter_tensor, input, output, op, None)
        _world.pg_coalesce_state[group].append(coll)
        if async_op:
            return _IllegalWork()
        else:
            return None

    work = group._reduce_scatter_base(output, input, opts)

    if async_op:
        return work
    else:
        work.wait()


def _reduce_scatter_base(output, input, op=ReduceOp.SUM, group=None, async_op=False):
    """

    Reduces, then scatters a flattened tensor to all processes in a group.



    Args:

        output (Tensor): Output tensor.

        input (Tensor): Input tensor that is of size output tensor size times world size

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op.



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group.



    .. warning::

        `_reduce_scatter_base` is a private function. Users should use

        `reduce_scatter_tensor` instead.



    """
    warnings.warn(
        "torch.distributed._reduce_scatter_base is a private function and will "
        "be deprecated. Please use torch.distributed.reduce_scatter_tensor "
        "instead."
    )
    return reduce_scatter_tensor(output, input, op, group, async_op)


@_exception_logger
def all_to_all_single(

    output,

    input,

    output_split_sizes=None,

    input_split_sizes=None,

    group=None,

    async_op=False,

):
    """

    Split input tensor and then scatter the split list to all processes in a group.



    Later the received tensors are concatenated from all the processes in the group

    and returned as a single output tensor.



    Complex tensors are supported.



    Args:

        output (Tensor): Gathered concatenated output tensor.

        input (Tensor): Input tensor to scatter.

        output_split_sizes: (list[Int], optional): Output split sizes for dim 0

            if specified None or empty, dim 0 of ``output`` tensor must divide

            equally by ``world_size``.

        input_split_sizes: (list[Int], optional): Input split sizes for dim 0

            if specified None or empty, dim 0 of ``input`` tensor must divide

            equally by ``world_size``.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op.



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group.



    .. warning::

        `all_to_all_single` is experimental and subject to change.



    Examples:

        >>> # xdoctest: +SKIP("Undefined rank")

        >>> input = torch.arange(4) + rank * 4

        >>> input

        tensor([0, 1, 2, 3])     # Rank 0

        tensor([4, 5, 6, 7])     # Rank 1

        tensor([8, 9, 10, 11])   # Rank 2

        tensor([12, 13, 14, 15]) # Rank 3

        >>> output = torch.empty([4], dtype=torch.int64)

        >>> dist.all_to_all_single(output, input)

        >>> output

        tensor([0, 4, 8, 12])    # Rank 0

        tensor([1, 5, 9, 13])    # Rank 1

        tensor([2, 6, 10, 14])   # Rank 2

        tensor([3, 7, 11, 15])   # Rank 3



        >>> # Essentially, it is similar to following operation:

        >>> scatter_list = list(input.chunk(world_size))

        >>> gather_list  = list(output.chunk(world_size))

        >>> for i in range(world_size):

        >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)



        >>> # Another example with uneven split

        >>> input

        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0

        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1

        tensor([20, 21, 22, 23, 24])                                     # Rank 2

        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3

        >>> input_splits

        [2, 2, 1, 1]                                                     # Rank 0

        [3, 2, 2, 2]                                                     # Rank 1

        [2, 1, 1, 1]                                                     # Rank 2

        [2, 2, 2, 1]                                                     # Rank 3

        >>> output_splits

        [2, 3, 2, 2]                                                     # Rank 0

        [2, 2, 1, 2]                                                     # Rank 1

        [1, 2, 1, 2]                                                     # Rank 2

        [1, 2, 1, 1]                                                     # Rank 3

        >>> output = ...

        >>> dist.all_to_all_single(output, input, output_splits, input_splits)

        >>> output

        tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0

        tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1

        tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2

        tensor([ 5, 17, 18, 24, 36])                                     # Rank 3





        >>> # Another example with tensors of torch.cfloat type.

