aphrodite-engine/aphrodite/distributed/parallel_state.py

# Copyright 2023 The PygmalionAI team.
# Copyright 2023 The vLLM team.
# Adapted from
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Tensor and pipeline parallel groups."""
import os
from typing import List, Optional

import torch
from loguru import logger
from torch.distributed import ProcessGroup

_ENABLE_CUSTOM_ALL_REDUCE = True
# Tensor model parallel group that the current rank belongs to.
_TP_DEVICE_GROUP: Optional[ProcessGroup] = None
_TP_CPU_GROUP: Optional[ProcessGroup] = None
_TP_PYNCCL_COMMUNICATOR = None
_TP_CA_COMMUNICATOR = None
# Pipeline model parallel group that the current rank belongs to.
_PP_DEVICE_GROUP: Optional[ProcessGroup] = None

# when people blindly call `torch.distributed.all_reduce` etc,
# it will use this group. It is initialized with the `backend`
# parameter of `init_distributed_environment` below.
# Essentially, this is `torch.distributed.group.WORLD`.
# We leave a line here to note that this is device-specific.
# Note that this variable is not safe to use, because when users
# call `init_distributed_environment` first, and then destroy
# the process group themselves, this variable will keep a reference to the
# destroyed process group, which is not useful.
_DEVICE_WORLD_GROUP = None

# duing `init_distributed_environment`, we will also initialize a
# group with `gloo` backend, to allow direct coordination between
# processes through the CPU.
_CPU_WORLD_GROUP = None

# In summary, after calling `init_distributed_environment`, we will
# always have two groups: one for device-specific (and is the default)
# and one for CPU. All processes will be part of both groups.

# A list of global ranks for each pipeline group to ease calculation of the
# source rank when broadcasting from the first or last pipeline stage.
_PP_GLOBAL_RANKS: Optional[List[int]] = None

_LOCAL_RANK = -1


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


def get_tp_pynccl_communicator():
    global _TP_PYNCCL_COMMUNICATOR
    return _TP_PYNCCL_COMMUNICATOR


def get_tp_ca_communicator():
    global _TP_CA_COMMUNICATOR
    return _TP_CA_COMMUNICATOR


def get_local_rank():
    global _LOCAL_RANK
    return _LOCAL_RANK


def init_distributed_environment(
    world_size: int = -1,
    rank: int = -1,
    distributed_init_method: str = "env://",
    local_rank: int = -1,
    backend: str = "nccl",
):
    logger.debug(f"{world_size=} {rank=} {local_rank=} "
                 f"{distributed_init_method=} {backend=}")
    if not torch.distributed.is_initialized():
        assert distributed_init_method is not None, (
            "distributed_init_method must be provided when initializing "
            "distributed environment")
        # this backend is used for WORLD
        torch.distributed.init_process_group(
            backend=backend,
            init_method=distributed_init_method,
            world_size=world_size,
            rank=rank)
        global _DEVICE_WORLD_GROUP, _CPU_WORLD_GROUP
        _DEVICE_WORLD_GROUP = torch.distributed.group.WORLD
        ranks = list(range(torch.distributed.get_world_size()))
        _CPU_WORLD_GROUP = torch.distributed.new_group(ranks=ranks,
                                                       backend="gloo")
        if local_rank == -1:
            # local rank not set, this usually happens in single-node
            # setting, where we can use rank as local rank
            if distributed_init_method == "env://":
                local_rank = int(os.environ.get("LOCAL_RANK", rank))
            else:
                local_rank = rank
        global _LOCAL_RANK
        _LOCAL_RANK = local_rank
        # A small all_reduce for warmup.
        data = torch.zeros(1)
        if torch.cuda.is_available():
            data = data.to(device=f"cuda:{local_rank}")
        torch.distributed.all_reduce(data)
        if torch.cuda.is_available():
            torch.cuda.synchronize()
        del data


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

    Arguments:
        tensor_model_parallel_size: number of GPUs used for tensor model
            parallelism.
        pipeline_model_parallel_size: number of GPUs used for pipeline model
            parallelism.

    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()
    # get the backend of _DEVICE_WORLD_GROUP
    backend = backend or torch.distributed.get_backend()

    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)
    num_pipeline_model_parallel_groups: int = (world_size //
                                               pipeline_model_parallel_size)
    rank = torch.distributed.get_rank()

    # Build the tensor model-parallel groups.
    global _TP_DEVICE_GROUP, _TP_CPU_GROUP
    global _TP_PYNCCL_COMMUNICATOR, _TP_CA_COMMUNICATOR
    assert _TP_DEVICE_GROUP is None, (
        "tensor model parallel group is already initialized")
    for i in range(num_tensor_model_parallel_groups):
        ranks = list(
            range(i * tensor_model_parallel_size,
                  (i + 1) * tensor_model_parallel_size))
        group = torch.distributed.new_group(ranks, backend=backend)
        cpu_group = torch.distributed.new_group(ranks, backend="gloo")
        if rank in ranks:
            _TP_DEVICE_GROUP = group
            _TP_CPU_GROUP = cpu_group

    from aphrodite.distributed.device_communicators.pynccl import \
        PyNcclCommunicator
    _TP_PYNCCL_COMMUNICATOR = PyNcclCommunicator(
        group=_TP_CPU_GROUP,
        device=_LOCAL_RANK,
    )

