venv/lib/python3.10/site-packages/deepspeed/runtime/pipe/p2p.py

# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

import pickle
import typing

import torch
from deepspeed import comm as dist

from deepspeed.utils.torch import required_torch_version
from deepspeed.accelerator import get_accelerator

_groups = None
_grid = None

_async = []


def can_send_recv() -> bool:
    return required_torch_version(min_version=1.8)


#initializes adjacent process groups
#run this only after deepspeed.init_distributed() has been called
def init_process_groups(grid):
    global _groups, _grid
    _grid = grid

    assert _grid.pipe_parallel_size > 1, "There is no pipeline parallelism"

    if not can_send_recv():
        _groups = [dist.new_group(ranks=group) for group in _grid.p2p_groups]


def _is_valid_send_recv(src_stage, dest_stage):
    first_stage = 0
    last_stage = _grid.pipe_parallel_size - 1
    assert abs(src_stage-dest_stage) == 1 or \
        (src_stage == first_stage and dest_stage == last_stage) or \
        (src_stage == last_stage and dest_stage == first_stage), \
    "Functionality currently limited to send and receive between adjacent ranks only"


def send(tensor, dest_stage, async_op=False):
    global _groups
    assert async_op == False, "Doesn't support async_op true"
    src_stage = _grid.get_stage_id()
    _is_valid_send_recv(src_stage, dest_stage)

    dest_rank = _grid.stage_to_global(stage_id=dest_stage)
    if async_op:
        global _async
        op = dist.isend(tensor, dest_rank)
        _async.append(op)
    else:

        if can_send_recv():
            return dist.send(tensor, dest_rank)
        else:
            group = _get_send_recv_group(src_stage, dest_stage)
            src_rank = _grid.stage_to_global(stage_id=src_stage)
            return dist.broadcast(tensor, src_rank, group=group, async_op=async_op)


def recv(tensor, src_stage, async_op=False):
    global _groups
    assert async_op == False, "Doesn't support async_op true"
    dest_stage = _grid.get_stage_id()
    _is_valid_send_recv(src_stage, dest_stage)

    src_rank = _grid.stage_to_global(stage_id=src_stage)

    if async_op:
        global _async
        op = dist.irecv(tensor, src_rank)
        _async.append(op)
    else:
        if can_send_recv():
            return dist.recv(tensor, src_rank)
        else:
            group = _get_send_recv_group(src_stage, dest_stage)
            return dist.broadcast(tensor, src_rank, group=group, async_op=async_op)


def wait():
    global _async
    for op in _async:
        op.wait()
    _async = []

    get_accelerator().synchronize()


def send_obj(msg: typing.Any, dest: int):
    """Send an arbitrary python object to ``dest``.

    Note: ``msg`` must be pickleable.

    WARN: This incurs a CPU -> GPU transfer and should be used sparingly
    for performance reasons.

    Args:
        msg (typing.Any): The object to send.
        dest (int): Destination rank.
    """
    # serialize the message
    msg = pickle.dumps(msg)
    # construct a tensor to send
    msg = torch.ByteTensor(torch.ByteStorage.from_buffer(msg)).to(get_accelerator().device_name())

    # Send meta and message
    length_tensor = torch.tensor([len(msg)], dtype=torch.long).to(get_accelerator().device_name())
    dist.send(length_tensor, dst=dest)
    dist.send(msg, dst=dest)


def recv_obj(sender: int) -> typing.Any:
    """Receive an arbitrary python object from ``sender``.

    WARN: This incur a CPU <-> GPU transfers and should be used sparingly
    for performance reasons.

    Args:
        sender (int): The rank sending the message.
    """
    # Get message meta
    length = torch.tensor([0], dtype=torch.long).to(get_accelerator().device_name())
    dist.recv(length, src=sender)

    # Receive and deserialize
    msg = torch.empty(length.item(), dtype=torch.uint8).to(get_accelerator().device_name())
    dist.recv(msg, src=sender)

    msg = pickle.loads(msg.cpu().numpy().tobytes())

    def _to(x):
        """Recursively move to the current device."""
        if torch.is_tensor(x):
            return x.to(get_accelerator().device_name())
        if isinstance(x, (tuple, list)):
            ret = [_to(x_) for x_ in x]
            if isinstance(x, tuple):
                ret = tuple(ret)
            return ret
        # handle kwargs
        if isinstance(x, dict):
            ret = dict()
            for key, val in x.items():
                ret[_to(key)] = _to(val)
            return ret

        # Anything else is a no-op
        return x

    msg = _to(msg)
    return msg


def _get_send_recv_group(src_stage, dest_stage):
    '''the group id is always the smaller rank unless its a wrap around'''

    stage_id = None

    first_stage = 0
    last_stage = _grid.pipe_parallel_size - 1

    if (src_stage == first_stage and dest_stage == last_stage
            or dest_stage == first_stage and src_stage == last_stage):
        stage_id = last_stage
    elif src_stage > dest_stage:
        stage_id = dest_stage
    else:
        stage_id = src_stage
    '''group_id corresponds to group of [group_id, group_id+1]
     unless group_id is the rank of the last stage
     in which case group_id corresponds to group[group_id-num_stages+1, group_id]
     '''
    group_id = _grid.stage_to_global(stage_id=stage_id)

    return _groups[group_id]