        >>> input = torch.tensor([1+1j, 2+2j, 3+3j, 4+4j], dtype=torch.cfloat) + 4 * rank * (1+1j)

        >>> input

        tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0

        tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1

        tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2

        tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3

        >>> output = torch.empty([4], dtype=torch.int64)

        >>> dist.all_to_all_single(output, input)

        >>> output

        tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0

        tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1

        tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2

        tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3

    """
    if _rank_not_in_group(group):
        _warn_not_in_group("all_to_all_single")
        return

    opts = AllToAllOptions()
    _check_single_tensor(output, "output")
    _check_single_tensor(input, "input")
    _ensure_all_tensors_same_dtype(output, input)

    if input.is_complex():
        input = torch.view_as_real(input)
    if output.is_complex():
        output = torch.view_as_real(output)

    output_split_sizes = [] if output_split_sizes is None else output_split_sizes
    input_split_sizes = [] if input_split_sizes is None else input_split_sizes

    if group is None:
        default_pg = _get_default_group()
        work = default_pg.alltoall_base(
            output, input, output_split_sizes, input_split_sizes, opts
        )
    else:
        work = group.alltoall_base(
            output, input, output_split_sizes, input_split_sizes, opts
        )

    if async_op:
        return work
    else:
        work.wait()


@_exception_logger
def all_to_all(output_tensor_list, input_tensor_list, group=None, async_op=False):
    """

    Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.



    Complex tensors are supported.



    Args:

        output_tensor_list (list[Tensor]): List of tensors to be gathered one

            per rank.

        input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op.



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group.



    .. warning::

        `all_to_all` is experimental and subject to change.



    Examples:

        >>> # xdoctest: +SKIP("Undefined rank")

        >>> input = torch.arange(4) + rank * 4

        >>> input = list(input.chunk(4))

        >>> input

        [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0

        [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1

        [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2

        [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3

        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))

        >>> dist.all_to_all(output, input)

        >>> output

        [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0

        [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1

        [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2

        [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3



        >>> # Essentially, it is similar to following operation:

        >>> scatter_list = input

        >>> gather_list  = output

        >>> for i in range(world_size):

        >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)



        >>> input

        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0

        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1

        tensor([20, 21, 22, 23, 24])                                     # Rank 2

        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3

        >>> input_splits

        [2, 2, 1, 1]                                                     # Rank 0

        [3, 2, 2, 2]                                                     # Rank 1

        [2, 1, 1, 1]                                                     # Rank 2

        [2, 2, 2, 1]                                                     # Rank 3

        >>> output_splits

        [2, 3, 2, 2]                                                     # Rank 0

        [2, 2, 1, 2]                                                     # Rank 1

        [1, 2, 1, 2]                                                     # Rank 2

        [1, 2, 1, 1]                                                     # Rank 3

        >>> input = list(input.split(input_splits))

        >>> input

        [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0

        [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1

        [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2

        [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3

        >>> output = ...

        >>> dist.all_to_all(output, input)

        >>> output

        [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0

        [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1

        [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2

        [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3



        >>> # Another example with tensors of torch.cfloat type.

        >>> input = torch.tensor([1+1j, 2+2j, 3+3j, 4+4j], dtype=torch.cfloat) + 4 * rank * (1+1j)

        >>> input = list(input.chunk(4))

        >>> input

        [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0

        [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1

        [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2

        [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3

        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))

        >>> dist.all_to_all(output, input)

        >>> output

        [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0

        [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1

        [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2

        [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3



    """
    if _rank_not_in_group(group):
        _warn_not_in_group("all_to_all")
        return

    opts = AllToAllOptions()
    _check_tensor_list(output_tensor_list, "output_tensor_list")
    _check_tensor_list(input_tensor_list, "input_tensor_list")
    _ensure_all_tensors_same_dtype(output_tensor_list, input_tensor_list)

    input_tensor_list = [
        t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list
    ]
    output_tensor_list = [
        t if not t.is_complex() else torch.view_as_real(t) for t in output_tensor_list
    ]

    if group is None:
        default_pg = _get_default_group()
        work = default_pg.alltoall(output_tensor_list, input_tensor_list, opts)
    else:
        work = group.alltoall(output_tensor_list, input_tensor_list, opts)

    if async_op:
        return work
    else:
        work.wait()

@_exception_logger
def barrier(group=GroupMember.WORLD, async_op=False, device_ids=None):
    """

    Synchronize all processes.



