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# Source code for torch.distributed.distributed_c10d

    import itertools
    import collections.abc
    import contextlib
    import hashlib
    import io
    import logging
    import os
    import pickle
    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 (
        AllreduceCoalescedOptions,
        AllreduceOptions,
        AllToAllOptions,
        _DistributedBackendOptions,
        BarrierOptions,
        BroadcastOptions,
        GatherOptions,
        PrefixStore,
        ProcessGroup,
        ReduceOp,
        ReduceOptions,
        ReduceScatterOptions,
        ScatterOptions,
        Store,
        DebugLevel,
        get_debug_level,
        Work
    )
    from .constants import default_pg_timeout
    from .c10d_logger import _exception_logger, _time_logger
    from .rendezvous import register_rendezvous_handler, rendezvous  # noqa: F401

    __all__ = [
        'Backend', 'BackendConfig', 'GroupMember', 'P2POp', 'all_gather', 'all_gather_coalesced',
        'all_gather_multigpu', 'all_gather_object', 'all_reduce',
        'all_reduce_coalesced', 'all_reduce_multigpu', 'all_to_all',
        'all_to_all_single', 'barrier', 'batch_isend_irecv', 'broadcast',
        'broadcast_multigpu', 'broadcast_object_list', 'destroy_process_group',
        'gather', 'gather_object', 'get_backend_config', 'get_backend', 'get_rank',
        'get_world_size', '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_multigpu',
        'reduce_scatter', 'reduce_scatter_multigpu', '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():
        _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:
        denyList = [
            ReduceOp.MAX,
            ReduceOp.MIN,
            ReduceOp.PRODUCT,
            ReduceOp.BAND,
            ReduceOp.BOR,
            ReduceOp.BXOR,
        ]
        return reduceOp not in denyList

    class Backend:
        """
        An enum-like class of 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"],
        }

        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):
            if not isinstance(name, str):
                raise ValueError(f"Backend name must be a string, but got: {name}")
            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):
            """
            Registers 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:

        def __init__(self, backend: Union[str, Backend]):
            self.device_backend_map: Dict[torch.device, Backend] = {}

            if backend == Backend.UNDEFINED:
                # default config when backend is not specified
                # supported since PyTorch 2.0
                for device in Backend.default_device_backend_map:
                    if is_backend_available(Backend.default_device_backend_map[device]):
                        self.device_backend_map[device] = Backend.default_device_backend_map[device]
            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 = {
                    device : backend_val for device in supported_devices
                }
            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):
            # string with 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):
            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):
            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):
            _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, Optional[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] = {}

    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[Union[_CollOp, P2POp]]] = {}
            self._pg_default_device: Dict[ProcessGroup, torch.device] = {}

        @property
        def default_pg(self):
            """
            The default ProcessGroup includes all ranks of the cluster.
            This 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):
            self._default_pg = value

        @property
        def pg_map(self) -> Dict[ProcessGroup, Tuple[str, Optional[Store]]]:
            """
            Cached process groups
            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):
            """
            Count is used when computing 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[Union[_CollOp, P2POp]]]:
            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, Union[int, str]]]:
            """
            Returns a list of dict with process groups and backends with their unique IDs
            and configurations (types and ranks).
            """
            config_info = []
            for pg, backend in self.pg_map.items():
                # backend is a tuple with the first element being the backend type ("nccl", etc.)
                backend_type = Backend.backend_type_map[backend[0]]
                config_info.append(
                    {
                        "pg_id": pg._id(),
                        "backend_id": pg._backend_id(backend_type),
                        "backend_config": self.pg_backend_config[pg],
                        "ranks": self.pg_group_ranks[pg],
                    }
                )
            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`` so they
        can 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):
        pass

    class GroupMember(metaclass=_WorldMeta):
        NON_GROUP_MEMBER = -100

    # Default process group state
    _default_pg_init_method = None

    STORE_BASED_BARRIER_PREFIX = "store_based_barrier_key"

