parallel_state.py 64.1 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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from typing_extensions import deprecated
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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.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.
            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)
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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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        """
        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_
579
580
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
581
        return self.device_communicator.gather(input_, dst, dim)
582
583
584
585
586
587
588
589
590
591
592

    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.
593
594
595
        torch.distributed.broadcast(
            input_, src=self.ranks[src], group=self.device_group
        )
596
597
        return input_

598
    def broadcast_object(self, obj: Any | None = None, src: int = 0):
599
600
601
602
603
604
605
606
        """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
607
608
609
        if self.mq_broadcaster is not None:
            assert src == 0, "Message queue broadcaster only supports src=0"
            return self.mq_broadcaster.broadcast_object(obj)
610
        if self.rank_in_group == src:
611
612
613
            torch.distributed.broadcast_object_list(
                [obj], src=self.ranks[src], group=self.cpu_group
            )
614
615
616
            return obj
        else:
            recv = [None]
617
618
619
            torch.distributed.broadcast_object_list(
                recv, src=self.ranks[src], group=self.cpu_group
            )
620
621
            return recv[0]

622
    def broadcast_object_list(
623
        self, obj_list: list[Any], src: int = 0, group: ProcessGroup | None = None
624
    ):
625
626
627
628
629
630
631
632
633
        """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.
634
635
636
        torch.distributed.broadcast_object_list(
            obj_list, src=self.ranks[src], group=self.device_group
        )
637
638
        return obj_list

639
640
641
642
643
644
    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})"

645
        assert dst != self.rank_in_group, (
646
            "Invalid destination rank. Destination rank is the same "
647
648
            "as the current rank."
        )
649
650
651
652

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

653
654
655
        size_tensor = torch.tensor(
            [object_tensor.numel()], dtype=torch.long, device="cpu"
        )
656
657
658

        # Send object size

659
        torch.distributed.send(size_tensor, dst=self.ranks[dst], group=self.cpu_group)
660
661

        # Send object
662
        torch.distributed.send(object_tensor, dst=self.ranks[dst], group=self.cpu_group)
663
664
665
666
667
668
669
670
671

        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})"

672
        assert src != self.rank_in_group, (
673
674
675
676
677
678
            "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
679
680
681
        rank_size = torch.distributed.recv(
            size_tensor, src=self.ranks[src], group=self.cpu_group
        )
682
683
684
685
686

        # Tensor to receive serialized objects into.
        object_tensor = torch.empty(  # type: ignore[call-overload]
            size_tensor.item(),  # type: ignore[arg-type]
            dtype=torch.uint8,
687
688
            device="cpu",
        )
689

690
691
692
        rank_object = torch.distributed.recv(
            object_tensor, src=self.ranks[src], group=self.cpu_group
        )
693
694

        assert rank_object == rank_size, (
695
696
            "Received object sender rank does not match the size sender rank."
        )
697
698
699
700
701

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

        return obj

702
703
    def broadcast_tensor_dict(
        self,
704
        tensor_dict: dict[str, torch.Tensor | Any] | None = None,
705
        src: int = 0,
706
707
708
        group: ProcessGroup | None = None,
        metadata_group: ProcessGroup | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
709
710
711
712
        """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.
713
        if not torch.distributed.is_initialized() or self.world_size == 1:
714
715
716
717
718
719
            return tensor_dict

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

720
721
        rank_in_group = self.rank_in_group
        if rank_in_group == src:
722
            metadata_list: list[tuple[Any, Any]] = []
723
724
725
            assert isinstance(tensor_dict, dict), (
                f"Expecting a dictionary, got {type(tensor_dict)}"
            )
726
727
728
729
            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.
730
            self.broadcast_object(metadata_list, src=src)
731
732
733
734
735
736
737
            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
738
739
740
                    handle = torch.distributed.broadcast(
                        tensor, src=self.ranks[src], group=metadata_group, async_op=True
                    )
741
742
                else:
                    # use group for GPU tensors
743
744
745
                    handle = torch.distributed.broadcast(
                        tensor, src=self.ranks[src], group=group, async_op=True
                    )
746
747
748
749
750
                async_handles.append(handle)
            for async_handle in async_handles:
                async_handle.wait()