    This collective blocks processes until the whole group enters this function,

    if async_op is False, or if async work handle is called on wait().



    Args:

        group (ProcessGroup, optional): The process group to work on. If None,

            the default process group will be used.

        async_op (bool, optional): Whether this op should be an async op

        device_ids ([int], optional): List of device/GPU ids.



    Returns:

        Async work handle, if async_op is set to True.

        None, if not async_op or if not part of the group



    .. note:: `ProcessGroupNCCL` now relies on stream synchronization instead of

              device synchronization to block the CPU. Thus, please do not assume that

              `barrier()` would perform a device synchronization.

    """
    if _rank_not_in_group(group):
        _warn_not_in_group("barrier")
        return

    opts = BarrierOptions()
    opts.device = _get_pg_default_device(group)
    if device_ids is not None:
        if isinstance(device_ids, list):
            opts.device_ids = device_ids
        else:
            raise TypeError(
                "Invalid function argument: device_ids type should be List[int]"
            )

    if group is None:
        default_pg = _get_default_group()
        work = default_pg.barrier(opts=opts)
    else:
        work = group.barrier(opts=opts)

    if async_op:
        return work
    else:
        work.wait()


def monitored_barrier(group=GroupMember.WORLD, timeout=None, wait_all_ranks=False):
    """

    Synchronize processes similar to ``torch.distributed.barrier``, but consider a configurable timeout.



    It is able to report ranks that did not pass this barrier within the provided timeout.

    Specifically, for non-zero ranks, will block until a send/recv is processed from rank 0.

    Rank 0 will block until all send /recv from other ranks are processed, and will report

    failures for ranks that failed to respond in time. Note that if one rank does not reach the

    monitored_barrier (for example due to a hang), all other ranks would fail in monitored_barrier.



    This collective will block all processes/ranks in the group, until the

    whole group exits the function successfully, making it useful for debugging

    and synchronizing. However, it can have a performance impact and should only

    be used for debugging or scenarios that require full synchronization points

    on the host-side. For debugging purposes, this barrier can be inserted

    before the application's collective calls to check if any ranks are

    desynchronized.



    .. note:: Note that this collective is only supported with the GLOO backend.



    Args:

        group (ProcessGroup, optional): The process group to work on. If

            ``None``, the default process group will be used.

        timeout (datetime.timedelta, optional): Timeout for monitored_barrier.

            If ``None``, the default process group timeout will be used.

        wait_all_ranks (bool, optional): Whether to collect all failed ranks or

            not. By default, this is ``False`` and ``monitored_barrier`` on rank 0

            will throw on the first failed rank it encounters in order to fail

            fast. By setting ``wait_all_ranks=True`` ``monitored_barrier`` will

            collect all failed ranks and throw an error containing information

            about all failed ranks.



    Returns:

        ``None``.



    Example::

        >>> # xdoctest: +SKIP("need process group init")

        >>> # Note: Process group initialization omitted on each rank.

        >>> import torch.distributed as dist

        >>> if dist.get_rank() != 1:

        >>>     dist.monitored_barrier() # Raises exception indicating that

        >>> # rank 1 did not call into monitored_barrier.

        >>> # Example with wait_all_ranks=True

        >>> if dist.get_rank() == 0:

        >>>     dist.monitored_barrier(wait_all_ranks=True) # Raises exception

        >>> # indicating that ranks 1, 2, ... world_size - 1 did not call into

        >>> # monitored_barrier.

