parallel_state.py 63.6 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2023 The vLLM team.
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# Adapted from
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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"""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.
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- call `initialize_model_parallel` or `ensure_model_parallel_initialized` to
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 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.
"""
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import contextlib
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import gc
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import pickle
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import weakref
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from collections import namedtuple
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from collections.abc import Callable
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from contextlib import contextmanager, nullcontext
from dataclasses import dataclass
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from datetime import timedelta
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from multiprocessing import shared_memory
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from typing import Any, Optional
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from unittest.mock import patch
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import torch
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import torch.distributed
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import torch.distributed._functional_collectives as funcol
import torch.distributed._symmetric_memory
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from torch.distributed import Backend, ProcessGroup
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import vllm.envs as envs
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from vllm.distributed.device_communicators.base_device_communicator import (
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    DeviceCommunicatorBase,
)
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from vllm.distributed.utils import StatelessProcessGroup
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from vllm.logger import init_logger
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from vllm.utils.import_utils import resolve_obj_by_qualname
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from vllm.utils.network_utils import get_distributed_init_method
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from vllm.utils.system_utils import suppress_stdout
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from vllm.utils.torch_utils import (
    direct_register_custom_op,
    supports_custom_op,
)
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@dataclass
class GraphCaptureContext:
    stream: torch.cuda.Stream
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TensorMetadata = namedtuple("TensorMetadata", ["device", "dtype", "size"])
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def _split_tensor_dict(
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    tensor_dict: dict[str, torch.Tensor | Any],
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) -> tuple[list[tuple[str, Any]], list[torch.Tensor]]:
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    """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.
    """
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    metadata_list: list[tuple[str, Any]] = []
    tensor_list: list[torch.Tensor] = []
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    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.
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            device = value.device.type
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            metadata_list.append(
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                (key, TensorMetadata(device, value.dtype, value.size()))
            )
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            tensor_list.append(value)
        else:
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            metadata_list.append((key, value))
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    return metadata_list, tensor_list


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_group_name_counter: dict[str, int] = {}
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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


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_groups: dict[str, Callable[[], Optional["GroupCoordinator"]]] = {}
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def _register_group(group: "GroupCoordinator") -> None:
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    _groups[group.unique_name] = weakref.ref(group)
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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)
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def all_reduce_fake(tensor: torch.Tensor, group_name: str) -> torch.Tensor:
    return torch.empty_like(tensor)
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def reduce_scatter(
    tensor: torch.Tensor, dim: int, world_size: int, group_name: str
) -> torch.Tensor:
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    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.")
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    return group._reduce_scatter_out_place(tensor, dim)
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def reduce_scatter_fake(
    tensor: torch.Tensor, dim: int, world_size: int, group_name: str
) -> torch.Tensor:
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    new_shape = list(tensor.shape)
    new_shape[dim] = tensor.shape[dim] // world_size
    return torch.empty(new_shape, dtype=tensor.dtype, device=tensor.device)


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


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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,
    )


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

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    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,
    )

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    # 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,
    )

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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
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        the processes in the group. It manages both CPU and device
        communication.
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    """

    # available attributes:
    rank: int  # global rank
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    ranks: list[int]  # global ranks in the group
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    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
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    cpu_group: ProcessGroup  # group for CPU communication
    device_group: ProcessGroup  # group for device communication
    # device communicator (if use_device_communicator=True)
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    device_communicator: DeviceCommunicatorBase | None
    mq_broadcaster: Any | None  # shared memory broadcaster
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    def __init__(
        self,
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        group_ranks: list[list[int]],
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        local_rank: int,
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        torch_distributed_backend: str | Backend,
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        use_device_communicator: bool,  # whether to use device communicator
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        use_message_queue_broadcaster: bool = False,
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        group_name: str | None = None,
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    ):
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        group_name = group_name or "anonymous"
        self.unique_name = _get_unique_name(group_name)
        _register_group(self)
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        self.rank = torch.distributed.get_rank()
        self.local_rank = local_rank
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        self_device_group = None
        self_cpu_group = None
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        for ranks in group_ranks:
            device_group = torch.distributed.new_group(
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                ranks, backend=torch_distributed_backend
            )
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            # a group with `gloo` backend, to allow direct coordination between
            # processes through the CPU.
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            with suppress_stdout():
                cpu_group = torch.distributed.new_group(ranks, backend="gloo")
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            if self.rank in ranks:
                self.ranks = ranks
                self.world_size = len(ranks)
                self.rank_in_group = ranks.index(self.rank)
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                self_device_group = device_group
                self_cpu_group = cpu_group

        assert self_cpu_group is not None
        assert self_device_group is not None
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        self.cpu_group = self_cpu_group
        self.device_group = self_device_group
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        from vllm.platforms import current_platform
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        if current_platform.is_cuda_alike():
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            self.device = torch.device(f"cuda:{local_rank}")
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        elif current_platform.is_xpu():
            self.device = torch.device(f"xpu:{local_rank}")
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        elif current_platform.is_out_of_tree():
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            self.device = torch.device(f"{current_platform.device_name}:{local_rank}")
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        else:
            self.device = torch.device("cpu")