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

        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 RuntimeError(
                        "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: ProcessGroup):
        """
        Helper that checks 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):
        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 RuntimeError(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 RuntimeError(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 RuntimeError(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 RuntimeError(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):
        """
        This method is deprecated, please use get_global_rank.
        """
        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):
        """
        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):
        """
        Helper that gets 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 _check_single_tensor(param, param_name):
        """
        Helper to check that the parameter ``param_name`` is a single tensor.
        """
        if not isinstance(param, torch.Tensor):
            raise RuntimeError(
                f"Invalid function argument. Expected parameter `{param_name}` to be of type torch.Tensor."
            )

    def _check_tensor_list(param, param_name):
        """
        Helper to check that the parameter ``param_name`` is a list of tensors.
        """
        if not isinstance(param, list) or not all(
            isinstance(p, torch.Tensor) for p in param
        ):
            raise RuntimeError(
                f"Invalid function argument. Expected parameter `{param_name}` to be of type List[torch.Tensor]."
            )

    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(*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 RuntimeError(
                        "Invalid usage of tensors with different dtypes"
                        f"Found {last_dtype} and  {tensor.dtype}"
                    )

    def _check_op(op):
        """
        Helper to check that the ``op`` is either isend or irecv.
        """
        if op not in [isend, irecv]:
            raise RuntimeError(
                "Invalid ``op``. Expected ``op`` "
                "to be of type ``torch.distributed.isend`` or "
                "``torch.distributed.irecv``."
            )

    def _check_p2p_op_list(p2p_op_list):
        """
        Helper to check that the ``p2p_op_list`` is a list of P2POp instances and
        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 RuntimeError(
                "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 RuntimeError("All ops need to use the same group.")

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

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

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

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

    def is_backend_available(backend: str) -> bool:
        """
        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:
        """
        Checking if the default process group has been initialized
        """
        return GroupMember.WORLD is not None

    def is_torchelastic_launched() -> bool:
        """
        Checks 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 _get_default_group():
        """
        Getting the default process group created by init_process_group
        """
        if not is_initialized():
            raise RuntimeError(
                "Default process group has not been initialized, "
                "please make sure to call init_process_group."
            )
        return GroupMember.WORLD

    def _get_default_store():
        """
        Getting the default store created by init_process_group
        """
        if not is_initialized():
            raise RuntimeError(
                "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):
        _world.default_pg = pg

    def get_backend_config(group: Optional[ProcessGroup] = None) -> str:
        if group is None:
            pg = _get_default_group()
        else:
            pg = group
        if _rank_not_in_group(pg):
            raise RuntimeError("Invalid process group specified")
        backend_config = _world.pg_backend_config.get(pg)
        assert backend_config is not None
        return str(backend_config)

    def get_backend(group: Optional[ProcessGroup] = None) -> str:
        """
        Returns 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 RuntimeError("Invalid process group specified")
        pg_store = _world.pg_map[pg] if pg in _world.pg_map else None
        assert pg_store is not None
        return pg_store[0]

    _exception_logger
    @_time_logger
    def init_process_group(
        backend: Union[str, Backend] = None,
        init_method: Optional[str] = None,
        timeout: timedelta = default_pg_timeout,
        world_size: int = -1,
        rank: int = -1,
        store: Optional[Store] = None,
        group_name: str = "",
        pg_options: Optional[Any] = None,
    ):
        """
        Initializes the default distributed process group, and 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 equals 30 minutes.
                This is applicable for the ``gloo`` backend. For ``nccl``, this is
                applicable only if the environment variable ``NCCL_BLOCKING_WAIT``
                or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When
                ``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the
                process will block and wait for collectives to complete before
                throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set,
                this is the duration after which collectives will be aborted
                asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT``
                will provide errors to the user which can be caught and handled,
                but due to its blocking nature, it has a performance overhead. On
                the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has very little
                performance overhead, but crashes the process on errors. 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. Only one of these two environment variables should be set.
                For ``ucc``, blocking wait is supported similar to NCCL. However,
                async error handling is done differently since with UCC we have
                progress thread and not watch-dog thread.
            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.

