parallel_state.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Copyright 2023 The vLLM team.
# Adapted from
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""vLLM distributed state.
It takes over the control of the distributed environment from PyTorch.
The typical workflow is:

- call `init_distributed_environment` to initialize the distributed environment.
- call `initialize_model_parallel` or `ensure_model_parallel_initialized` to
 initialize the model parallel groups.

- any code dealing with the distributed stuff

- call `destroy_model_parallel` to destroy the model parallel groups.
- call `destroy_distributed_environment` to destroy the distributed environment.

If you only need to use the distributed environment without model/pipeline
 parallelism, you can skip the model parallel initialization and destruction
 steps.
"""

import contextlib
import gc
import pickle
import weakref
from collections import namedtuple
from collections.abc import Callable
from contextlib import contextmanager, nullcontext
from dataclasses import dataclass
from datetime import timedelta
from multiprocessing import shared_memory
from typing import Any, Optional
from unittest.mock import patch

import torch
import torch.distributed
import torch.distributed._functional_collectives as funcol
import torch.distributed._symmetric_memory
from torch.distributed import Backend, ProcessGroup
from typing_extensions import deprecated

import vllm.envs as envs
from vllm.distributed.device_communicators.base_device_communicator import (
    DeviceCommunicatorBase,
)
from vllm.distributed.utils import StatelessProcessGroup
from vllm.logger import init_logger
from vllm.utils import (
    direct_register_custom_op,
    get_distributed_init_method,
    resolve_obj_by_qualname,
    supports_custom_op,
)


@dataclass
class GraphCaptureContext:
    stream: torch.cuda.Stream


TensorMetadata = namedtuple("TensorMetadata", ["device", "dtype", "size"])


def _split_tensor_dict(
    tensor_dict: dict[str, torch.Tensor | Any],
) -> tuple[list[tuple[str, Any]], list[torch.Tensor]]:
    """Split the tensor dictionary into two parts:
    1. A list of (key, value) pairs. If the value is a tensor, it is replaced
         by its metadata.
    2. A list of tensors.
    """
    metadata_list: list[tuple[str, Any]] = []
    tensor_list: list[torch.Tensor] = []
    for key, value in tensor_dict.items():
        if isinstance(value, torch.Tensor):
            # Note: we cannot use `value.device` here,
            # because it contains not only the device type but also the device
            # index (e.g. "cuda:0"). We only need the device type.
            # receiving side will set the device index.
            device = value.device.type
            metadata_list.append(
                (key, TensorMetadata(device, value.dtype, value.size()))
            )
            tensor_list.append(value)
        else:
            metadata_list.append((key, value))
    return metadata_list, tensor_list


_group_name_counter: dict[str, int] = {}


def _get_unique_name(name: str) -> str:
    """Get a unique name for the group.
    Example:
    _get_unique_name("tp") -> "tp:0"
    _get_unique_name("tp") -> "tp:1"
    """
    if name not in _group_name_counter:
        _group_name_counter[name] = 0
    newname = f"{name}:{_group_name_counter[name]}"
    _group_name_counter[name] += 1
    return newname


_groups: dict[str, Callable[[], Optional["GroupCoordinator"]]] = {}


def _register_group(group: "GroupCoordinator") -> None:
    _groups[group.unique_name] = weakref.ref(group)


def all_reduce(tensor: torch.Tensor, group_name: str) -> torch.Tensor:
    assert group_name in _groups, f"Group {group_name} is not found."
    group = _groups[group_name]()
    if group is None:
        raise ValueError(f"Group {group_name} is destroyed.")
    return group._all_reduce_out_place(tensor)


def all_reduce_fake(tensor: torch.Tensor, group_name: str) -> torch.Tensor:
    return torch.empty_like(tensor)


def reduce_scatter(
    tensor: torch.Tensor, dim: int, world_size: int, group_name: str
) -> torch.Tensor:
    assert group_name in _groups, f"Group {group_name} is not found."
    group = _groups[group_name]()
    if group is None:
        raise ValueError(f"Group {group_name} is destroyed.")
    return group._reduce_scatter_out_place(tensor, dim)


def reduce_scatter_fake(
    tensor: torch.Tensor, dim: int, world_size: int, group_name: str
) -> torch.Tensor:
    new_shape = list(tensor.shape)
    new_shape[dim] = tensor.shape[dim] // world_size
    return torch.empty(new_shape, dtype=tensor.dtype, device=tensor.device)


def all_gather(
    tensor: torch.Tensor, dim: int, world_size: int, group_name: str
) -> torch.Tensor:
    assert group_name in _groups, f"Group {group_name} is not found."
    group = _groups[group_name]()
    if group is None:
        raise ValueError(f"Group {group_name} is destroyed.")
    return group._all_gather_out_place(tensor, dim)


def all_gather_fake(
    tensor: torch.Tensor, dim: int, world_size: int, group_name: str
) -> torch.Tensor:
    new_shape = list(tensor.shape)
    new_shape[dim] = tensor.shape[dim] * world_size
    return torch.empty(new_shape, dtype=tensor.dtype, device=tensor.device)


def patched_fused_scaled_matmul_reduce_scatter_fake(
    A: torch.Tensor,
    B: torch.Tensor,
    A_scale: torch.Tensor,
    B_scale: torch.Tensor,
    reduce_op: str,
    orig_scatter_dim: int,
    scatter_dim_after_maybe_reshape: int,
    group_name: str,
    output_shape: list[int],
    bias: torch.Tensor | None = None,
    result_scale: torch.Tensor | None = None,
    out_dtype: torch.dtype | None = None,
    use_fast_accum: bool = False,
) -> torch.Tensor:
    # Copied from
    # https://github.com/pytorch/pytorch/blob/50c338c2da905062449e4d9ac807832d1b5cd90e/torch/distributed/_symmetric_memory/__init__.py#L1189
    if A_scale.numel() > 1:
        if A_scale.shape[:-1] != A.shape[:-1]:
            raise ValueError(
                "For row-wise scaling, the leading dims of A_scale "
                "must match the leading dims of A "
                f"(A shape: {A.shape}, A_scale shape: {A_scale.shape})"
            )
        A_scale = A_scale.flatten(0, -2).contiguous()
    elif A_scale.numel() != 1:
        raise ValueError(
            "Invalid A_scale shape "
            f"(A shape: {A.shape}, A_scale shape: {A_scale.shape})"
        )