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

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        from vllm.distributed.device_communicators.shm_broadcast import MessageQueue

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

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        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"
        )
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    def create_mq_broadcaster(
        self, writer_rank=0, external_writer_handle=None, blocking=True
    ):
        from vllm.distributed.device_communicators.shm_broadcast import MessageQueue

        return MessageQueue.create_from_process_group(
            self.cpu_group,
            1 << 22,
            6,
            writer_rank=writer_rank,
            external_writer_handle=external_writer_handle,
            blocking=blocking,
        )

    def create_single_reader_mq_broadcasters(
        self, reader_rank_in_group=0, blocking=False
    ):
        from vllm.distributed.device_communicators.shm_broadcast import MessageQueue

        return MessageQueue.create_from_process_group_single_reader(
            self.cpu_group,
            1 << 22,
            6,
            reader_rank=self.ranks[reader_rank_in_group],
            blocking=blocking,
        )

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    @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]

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    @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

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

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        # 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 (
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            CudaCommunicator,
        )

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        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
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        # 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)

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        with torch.cuda.stream(stream), maybe_ca_context:
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            yield graph_capture_context
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    def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
        """
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        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
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        a new tensor in the same op. So we always make the all-reduce operation
        out-of-place.
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        """
        # Bypass the function if we are using only 1 GPU.
        if self.world_size == 1:
            return input_

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        if self.use_custom_op_call:
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            return torch.ops.vllm.all_reduce(input_, group_name=self.unique_name)
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        else:
            return self._all_reduce_out_place(input_)
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    def _all_reduce_out_place(self, input_: torch.Tensor) -> torch.Tensor:
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        if self.device_communicator is None:
            raise ValueError("No device communicator found")
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        return self.device_communicator.all_reduce(input_)
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    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(), (
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            f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
        )
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        if self.use_custom_op_call:
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            return torch.ops.vllm.all_gather(
                input_, dim, world_size, group_name=self.unique_name
            )
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        else:
            return self._all_gather_out_place(input_, dim)

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

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    def reduce_scatter(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
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        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(), (
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            f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
        )
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        if self.use_custom_op_call:
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            return torch.ops.vllm.reduce_scatter(
                input_, dim, world_size, group_name=self.unique_name
            )
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        else:
            return self._reduce_scatter_out_place(input_, dim)

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

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

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    def gather(
        self, input_: torch.Tensor, dst: int = 0, dim: int = -1
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    ) -> torch.Tensor | None:
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574
575
576
577
578
579
        """
        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_
580
581
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
582
        return self.device_communicator.gather(input_, dst, dim)
583
584
585
586
587
588
589
590
591
592
593

    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.
594
595
596
        torch.distributed.broadcast(
            input_, src=self.ranks[src], group=self.device_group
        )
597
598
        return input_

599
    def broadcast_object(self, obj: Any | None = None, src: int = 0):
600
601
602
603
604
605
606
607
        """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
608
609
610
        if self.mq_broadcaster is not None:
            assert src == 0, "Message queue broadcaster only supports src=0"
            return self.mq_broadcaster.broadcast_object(obj)
611
        if self.rank_in_group == src:
612
613
614
            torch.distributed.broadcast_object_list(
                [obj], src=self.ranks[src], group=self.cpu_group
            )
615
616
617
            return obj
        else:
            recv = [None]
618
619
620
            torch.distributed.broadcast_object_list(
                recv, src=self.ranks[src], group=self.cpu_group
            )
621
622
            return recv[0]

623
    def broadcast_object_list(
624
        self, obj_list: list[Any], src: int = 0, group: ProcessGroup | None = None
625
    ):
626
627
628
629
630
631
632
633
634
        """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.
635
636
637
        torch.distributed.broadcast_object_list(
            obj_list, src=self.ranks[src], group=self.device_group
        )
638
639
        return obj_list

640
641
642
643
644
645
    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})"

646
        assert dst != self.rank_in_group, (
647
            "Invalid destination rank. Destination rank is the same "
648
649
            "as the current rank."
        )
650
651
652
653

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

654
655
656
        size_tensor = torch.tensor(
            [object_tensor.numel()], dtype=torch.long, device="cpu"
        )
657
658
659