        .. 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 not isinstance(timeout, timedelta):
            raise RuntimeError(
                "Expected timeout argument to be of type datetime.timedelta"
            )

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

        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")

        """
        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=group_name, timeout=timeout
            )
            _update_default_pg(default_pg)
        else:
            # backward compatible API
            if store is None:
                rendezvous_iterator = rendezvous(
                    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,
                pg_options=pg_options,
                group_name=group_name,
                timeout=timeout
            )
            _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[GroupMember.WORLD][0]  # type: ignore[index]
        _default_pg_init_method = init_method

        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 _new_process_group_helper(
        group_size,
        group_rank,
        global_ranks_in_group,
        backend,
        store,
        pg_options=None,
        group_name=None,
        timeout=default_pg_timeout,
        pg_tag=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 RuntimeError(
                "The specified group name has already been "
                "created, please use a different group name"
            )

        if not isinstance(timeout, timedelta):
            raise RuntimeError(
                "Expected timeout argument to be of type datetime.timedelta"
            )

        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

        # 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:
                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)
        backend_config = BackendConfig(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
                # 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"
                else:
                    # default pg_options for NCCL
                    pg_options = ProcessGroupNCCL.Options()
                    pg_options.is_high_priority_stream = False
                    pg_options._timeout = timeout

                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 in [Backend.GLOO, Backend.NCCL]:
                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
                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)

        # update global state
        _world.pg_map[pg] = (backend, prefix_store)
        _world.pg_names[pg] = group_name
        _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 RuntimeError("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:
            _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()

            # 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:
            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

    def get_rank(group: Optional[ProcessGroup] = None) -> int:
        """
        Returns the rank of the current process in the provided ``group`` or the
        default group if none was provided.

        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:
        """
        Returns 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) -> Work:
        """
        Sends 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.
            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

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

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

        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:
        """
        Sends a tensor synchronously.

        Args:
            tensor (Tensor): Tensor to send.
            dst (int): Destination rank. 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

        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. 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 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 RuntimeError(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,
    ):
        """
        A 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 RuntimeError("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)
                opts = AllreduceCoalescedOptions()
                opts.reduceOp = op_list[0].redop
                work = group.allreduce_coalesced(tensors, opts)
            elif op0 == all_gather_into_tensor:
                inputs = []
                outputs = []
                for op in op_list:
                    inputs.append(op.tensor)
                    outputs.append(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(op.dst_tensor)
                    opts = ReduceScatterOptions()
                    opts.reduceOp = op_list[0].redop
                work = group.reduce_scatter_tensor_coalesced(outputs, inputs, 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)
        else:
            work.wait()

    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) + 2 * rank
            >>> recv_tensor = torch.randn(2)
            >>> 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_multigpu(tensor_list, src, group=None, async_op=False, src_tensor=0):
        """
        Broadcasts the tensor to the whole group with multiple GPU tensors
        per node.

        ``tensor`` must have the same number of elements in all the GPUs from
        all processes participating in the collective. each tensor in the list must
        be on a different GPU

        Only nccl and gloo backend are currently supported
        tensors should only be GPU tensors

        Args:
            tensor_list (List[Tensor]): Tensors that participate in the collective
                operation. If ``src`` is the rank, then the specified ``src_tensor``
                element of ``tensor_list`` (``tensor_list[src_tensor]``) will be
                broadcast to all other tensors (on different GPUs) in the src process
                and all tensors in ``tensor_list`` of other non-src processes.
                You also need to make sure that ``len(tensor_list)`` is the same
                for all the distributed processes calling this function.

            src (int): Source 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
            src_tensor (int, optional): Source tensor rank within ``tensor_list``

        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.broadcast_multigpu will be deprecated. If you must "
            "use it, please revisit our documentation later at "
            "https://pytorch.org/docs/master/distributed.html#multi-gpu-collective-functions"
        )

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

        opts = BroadcastOptions()
        opts.rootRank = src
        opts.rootTensor = src_tensor

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

    @_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.
            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

        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_multigpu(tensor_list, op=ReduceOp.SUM, group=None, async_op=False):
        r"""
        Reduces the tensor data across all machines in such a way that all get
        the final result. This function reduces a number of tensors on every node,
        while each tensor resides on different GPUs.
        Therefore, the input tensor in the tensor list needs to be GPU tensors.
        Also, each tensor in the tensor list needs to reside on a different GPU.

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

        Complex tensors are supported.