    C = torch._scaled_mm(
        A.flatten(0, -2).contiguous(),
        B,
        A_scale,
        B_scale,
        bias,
        result_scale,
        out_dtype,
        use_fast_accum,
    )
    C = C.view(*output_shape[:-1], B.shape[1])
    res = funcol.reduce_scatter_tensor(
        C,
        reduce_op,
        orig_scatter_dim,  # need original scatter dim for 3D+ output tensor here
        group_name,
    )
    res = funcol.wait_tensor(res)
    return res


def patched_fused_scaled_matmul_reduce_scatter(
    A: torch.Tensor,
    B: torch.Tensor,
    A_scale: torch.Tensor,
    B_scale: torch.Tensor,
    reduce_op: str,
    orig_scatter_dim: int,
    scatter_dim_after_maybe_reshape: int,
    group_name: str,
    output_shape: list[int],
    bias: torch.Tensor | None = None,
    result_scale: torch.Tensor | None = None,
    out_dtype: torch.dtype | None = None,
    use_fast_accum: bool = False,
) -> torch.Tensor:
    return torch.ops.symm_mem.fused_scaled_matmul_reduce_scatter(
        A,
        B,
        A_scale,
        B_scale,
        reduce_op,
        orig_scatter_dim,
        scatter_dim_after_maybe_reshape,
        group_name,
        output_shape,
        bias,
        result_scale,
        out_dtype,
        use_fast_accum,
    )


if supports_custom_op():
    direct_register_custom_op(
        op_name="all_reduce",
        op_func=all_reduce,
        fake_impl=all_reduce_fake,
    )

    direct_register_custom_op(
        op_name="reduce_scatter",
        op_func=reduce_scatter,
        fake_impl=reduce_scatter_fake,
    )

    direct_register_custom_op(
        op_name="all_gather",
        op_func=all_gather,
        fake_impl=all_gather_fake,
    )

    # TODO: Remove this once the pytorch fix
    # (https://github.com/pytorch/pytorch/pull/165086) gets released,
    # in either 2.9.1 or 2.10
    direct_register_custom_op(
        op_name="patched_fused_scaled_matmul_reduce_scatter",
        op_func=patched_fused_scaled_matmul_reduce_scatter,
        fake_impl=patched_fused_scaled_matmul_reduce_scatter_fake,
    )


class GroupCoordinator:
    """
    PyTorch ProcessGroup wrapper for a group of processes.
    PyTorch ProcessGroup is bound to one specific communication backend,
        e.g. NCCL, Gloo, MPI, etc.
    GroupCoordinator takes charge of all the communication operations among
        the processes in the group. It manages both CPU and device
        communication.
    """

    # available attributes:
    rank: int  # global rank
    ranks: list[int]  # global ranks in the group
    world_size: int  # size of the group
    # difference between `local_rank` and `rank_in_group`:
    # if we have a group of size 4 across two nodes:
    # Process | Node | Rank | Local Rank | Rank in Group
    #   0     |   0  |  0   |     0      |       0
    #   1     |   0  |  1   |     1      |       1
    #   2     |   1  |  2   |     0      |       2
    #   3     |   1  |  3   |     1      |       3
    local_rank: int  # local rank used to assign devices
    rank_in_group: int  # rank inside the group
    cpu_group: ProcessGroup  # group for CPU communication
    device_group: ProcessGroup  # group for device communication
    # device communicator (if use_device_communicator=True)
    device_communicator: DeviceCommunicatorBase | None
    mq_broadcaster: Any | None  # shared memory broadcaster

    def __init__(
        self,
        group_ranks: list[list[int]],
        local_rank: int,
        torch_distributed_backend: str | Backend,
        use_device_communicator: bool,  # whether to use device communicator
        use_message_queue_broadcaster: bool = False,
        group_name: str | None = None,
    ):
        group_name = group_name or "anonymous"
        self.unique_name = _get_unique_name(group_name)
        _register_group(self)

        self.rank = torch.distributed.get_rank()
        self.local_rank = local_rank

        self_device_group = None
        self_cpu_group = None

        for ranks in group_ranks:
            device_group = torch.distributed.new_group(
                ranks, backend=torch_distributed_backend
            )
            # a group with `gloo` backend, to allow direct coordination between
            # processes through the CPU.
            cpu_group = torch.distributed.new_group(ranks, backend="gloo")
            if self.rank in ranks:
                self.ranks = ranks
                self.world_size = len(ranks)
                self.rank_in_group = ranks.index(self.rank)
                self_device_group = device_group
                self_cpu_group = cpu_group

        assert self_cpu_group is not None
        assert self_device_group is not None

        self.cpu_group = self_cpu_group
        self.device_group = self_device_group

        from vllm.platforms import current_platform

        if current_platform.is_cuda_alike():
            self.device = torch.device(f"cuda:{local_rank}")
        elif current_platform.is_xpu():
            self.device = torch.device(f"xpu:{local_rank}")
        elif current_platform.is_out_of_tree():
            self.device = torch.device(f"{current_platform.device_name}:{local_rank}")
        else:
            self.device = torch.device("cpu")

        self.use_device_communicator = use_device_communicator
        self.device_communicator = None
        if use_device_communicator and self.world_size > 1:
            device_comm_cls = resolve_obj_by_qualname(
                current_platform.get_device_communicator_cls()
            )
            self.device_communicator = device_comm_cls(
                cpu_group=self.cpu_group,
                device=self.device,
                device_group=self.device_group,
                unique_name=self.unique_name,
            )

        from vllm.distributed.device_communicators.shm_broadcast import MessageQueue

        self.mq_broadcaster: MessageQueue | None = None
        if use_message_queue_broadcaster and self.world_size > 1:
            self.mq_broadcaster = MessageQueue.create_from_process_group(
                self.cpu_group, 1 << 22, 6
            )

        from vllm.platforms import current_platform

        self.use_custom_op_call = (
            current_platform.is_cuda_alike() or current_platform.is_tpu()
        )

        self.use_cpu_custom_send_recv = current_platform.is_cpu() and hasattr(
            torch.ops._C, "init_shm_manager"
        )

    @property
    def first_rank(self):
        """Return the global rank of the first process in the group"""
        return self.ranks[0]

    @property
    def last_rank(self):
        """Return the global rank of the last process in the group"""
        return self.ranks[-1]

    @property
    def is_first_rank(self):
        """Return whether the caller is the first process in the group"""
        return self.rank == self.first_rank

    @property
    def is_last_rank(self):
        """Return whether the caller is the last process in the group"""
        return self.rank == self.last_rank

    @property
    def next_rank(self):
        """Return the global rank of the process that follows the caller"""
        rank_in_group = self.rank_in_group
        world_size = self.world_size
        return self.ranks[(rank_in_group + 1) % world_size]