        # Send object size

660
        torch.distributed.send(size_tensor, dst=self.ranks[dst], group=self.cpu_group)
661
662

        # Send object
663
        torch.distributed.send(object_tensor, dst=self.ranks[dst], group=self.cpu_group)
664
665
666
667
668
669
670
671
672

        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})"

673
        assert src != self.rank_in_group, (
674
675
676
677
678
679
            "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
680
681
682
        rank_size = torch.distributed.recv(
            size_tensor, src=self.ranks[src], group=self.cpu_group
        )
683
684
685
686
687

        # Tensor to receive serialized objects into.
        object_tensor = torch.empty(  # type: ignore[call-overload]
            size_tensor.item(),  # type: ignore[arg-type]
            dtype=torch.uint8,
688
689
            device="cpu",
        )
690

691
692
693
        rank_object = torch.distributed.recv(
            object_tensor, src=self.ranks[src], group=self.cpu_group
        )
694
695

        assert rank_object == rank_size, (
696
697
            "Received object sender rank does not match the size sender rank."
        )
698
699
700
701
702

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

        return obj

703
704
    def broadcast_tensor_dict(
        self,
705
        tensor_dict: dict[str, torch.Tensor | Any] | None = None,
706
        src: int = 0,
707
708
709
        group: ProcessGroup | None = None,
        metadata_group: ProcessGroup | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
710
711
712
713
        """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.
714
        if not torch.distributed.is_initialized() or self.world_size == 1:
715
716
717
718
719
720
            return tensor_dict

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

721
722
        rank_in_group = self.rank_in_group
        if rank_in_group == src:
723
            metadata_list: list[tuple[Any, Any]] = []
724
725
726
            assert isinstance(tensor_dict, dict), (
                f"Expecting a dictionary, got {type(tensor_dict)}"
            )
727
728
729
730
            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.
731
            self.broadcast_object(metadata_list, src=src)
732
733
734
735
736
737
738
            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
739
740
741
                    handle = torch.distributed.broadcast(
                        tensor, src=self.ranks[src], group=metadata_group, async_op=True
                    )
742
743
                else:
                    # use group for GPU tensors
744
745
746
                    handle = torch.distributed.broadcast(
                        tensor, src=self.ranks[src], group=group, async_op=True
                    )
747
748
749
750
751
                async_handles.append(handle)
            for async_handle in async_handles:
                async_handle.wait()

        else:
752
            metadata_list = self.broadcast_object(None, src=src)
753
754
            tensor_dict = {}
            async_handles = []
755
            for key, value in metadata_list:
756
                if isinstance(value, TensorMetadata):
757
758
759
                    tensor = torch.empty(
                        value.size, dtype=value.dtype, device=value.device
                    )
760
761
                    if tensor.numel() == 0:
                        # Skip broadcasting empty tensors.
762
                        tensor_dict[key] = tensor
763
764
765
766
767
                        continue
                    if tensor.is_cpu:
                        # use metadata_group for CPU tensors
                        handle = torch.distributed.broadcast(
                            tensor,
768
                            src=self.ranks[src],
769
                            group=metadata_group,
770
771
                            async_op=True,
                        )
772
773
                    else:
                        # use group for GPU tensors
774
                        handle = torch.distributed.broadcast(
775
776
                            tensor, src=self.ranks[src], group=group, async_op=True
                        )
777
                    async_handles.append(handle)
778
                    tensor_dict[key] = tensor
779
                else:
780
                    tensor_dict[key] = value
781
782
783
784
            for async_handle in async_handles:
                async_handle.wait()
        return tensor_dict

785
786
    def send_tensor_dict(
        self,
787
788
        tensor_dict: dict[str, torch.Tensor | Any],
        dst: int | None = None,
789
        all_gather_group: Optional["GroupCoordinator"] = None,
790
791
        all_gather_tensors: dict[str, bool] | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
792
793
        """Send the input tensor dictionary.
        NOTE: `dst` is the local rank of the source rank.
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808

        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.
809
810
811
812
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return tensor_dict
813
814
815
816
        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
        )
817

818
819
820
821
        group = self.device_group
        metadata_group = self.cpu_group

        if dst is None:
822
            dst = (self.rank_in_group + 1) % self.world_size
823
824
        assert dst < self.world_size, f"Invalid dst rank ({dst})"

825
        if self.use_cpu_custom_send_recv:
826
827
            if self.device_communicator is None:
                raise ValueError("No device communicator found")
828
            self.device_communicator.send_tensor_dict(  # type: ignore
829
830
                tensor_dict, dst
            )
831
832
            return None