    """
    # Need to call rank not in group before using the group, otherwise
    # "Invalid process group" error is raised.
    if _rank_not_in_group(group):
        _warn_not_in_group("monitored_barrier")
        return

    if get_backend(group) != Backend.GLOO:
        raise ValueError("monitored_barrier is only implemented for GLOO backend.")

    if timeout is None:
        timeout = _get_default_timeout(get_backend(group))
    elif isinstance(timeout, float):
        # TODO(whc) aparently some existing test case for monitored_barrier passes in a timeout in float format?
        warnings.warn(
            "Please specify timeout arg as a timedelta. "
            f"Converting current value of {timeout} assuming it represents seconds",
        )
        timeout = timedelta(seconds=timeout)

    _check_valid_timeout(timeout)

    group_to_use = _get_default_group() if group is None else group
    return group_to_use.monitored_barrier(timeout, wait_all_ranks=wait_all_ranks)


def _create_process_group_wrapper(

    wrapped_pg: torch._C._distributed_c10d.Backend,

    store_prefix: str,

    store: Store,

    rank: int,

    world_size: int,

    timeout: timedelta = default_pg_timeout,

):
    # (whc) this appears to be just for the gloo backend? if so, `default_pg_timeout` is appropriate...

    # Create a separate prefix store for the helper process group.
    prefix = f"{PG_WRAPPER_STORE_PREFIX}:{store_prefix}"
    store = PrefixStore(prefix, store)
    helper_pg = ProcessGroupGloo(store, rank, world_size, timeout=timeout)
    # Wrap the underlying pg with ProcessGroupWrapper.
    wrapped_pg = _ProcessGroupWrapper(wrapped_pg, helper_pg)
    return wrapped_pg

# helper function for deterministically hashing a list of ranks
def _hash_ranks(ranks: List[int]):
    return hashlib.sha1(bytes("_".join(map(str, ranks)), "utf-8")).hexdigest()

# Takes a list of ranks and computes an integer color
def _process_group_color(ranks: List[int]) -> int:
    # Convert our hash to an int, but avoid negative numbers by shifting a bit.
    return int(_hash_ranks(ranks), 16) % (sys.maxsize >> 1)

def _process_group_name(ranks, use_hashed_name):
    global _world
    if use_hashed_name:
        pg_name = _hash_ranks(ranks)
        while pg_name in _world.pg_names.values():
            pg_name = hashlib.sha1(bytes(pg_name + "_", "utf-8")).hexdigest()
    else:
        pg_name = str(_world.group_count)
        _world.group_count += 1
    return pg_name

def _get_backend_from_str(backend: Optional[str] = None) -> Backend:
    # Default to the same backend as the global process group
    #  if backend is not specified.
    if not backend:
        backend = get_backend(_get_default_group())
    return Backend(backend)


@_time_logger
def new_group(ranks=None, timeout=None, backend=None, pg_options=None, use_local_synchronization=False):
    """

    Create a new distributed group.



    This function requires that all processes in the main group (i.e. all

    processes that are part of the distributed job) enter this function, even

    if they are not going to be members of the group. Additionally, groups

    should be created in the same order in all processes.



    .. warning::

        Using multiple process groups with the ``NCCL`` backend concurrently

        is not safe and the user should perform explicit synchronization in

        their application to ensure only one process group is used at a time.

        This means collectives from one process group should have completed

        execution on the device (not just enqueued since CUDA execution is

        async) before collectives from another process group are enqueued.

        See `Using multiple NCCL communicators concurrently <https://docs.nvid

        ia.com/deeplearning/nccl/user-guide/docs/usage/communicators.html#using

        -multiple-nccl-communicators-concurrently>`_ for more details.



    Args:

        ranks (list[int]): List of ranks of group members. If ``None``, will be

            set to all ranks. Default is ``None``.

        timeout (timedelta, optional): see `init_process_group` for details and default value.

        backend (str or Backend, optional): The backend to use. Depending on

            build-time configurations, valid values are ``gloo`` and ``nccl``.