    @property
    def prev_rank(self):
        """Return the global rank of the process that precedes the caller"""
        rank_in_group = self.rank_in_group
        world_size = self.world_size
        return self.ranks[(rank_in_group - 1) % world_size]

    @contextmanager
    def graph_capture(self, graph_capture_context: GraphCaptureContext | None = None):
        if graph_capture_context is None:
            stream = torch.cuda.Stream()
            graph_capture_context = GraphCaptureContext(stream)
        else:
            stream = graph_capture_context.stream

        # only cuda uses this function,
        # so we don't abstract it into the base class
        maybe_ca_context = nullcontext()
        from vllm.distributed.device_communicators.cuda_communicator import (
            CudaCommunicator,
        )

        if self.device_communicator is not None:
            assert isinstance(self.device_communicator, CudaCommunicator)
            ca_comm = self.device_communicator.ca_comm
            if ca_comm is not None:
                maybe_ca_context = ca_comm.capture()  # type: ignore

        # ensure all initialization operations complete before attempting to
        # capture the graph on another stream
        curr_stream = torch.cuda.current_stream()
        if curr_stream != stream:
            stream.wait_stream(curr_stream)

        with torch.cuda.stream(stream), maybe_ca_context:
            yield graph_capture_context

    def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
        """
        User-facing all-reduce function before we actually call the
        all-reduce operation.

        We need this because Dynamo does not support passing an arbitrary
        object (`self` in this case) to a custom op. We need to pass the
         group name as a string, and then look up the group coordinator from
         the group name, dispatch the all-reduce operation to the group
         coordinator.

        In addition, PyTorch custom ops do not support mutation or returning
        a new tensor in the same op. So we always make the all-reduce operation
        out-of-place.
        """
        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return input_

        if self.use_custom_op_call:
            return torch.ops.vllm.all_reduce(input_, group_name=self.unique_name)
        else:
            return self._all_reduce_out_place(input_)

    def _all_reduce_out_place(self, input_: torch.Tensor) -> torch.Tensor:
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
        return self.device_communicator.all_reduce(input_)

    def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
        world_size = self.world_size
        # Bypass the function if we are using only 1 GPU.
        if world_size == 1:
            return input_
        assert -input_.dim() <= dim < input_.dim(), (
            f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
        )

        if self.use_custom_op_call:
            return torch.ops.vllm.all_gather(
                input_, dim, world_size, group_name=self.unique_name
            )
        else:
            return self._all_gather_out_place(input_, dim)

    def _all_gather_out_place(self, input_: torch.Tensor, dim: int) -> torch.Tensor:
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
        return self.device_communicator.all_gather(input_, dim)

    def all_gatherv(
        self,
        input_: torch.Tensor | list[torch.Tensor],
        dim: int = 0,
        sizes: list[int] | None = None,
    ):
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
        return self.device_communicator.all_gatherv(input_, dim, sizes)

    def reduce_scatter(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
        world_size = self.world_size
        # Bypass the function if we are using only 1 GPU.
        if world_size == 1:
            return input_
        assert -input_.dim() <= dim < input_.dim(), (
            f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
        )

        if self.use_custom_op_call:
            return torch.ops.vllm.reduce_scatter(
                input_, dim, world_size, group_name=self.unique_name
            )
        else:
            return self._reduce_scatter_out_place(input_, dim)

    def reduce_scatterv(
        self, input_: torch.Tensor, dim: int = -1, sizes: list[int] | None = None
    ) -> torch.Tensor:
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
        return self.device_communicator.reduce_scatterv(input_, dim, sizes)

    def _reduce_scatter_out_place(self, input_: torch.Tensor, dim: int) -> torch.Tensor:
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
        return self.device_communicator.reduce_scatter(input_, dim)

    def gather(
        self, input_: torch.Tensor, dst: int = 0, dim: int = -1
    ) -> torch.Tensor | None:
        """
        NOTE: We assume that the input tensor is on the same device across
        all the ranks.
        NOTE: `dst` is the local rank of the destination rank.
        """
        world_size = self.world_size
        # Bypass the function if we are using only 1 GPU.
        if world_size == 1:
            return input_
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
        return self.device_communicator.gather(input_, dst, dim)

    def broadcast(self, input_: torch.Tensor, src: int = 0):
        """Broadcast the input tensor.
        NOTE: `src` is the local rank of the source rank.
        """
        assert src < self.world_size, f"Invalid src rank ({src})"

        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return input_
        # Broadcast.
        torch.distributed.broadcast(
            input_, src=self.ranks[src], group=self.device_group
        )
        return input_

    def broadcast_object(self, obj: Any | None = None, src: int = 0):
        """Broadcast the input object.
        NOTE: `src` is the local rank of the source rank.
        """
        assert src < self.world_size, f"Invalid src rank ({src})"

        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return obj
        if self.mq_broadcaster is not None:
            assert src == 0, "Message queue broadcaster only supports src=0"
            return self.mq_broadcaster.broadcast_object(obj)
        if self.rank_in_group == src:
            torch.distributed.broadcast_object_list(
                [obj], src=self.ranks[src], group=self.cpu_group
            )
            return obj
        else:
            recv = [None]
            torch.distributed.broadcast_object_list(
                recv, src=self.ranks[src], group=self.cpu_group
            )
            return recv[0]

    def broadcast_object_list(
        self, obj_list: list[Any], src: int = 0, group: ProcessGroup | None = None
    ):
        """Broadcast the input object list.
        NOTE: `src` is the local rank of the source rank.
        """
        assert src < self.world_size, f"Invalid src rank ({src})"

        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return obj_list
        # Broadcast.
        torch.distributed.broadcast_object_list(
            obj_list, src=self.ranks[src], group=self.device_group
        )
        return obj_list

    def send_object(self, obj: Any, dst: int) -> None:
        """Send the input object list to the destination rank."""
        """NOTE: `dst` is the local rank of the destination rank."""

        assert dst < self.world_size, f"Invalid dst rank ({dst})"

        assert dst != self.rank_in_group, (
            "Invalid destination rank. Destination rank is the same "
            "as the current rank."
        )

        # Serialize object to tensor and get the size as well
        object_tensor = torch.frombuffer(pickle.dumps(obj), dtype=torch.uint8)

        size_tensor = torch.tensor(
            [object_tensor.numel()], dtype=torch.long, device="cpu"
        )

        # Send object size

        torch.distributed.send(size_tensor, dst=self.ranks[dst], group=self.cpu_group)