    # Initialize a custom fast all-reduce implementation.
    if _ENABLE_CUSTOM_ALL_REDUCE:
        from aphrodite.distributed.device_communicators.custom_all_reduce \
            import CustomAllreduce
        _TP_CA_COMMUNICATOR = CustomAllreduce(
            group=_TP_CPU_GROUP,
            device=_LOCAL_RANK,
        )

    # Build the pipeline model-parallel groups.
    global _PP_DEVICE_GROUP
    global _PP_GLOBAL_RANKS
    assert _PP_DEVICE_GROUP is None, (
        "pipeline model parallel group is already initialized")
    for i in range(num_pipeline_model_parallel_groups):
        ranks = list(range(i, world_size, num_pipeline_model_parallel_groups))
        group = torch.distributed.new_group(ranks, backend=backend)
        if rank in ranks:
            _PP_DEVICE_GROUP = group
            _PP_GLOBAL_RANKS = ranks


def ensure_model_parallel_initialized(
    tensor_model_parallel_size: int,
    pipeline_model_parallel_size: int,
    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()
    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
    ), ("tensor parallel group already initialized, but of unexpected size: "
        f"{get_tensor_model_parallel_world_size()=} vs. "
        f"{tensor_model_parallel_size=}")
    assert (get_pipeline_model_parallel_world_size(
    ) == pipeline_model_parallel_size), (
        "pipeline parallel group already initialized, but of unexpected size: "
        f"{get_pipeline_model_parallel_world_size()=} vs. "
        f"{pipeline_model_parallel_size=}")


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


def get_cpu_world_group():
    """Get the CPU world group."""
    assert _CPU_WORLD_GROUP is not None, ("CPU world group is not initialized")
    return _CPU_WORLD_GROUP


def get_tensor_model_parallel_group():
    """Get the tensor model parallel group the caller rank belongs to."""
    assert _TP_DEVICE_GROUP is not None, (
        "tensor model parallel group is not initialized")
    return _TP_DEVICE_GROUP


def get_tensor_model_parallel_cpu_group():
    """Get the tensor model parallel cpu group the caller rank belongs to."""
    assert _TP_CPU_GROUP is not None, (
        "tensor model parallel cpu group is not initialized")
    return _TP_CPU_GROUP


def get_pipeline_model_parallel_group():
    """Get the pipeline model parallel group the caller rank belongs to."""
    assert _PP_DEVICE_GROUP is not None, (
        "pipeline model parallel group is not initialized")
    return _PP_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_pipeline_model_parallel_world_size():
    """Return world size for the pipeline model parallel group."""
    return torch.distributed.get_world_size(
        group=get_pipeline_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_pipeline_model_parallel_rank():
    """Return my rank for the pipeline model parallel group."""
    return torch.distributed.get_rank(
        group=get_pipeline_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


def get_pipeline_model_parallel_first_rank():
    """Return the global rank of the first process in the pipeline for the
    current tensor parallel group"""
    assert _PP_GLOBAL_RANKS is not None, (
        "Pipeline parallel group is not initialized")
    return _PP_GLOBAL_RANKS[0]


def get_pipeline_model_parallel_last_rank():
    """Return the global rank of the last process in the pipeline for the
    current tensor parallel group"""
    assert _PP_GLOBAL_RANKS is not None, (
        "Pipeline parallel group is not initialized")
    last_rank_local = get_pipeline_model_parallel_world_size() - 1
    return _PP_GLOBAL_RANKS[last_rank_local]


def get_pipeline_model_parallel_next_rank():
    """Return the global rank that follows the caller in the pipeline"""
    assert _PP_GLOBAL_RANKS is not None, (
        "Pipeline parallel group is not initialized")
    rank_in_pipeline = get_pipeline_model_parallel_rank()
    world_size = get_pipeline_model_parallel_world_size()
    return _PP_GLOBAL_RANKS[(rank_in_pipeline + 1) % world_size]


def get_pipeline_model_parallel_prev_rank():
    """Return the global rank that precedes the caller in the pipeline"""
    assert _PP_GLOBAL_RANKS is not None, (
        "Pipeline parallel group is not initialized")
    rank_in_pipeline = get_pipeline_model_parallel_rank()
    world_size = get_pipeline_model_parallel_world_size()
    return _PP_GLOBAL_RANKS[(rank_in_pipeline - 1) % world_size]


def destroy_model_parallel():
    """Set the groups to none and destroy them."""
    global _TP_DEVICE_GROUP
    if _TP_DEVICE_GROUP:
        torch.distributed.destroy_process_group(_TP_DEVICE_GROUP)
    _TP_DEVICE_GROUP = None
    global _TP_CPU_GROUP
    if _TP_CPU_GROUP:
        torch.distributed.destroy_process_group(_TP_CPU_GROUP)
    _TP_CPU_GROUP = None
    global _TP_PYNCCL_COMMUNICATOR
    _TP_PYNCCL_COMMUNICATOR = None

    global _PP_DEVICE_GROUP
    if _PP_DEVICE_GROUP:
        torch.distributed.destroy_process_group(_PP_DEVICE_GROUP)
    _PP_DEVICE_GROUP = None
    global _PP_GLOBAL_RANKS
    _PP_GLOBAL_RANKS = None