            By default uses the same backend as the global group. This field

            should be given as a lowercase string (e.g., ``"gloo"``), which can

            also be accessed via :class:`Backend` attributes (e.g.,

            ``Backend.GLOO``). If ``None`` is passed in, the backend

            corresponding to the default process group will be used. Default is

            ``None``.

        pg_options (ProcessGroupOptions, optional): process group options

            specifying what additional options need to be passed in during

            the construction of specific process groups. i.e. for the ``nccl``

            backend, ``is_high_priority_stream`` can be specified so that

            process group can pick up high priority cuda streams.

        use_local_synchronization (bool, optional): perform a group-local

            barrier at the end of the process group creation. This is different

            in that non-member ranks don't need to call into API and don't

            join the barrier.



    Returns:

        A handle of distributed group that can be given to collective calls or None if the rank is not part of ``ranks``.



    N.B. use_local_synchronization doesn't work with MPI.



    N.B. While use_local_synchronization=True can be significantly faster with larger

    clusters and small process groups, care must be taken since it changes cluster behavior

    as non-member ranks don't join the group barrier().



    N.B. use_local_synchronization=True can lead to deadlocks when each rank creates

    multiple overlaping process groups. To avoid that, make sure all ranks follow the

    same global creation order.

    """
    return _new_group_with_tag(ranks, timeout, backend, pg_options, None, use_local_synchronization=use_local_synchronization)

def _new_group_with_tag(

    ranks=None,

    timeout=None,

    backend=None,

    pg_options=None,

    pg_tag=None,

    use_local_synchronization=False

):
    """

    Variant of ``new_group`` that exposes tag creation.



    :: N.B. The mechanism is experimental and tied to the functional collectives effort, see

    ``torch.distributed._functional_collectives`` for reference on how to use it.

    """
    global _world

    default_pg = _get_default_group()
    default_backend, default_store = _world.pg_map[default_pg]
    global_rank = default_pg.rank()
    global_world_size = default_pg.size()


    # Default to the same backend as the global process group
    # if the backend is not specified.
    if not backend:
        backend = default_backend
    backend = Backend(backend)

    # this timeout defaulting/validation is used for all the new_groups/new_subgroups variants,
    # which may just pass their timeout value (or None)
    if timeout is None:
        timeout = _get_default_timeout(backend)
    _check_valid_timeout(timeout)

    if use_local_synchronization:
        # MPI backend doesn't have have a way for us to perform a partial sync
        if backend == Backend.MPI:
            raise ValueError("MPI backend doesn't support use_local_synchronization=True")
        if ranks is not None and get_rank() not in ranks:
            return None

    # checks the input ranks
    if ranks is not None:
        ranks = sorted(ranks)
        group_world_size = len(ranks)
        if group_world_size > global_world_size:
            raise ValueError(
                "the new group's world size should be less or "
                "equal to the world size set by "
                "init_process_group"
            )
        # check ranks' sanity
        for rank in ranks:
            if rank < 0 or rank >= global_world_size:
                raise ValueError(
                    "The new group's rank should be within "
                    "the world_size set by init_process_group"
                )
        if global_rank in ranks:
            group_rank = ranks.index(global_rank)
        else:
            group_rank = None
    else:
        ranks = list(range(global_world_size))
        group_world_size = global_world_size
        group_rank = global_rank

    group_name = _process_group_name(ranks, use_hashed_name=use_local_synchronization)

    pg, pg_store = _new_process_group_helper(
        group_world_size,
        group_rank,
        ranks,
        backend,
        default_store,
        group_name,
        pg_options=pg_options,
        timeout=timeout,
        pg_tag=pg_tag
    )