833
        metadata_list: list[tuple[Any, Any]] = []
834
835
836
        assert isinstance(tensor_dict, dict), (
            f"Expecting a dictionary, got {type(tensor_dict)}"
        )
837
838
839
840
841
        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)
842

843
        tensor_keys = [k for k, v in tensor_dict.items() if isinstance(v, torch.Tensor)]
844
845
846
        assert len(tensor_keys) == len(tensor_list)

        for key, tensor in zip(tensor_keys, tensor_list):
847
848
849
            if tensor.numel() == 0:
                # Skip sending empty tensors.
                continue
850
851

            # send-allgather: send only a slice, then do allgather.
852
853
854
855
856
857
858
859
            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
            )
860
            if use_all_gather:
861
862
                tensor = tensor.reshape(all_gather_size, -1)[all_gather_rank]

863
864
            if tensor.is_cpu:
                # use metadata_group for CPU tensors
865
866
867
                torch.distributed.send(
                    tensor, dst=self.ranks[dst], group=metadata_group
                )
868
869
            else:
                # use group for GPU tensors
870
                torch.distributed.send(tensor, dst=self.ranks[dst], group=group)
871
872
873
874
        return None

    def recv_tensor_dict(
        self,
875
        src: int | None = None,
876
        all_gather_group: Optional["GroupCoordinator"] = None,
877
878
        all_gather_tensors: dict[str, bool] | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
879
880
        """Recv the input tensor dictionary.
        NOTE: `src` is the local rank of the source rank.
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895

        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.
896
897
898
899
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return None
900
901
902
903
        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
        )
904

905
906
907
908
        group = self.device_group
        metadata_group = self.cpu_group

        if src is None:
909
            src = (self.rank_in_group - 1) % self.world_size
910
911
        assert src < self.world_size, f"Invalid src rank ({src})"

912
        if self.use_cpu_custom_send_recv:
913
914
            if self.device_communicator is None:
                raise ValueError("No device communicator found")
915
            return self.device_communicator.recv_tensor_dict(  # type: ignore
916
917
                src
            )
918

919
        recv_metadata_list = self.recv_object(src=src)
920
        tensor_dict: dict[str, Any] = {}
921
922
        for key, value in recv_metadata_list:
            if isinstance(value, TensorMetadata):
923
                tensor = torch.empty(value.size, dtype=value.dtype, device=value.device)
924
925
                if tensor.numel() == 0:
                    # Skip broadcasting empty tensors.
926
                    tensor_dict[key] = tensor
927
                    continue
928
929

                # send-allgather: send only a slice, then do allgather.
930
931
932
933
934
935
936
937
938
                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
                )
939
940
941

                if use_all_gather:
                    orig_shape = tensor.shape
942
                    tensor = tensor.reshape(all_gather_size, -1)[all_gather_rank]
943

944
945
                if tensor.is_cpu:
                    # use metadata_group for CPU tensors
946
947
948
                    torch.distributed.recv(
                        tensor, src=self.ranks[src], group=metadata_group
                    )
949
950
                else:
                    # use group for GPU tensors
951
                    torch.distributed.recv(tensor, src=self.ranks[src], group=group)
952
953
954
                if use_all_gather:
                    # do the allgather
                    tensor = all_gather_group.all_gather(  # type: ignore
955
956
                        tensor, dim=0
                    )
957
958
                    tensor = tensor.reshape(orig_shape)

959
                tensor_dict[key] = tensor
960
            else:
961
                tensor_dict[key] = value
962
963
        return tensor_dict

964
965
966
967
968
969
970
971
972
    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)

973
    def send(self, tensor: torch.Tensor, dst: int | None = None) -> None:
974
        """Sends a tensor to the destination rank in a blocking way"""
975
        """NOTE: `dst` is the local rank of the destination rank."""
976
977
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
978
        self.device_communicator.send(tensor, dst)
979

980
    def recv(
981
        self, size: torch.Size, dtype: torch.dtype, src: int | None = None
982
    ) -> torch.Tensor:
983
984
        """Receives a tensor from the source rank."""
        """NOTE: `src` is the local rank of the source rank."""
985
986
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
987
        return self.device_communicator.recv(size, dtype, src)
988

989
    def destroy(self):
990
        if hasattr(self, "device_group"):
991
            torch.distributed.destroy_process_group(self.device_group)
992
993
            del self.device_group
        if hasattr(self, "cpu_group"):
994
            torch.distributed.destroy_process_group(self.cpu_group)
995
            del self.cpu_group
996
997
        if self.device_communicator is not None:
            self.device_communicator.destroy()
998
999
        if self.mq_broadcaster is not None:
            self.mq_broadcaster = None
1000