        # Send object
        torch.distributed.send(object_tensor, dst=self.ranks[dst], group=self.cpu_group)

        return None

    def recv_object(self, src: int) -> Any:
        """Receive the input object list from the source rank."""
        """NOTE: `src` is the local rank of the source rank."""

        assert src < self.world_size, f"Invalid src rank ({src})"

        assert src != self.rank_in_group, (
            "Invalid source rank. Source rank is the same as the current rank."
        )

        size_tensor = torch.empty(1, dtype=torch.long, device="cpu")

        # Receive object size
        rank_size = torch.distributed.recv(
            size_tensor, src=self.ranks[src], group=self.cpu_group
        )

        # Tensor to receive serialized objects into.
        object_tensor = torch.empty(  # type: ignore[call-overload]
            size_tensor.item(),  # type: ignore[arg-type]
            dtype=torch.uint8,
            device="cpu",
        )

        rank_object = torch.distributed.recv(
            object_tensor, src=self.ranks[src], group=self.cpu_group
        )

        assert rank_object == rank_size, (
            "Received object sender rank does not match the size sender rank."
        )

        obj = pickle.loads(object_tensor.numpy().tobytes())

        return obj

    def broadcast_tensor_dict(
        self,
        tensor_dict: dict[str, torch.Tensor | Any] | None = None,
        src: int = 0,
        group: ProcessGroup | None = None,
        metadata_group: ProcessGroup | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
        """Broadcast the input tensor dictionary.
        NOTE: `src` is the local rank of the source rank.
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return tensor_dict

        group = self.device_group
        metadata_group = self.cpu_group
        assert src < self.world_size, f"Invalid src rank ({src})"

        rank_in_group = self.rank_in_group
        if rank_in_group == src:
            metadata_list: list[tuple[Any, Any]] = []
            assert isinstance(tensor_dict, dict), (
                f"Expecting a dictionary, got {type(tensor_dict)}"
            )
            metadata_list, tensor_list = _split_tensor_dict(tensor_dict)
            # `metadata_list` lives in CPU memory.
            # `broadcast_object_list` has serialization & deserialization,
            # all happening on CPU. Therefore, we can use the CPU group.
            self.broadcast_object(metadata_list, src=src)
            async_handles = []
            for tensor in tensor_list:
                if tensor.numel() == 0:
                    # Skip broadcasting empty tensors.
                    continue
                if tensor.is_cpu:
                    # use metadata_group for CPU tensors
                    handle = torch.distributed.broadcast(
                        tensor, src=self.ranks[src], group=metadata_group, async_op=True
                    )
                else:
                    # use group for GPU tensors
                    handle = torch.distributed.broadcast(
                        tensor, src=self.ranks[src], group=group, async_op=True
                    )
                async_handles.append(handle)
            for async_handle in async_handles:
                async_handle.wait()

        else:
            metadata_list = self.broadcast_object(None, src=src)
            tensor_dict = {}
            async_handles = []
            for key, value in metadata_list:
                if isinstance(value, TensorMetadata):
                    tensor = torch.empty(
                        value.size, dtype=value.dtype, device=value.device
                    )
                    if tensor.numel() == 0:
                        # Skip broadcasting empty tensors.
                        tensor_dict[key] = tensor
                        continue
                    if tensor.is_cpu:
                        # use metadata_group for CPU tensors
                        handle = torch.distributed.broadcast(
                            tensor,
                            src=self.ranks[src],
                            group=metadata_group,
                            async_op=True,
                        )
                    else:
                        # use group for GPU tensors
                        handle = torch.distributed.broadcast(
                            tensor, src=self.ranks[src], group=group, async_op=True
                        )
                    async_handles.append(handle)
                    tensor_dict[key] = tensor
                else:
                    tensor_dict[key] = value
            for async_handle in async_handles:
                async_handle.wait()
        return tensor_dict

    def send_tensor_dict(
        self,
        tensor_dict: dict[str, torch.Tensor | Any],
        dst: int | None = None,
        all_gather_group: Optional["GroupCoordinator"] = None,
        all_gather_tensors: dict[str, bool] | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
        """Send the input tensor dictionary.
        NOTE: `dst` is the local rank of the source rank.

        all_gather_group: The group for the all-gather operation. If provided,
            an optimization is enabled where each rank in the group sends a
            slice of a tensor and the receiver reconstructs it using an
            all-gather, which can improve performance. This is typically the
            tensor-parallel group.
        all_gather_tensors: A dictionary to specify which tensors should use
            the all-gather optimization, which is only effective when
            `all_gather_group` is provided. By default, this optimization is
            on for any tensor whose size is divisible by the
            `all_gather_group`'s world size. However, it should be disabled
            for tensors that are not fully replicated across the group (e.g.,
            the residual tensor when sequence parallelism is enabled). This
            dictionary allows overriding the default behavior on a per-tensor
            basis.
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return tensor_dict
        all_gather_size = 1 if all_gather_group is None else all_gather_group.world_size
        all_gather_rank = (
            0 if all_gather_group is None else all_gather_group.rank_in_group
        )

        group = self.device_group
        metadata_group = self.cpu_group

        if dst is None:
            dst = (self.rank_in_group + 1) % self.world_size
        assert dst < self.world_size, f"Invalid dst rank ({dst})"

        if self.use_cpu_custom_send_recv:
            if self.device_communicator is None:
                raise ValueError("No device communicator found")
            self.device_communicator.send_tensor_dict(  # type: ignore
                tensor_dict, dst
            )
            return None

        metadata_list: list[tuple[Any, Any]] = []
        assert isinstance(tensor_dict, dict), (
            f"Expecting a dictionary, got {type(tensor_dict)}"
        )
        metadata_list, tensor_list = _split_tensor_dict(tensor_dict)
        # `metadata_list` lives in CPU memory.
        # `send_object_list` has serialization & deserialization,
        # all happening on CPU. Therefore, we can use the CPU group.
        self.send_object(metadata_list, dst=dst)

        tensor_keys = [k for k, v in tensor_dict.items() if isinstance(v, torch.Tensor)]
        assert len(tensor_keys) == len(tensor_list)

        for key, tensor in zip(tensor_keys, tensor_list):
            if tensor.numel() == 0:
                # Skip sending empty tensors.
                continue

