verl/third_party/sglang/parallel_state.py

# Copyright 2024 Bytedance Ltd. and/or its affiliates
# Copyright 2023 The SGlang team.
# Adapted from
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Model and data parallel groups."""

import os
from typing import Optional

import sglang.srt.distributed.parallel_state as ps
import torch
import torch.distributed
from sglang.srt.distributed.parallel_state import (
    get_pp_group,
    get_world_group,
    init_distributed_environment,
    init_model_parallel_group,
)

"""

This version is strongly tied with Megatron to implement HybridEngine and weight sharing between vllm and Megatron.

- We assume the Megatron tp+dp+pp world is already established before calling this function.



"""

# Device mesh for using DTensor
_DEVICE_MESH = None

# Tensor model parallel group that the current rank belongs to.
_TP = None
# Pipeline model parallel group that the current rank belongs to.
_PP = None


# This method is for initializing the ParallelGroup when using HybridEngine
# NOTE(linjunrong): this function is for megatron
def initialize_parallel_state(

    distributed_init_method: str = "env://",

    backend: str = "nccl",

    tensor_model_parallel_size: int = 1,

    num_tp_per_train_tp: int = 1,

    pipeline_model_parallel_size: int = 1,

):
    # torch.distributed.all_reduce does not free the input tensor until
    # the synchronization point. This causes the memory usage to grow
    # as the number of all_reduce calls increases. This env var disables
    # this behavior.
    # Related issue:
    # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
    os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"

    # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN.
    rank = int(os.getenv("RANK", "-1"))
    local_rank = int(os.getenv("LOCAL_RANK", "0"))

    # Use the world_size set by TORCHRUN
    world_size = int(os.getenv("WORLD_SIZE", "-1"))
    assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN"
    init_distributed_environment(world_size, rank, distributed_init_method, local_rank, backend)
    if torch.distributed.get_world_size() > 1:
        # NOTE: build a sepearate inference group with infer tp & micro dp
        initialize_model_parallel_for_sglang(
            tensor_model_parallel_size=tensor_model_parallel_size,
            num_tensor_model_parallel_groups_per_train_tp=num_tp_per_train_tp,
        )
    else:
        initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend)


# NOTE(linjunrong): After init SGLang rollout using class EngineFragment, user should always remember to call
# this function to sync the _TP, _PP define at the beginning of this file. Otherwise, only the conterparts
# inside sglang.srt.distributed are init as ProcessGroup, the symbols defined in this file remain as None.
# It could be weird to maintain two _TP and _PP, I follow the same way to maintain an extra ones for
# verl itself as how it was done in verl.third_party.vllm.parallel_state. Note that the process is a little
# bit different
def ensure_model_parallel_initialized(

    tensor_model_parallel_size: int,

    pipeline_model_parallel_size: int = 1,

    backend: Optional[str] = None,

) -> None:
    """Helper to initialize model parallel groups if they are not initialized,

    or ensure tensor-parallel and pipeline-parallel sizes are equal to expected

    values if the model parallel groups are initialized.

    """
    # get the backend of _DEVICE_WORLD_GROUP
    backend = backend or torch.distributed.get_backend(get_world_group().device_group)
    if not model_parallel_is_initialized():
        initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend)
        return

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


# TODO(sgm): deviate from the v0.5.4, not pp now
# NOTE(linjunrong): the SGLang version using _TP instead of ps._TP
def model_parallel_is_initialized():
    """Check if tensor and pipeline parallel groups are initialized."""
    return _TP is not None
    # and _PIPELINE_MODEL_PARALLEL_GROUP is not None)


def initialize_model_parallel_for_sglang(

    tensor_model_parallel_size: int,

    num_tensor_model_parallel_groups_per_train_tp: int = 1,

    pipeline_model_parallel_size: int = 1,

) -> None:
    pass

    # Get world size and rank. Ensure some consistencies.
    assert torch.distributed.is_initialized()

    assert isinstance(tensor_model_parallel_size, int)