1001
1002
    def prepare_communication_buffer_for_model(self, model: torch.nn.Module):
        if self.device_communicator is not None:
1003
            self.device_communicator.prepare_communication_buffer_for_model(model)
1004
1005

    def dispatch(
1006
1007
1008
        self,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
1009
        is_sequence_parallel: bool = False,
1010
    ) -> tuple[torch.Tensor, torch.Tensor]:
1011
        if self.device_communicator is not None:
1012
1013
1014
            return self.device_communicator.dispatch(
                hidden_states, router_logits, is_sequence_parallel
            )
1015
1016
        else:
            return hidden_states, router_logits
1017

1018
1019
1020
    def combine(
        self, hidden_states, is_sequence_parallel: bool = False
    ) -> torch.Tensor:
1021
        if self.device_communicator is not None:
1022
            return self.device_communicator.combine(hidden_states, is_sequence_parallel)
1023
1024
        else:
            return hidden_states
1025

1026

1027
_WORLD: GroupCoordinator | None = None
1028
_INNER_DP_WORLD: GroupCoordinator | None = None
1029
_NODE_COUNT: int | None = None
1030
1031
1032


def get_world_group() -> GroupCoordinator:
1033
    assert _WORLD is not None, "world group is not initialized"
1034
1035
1036
    return _WORLD


1037
1038
1039
1040
1041
def get_inner_dp_world_group() -> GroupCoordinator:
    assert _INNER_DP_WORLD is not None, "inner dp world group is not initialized"
    return _INNER_DP_WORLD


1042
1043
1044
def init_world_group(
    ranks: list[int], local_rank: int, backend: str
) -> GroupCoordinator:
1045
1046
1047
1048
    return GroupCoordinator(
        group_ranks=[ranks],
        local_rank=local_rank,
        torch_distributed_backend=backend,
1049
        use_device_communicator=False,
1050
        group_name="world",
1051
1052
1053
    )


1054
def init_model_parallel_group(
1055
    group_ranks: list[list[int]],
1056
1057
1058
    local_rank: int,
    backend: str,
    use_message_queue_broadcaster: bool = False,
1059
    group_name: str | None = None,
1060
    use_device_communicator: bool = True,
1061
) -> GroupCoordinator:
1062
1063
1064
1065
    return GroupCoordinator(
        group_ranks=group_ranks,
        local_rank=local_rank,
        torch_distributed_backend=backend,
1066
        use_device_communicator=use_device_communicator,
1067
        use_message_queue_broadcaster=use_message_queue_broadcaster,
1068
        group_name=group_name,
1069
1070
1071
    )


1072
_TP: GroupCoordinator | None = None
1073
1074
1075


def get_tp_group() -> GroupCoordinator:
1076
    assert _TP is not None, "tensor model parallel group is not initialized"
1077
1078
1079
    return _TP


1080
_DCP: GroupCoordinator | None = None
1081
1082
1083


def get_dcp_group() -> GroupCoordinator:
1084
    assert _DCP is not None, "decode context model parallel group is not initialized"
1085
1086
1087
1088
1089
1090
    return _DCP


# kept for backward compatibility
get_context_model_parallel_group = get_dcp_group

1091
_PP: GroupCoordinator | None = None
1092

1093
1094
1095
1096
1097
1098

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


1099
_DP: GroupCoordinator | None = None
1100
1101
1102


def get_dp_group() -> GroupCoordinator:
1103
    assert _DP is not None, "data parallel group is not initialized"
1104
1105
    return _DP

1106

1107
_EP: GroupCoordinator | None = None
1108
1109
1110


def get_ep_group() -> GroupCoordinator:
1111
    assert _EP is not None, "expert parallel group is not initialized"
1112
1113
1114
    return _EP


1115
1116
1117
1118
1119
1120
_PCP: GroupCoordinator | None = None


def get_pcp_group() -> GroupCoordinator:
    assert _PCP is not None, "prefill context parallel group is not initialized"
    return _PCP
1121
1122


1123
@contextmanager
1124
def graph_capture(device: torch.device):
1125
1126
    """
    `graph_capture` is a context manager which should surround the code that
1127
1128
    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
1129
1130
1131
1132
1133
1134
1135
1136
1137
    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.
    """
1138
    context = GraphCaptureContext(torch.cuda.Stream(device=device))
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    with get_tp_group().graph_capture(context), get_pp_group().graph_capture(context):
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        yield context