            # send-allgather: send only a slice, then do allgather.
            use_all_gather = (
                all_gather_group is not None and tensor.numel() % all_gather_size == 0
            )
            use_all_gather = (
                all_gather_tensors.get(key, use_all_gather)
                if all_gather_tensors
                else use_all_gather
            )
            if use_all_gather:
                tensor = tensor.reshape(all_gather_size, -1)[all_gather_rank]

            if tensor.is_cpu:
                # use metadata_group for CPU tensors
                torch.distributed.send(
                    tensor, dst=self.ranks[dst], group=metadata_group
                )
            else:
                # use group for GPU tensors
                torch.distributed.send(tensor, dst=self.ranks[dst], group=group)
        return None

    def recv_tensor_dict(
        self,
        src: int | None = None,
        all_gather_group: Optional["GroupCoordinator"] = None,
        all_gather_tensors: dict[str, bool] | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
        """Recv the input tensor dictionary.
        NOTE: `src` is the local rank of the source rank.

        all_gather_group: The group for the all-gather operation. If provided,
            an optimization is enabled where each rank in the group sends a
            slice of a tensor and the receiver reconstructs it using an
            all-gather, which can improve performance. This is typically the
            tensor-parallel group.
        all_gather_tensors: A dictionary to specify which tensors should use
            the all-gather optimization, which is only effective when
            `all_gather_group` is provided. By default, this optimization is
            on for any tensor whose size is divisible by the
            `all_gather_group`'s world size. However, it should be disabled
            for tensors that are not fully replicated across the group (e.g.,
            the residual tensor when sequence parallelism is enabled). This
            dictionary allows overriding the default behavior on a per-tensor
            basis.
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return None
        all_gather_size = 1 if all_gather_group is None else all_gather_group.world_size
        all_gather_rank = (
            0 if all_gather_group is None else all_gather_group.rank_in_group
        )

        group = self.device_group
        metadata_group = self.cpu_group

        if src is None:
            src = (self.rank_in_group - 1) % self.world_size
        assert src < self.world_size, f"Invalid src rank ({src})"

        if self.use_cpu_custom_send_recv:
            if self.device_communicator is None:
                raise ValueError("No device communicator found")
            return self.device_communicator.recv_tensor_dict(  # type: ignore
                src
            )

        recv_metadata_list = self.recv_object(src=src)
        tensor_dict: dict[str, Any] = {}
        for key, value in recv_metadata_list:
            if isinstance(value, TensorMetadata):
                tensor = torch.empty(value.size, dtype=value.dtype, device=value.device)
                if tensor.numel() == 0:
                    # Skip broadcasting empty tensors.
                    tensor_dict[key] = tensor
                    continue

                # send-allgather: send only a slice, then do allgather.
                use_all_gather = (
                    all_gather_group is not None
                    and tensor.numel() % all_gather_size == 0
                )
                use_all_gather = (
                    all_gather_tensors.get(key, use_all_gather)
                    if all_gather_tensors
                    else use_all_gather
                )

                if use_all_gather:
                    orig_shape = tensor.shape
                    tensor = tensor.reshape(all_gather_size, -1)[all_gather_rank]

                if tensor.is_cpu:
                    # use metadata_group for CPU tensors
                    torch.distributed.recv(
                        tensor, src=self.ranks[src], group=metadata_group
                    )
                else:
                    # use group for GPU tensors
                    torch.distributed.recv(tensor, src=self.ranks[src], group=group)
                if use_all_gather:
                    # do the allgather
                    tensor = all_gather_group.all_gather(  # type: ignore
                        tensor, dim=0
                    )
                    tensor = tensor.reshape(orig_shape)

                tensor_dict[key] = tensor
            else:
                tensor_dict[key] = value
        return tensor_dict

    def barrier(self):
        """Barrier synchronization among the group.
        NOTE: don't use `device_group` here! `barrier` in NCCL is
        terrible because it is internally a broadcast operation with
        secretly created GPU tensors. It is easy to mess up the current
        device. Use the CPU group instead.
        """
        torch.distributed.barrier(group=self.cpu_group)

    def send(self, tensor: torch.Tensor, dst: int | None = None) -> None:
        """Sends a tensor to the destination rank in a blocking way"""
        """NOTE: `dst` is the local rank of the destination rank."""
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
        self.device_communicator.send(tensor, dst)

    def recv(
        self, size: torch.Size, dtype: torch.dtype, src: int | None = None
    ) -> torch.Tensor:
        """Receives a tensor from the source rank."""
        """NOTE: `src` is the local rank of the source rank."""
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
        return self.device_communicator.recv(size, dtype, src)

    def destroy(self):
        if hasattr(self, "device_group"):
            torch.distributed.destroy_process_group(self.device_group)
            del self.device_group
        if hasattr(self, "cpu_group"):
            torch.distributed.destroy_process_group(self.cpu_group)
            del self.cpu_group
        if self.device_communicator is not None:
            self.device_communicator.destroy()
        if self.mq_broadcaster is not None:
            self.mq_broadcaster = None

    def prepare_communication_buffer_for_model(self, model: torch.nn.Module):
        if self.device_communicator is not None:
            self.device_communicator.prepare_communication_buffer_for_model(model)

    def dispatch(
        self,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
        is_sequence_parallel: bool = False,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if self.device_communicator is not None:
            return self.device_communicator.dispatch(
                hidden_states, router_logits, is_sequence_parallel
            )
        else:
            return hidden_states, router_logits

    def combine(
        self, hidden_states, is_sequence_parallel: bool = False
    ) -> torch.Tensor:
        if self.device_communicator is not None:
            return self.device_communicator.combine(hidden_states, is_sequence_parallel)
        else:
            return hidden_states


_WORLD: GroupCoordinator | None = None
_NODE_COUNT: int | None = None


def get_world_group() -> GroupCoordinator:
    assert _WORLD is not None, "world group is not initialized"
    return _WORLD


def init_world_group(
    ranks: list[int], local_rank: int, backend: str
) -> GroupCoordinator:
    return GroupCoordinator(
        group_ranks=[ranks],
        local_rank=local_rank,
        torch_distributed_backend=backend,
        use_device_communicator=False,
        group_name="world",
    )


def init_model_parallel_group(
    group_ranks: list[list[int]],
    local_rank: int,
    backend: str,
    use_message_queue_broadcaster: bool = False,
    group_name: str | None = None,
) -> GroupCoordinator:
    return GroupCoordinator(
        group_ranks=group_ranks,
        local_rank=local_rank,
        torch_distributed_backend=backend,
        use_device_communicator=True,
        use_message_queue_broadcaster=use_message_queue_broadcaster,
        group_name=group_name,
    )


_TP: GroupCoordinator | None = None


def get_tp_group() -> GroupCoordinator:
    assert _TP is not None, "tensor model parallel group is not initialized"
    return _TP