    # Create the global rank to group rank mapping
    _world.pg_group_ranks[pg] = {
        global_rank: group_rank for group_rank, global_rank in enumerate(ranks)
    }

    if _is_barrier_after_init() == 1:
        # barrier at the end to ensure that once we return from this method, all
        # process groups including global variables (if any) are updated
        # correctly on all ranks.
        # Update 04/2023: for large-scale runs, this barrier (esp. store-based
        # barrier) may be costly and/or unscalable. Also, in a lot of cases,
        # these barriers may be unnecessary, as proven by a green CI after
        # removal. An environment variable `TORCH_DIST_INIT_BARRIER` has been
        # added which enables this barrier only when set to 1.
        logger.info(
            "Performing barrier after ProcessGroup initialization since "
            "TORCH_DIST_INIT_BARRIER = 1"
        )
        if backend == Backend.MPI:
            # MPI doesn't have store.
            barrier()
        else:
            barrier_store = pg_store if use_local_synchronization else default_store
            world_size = len(ranks) if use_local_synchronization else get_world_size()
            # Use store based barrier here since barrier() used a bunch of
            # default devices and messes up NCCL internal state.
            _store_based_barrier(global_rank, barrier_store, group_name, world_size, timeout)

    return pg


def new_subgroups(

    group_size=None,

    group=None,

    timeout=None,

    backend=None,

    pg_options=None,

):
    """

    Create subgroups of equal size.



    By default, it creates intra-machine subgroups,

    where each of which contains all the ranks of a machine, based on the assumption

    that each machine has the same number of devices.



    This is a convenience API that calls ``new_group`` to generate multiple subgroups.

    It requires that all processes in the main group (i.e. all

    processes that are part of the distributed job) enter this function, even

    if they are not going to be members of the group.



    .. warning::

        If ``group_size`` is passed in, the world size must be divisible by ``group_size``.

        If no ``group_size`` is passed in, it believe that you are creating a group based

        on CUDA and determining the group size by number of CUDA devices, and if not all

        the machines have the same number of devices, the subgroup division will be

        different across nodes and can cause unexpected behaviors. Therefore, if you are

        creating a subgroup that does not depend on CUDA (such as Gloo on CPU), please

        pass in ``group_size`` correctly.



    .. warning::

        Using multiple process groups with the ``NCCL`` backend concurrently

        is not safe and the user should perform explicit synchronization in

        their application to ensure only one process group is used at a time.

        This means collectives from one process group should have completed

        execution on the device (not just enqueued since CUDA execution is

        async) before collectives from another process group are enqueued.

        See `Using multiple NCCL communicators concurrently <https://docs.nvid

        ia.com/deeplearning/nccl/user-guide/docs/usage/communicators.html#using

        -multiple-nccl-communicators-concurrently>`_ for more details.



    Args:

        group_size (int, optional): The size of each subgroup. If ``None``,

            the default subgroup size is equal to the number of devices on each machine,

            based on the assumption that each machine has exactly the same

            number of devices. Default is ``None``.

        timeout (timedelta, optional): see `init_process_group` for details and default value.

        backend (str or Backend, optional): The backend to use. Depending on

            build-time configurations, valid values are ``gloo`` and ``nccl``.

            By default uses the same backend as the global group. This field

            should be given as a lowercase string (e.g., ``"gloo"``), which can

            also be accessed via :class:`Backend` attributes (e.g.,

            ``Backend.GLOO``). If ``None`` is passed in, the backend

            corresponding to the default process group will be used. Default is

            ``None``.

        pg_options (ProcessGroupOptions, optional): process group options

            specifying what additional options need to be passed in during

            the construction of specific process groups. i.e. for the ``nccl``

            backend, ``is_high_priority_stream`` can be specified so that

            process group can pick up high priority cuda streams.



    Returns:

        The subgroup containing the current rank, and all the subgroups used for cleanup.



    Examples:

        >>> # Create intra-machine subgroups.

        >>> # xdoctest: +SKIP("need process group init")

        >>> cur_subgroup, subgroups = dist.new_subgroups()

        >>> # Allreduce within the machine.

        >>> rank = dist.get_rank()

        >>> tensor = torch.ones(1, device=rank) * rank

        >>> dist.all_reduce(tensor, group=cur_subgroup)

        >>> tensor

        tensor([8])     # Assume 8 is the number of CUDA devices per machine.