        Only nccl and gloo backend is currently supported
        tensors should only be GPU tensors

        Args:
            tensor_list (List[Tensor]): List of input and output tensors of
                the collective. The function operates in-place and requires that
                each tensor to be a GPU tensor on different GPUs.
                You also need to make sure that ``len(tensor_list)`` is the same for
                all the distributed processes calling this function.
            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

        """
        warnings.warn(
            "torch.distributed.all_reduce_multigpu will be deprecated. If you must "
            "use it, please revisit our documentation later at "
            "https://pytorch.org/docs/master/distributed.html#multi-gpu-collective-functions"
        )

        if _rank_not_in_group(group):
            return

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

        opts = AllreduceOptions()
        opts.reduceOp = op
        if group is None:
            default_pg = _get_default_group()
            work = default_pg.allreduce(tensor_list, opts)
        else:
            work = group.allreduce(tensor_list, 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 such 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.
            >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
            >>> tensor
            tensor([1, 2]) # Rank 0
            tensor([3, 4]) # Rank 1
            >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
            >>> tensor
            tensor([4, 6]) # Rank 0
            tensor([4, 6]) # 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) + 2 * rank * (1+1j)
            >>> tensor
            tensor([1.+1.j, 2.+2.j]) # Rank 0
            tensor([3.+3.j, 4.+4.j]) # Rank 1
            >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
            >>> tensor
            tensor([4.+4.j, 6.+6.j]) # Rank 0
            tensor([4.+4.j, 6.+6.j]) # 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 RuntimeError(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 (List[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"
        )
        _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 RuntimeError(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_multigpu(
        tensor_list, dst, op=ReduceOp.SUM, group=None, async_op=False, dst_tensor=0
    ):
        """
        Reduces the tensor data on multiple GPUs across all machines. Each tensor
        in ``tensor_list`` should reside on a separate GPU

        Only the GPU of ``tensor_list[dst_tensor]`` on the process with rank ``dst``
        is going to receive the final result.

        Only nccl backend is currently supported
        tensors should only be GPU tensors

        Args:
            tensor_list (List[Tensor]): Input and output GPU tensors of the
                collective. The function operates in-place.
                You also need to make sure that ``len(tensor_list)`` is the same for
                all the distributed processes calling this function.
            dst (int): Destination rank
            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
            dst_tensor (int, optional): Destination tensor rank within
                                        ``tensor_list``

        Returns:
            Async work handle, if async_op is set to True.
            None, otherwise

        """
        warnings.warn(
            "torch.distributed.reduce_multigpu will be deprecated. If you must "
            "use it, please revisit our documentation later at "
            "https://pytorch.org/docs/master/distributed.html#multi-gpu-collective-functions"
        )

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

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

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

        if async_op:
            return work
        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
            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()

    @_exception_logger
    def all_gather_multigpu(
        output_tensor_lists, input_tensor_list, group=None, async_op=False
    ):
        """
        Gathers tensors from the whole group in a list.
        Each tensor in ``tensor_list`` should reside on a separate GPU

        Only nccl backend is currently supported
        tensors should only be GPU tensors

        Complex tensors are supported.

        Args:
            output_tensor_lists (List[List[Tensor]]): Output lists. It should
                contain correctly-sized tensors on each GPU to be used for output
                of the collective, e.g. ``output_tensor_lists[i]`` contains the
                all_gather result that resides on the GPU of
                ``input_tensor_list[i]``.

                Note that each element of ``output_tensor_lists`` has the size of
                ``world_size * len(input_tensor_list)``, since the function all
                gathers the result from every single GPU in the group. To interpret
                each element of ``output_tensor_lists[i]``, note that
                ``input_tensor_list[j]`` of rank k will be appear in
                ``output_tensor_lists[i][k * world_size + j]``

                Also note that ``len(output_tensor_lists)``, and the size of each
                element in ``output_tensor_lists`` (each element is a list,
                therefore ``len(output_tensor_lists[i])``) need to be the same
                for all the distributed processes calling this function.

            input_tensor_list (List[Tensor]): List of tensors(on different GPUs) to
                be broadcast from current process.
                Note that ``len(input_tensor_list)`` needs to be the same for
                all the distributed processes calling this function.