@deprecated(
    "`get_tensor_model_parallel_group` has been replaced with "
    "`get_tp_group` and may be removed after v0.12. Please use "
    "`get_tp_group` instead."
)
def get_tensor_model_parallel_group():
    return get_tp_group()


_DCP: GroupCoordinator | None = None


def get_dcp_group() -> GroupCoordinator:
    assert _DCP is not None, "decode context model parallel group is not initialized"
    return _DCP


# kept for backward compatibility
get_context_model_parallel_group = get_dcp_group

_PP: GroupCoordinator | None = None

_DP: GroupCoordinator | None = None


def get_dp_group() -> GroupCoordinator:
    assert _DP is not None, "data parallel group is not initialized"
    return _DP


_EP: GroupCoordinator | None = None


def get_ep_group() -> GroupCoordinator:
    assert _EP is not None, "expert parallel group is not initialized"
    return _EP


def get_pp_group() -> GroupCoordinator:
    assert _PP is not None, "pipeline model parallel group is not initialized"
    return _PP


@deprecated(
    "`get_pipeline_model_parallel_group` has been replaced with "
    "`get_pp_group` and may be removed in v0.12. Please use "
    "`get_pp_group` instead."
)
def get_pipeline_model_parallel_group():
    return get_pp_group()


@contextmanager
def graph_capture(device: torch.device):
    """
    `graph_capture` is a context manager which should surround the code that
    is capturing the CUDA graph. Its main purpose is to ensure that some
    operations will be run after the graph is captured, before the graph
    is replayed. It returns a `GraphCaptureContext` object which contains the
    necessary data for the graph capture. Currently, it only contains the
    stream that the graph capture is running on. This stream is set to the
    current CUDA stream when the context manager is entered and reset to the
    default stream when the context manager is exited. This is to ensure that
    the graph capture is running on a separate stream from the default stream,
    in order to explicitly distinguish the kernels to capture
    from other kernels possibly launched on background in the default stream.
    """
    context = GraphCaptureContext(torch.cuda.Stream(device=device))
    with get_tp_group().graph_capture(context), get_pp_group().graph_capture(context):
        yield context


logger = init_logger(__name__)

_ENABLE_CUSTOM_ALL_REDUCE = True


def set_custom_all_reduce(enable: bool):
    global _ENABLE_CUSTOM_ALL_REDUCE
    _ENABLE_CUSTOM_ALL_REDUCE = enable


def init_distributed_environment(
    world_size: int = -1,
    rank: int = -1,
    distributed_init_method: str = "env://",
    local_rank: int = -1,
    backend: str = "nccl",
    timeout: timedelta | None = None,
):
    logger.debug(
        "world_size=%d rank=%d local_rank=%d distributed_init_method=%s backend=%s",
        world_size,
        rank,
        local_rank,
        distributed_init_method,
        backend,
    )
    from vllm.config import get_current_vllm_config

    config = get_current_vllm_config()
    if (
        config is not None
        and config.parallel_config.data_parallel_size > 1
        and config.parallel_config.distributed_executor_backend != "external_launcher"
    ):
        parallel_config = config.parallel_config
        # adjust to take into account data parallelism
        # offset the rank by the data parallel rank
        rank = parallel_config.data_parallel_rank * world_size + rank
        # adjust the world size to take into account data parallelism
        world_size = parallel_config.world_size_across_dp
        ip = parallel_config.data_parallel_master_ip
        port = parallel_config.get_next_dp_init_port()
        distributed_init_method = get_distributed_init_method(ip, port)
        logger.info(
            "Adjusting world_size=%d rank=%d distributed_init_method=%s for DP",
            world_size,
            rank,
            distributed_init_method,
        )
    if not torch.distributed.is_initialized():
        assert distributed_init_method is not None, (
            "distributed_init_method must be provided when initializing "
            "distributed environment"
        )
        if not torch.distributed.is_backend_available(backend):
            logger.warning(
                "Distributed backend %s is not available; falling back to gloo.",
                backend,
            )
            assert torch.distributed.is_gloo_available(), (
                "Fallback Gloo backend is not available."
            )
            backend = "gloo"
        # this backend is used for WORLD
        torch.distributed.init_process_group(
            backend=backend,
            init_method=distributed_init_method,
            world_size=world_size,
            rank=rank,
            timeout=timeout,
        )
    # set the local rank
    # local_rank is not available in torch ProcessGroup,
    # see https://github.com/pytorch/pytorch/issues/122816
    if local_rank == -1:
        # local rank not set, this usually happens in single-node
        # setting, where we can use rank as local rank
        local_rank = envs.LOCAL_RANK if distributed_init_method == "env://" else rank
    global _WORLD, _NODE_COUNT
    if _WORLD is None:
        ranks = list(range(torch.distributed.get_world_size()))
        _WORLD = init_world_group(ranks, local_rank, backend)
        _NODE_COUNT = _node_count(_WORLD.cpu_group)
        logger.debug("Detected %d nodes in the distributed environment", _NODE_COUNT)
    else:
        assert _WORLD.world_size == torch.distributed.get_world_size(), (
            "world group already initialized with a different world size"
        )


def initialize_model_parallel(
    tensor_model_parallel_size: int = 1,
    pipeline_model_parallel_size: int = 1,
    decode_context_model_parallel_size: int | None = 1,
    backend: str | None = None,
) -> None:
    """
    Initialize model parallel groups.

    Arguments:
        tensor_model_parallel_size: number of GPUs used for tensor model
            parallelism.
        pipeline_model_parallel_size: number of GPUs used for pipeline model
            parallelism.
        backend: name of torch distributed communication backend.

    Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
    use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
    the model pipeline. The present function will
    create 4 tensor model-parallel groups and 2 pipeline model-parallel groups:
        4 tensor model-parallel groups:
            [g0, g1], [g2, g3], [g4, g5], [g6, g7]
        2 pipeline model-parallel groups:
            [g0, g2, g4, g6], [g1, g3, g5, g7]
    Note that for efficiency, the caller should make sure adjacent ranks
    are on the same DGX box. For example if we are using 2 DGX-1 boxes
    with a total of 16 GPUs, rank 0 to 7 belong to the first box and
    ranks 8 to 15 belong to the second box.
    """
    # Get world size and rank. Ensure some consistencies.
    assert torch.distributed.is_initialized()
    world_size: int = torch.distributed.get_world_size()
    rank = torch.distributed.get_rank()
    backend = backend or torch.distributed.get_backend(get_world_group().device_group)

    data_parallel_size = 1
    from vllm.config import get_current_vllm_config

    config = get_current_vllm_config()
    if config is not None:
        data_parallel_size = config.parallel_config.data_parallel_size