        >>> # Cleanup.

        >>> for subgroup in subgroups:

        >>>     dist.destroy_process_group(subgroup)

    """
    if group_size is None:
        if not torch.cuda.is_available():
            raise ValueError("Default group size only takes effect when CUDA is available."
                             "If your subgroup using a backend that does not depend on CUDA,"
                             "please pass in 'group_size' correctly.")
        group_size = torch.cuda.device_count()
    if group_size <= 0:
        raise ValueError(f"The arg 'group_size' ({group_size}) must be positive")

    world_size = get_world_size()
    if world_size < group_size:
        raise ValueError(f"The arg 'group_size' ({group_size}) must not exceed the world size ({world_size})")
    if world_size % group_size != 0:
        raise ValueError("The world size must be divisible by 'group_size'")

    subgroups = []
    cur_subgroup = None

    for subgroup_id in range(world_size // group_size):
        start_rank = subgroup_id * group_size
        end_rank = start_rank + group_size
        ranks_in_subgroup = list(range(start_rank, end_rank))
        subgroup = new_group(
            ranks=ranks_in_subgroup,
            timeout=timeout,
            backend=backend,
            pg_options=pg_options,
        )
        subgroups.append(subgroup)

        rank = get_rank()
        if rank in ranks_in_subgroup:
            cur_subgroup = subgroup
            logger.info(
                "Rank %s is assigned to subgroup %s",
                rank, ranks_in_subgroup
            )

    return cur_subgroup, subgroups


def new_subgroups_by_enumeration(

    ranks_per_subgroup_list,

    timeout=None,

    backend=None,

    pg_options=None,

):
    """

    Create subgroups by dividing the global world.



    The division is specified by a nested list of ranks. The subgroups cannot have

    overlap, and some ranks may not have to be in any subgroup.



    This is a convenience API that calls ``new_group`` to generate multiple subgroups.

    It requires that all processes in the main group (i.e. all

    processes that are part of the distributed job) enter this function, even

    if they are not going to be members of the group.



    .. warning::

        Using multiple process groups with the ``NCCL`` backend concurrently

        is not safe and the user should perform explicit synchronization in

        their application to ensure only one process group is used at a time.

        This means collectives from one process group should have completed

        execution on the device (not just enqueued since CUDA execution is

        async) before collectives from another process group are enqueued.

        See `Using multiple NCCL communicators concurrently <https://docs.nvid

        ia.com/deeplearning/nccl/user-guide/docs/usage/communicators.html#using

        -multiple-nccl-communicators-concurrently>`_ for more details.



    Args:

        ranks_per_subgroup_list (list[list[int]]): A nested list of ranks of

            group members.

        timeout (timedelta, optional): see `init_process_group` for details and default value.

        backend (str or Backend, optional): The backend to use. Depending on

             build-time configurations, valid values are ``gloo`` and ``nccl``.

             By default uses the same backend as the global group. This field

             should be given as a lowercase string (e.g., ``"gloo"``), which can

             also be accessed via :class:`Backend` attributes (e.g.,

             ``Backend.GLOO``). If ``None`` is passed in, the backend

             corresponding to the default process group will be used. Default is

             ``None``.

        pg_options (ProcessGroupOptions, optional): process group options

            specifying what additional options need to be passed in during

            the construction of specific process groups. i.e. for the ``nccl``

            backend, ``is_high_priority_stream`` can be specified so that

            process group can pick up high priority cuda streams.



    Returns:

        The subgroup containing the current rank, and all the subgroups used for cleanup.



    Examples:

        >>> # Create two subgroups, where each has 2 processes.