        else:
751
            metadata_list = self.broadcast_object(None, src=src)
752
753
            tensor_dict = {}
            async_handles = []
754
            for key, value in metadata_list:
755
                if isinstance(value, TensorMetadata):
756
757
758
                    tensor = torch.empty(
                        value.size, dtype=value.dtype, device=value.device
                    )
759
760
                    if tensor.numel() == 0:
                        # Skip broadcasting empty tensors.
761
                        tensor_dict[key] = tensor
762
763
764
765
766
                        continue
                    if tensor.is_cpu:
                        # use metadata_group for CPU tensors
                        handle = torch.distributed.broadcast(
                            tensor,
767
                            src=self.ranks[src],
768
                            group=metadata_group,
769
770
                            async_op=True,
                        )
771
772
                    else:
                        # use group for GPU tensors
773
                        handle = torch.distributed.broadcast(
774
775
                            tensor, src=self.ranks[src], group=group, async_op=True
                        )
776
                    async_handles.append(handle)
777
                    tensor_dict[key] = tensor
778
                else:
779
                    tensor_dict[key] = value
780
781
782
783
            for async_handle in async_handles:
                async_handle.wait()
        return tensor_dict

784
785
    def send_tensor_dict(
        self,
786
787
        tensor_dict: dict[str, torch.Tensor | Any],
        dst: int | None = None,
788
        all_gather_group: Optional["GroupCoordinator"] = None,
789
790
        all_gather_tensors: dict[str, bool] | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
791
792
        """Send the input tensor dictionary.
        NOTE: `dst` is the local rank of the source rank.
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807

        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.
808
809
810
811
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return tensor_dict
812
813
814
815
        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
        )
816

817
818
819
820
        group = self.device_group
        metadata_group = self.cpu_group

        if dst is None:
821
            dst = (self.rank_in_group + 1) % self.world_size
822
823
        assert dst < self.world_size, f"Invalid dst rank ({dst})"

824
        if self.use_cpu_custom_send_recv:
825
826
            if self.device_communicator is None:
                raise ValueError("No device communicator found")
827
            self.device_communicator.send_tensor_dict(  # type: ignore
828
829
                tensor_dict, dst
            )
830
831
            return None

832
        metadata_list: list[tuple[Any, Any]] = []
833
834
835
        assert isinstance(tensor_dict, dict), (
            f"Expecting a dictionary, got {type(tensor_dict)}"
        )
836
837
838
839
840
        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)
841

842
        tensor_keys = [k for k, v in tensor_dict.items() if isinstance(v, torch.Tensor)]
843
844
845
        assert len(tensor_keys) == len(tensor_list)

        for key, tensor in zip(tensor_keys, tensor_list):
846
847
848
            if tensor.numel() == 0:
                # Skip sending empty tensors.
                continue
849
850

            # send-allgather: send only a slice, then do allgather.
851
852
853
854
855
856
857
858
            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
            )
859
            if use_all_gather:
860
861
                tensor = tensor.reshape(all_gather_size, -1)[all_gather_rank]

862
863
            if tensor.is_cpu:
                # use metadata_group for CPU tensors
864
865
866
                torch.distributed.send(
                    tensor, dst=self.ranks[dst], group=metadata_group
                )
867
868
            else:
                # use group for GPU tensors
869
                torch.distributed.send(tensor, dst=self.ranks[dst], group=group)
870
871
872
873
        return None

    def recv_tensor_dict(
        self,
874
        src: int | None = None,
875
        all_gather_group: Optional["GroupCoordinator"] = None,
876
877
        all_gather_tensors: dict[str, bool] | None = None,
    ) -> dict[str, torch.Tensor | Any] | None:
878
879
        """Recv the input tensor dictionary.
        NOTE: `src` is the local rank of the source rank.
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894

        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.
895
896
897
898
        """
        # Bypass the function if we are using only 1 GPU.
        if not torch.distributed.is_initialized() or self.world_size == 1:
            return None
899
900
901
902
        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
        )
903