    # the layout order is: ExternalDP x DP x PP x TP
    # ExternalDP is the data parallel group that is not part of the model,
    # every dp rank can generate independently (in verl integration).
    # DP is the data parallel group that is part of the model,
    # all the ranks in the same DP group should generate simultaneously,
    # i.e. the `generate` call in the same DP group should be called together,
    # otherwise it will cause deadlock.
    # to get group_ranks for each dimension, transpose that dimension to the
    # last dimension, then reshape to 2D, then unbind the last dimension
    all_ranks = torch.arange(world_size).reshape(
        -1, data_parallel_size, pipeline_model_parallel_size, tensor_model_parallel_size
    )  # noqa

    # Build the tensor model-parallel groups.
    global _TP
    assert _TP is None, "tensor model parallel group is already initialized"
    group_ranks = all_ranks.view(-1, tensor_model_parallel_size).unbind(0)
    group_ranks = [x.tolist() for x in group_ranks]

    # message queue broadcaster is only used in tensor model parallel group
    _TP = init_model_parallel_group(
        group_ranks,
        get_world_group().local_rank,
        backend,
        use_message_queue_broadcaster=True,
        group_name="tp",
    )

    # Build the DCP model-parallel groups.
    global _DCP
    assert _DCP is None, "decode context model parallel group is already initialized"
    # Note(hc): In the current implementation of decode context parallel,
    # dcp_size must not exceed tp_size, because the world size does not
    # change by DCP, it simply reuses the GPUs of TP group, and split one
    # TP group into tp_size//dcp_size DCP groups.
    group_ranks = all_ranks.reshape(-1, decode_context_model_parallel_size).unbind(0)
    group_ranks = [x.tolist() for x in group_ranks]
    _DCP = init_model_parallel_group(
        group_ranks,
        get_world_group().local_rank,
        backend,
        use_message_queue_broadcaster=True,
        group_name="dcp",
    )

    # Build the pipeline model-parallel groups.
    global _PP
    assert _PP is None, "pipeline model parallel group is already initialized"
    group_ranks = (
        all_ranks.transpose(2, 3).reshape(-1, pipeline_model_parallel_size).unbind(0)
    )
    group_ranks = [x.tolist() for x in group_ranks]
    _PP = init_model_parallel_group(
        group_ranks, get_world_group().local_rank, backend, group_name="pp"
    )

    global _DP
    assert _DP is None, "data parallel group is already initialized"
    group_ranks = all_ranks.transpose(1, 3).reshape(-1, data_parallel_size).unbind(0)
    group_ranks = [x.tolist() for x in group_ranks]
    _DP = init_model_parallel_group(
        group_ranks, get_world_group().local_rank, backend, group_name="dp"
    )

    global _EP
    assert _EP is None, "expert parallel group is already initialized"
    group_ranks = (
        all_ranks.transpose(1, 2)
        .reshape(-1, data_parallel_size * tensor_model_parallel_size)
        .unbind(0)
    )
    group_ranks = [x.tolist() for x in group_ranks]
    _EP = init_model_parallel_group(
        group_ranks, get_world_group().local_rank, backend, group_name="ep"
    )

    logger.info(
        "rank %s in world size %s is assigned as "
        "DP rank %s, PP rank %s, TP rank %s, EP rank %s",
        rank,
        world_size,
        _DP.rank_in_group,
        _PP.rank_in_group,
        _TP.rank_in_group,
        _EP.rank_in_group,
    )


def ensure_model_parallel_initialized(
    tensor_model_parallel_size: int,
    pipeline_model_parallel_size: int,
    decode_context_model_parallel_size: int | None = 1,
    backend: str | None = None,
) -> None:
    """Helper to initialize model parallel groups if they are not initialized,
    or ensure tensor-parallel and pipeline-parallel sizes are equal to expected
    values if the model parallel groups are initialized.
    """
    backend = backend or torch.distributed.get_backend(get_world_group().device_group)
    if not model_parallel_is_initialized():
        initialize_model_parallel(
            tensor_model_parallel_size,
            pipeline_model_parallel_size,
            decode_context_model_parallel_size,
            backend,
        )
        return

    assert get_tensor_model_parallel_world_size() == tensor_model_parallel_size, (
        "tensor parallel group already initialized, but of unexpected size. "
        f"got: {get_tensor_model_parallel_world_size()=} vs. "
        f"wanted: {tensor_model_parallel_size=}"
    )
    pp_world_size = get_pp_group().world_size
    assert pp_world_size == pipeline_model_parallel_size, (
        "pipeline parallel group already initialized, but of unexpected size. "
        f"got: {pp_world_size=} vs. "
        f"wanted: {pipeline_model_parallel_size=}"
    )


def prepare_communication_buffer_for_model(model: torch.nn.Module):
    """Prepare the communication buffer for the model.
    Traditional communication libraries like NCCL are almost
    model agnostic. However, emerging new communication libraries like
    MoE all2all (DeepEP) usually allocate the communication buffer
    based on the model shape for optimal performance.
    """
    if _TP is not None:
        _TP.prepare_communication_buffer_for_model(model)
    if _PP is not None:
        _PP.prepare_communication_buffer_for_model(model)
    if _DP is not None:
        _DP.prepare_communication_buffer_for_model(model)
    if _EP is not None:
        _EP.prepare_communication_buffer_for_model(model)


def model_parallel_is_initialized():
    """Check if tensor and pipeline parallel groups are initialized."""
    return _TP is not None and _PP is not None


_TP_STATE_PATCHED = False


@contextmanager
def patch_tensor_parallel_group(tp_group: GroupCoordinator):
    """Patch the tp group temporarily until this function ends.

    This method is for draft workers of speculative decoding to run draft model
    with different tp degree from that of target model workers.