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logger = init_logger(__name__)
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_ENABLE_CUSTOM_ALL_REDUCE = True
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def set_custom_all_reduce(enable: bool):
    global _ENABLE_CUSTOM_ALL_REDUCE
    _ENABLE_CUSTOM_ALL_REDUCE = enable
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def init_distributed_environment(
    world_size: int = -1,
    rank: int = -1,
    distributed_init_method: str = "env://",
    local_rank: int = -1,
    backend: str = "nccl",
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    timeout: timedelta | None = None,
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):
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    logger.debug(
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        "world_size=%d rank=%d local_rank=%d distributed_init_method=%s backend=%s",
        world_size,
        rank,
        local_rank,
        distributed_init_method,
        backend,
    )
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    from vllm.config import get_current_vllm_config
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    config = get_current_vllm_config()
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    if config is not None and config.parallel_config.nnodes > 1:
        parallel_config = config.parallel_config
        ip = parallel_config.master_addr
        rank = parallel_config.data_parallel_rank * world_size + rank
        world_size = parallel_config.world_size_across_dp
        port = parallel_config.master_port
        distributed_init_method = get_distributed_init_method(ip, port)
    elif (
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        config is not None
        and config.parallel_config.data_parallel_size > 1
        and config.parallel_config.distributed_executor_backend != "external_launcher"
    ):
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        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()
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        distributed_init_method = get_distributed_init_method(ip, port)
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        logger.debug(
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            "Adjusting world_size=%d rank=%d distributed_init_method=%s for DP",
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            world_size,
            rank,
            distributed_init_method,
        )
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    if not torch.distributed.is_initialized():
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        logger.info(
            "world_size=%d rank=%d local_rank=%d distributed_init_method=%s backend=%s",
            world_size,
            rank,
            local_rank,
            distributed_init_method,
            backend,
        )
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        assert distributed_init_method is not None, (
            "distributed_init_method must be provided when initializing "
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            "distributed environment"
        )
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        if not torch.distributed.is_backend_available(backend):
            logger.warning(
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                "Distributed backend %s is not available; falling back to gloo.",
                backend,
            )
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            assert torch.distributed.is_gloo_available(), (
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                "Fallback Gloo backend is not available."
            )
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            backend = "gloo"
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        # this backend is used for WORLD
        torch.distributed.init_process_group(
            backend=backend,
            init_method=distributed_init_method,
            world_size=world_size,
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            rank=rank,
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            timeout=timeout,
        )
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    # 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
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        local_rank = envs.LOCAL_RANK if distributed_init_method == "env://" else rank
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    global _WORLD, _NODE_COUNT, _INNER_DP_WORLD
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    if _WORLD is None:
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        ranks = list(range(torch.distributed.get_world_size()))
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        _WORLD = init_world_group(ranks, local_rank, backend)
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        if config.parallel_config.nnodes > 1:
            _NODE_COUNT = config.parallel_config.nnodes
        else:
            _NODE_COUNT = _node_count(_WORLD.cpu_group)
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        logger.debug("Detected %d nodes in the distributed environment", _NODE_COUNT)
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    else:
        assert _WORLD.world_size == torch.distributed.get_world_size(), (
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            "world group already initialized with a different world size"
        )
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    if config.parallel_config.nnodes_within_dp > 1:
        if parallel_config.data_parallel_size > 1:
            world_size_inner_dp = parallel_config.world_size
            group_ranks = [
                [dp_rank * world_size_inner_dp + i for i in range(world_size_inner_dp)]
                for dp_rank in range(parallel_config.data_parallel_size)
            ]
            _INNER_DP_WORLD = init_model_parallel_group(
                group_ranks,
                get_world_group().local_rank,
                backend,
                use_message_queue_broadcaster=True,
                group_name="inner_dp_world",
                use_device_communicator=False,
            )
        else:
            _INNER_DP_WORLD = _WORLD
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def initialize_model_parallel(
    tensor_model_parallel_size: int = 1,
    pipeline_model_parallel_size: int = 1,
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    prefill_context_model_parallel_size: int = 1,
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    decode_context_model_parallel_size: int | None = 1,
    backend: str | None = None,
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) -> None:
    """
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    Initialize model parallel groups.
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    Arguments:
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        tensor_model_parallel_size: number of GPUs used for tensor model
            parallelism.
        pipeline_model_parallel_size: number of GPUs used for pipeline model
            parallelism.
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        backend: name of torch distributed communication backend.
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    Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
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    use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
    the model pipeline. The present function will
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    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]
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    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()
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    rank = torch.distributed.get_rank()
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    backend = backend or torch.distributed.get_backend(get_world_group().device_group)
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    data_parallel_size = 1
    from vllm.config import get_current_vllm_config
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    config = get_current_vllm_config()
    if config is not None:
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        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.
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    # 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(
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        -1,
        data_parallel_size,
        pipeline_model_parallel_size,
        prefill_context_model_parallel_size,
        tensor_model_parallel_size,
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    )  # noqa
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    # Build the tensor model-parallel groups.
    global _TP
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    assert _TP is None, "tensor model parallel group is already initialized"
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    group_ranks = all_ranks.view(-1, tensor_model_parallel_size).unbind(0)
    group_ranks = [x.tolist() for x in group_ranks]
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    # message queue broadcaster is only used in tensor model parallel group
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    _TP = init_model_parallel_group(
        group_ranks,
        get_world_group().local_rank,
        backend,
        use_message_queue_broadcaster=True,
        group_name="tp",
    )
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    # Build the DCP model-parallel groups.
    global _DCP
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    assert _DCP is None, "decode context model parallel group is already initialized"
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    # Note(hc): In the current implementation of decode context parallel,
    # dcp_size must not exceed tp_size, because the world size does not
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    # change by DCP, it simply reuses the GPUs of TP group, and split one
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    # TP group into tp_size//dcp_size DCP groups.
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    group_ranks = all_ranks.reshape(-1, decode_context_model_parallel_size).unbind(0)
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    group_ranks = [x.tolist() for x in group_ranks]
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    _DCP = init_model_parallel_group(
        group_ranks,
        get_world_group().local_rank,
        backend,
        use_message_queue_broadcaster=True,
        group_name="dcp",
    )
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    global _PCP
    assert _PCP is None, "prefill context parallel group is already initialized"
    group_ranks = (
        all_ranks.transpose(3, 4)
        .reshape(-1, prefill_context_model_parallel_size)
        .unbind(0)
    )
    group_ranks = [x.tolist() for x in group_ranks]
    _PCP = init_model_parallel_group(
        group_ranks, get_world_group().local_rank, backend, group_name="pcp"
    )