            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

        """
        warnings.warn(
            "torch.distributed.all_gather_multigpu will be deprecated. If you must "
            "use it, please revisit our documentation later at "
            "https://pytorch.org/docs/master/distributed.html#multi-gpu-collective-functions"
        )

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

        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(output_tensor_lists, input_tensor_list)
        else:
            work = group.allgather(output_tensor_lists, input_tensor_list)

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

    def _object_to_tensor(obj, device):
        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)
        local_size = torch.LongTensor([byte_tensor.numel()]).to(device)
        return byte_tensor, local_size

    def _tensor_to_object(tensor, tensor_size):
        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)

        # 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)
            if tensor.device != torch.device("cpu"):
                tensor = tensor.cpu()
            tensor_size = object_size_list[i]
            object_list[i] = _tensor_to_object(tensor, tensor_size)

    @_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. (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)

        # 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,
            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)

    @_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``.
            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) 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:
                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)
                if obj_view.device != torch.device("cpu"):
                    obj_view = obj_view.cpu()
                offset += obj_size
                object_list[i] = _tensor_to_object(obj_view, obj_size)

    @_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``.
            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 RuntimeError(
                "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) 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)
            for tensor in tensor_list:
                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,
            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,
            src=src,
            group=group,
        )

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

    @_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.
            >>> tensor_list = [torch.zeros(2, dtype=torch.int64) for _ in range(2)]
            >>> tensor_list
            [tensor([0, 0]), tensor([0, 0])] # Rank 0 and 1
            >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
            >>> tensor
            tensor([1, 2]) # Rank 0
            tensor([3, 4]) # Rank 1
            >>> dist.all_gather(tensor_list, tensor)
            >>> tensor_list
            [tensor([1, 2]), tensor([3, 4])] # Rank 0
            [tensor([1, 2]), tensor([3, 4])] # 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) for _ in range(2)]
            >>> tensor_list
            [tensor([0.+0.j, 0.+0.j]), tensor([0.+0.j, 0.+0.j])] # Rank 0 and 1
            >>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * rank * (1+1j)
            >>> tensor
            tensor([1.+1.j, 2.+2.j]) # Rank 0
            tensor([3.+3.j, 4.+4.j]) # Rank 1
            >>> dist.all_gather(tensor_list, tensor)
            >>> tensor_list
            [tensor([1.+1.j, 2.+2.j]), tensor([3.+3.j, 4.+4.j])] # Rank 0
            [tensor([1.+1.j, 2.+2.j]), tensor([3.+3.j, 4.+4.j])] # 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)
        )

        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)

        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 RuntimeError(
                "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 (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 (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

        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_multigpu(
        output_tensor_list, input_tensor_lists, op=ReduceOp.SUM, group=None, async_op=False
    ):
        """
        Reduce and scatter a list of tensors to the whole group.  Only nccl backend
        is currently supported.

        Each tensor in ``output_tensor_list`` should reside on a separate GPU, as
        should each list of tensors in ``input_tensor_lists``.

        Args:
            output_tensor_list (List[Tensor]): Output tensors (on different GPUs)
                to receive the result of the operation.

                Note that ``len(output_tensor_list)`` needs to be the same for all
                the distributed processes calling this function.

            input_tensor_lists (List[List[Tensor]]): Input lists.  It should
                contain correctly-sized tensors on each GPU to be used for input of
                the collective, e.g. ``input_tensor_lists[i]`` contains the
                reduce_scatter input that resides on the GPU of
                ``output_tensor_list[i]``.

                Note that each element of ``input_tensor_lists`` has the size of
                ``world_size * len(output_tensor_list)``, since the function
                scatters the result from every single GPU in the group.  To
                interpret each element of ``input_tensor_lists[i]``, note that
                ``output_tensor_list[j]`` of rank k receives the reduce-scattered
                result from ``input_tensor_lists[i][k * world_size + j]``

                Also note that ``len(input_tensor_lists)``, and the size of each
                element in ``input_tensor_lists`` (each element is a list,
                therefore ``len(input_tensor_lists[i])``) need to be the same for
                all the distributed processes calling this function.

            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.