    Args:
        tp_group (GroupCoordinator): the tp group coordinator
    """
    global _TP_STATE_PATCHED
    assert not _TP_STATE_PATCHED, "Should not call when it's already patched"

    _TP_STATE_PATCHED = True
    old_tp_group = get_tp_group()
    global _TP
    _TP = tp_group
    try:
        yield
    finally:
        # restore the original state
        _TP_STATE_PATCHED = False
        _TP = old_tp_group


def get_tensor_model_parallel_world_size():
    """Return world size for the tensor model parallel group."""
    return get_tp_group().world_size


def get_tensor_model_parallel_rank():
    """Return my rank for the tensor model parallel group."""
    return get_tp_group().rank_in_group


def get_decode_context_model_parallel_world_size():
    """Return world size for the decode context model parallel group."""
    return get_dcp_group().world_size


def get_decode_context_model_parallel_rank():
    """Return my rank for the decode context model parallel group."""
    return get_dcp_group().rank_in_group


def get_node_count() -> int:
    """Return the total number of nodes in the distributed environment."""
    assert _NODE_COUNT is not None, "distributed environment is not initialized"
    return _NODE_COUNT


def destroy_model_parallel():
    """Set the groups to none and destroy them."""
    global _TP

    if _TP:
        _TP.destroy()
    _TP = None

    global _PP
    if _PP:
        _PP.destroy()
    _PP = None

    global _DCP
    if _DCP:
        _DCP.destroy()
    _DCP = None

    global _DP
    if _DP:
        _DP.destroy()
    _DP = None

    global _EP
    if _EP:
        _EP.destroy()
    _EP = None


def destroy_distributed_environment():
    global _WORLD, _NODE_COUNT
    if _WORLD:
        _WORLD.destroy()
    _WORLD = None
    _NODE_COUNT = None
    if torch.distributed.is_initialized():
        torch.distributed.destroy_process_group()


def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
    destroy_model_parallel()
    destroy_distributed_environment()
    if shutdown_ray:
        import ray  # Lazy import Ray

        ray.shutdown()
    gc.collect()
    from vllm.platforms import current_platform

    empty_cache = current_platform.empty_cache
    if empty_cache is not None:
        empty_cache()
    try:
        if not current_platform.is_cpu():
            torch._C._host_emptyCache()
    except AttributeError:
        logger.warning("torch._C._host_emptyCache() only available in Pytorch >=2.5")


def in_the_same_node_as(
    pg: ProcessGroup | StatelessProcessGroup, source_rank: int = 0
) -> list[bool]:
    """
    This is a collective operation that returns if each rank is in the same node
    as the source rank. It tests if processes are attached to the same
    memory system (shared access to shared memory).
    """
    if isinstance(pg, ProcessGroup):
        assert torch.distributed.get_backend(pg) != torch.distributed.Backend.NCCL, (
            "in_the_same_node_as should be tested with a non-NCCL group."
        )
        # local rank inside the group
        rank = torch.distributed.get_rank(group=pg)
        world_size = torch.distributed.get_world_size(group=pg)

        # global ranks of the processes in the group
        ranks = torch.distributed.get_process_group_ranks(pg)
    else:
        rank = pg.rank
        world_size = pg.world_size
        ranks = list(range(world_size))

    # local tensor in each process to store the result
    is_in_the_same_node = torch.tensor([0] * world_size, dtype=torch.int32)

    magic_message = b"magic_message"
    shm = None

    try:
        with contextlib.suppress(OSError):
            if rank == source_rank:
                # create a shared memory segment
                shm = shared_memory.SharedMemory(create=True, size=128)
                shm.buf[: len(magic_message)] = magic_message
                if isinstance(pg, ProcessGroup):
                    torch.distributed.broadcast_object_list(
                        [shm.name], src=ranks[source_rank], group=pg
                    )
                else:
                    pg.broadcast_obj(shm.name, src=source_rank)
                is_in_the_same_node[rank] = 1
            else:
                # try to open the shared memory segment
                if isinstance(pg, ProcessGroup):
                    recv = [None]
                    torch.distributed.broadcast_object_list(
                        recv, src=ranks[source_rank], group=pg
                    )
                    name = recv[0]
                else:
                    name = pg.broadcast_obj(None, src=source_rank)
                # fix to https://stackoverflow.com/q/62748654/9191338
                # Python incorrectly tracks shared memory even if it is not
                # created by the process. The following patch is a workaround.
                with patch(
                    "multiprocessing.resource_tracker.register",
                    lambda *args, **kwargs: None,
                ):
                    shm = shared_memory.SharedMemory(name=name)
                if shm.buf[: len(magic_message)] == magic_message:
                    is_in_the_same_node[rank] = 1
    except Exception as e:
        logger.error("Error ignored in is_in_the_same_node: %s", e)
    finally:
        if shm:
            shm.close()

    if isinstance(pg, ProcessGroup):
        torch.distributed.barrier(group=pg)
    else:
        pg.barrier()

    # clean up the shared memory segment
    with contextlib.suppress(OSError):
        if rank == source_rank and shm:
            shm.unlink()

    if isinstance(pg, ProcessGroup):
        torch.distributed.all_reduce(is_in_the_same_node, group=pg)
        aggregated_data = is_in_the_same_node
    else:
        aggregated_data = torch.zeros_like(is_in_the_same_node)
        for i in range(world_size):
            rank_data = pg.broadcast_obj(is_in_the_same_node, src=i)
            aggregated_data += rank_data

    return [x == 1 for x in aggregated_data.tolist()]


def is_global_first_rank() -> bool:
    """
    Check if the current process is the first rank globally across all
    parallelism strategies (PP, TP, DP, EP, etc.).

    Unlike group-specific checks like `get_tensor_model_parallel_rank() == 0`
    or `get_pp_group().is_first_rank`, this function checks the global rank
    across all parallelism dimensions.

    Returns:
        bool: True if this is the global first rank (rank 0), False otherwise.
              Returns True if distributed is not initialized (single process).
    """
    try:
        # If world group is available, use it for the most accurate check
        global _WORLD
        if _WORLD is not None:
            return _WORLD.is_first_rank

        # If torch distributed is not initialized, assume single process
        if not torch.distributed.is_initialized():
            return True

        # Fallback to torch's global rank
        return torch.distributed.get_rank() == 0

    except Exception:
        # If anything goes wrong, assume this is the first rank
        return True


def _node_count(pg: ProcessGroup | StatelessProcessGroup) -> int:
    """
    Returns the total number of nodes in the process group.

    Args:
        pg: The process group to analyze

    Returns:
        int: The total number of nodes
    """
    if isinstance(pg, ProcessGroup):
        world_size = torch.distributed.get_world_size(group=pg)
    else:
        world_size = pg.world_size

    if world_size == 1:
        return 1

    # Build node assignment map
    node_assignment = [0] * world_size  # rank -> node_id
    next_node_id = 0

    for current_rank in range(world_size):
        if node_assignment[current_rank] != 0:
            continue  # Already assigned to a node

        # Assign current rank to a new node
        next_node_id += 1
        node_assignment[current_rank] = next_node_id

        # Find all ranks on the same node as current_rank
        same_node_flags = in_the_same_node_as(pg, current_rank)
        for other_rank, is_same_node in enumerate(same_node_flags):
            if is_same_node and node_assignment[other_rank] == 0:
                node_assignment[other_rank] = next_node_id

    return next_node_id