    # assert num_tensor_model_parallel_groups_per_train_tp == 1 and not different_tp_group
    # assert num_tensor_model_parallel_groups_per_train_tp > 1 and different_tp_group

    # Build the tensor model-parallel groups.
    assert ps._TP is None, "tensor model parallel group is already initialized"

    global _TP

    world_size: int = torch.distributed.get_world_size()

    backend = torch.distributed.get_backend()

    num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size

    if num_tensor_model_parallel_groups_per_train_tp == 1:
        # if tensor_model_parallel_size == train_tensor_parallel_size:
        # using the same tp group as Megatron/vllm
        assert _TP is None, "tensor model parallel group is already initialized"
        group_ranks = []
        for i in range(num_tensor_model_parallel_groups):
            ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)
            group_ranks.append(ranks)
        _TP = init_model_parallel_group(
            group_ranks=group_ranks,
            local_rank=get_world_group().local_rank,
            backend=backend,
            use_custom_allreduce=False,  # TODO: check why True is not work in Ray trainer
            use_message_queue_broadcaster=True,
        )
        ps._TP = _TP
        # _MICRO_DATA_PARALLEL_GROUP is move to hybrid engine
    else:
        # initialize a micro_dp group and a tp group
        # assume training tp=4, infer tp=2, then, weight is partitioned as
        # [1], [2], [3], [4] for training and [1,2], [1,2], [3,4], [3,4] for inference

        # Build the inference tp groups
        # train_tp = train_tensor_parallel_size
        train_tp = num_tensor_model_parallel_groups_per_train_tp * tensor_model_parallel_size
        # num_tensor_model_parallel_groups_per_train_tp = train_tp // tensor_model_parallel_size
        assert _TP is None, "tensor model parallel group is already initialized"
        group_ranks = []
        for i in range(num_tensor_model_parallel_groups // num_tensor_model_parallel_groups_per_train_tp):
            start = train_tp * i
            end = train_tp * (i + 1)
            for j in range(num_tensor_model_parallel_groups_per_train_tp):
                ranks = list(range(start, end, num_tensor_model_parallel_groups_per_train_tp))
                for i in range(len(ranks)):
                    ranks[i] += j
                group_ranks.append(ranks)
        _TP = init_model_parallel_group(
            group_ranks=group_ranks,
            local_rank=get_world_group().local_rank,
            backend=backend,
            use_custom_allreduce=False,  # TODO: check why True is not work in Ray trainer
            use_message_queue_broadcaster=True,
        )
        ps._TP = _TP

    # Build the pipeline model-parallel groups.
    # global _PIPELINE_MODEL_PARALLEL_GROUP
    # global _PIPELINE_GLOBAL_RANKS
    # assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized")

    # ps._PIPELINE_MODEL_PARALLEL_GROUP = mpu.get_pipeline_model_parallel_group()
    # ps._PIPELINE_GLOBAL_RANKS = mpu.get_pipeline_model_parallel_ranks()

    # TODO: init using device mesh (not support hybrid engine now)
    # Build the pipeline model-parallel groups.
    num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
    global _PP
    assert _PP is None, "pipeline model parallel group is already initialized"
    group_ranks = []
    for i in range(num_pipeline_model_parallel_groups):
        ranks = list(range(i, world_size, num_pipeline_model_parallel_groups))
        group_ranks.append(ranks)
    # pipeline parallel does not need custom allreduce
    _PP = init_model_parallel_group(group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False)
    ps._PP = _PP  # for verl


def initialize_model_parallel(

    tensor_model_parallel_size: int = 1,

    pipeline_model_parallel_size: int = 1,

    backend: Optional[str] = None,

) -> None:
    """

    NOTE: This method is a hack from the open-sourced version without

    asertion of world_size = tp * pp



    Initialize model parallel groups.



    Arguments:

        tensor_model_parallel_size: number of GPUs used for tensor model

            parallelism.

        pipeline_model_parallel_size: number of GPUs used for pipeline model

            parallelism.



    Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we

    use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize

    the model pipeline. The present function will

    create 4 tensor model-parallel groups and 2 pipeline model-parallel groups:

        4 tensor model-parallel groups:

            [g0, g1], [g2, g3], [g4, g5], [g6, g7]

        2 pipeline model-parallel groups:

            [g0, g2, g4, g6], [g1, g3, g5, g7]

    Note that for efficiency, the caller should make sure adjacent ranks

    are on the same DGX box. For example if we are using 2 DGX-1 boxes

    with a total of 16 GPUs, rank 0 to 7 belong to the first box and

    ranks 8 to 15 belong to the second box.

    """
    # Get world size and rank. Ensure some consistencies.
    assert torch.distributed.is_initialized()
    world_size: int = torch.distributed.get_world_size()
    backend = backend or torch.distributed.get_backend(ps.get_world_group().device_group)

    # NOTE(sgm) we don't assert world_size == tp * pp
    # DP is not managed by vllm but by the VeRL WorkerGroup
    # if (world_size !=
    #         tensor_model_parallel_size * pipeline_model_parallel_size):
    #     raise RuntimeError(
    #         f"world_size ({world_size}) is not equal to "
    #         f"tensor_model_parallel_size ({tensor_model_parallel_size}) x "
    #         f"pipeline_model_parallel_size ({pipeline_model_parallel_size})")

    num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size

    global _TP
    assert _TP is None, "tensor model parallel group is already initialized"
    group_ranks = []
    for i in range(num_tensor_model_parallel_groups):
        ranks = list(range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size))
        group_ranks.append(ranks)

    # message queue broadcaster is only used in tensor model parallel group
    if ps._TP is not None:
        _TP = ps._TP
    else:
        _TP = init_model_parallel_group(
            group_ranks,
            get_world_group().local_rank,
            backend,
            use_custom_allreduce=False,  # TODO: check why True is not work in Ray trainer
            use_message_queue_broadcaster=True,
        )
        ps._TP = _TP

    # TODO: init using device mesh (not support hybrid engine now)
    # Build the pipeline model-parallel groups.
    num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
    global _PP
    assert _PP is None, "pipeline model parallel group is already initialized"
    group_ranks = []
    for i in range(num_pipeline_model_parallel_groups):
        ranks = list(range(i, world_size, num_pipeline_model_parallel_groups))
        group_ranks.append(ranks)
    # pipeline parallel does not need custom allreduce
    if ps._TP is not None:
        _PP = ps._TP
    else:
        _PP = init_model_parallel_group(group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False)
        ps._PP = _PP


"""

Device mesh utilities

"""


def get_device_mesh():
    assert _DEVICE_MESH is not None, "device mesh is not initialized"
    return _DEVICE_MESH


"""

Tensor model parallel utilities

"""


# NOTE(linjunrong): In the vllm version parallel_state.py. verl created its own _TP and _PP as verl want to use
# the process group for some extra purpose. Under the hood, there is no difference between them and the original
# one in vllm.distributed.parallel_state. However, the implementation need to hack the init process of inference
# engine, as we do not maintain another SGLang here, I just use the original _TP and _PP directly.
def get_tensor_model_parallel_group():
    """Get the tensor model parallel group the caller rank belongs to."""

    assert _TP is not None, "tensor model parallel group is not initialized"
    return _TP.device_group


def get_tensor_model_parallel_world_size():
    """Return world size for the tensor model parallel group."""
    return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())


def get_tensor_model_parallel_rank():
    """Return my rank for the tensor model parallel group."""
    return torch.distributed.get_rank(group=get_tensor_model_parallel_group())


def get_tensor_model_parallel_src_rank():
    """Calculate the global rank corresponding to the first local rank

    in the tensor model parallel group."""
    global_rank = torch.distributed.get_rank()
    local_world_size = get_tensor_model_parallel_world_size()
    return (global_rank // local_world_size) * local_world_size