        """
        warnings.warn(
            "torch.distributed.reduce_scatter_multigpu will be deprecated. If you must "
            "use it, please revisit our documentation later at "
            "https://pytorch.org/docs/master/distributed.html#multi-gpu-collective-functions"
        )

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

        opts = ReduceScatterOptions()
        opts.reduceOp = op

        if group is None:
            default_pg = _get_default_group()
            work = default_pg.reduce_scatter(output_tensor_list, input_tensor_lists, opts)
        else:
            work = group.reduce_scatter(output_tensor_list, input_tensor_lists, 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

        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,
    ):
        """
        Each process splits input tensor and then scatters the split list
        to all processes in a group. Then concatenate the received tensors from all
        the processes in the group and return 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):
        """
        Each process 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):

        """
        Synchronizes 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
        """
        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 RuntimeError(
                    "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):
        """
        Synchronizes all processes similar to ``torch.distributed.barrier``, but takes
        a configurable timeout and is able to report ranks that did not pass this
        barrier within that 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 RuntimeError("monitored_barrier is only implemented for GLOO backend.")

        if timeout is None:
            timeout = default_pg_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: ProcessGroup,
        store_prefix: str,
        store: Store,
        rank: int,
        world_size: int,
        timeout: timedelta = default_pg_timeout,
    ):
        # 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

    def _process_group_name(ranks, use_hashed_name):
        global _world
        if use_hashed_name:
            pg_name = hashlib.sha1(bytes("_".join(map(str, ranks)), "utf-8")).hexdigest()
            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=default_pg_timeout, backend=None, pg_options=None, use_local_synchronization=False):
        """
        Creates 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): Timeout for operations executed against
                the process group. Default value equals 30 minutes.
                This is applicable for the ``gloo`` backend. For ``nccl``, this is
                applicable only if the environment variable ``NCCL_BLOCKING_WAIT``
                or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When
                ``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the
                process will block and wait for collectives to complete before
                throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set,
                this is the duration after which collectives will be aborted
                asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT``
                will provide errors to the user which can be caught and handled,
                but due to its blocking nature, it has a performance overhead. On
                the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has very little
                performance overhead, but crashes the process on errors. 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. Only one of these two environment variables should be set.
            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=default_pg_timeout,
        backend=None,
        pg_options=None,
        pg_tag=None,
        use_local_synchronization=False
    ):
        """
        This is a 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)
        if use_local_synchronization:
            # MPI backend doesn't have have a way for us to perform a partial sync
            if backend == Backend.MPI:
                raise RuntimeError("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 RuntimeError(
                    "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 RuntimeError(
                        "The new group's rank should be within the "
                        "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=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=default_pg_timeout,
        backend=None,
        pg_options=None,
    ):
        """
        Creates 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): Timeout for operations executed against
                the process group. Default value equals 30 minutes.
                This is applicable for the ``gloo`` backend. For ``nccl``, this is
                applicable only if the environment variable ``NCCL_BLOCKING_WAIT``
                or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When
                ``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the
                process will block and wait for collectives to complete before
                throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set,
                this is the duration after which collectives will be aborted
                asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT``
                will provide errors to the user which can be caught and handled,
                but due to its blocking nature, it has a performance overhead. On
                the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has very little
                performance overhead, but crashes the process on errors. 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. Only one of these two environment variables should be set.
            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=default_pg_timeout,
        backend=None,
        pg_options=None,
    ):
        """
        Creates subgroups by dividing the global world, where 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): Timeout for operations executed against
                the process group. Default value equals 30 minutes.
                This is applicable for the ``gloo`` backend. For ``nccl``, this is
                applicable only if the environment variable ``NCCL_BLOCKING_WAIT``
                or ``NCCL_ASYNC_ERROR_HANDLING`` is set to 1. When
                ``NCCL_BLOCKING_WAIT`` is set, this is the duration for which the
                process will block and wait for collectives to complete before
                throwing an exception. When ``NCCL_ASYNC_ERROR_HANDLING`` is set,
                this is the duration after which collectives will be aborted
                asynchronously and the process will crash. ``NCCL_BLOCKING_WAIT``
                will provide errors to the user which can be caught and handled,
                but due to its blocking nature, it has a performance overhead. On
                the other hand, ``NCCL_ASYNC_ERROR_HANDLING`` has very little
                performance overhead, but crashes the process on errors. 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. Only one of these two environment variables should be set.
             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]) -> 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:
        """
        Returns 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[pg]

    # This ops are not friently 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 = [
        all_reduce_multigpu,
        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,
        reduce_multigpu,
        scatter,
        scatter_object_list,
        reduce,
        reduce_scatter_multigpu,
        all_gather,
        broadcast_multigpu,
        all_gather_multigpu,
        reduce_scatter,
        all_gather_into_tensor,
        broadcast,
        reduce_scatter_tensor,
        send,
    ]

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