        >>> # xdoctest: +SKIP("need process group init")

        >>> cur_subgroup, subgroups = dist.new_subgroups(ranks=[[0, 2], [1, 3]])

        >>> rank = dist.get_rank()

        >>> tensor = torch.ones(1, device=rank) * rank

        >>> dist.all_reduce(tensor, group=cur_subgroup)

        >>> tensor

        tensor([2])     # Subgroup 0: ranks 0 and 2

        tensor([4])     # Subgroup 1: ranks 1 and 3

    """
    if ranks_per_subgroup_list is None or len(ranks_per_subgroup_list) == 0:
        raise ValueError("The arg 'ranks_per_subgroup_list' cannot be empty")

    subgroups = []
    cur_subgroup = None
    # Create a mapping from rank to subgroup to check if there is any subgroup overlap.
    rank_to_ranks_dict = {}  # type: ignore[var-annotated]
    for ranks in ranks_per_subgroup_list:
        subgroup = new_group(
            ranks=ranks,
            timeout=timeout,
            backend=backend,
            pg_options=pg_options,
        )
        subgroups.append(subgroup)
        my_rank = get_rank()
        for rank in ranks:
            if rank in rank_to_ranks_dict:
                raise ValueError(
                    f"Rank {rank} has appeared in both subgroup {rank_to_ranks_dict[rank]} and {ranks}"
                )
            rank_to_ranks_dict[rank] = ranks
            if my_rank == rank:
                cur_subgroup = subgroup
                logger.info("Rank %s is assigned to subgroup %s", rank, ranks)

    return cur_subgroup, subgroups


def _find_pg_by_ranks_and_tag(tag: str, ranks: List[int]) -> Optional[ProcessGroup]:
    if len(tag) > 0 and not tag.startswith("ptd:") and not tag.startswith("user:"):
        tag = f"user:{tag}"

    for group in _world.tags_to_pg.get(tag, []):
        if group.size() != len(ranks):
            continue

        group_ranks = get_process_group_ranks(group)
        good = all(r in group_ranks for r in ranks)
        if good:
            return group
    return None

def _find_or_create_pg_by_ranks_and_tag(tag: str, ranks: List[int], stride: int) -> ProcessGroup:
    assert len(ranks) % stride == 0, f"Ranks length ({len(ranks)}) must be divisible by stride ({stride})"

    my_rank = get_rank()
    my_ranks = None

    if stride == len(ranks):
        my_ranks = ranks.copy()
        assert my_rank in my_ranks, "rankset doesn't include the current node"
    else:
        for i in range(0, len(ranks), stride):
            rank_set = ranks[i : i + stride]
            if my_rank in rank_set:
                my_ranks = rank_set
        assert my_ranks is not None, "rankset doesn't include the current node"

    my_ranks.sort()

    pg = _find_pg_by_ranks_and_tag(tag, my_ranks)
    if pg is not None:
        return pg
    if tag == "":
        raise ValueError("Cannot automatically create PG with empty tag")
    # TODO copy settings and timeout from default PG
    return _new_group_with_tag(my_ranks, pg_tag=tag)

def _get_group_tag(pg: ProcessGroup) -> str:
    """Return the tag associated with ``pg``."""
    tag = _world.pg_to_tag[pg]
    if tag.startswith("user:"):
        tag = tag[5:]
    return tag

def _get_process_group_name(pg: ProcessGroup) -> str:
    return _world.pg_names.get(pg, "None")

def _get_process_group_store(pg: ProcessGroup) -> Store:
    return _world.pg_map[pg][1]

# This ops are not friendly to TorchDynamo. So, we decide to disallow these ops
# in FX graph, allowing them to run them on eager, with torch.compile.
dynamo_unsupported_distributed_c10d_ops = [
    recv,
    all_gather_object,
    all_gather_coalesced,
    all_to_all_single,
    all_reduce,
    gather_object,
    all_to_all,
    all_reduce_coalesced,
    gather,
    broadcast_object_list,
    barrier,
    scatter,
    scatter_object_list,
    reduce,
    all_gather,
    reduce_scatter,
    all_gather_into_tensor,
    broadcast,
    reduce_scatter_tensor,
    send,
]