904
905
906
907
        group = self.device_group
        metadata_group = self.cpu_group

        if src is None:
908
            src = (self.rank_in_group - 1) % self.world_size
909
910
        assert src < self.world_size, f"Invalid src rank ({src})"

911
        if self.use_cpu_custom_send_recv:
912
913
            if self.device_communicator is None:
                raise ValueError("No device communicator found")
914
            return self.device_communicator.recv_tensor_dict(  # type: ignore
915
916
                src
            )
917

918
        recv_metadata_list = self.recv_object(src=src)
919
        tensor_dict: dict[str, Any] = {}
920
921
        for key, value in recv_metadata_list:
            if isinstance(value, TensorMetadata):
922
                tensor = torch.empty(value.size, dtype=value.dtype, device=value.device)
923
924
                if tensor.numel() == 0:
                    # Skip broadcasting empty tensors.
925
                    tensor_dict[key] = tensor
926
                    continue
927
928

                # send-allgather: send only a slice, then do allgather.
929
930
931
932
933
934
935
936
937
                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
                )
938
939
940

                if use_all_gather:
                    orig_shape = tensor.shape
941
                    tensor = tensor.reshape(all_gather_size, -1)[all_gather_rank]
942

943
944
                if tensor.is_cpu:
                    # use metadata_group for CPU tensors
945
946
947
                    torch.distributed.recv(
                        tensor, src=self.ranks[src], group=metadata_group
                    )
948
949
                else:
                    # use group for GPU tensors
950
                    torch.distributed.recv(tensor, src=self.ranks[src], group=group)
951
952
953
                if use_all_gather:
                    # do the allgather
                    tensor = all_gather_group.all_gather(  # type: ignore
954
955
                        tensor, dim=0
                    )
956
957
                    tensor = tensor.reshape(orig_shape)

958
                tensor_dict[key] = tensor
959
            else:
960
                tensor_dict[key] = value
961
962
        return tensor_dict

963
964
965
966
967
968
969
970
971
    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)

972
    def send(self, tensor: torch.Tensor, dst: int | None = None) -> None:
973
        """Sends a tensor to the destination rank in a blocking way"""
974
        """NOTE: `dst` is the local rank of the destination rank."""
975
976
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
977
        self.device_communicator.send(tensor, dst)
978

979
    def recv(
980
        self, size: torch.Size, dtype: torch.dtype, src: int | None = None
981
    ) -> torch.Tensor:
982
983
        """Receives a tensor from the source rank."""
        """NOTE: `src` is the local rank of the source rank."""
984
985
        if self.device_communicator is None:
            raise ValueError("No device communicator found")
986
        return self.device_communicator.recv(size, dtype, src)
987

988
    def destroy(self):
989
        if hasattr(self, "device_group"):
990
            torch.distributed.destroy_process_group(self.device_group)
991
992
            del self.device_group
        if hasattr(self, "cpu_group"):
993
            torch.distributed.destroy_process_group(self.cpu_group)
994
            del self.cpu_group
995
996
        if self.device_communicator is not None:
            self.device_communicator.destroy()
997
998
        if self.mq_broadcaster is not None:
            self.mq_broadcaster = None
999

1000
1001
    def prepare_communication_buffer_for_model(self, model: torch.nn.Module):
        if self.device_communicator is not None:
1002
            self.device_communicator.prepare_communication_buffer_for_model(model)
1003
1004

    def dispatch(
1005
1006
1007
        self,
        hidden_states: torch.Tensor,
        router_logits: torch.Tensor,
1008
        is_sequence_parallel: bool = False,
1009
    ) -> tuple[torch.Tensor, torch.Tensor]:
1010
        if self.device_communicator is not None:
1011
1012
1013
            return self.device_communicator.dispatch(
                hidden_states, router_logits, is_sequence_parallel
            )
1014
1015
        else:
            return hidden_states, router_logits
1016

1017
1018
1019
    def combine(
        self, hidden_states, is_sequence_parallel: bool = False
    ) -> torch.Tensor:
1020
        if self.device_communicator is not None:
1021
            return self.device_communicator.combine(hidden_states, is_sequence_parallel)
1022
1023
        else:
            return hidden_states
1024