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    # Build the pipeline model-parallel groups.
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    global _PP
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    assert _PP is None, "pipeline model parallel group is already initialized"
    group_ranks = (
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        all_ranks.transpose(2, 4).reshape(-1, pipeline_model_parallel_size).unbind(0)
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    )
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    group_ranks = [x.tolist() for x in group_ranks]
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    _PP = init_model_parallel_group(
        group_ranks, get_world_group().local_rank, backend, group_name="pp"
    )
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    global _DP
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    assert _DP is None, "data parallel group is already initialized"
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    group_ranks = all_ranks.transpose(1, 4).reshape(-1, data_parallel_size).unbind(0)
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    group_ranks = [x.tolist() for x in group_ranks]
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    _DP = init_model_parallel_group(
        group_ranks, get_world_group().local_rank, backend, group_name="dp"
    )
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    global _EP
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    assert _EP is None, "expert parallel group is already initialized"
    group_ranks = (
        all_ranks.transpose(1, 2)
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        .reshape(
            -1,
            data_parallel_size
            * prefill_context_model_parallel_size
            * tensor_model_parallel_size,
        )
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        .unbind(0)
    )
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    group_ranks = [x.tolist() for x in group_ranks]
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    _EP = init_model_parallel_group(
        group_ranks, get_world_group().local_rank, backend, group_name="ep"
    )
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    logger.info_once(
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        "rank %s in world size %s is assigned as "
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        "DP rank %s, PP rank %s, PCP rank %s, "
        "TP rank %s, EP rank %s",
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        rank,
        world_size,
        _DP.rank_in_group,
        _PP.rank_in_group,
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        _PCP.rank_in_group,
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        _TP.rank_in_group,
        _EP.rank_in_group,
    )
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def ensure_model_parallel_initialized(
    tensor_model_parallel_size: int,
    pipeline_model_parallel_size: int,
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    prefill_context_model_parallel_size: int = 1,
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    decode_context_model_parallel_size: int | None = 1,
    backend: str | None = None,
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) -> 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.
    """
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    backend = backend or torch.distributed.get_backend(get_world_group().device_group)
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    if not model_parallel_is_initialized():
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        initialize_model_parallel(
            tensor_model_parallel_size,
            pipeline_model_parallel_size,
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            prefill_context_model_parallel_size,
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            decode_context_model_parallel_size,
            backend,
        )
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        return