1025

1026
_WORLD: GroupCoordinator | None = None
1027
_INNER_DP_WORLD: GroupCoordinator | None = None
1028
_NODE_COUNT: int | None = None
1029
1030
1031


def get_world_group() -> GroupCoordinator:
1032
    assert _WORLD is not None, "world group is not initialized"
1033
1034
1035
    return _WORLD


1036
1037
1038
1039
1040
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


1041
1042
1043
def init_world_group(
    ranks: list[int], local_rank: int, backend: str
) -> GroupCoordinator:
1044
1045
1046
1047
    return GroupCoordinator(
        group_ranks=[ranks],
        local_rank=local_rank,
        torch_distributed_backend=backend,
1048
        use_device_communicator=False,
1049
        group_name="world",
1050
1051
1052
    )


1053
def init_model_parallel_group(
1054
    group_ranks: list[list[int]],
1055
1056
1057
    local_rank: int,
    backend: str,
    use_message_queue_broadcaster: bool = False,
1058
    group_name: str | None = None,
1059
    use_device_communicator: bool = True,
1060
) -> GroupCoordinator:
1061
1062
1063
1064
    return GroupCoordinator(
        group_ranks=group_ranks,
        local_rank=local_rank,
        torch_distributed_backend=backend,
1065
        use_device_communicator=use_device_communicator,
1066
        use_message_queue_broadcaster=use_message_queue_broadcaster,
1067
        group_name=group_name,
1068
1069
1070
    )


1071
_TP: GroupCoordinator | None = None
1072
1073
1074


def get_tp_group() -> GroupCoordinator:
1075
    assert _TP is not None, "tensor model parallel group is not initialized"
1076
1077
1078
    return _TP


1079
1080
1081
1082
1083
@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."
)
1084
1085
1086
def get_tensor_model_parallel_group():
    return get_tp_group()

1087

1088
_DCP: GroupCoordinator | None = None
1089
1090
1091


def get_dcp_group() -> GroupCoordinator:
1092
    assert _DCP is not None, "decode context model parallel group is not initialized"
1093
1094
1095
1096
1097
1098
    return _DCP


# kept for backward compatibility
get_context_model_parallel_group = get_dcp_group

1099
_PP: GroupCoordinator | None = None
1100

1101
1102
1103
1104
1105
1106

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


1107
_DP: GroupCoordinator | None = None
1108
1109
1110


def get_dp_group() -> GroupCoordinator:
1111
    assert _DP is not None, "data parallel group is not initialized"
1112
1113
    return _DP

1114

1115
_EP: GroupCoordinator | None = None
1116
1117
1118


def get_ep_group() -> GroupCoordinator:
1119
    assert _EP is not None, "expert parallel group is not initialized"
1120
1121
1122
    return _EP


1123
1124
1125
1126
1127
1128
_PCP: GroupCoordinator | None = None


def get_pcp_group() -> GroupCoordinator:
    assert _PCP is not None, "prefill context parallel group is not initialized"
    return _PCP
1129
1130


1131
1132
1133
1134
1135
@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."
)
1136
1137
def get_pipeline_model_parallel_group():
    return get_pp_group()
1138
1139


1140
@contextmanager
1141
def graph_capture(device: torch.device):
1142
1143
    """
    `graph_capture` is a context manager which should surround the code that
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    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
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    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.
    """
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    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 freezed before cleanup
    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)
1668
                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,
                ):
1686
                    shm = shared_memory.SharedMemory(name=name)
1687
                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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1701

    # clean up the shared memory segment
    with contextlib.suppress(OSError):
1702
        if rank == source_rank and shm:
1703
            shm.unlink()
1704

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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()]
1715
1716


1717
1718
def is_global_first_rank() -> bool:
    """
1719
    Check if the current process is the first rank globally across all
1720
    parallelism strategies (PP, TP, DP, EP, etc.).
1721

1722
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1724
    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.
1725

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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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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


1771
def _node_count(pg: ProcessGroup | StatelessProcessGroup) -> int:
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1776
    """
    Returns the total number of nodes in the process group.

    Args:
        pg: The process group to analyze
1777

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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