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    assert get_tensor_model_parallel_world_size() == tensor_model_parallel_size, (
        "tensor parallel group already initialized, but of unexpected size. "
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        f"got: {get_tensor_model_parallel_world_size()=} vs. "
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        f"wanted: {tensor_model_parallel_size=}"
    )
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    pp_world_size = get_pp_group().world_size
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    assert pp_world_size == pipeline_model_parallel_size, (
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        "pipeline parallel group already initialized, but of unexpected size. "
        f"got: {pp_world_size=} vs. "
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        f"wanted: {pipeline_model_parallel_size=}"
    )
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    pcp_world_size = get_pcp_group().world_size
    assert pcp_world_size == prefill_context_model_parallel_size, (
        "prefill context parallel group already initialized, but of unexpected size: "
        f"{pcp_world_size=} vs. "
        f"{prefill_context_model_parallel_size=}"
    )
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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)
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    if _PCP is not None:
        _PCP.prepare_communication_buffer_for_model(model)
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    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)


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


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


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


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

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

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

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

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

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

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def destroy_distributed_environment():
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    global _WORLD, _NODE_COUNT
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    if _WORLD:
        _WORLD.destroy()
    _WORLD = None
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    _NODE_COUNT = None
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    if torch.distributed.is_initialized():
        torch.distributed.destroy_process_group()
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def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
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    # Ensure all objects are not frozen before cleanup
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    gc.unfreeze()

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    destroy_model_parallel()
    destroy_distributed_environment()
    if shutdown_ray:
        import ray  # Lazy import Ray
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        ray.shutdown()
    gc.collect()
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    from vllm.platforms import current_platform
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    empty_cache = current_platform.empty_cache
    if empty_cache is not None:
        empty_cache()
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    try:
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        if not current_platform.is_cpu():
            torch._C._host_emptyCache()
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    except AttributeError:
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        logger.warning("torch._C._host_emptyCache() only available in Pytorch >=2.5")
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def in_the_same_node_as(
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    pg: ProcessGroup | StatelessProcessGroup, source_rank: int = 0
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) -> list[bool]:
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    """
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    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
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    memory system (shared access to shared memory).
    """
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    if isinstance(pg, ProcessGroup):
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        assert torch.distributed.get_backend(pg) != torch.distributed.Backend.NCCL, (
            "in_the_same_node_as should be tested with a non-NCCL group."
        )
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        # 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))
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    # local tensor in each process to store the result
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    is_in_the_same_node = torch.tensor(
        [0] * world_size, dtype=torch.int32, device="cpu"
    )
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    magic_message = b"magic_message"
    shm = None

    try:
        with contextlib.suppress(OSError):
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            if rank == source_rank:
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                # create a shared memory segment
                shm = shared_memory.SharedMemory(create=True, size=128)
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                shm.buf[: len(magic_message)] = magic_message
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                if isinstance(pg, ProcessGroup):
                    torch.distributed.broadcast_object_list(
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                        [shm.name], src=ranks[source_rank], group=pg
                    )
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                else:
                    pg.broadcast_obj(shm.name, src=source_rank)
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                is_in_the_same_node[rank] = 1
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            else:
                # try to open the shared memory segment
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                if isinstance(pg, ProcessGroup):
                    recv = [None]
                    torch.distributed.broadcast_object_list(
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                        recv, src=ranks[source_rank], group=pg
                    )
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                    name = recv[0]
                else:
                    name = pg.broadcast_obj(None, src=source_rank)
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                # 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.
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                with patch(
                    "multiprocessing.resource_tracker.register",
                    lambda *args, **kwargs: None,
                ):
1669
                    shm = shared_memory.SharedMemory(name=name)
1670
                if shm.buf[: len(magic_message)] == magic_message:
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                    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()

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    if isinstance(pg, ProcessGroup):
        torch.distributed.barrier(group=pg)
    else:
        pg.barrier()
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    # clean up the shared memory segment
    with contextlib.suppress(OSError):
1685
        if rank == source_rank and shm:
1686
            shm.unlink()
1687

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    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()]
1698
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1700
1701
def is_global_first_rank() -> bool:
    """
1702
    Check if the current process is the first rank globally across all
1703
    parallelism strategies (PP, TP, DP, EP, etc.).
1704

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    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.
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    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


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1753
def is_local_first_rank() -> bool:
    """
    Check if the current process is the first local rank (rank 0 on its node).
    """
    try:
        # prefer the initialized world group if available
        global _WORLD
        if _WORLD is not None:
            return _WORLD.local_rank == 0

        if not torch.distributed.is_initialized():
            return True

        # fallback to environment-provided local rank if available
        # note: envs.LOCAL_RANK is set when using env:// launchers (e.g., torchrun)
        try:
            return int(envs.LOCAL_RANK) == 0  # type: ignore[arg-type]
        except Exception:
            return torch.distributed.get_rank() == 0
    except Exception:
        return True


1754
def _node_count(pg: ProcessGroup | StatelessProcessGroup) -> int:
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1759
    """
    Returns the total number of nodes in the process group.

    Args:
        pg: The process group to analyze
1760

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    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