'''
Copyright 2021 The Microsoft DeepSpeed Team
'''

# The file has been adapted from https://github.com/NVIDIA/Megatron-LM and retains the following license from the original file

# Copyright (c) 2019, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
 Support different forms of parallelism in DeepSped using multiple process groups.
 Given that there are multiple scenarios and use-cases, this file is going to be updated
 frequently. For now, the group creation needed for the training scenario is being implemented.
 For inference and other new scenarios, the code will be either reused or added to this file.
"""

import torch
from torch.distributed.distributed_c10d import _get_global_rank
from deepspeed.utils import logger, log_dist
from deepspeed.utils.exceptions import DeprecatedException

# Expert parallel group that the current rank belongs to.
_EXPERT_PARALLEL_GROUP = {}
# Expert data parallel group that the current rank belongs to.
_EXPERT_DATA_PARALLEL_GROUP = {}
# torch.distributed world group needs to be cloned for some cases
_WORLD_GROUP = None
# global object to maintain mpu object if passed by a Megatron client
mpu = None


# Deprecated groups initialize function.
def initialize(ep_size=1, mpu=None):
    """ Deprecated function. Retained to inform the users."""
    raise DeprecatedException(
        "Please do not use the groups.initialize() API as it is deprecated. Instead, pass the desired ep_size to deepspeed.moe.layer.MoE(..,ep_size,..)"
    )


def _ensure_divisibility(numerator, denominator):
    """Ensure that numerator is divisible by the denominator."""
    assert numerator % denominator == 0, '{} is not divisible by {}'.format(
        numerator, denominator)


# Not currently used. Helper function to create a model (tensor) parallel group.
def _create_model_parallel(model_parallel_size_):
    """
    Initialize model data parallel groups.

    Arguments:
        model_parallel_size: number of GPUs used to parallelize model.

    Returns:
        Tuple of data parallel group and model parallel group

    Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
    use 2 GPUs to parallelize the model. The present function will
    create 4 model parallel groups and 2 data parallel groups as:
        4 model parallel groups:
            [g0, g1], [g2, g3], [g4, g5], [g6, g7]
        2 data 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.
    """
    log_dist(f'Creating model parallel group with size {model_parallel_size_}',
             ranks=[0])
    # Get world size and rank. Ensure some consistencies.
    assert torch.distributed.is_initialized()
    world_size = torch.distributed.get_world_size()
    model_parallel_size = min(model_parallel_size_, world_size)
    _ensure_divisibility(world_size, model_parallel_size)
    rank = torch.distributed.get_rank()

    _DATA_PARALLEL_GROUP = None
    _MODEL_PARALLEL_GROUP = None
    # Build the data parallel groups.
    for i in range(model_parallel_size):
        ranks = range(i, world_size, model_parallel_size)
        group = torch.distributed.new_group(ranks)
        if i == (rank % model_parallel_size):
            _DATA_PARALLEL_GROUP = group

    # Build the model parallel groups.
    for i in range(world_size // model_parallel_size):
        ranks = range(i * model_parallel_size, (i + 1) * model_parallel_size)
        group = torch.distributed.new_group(ranks)
        if i == (rank // model_parallel_size):
            _MODEL_PARALLEL_GROUP = group

    return _DATA_PARALLEL_GROUP, _MODEL_PARALLEL_GROUP


def _create_expert_and_data_parallel(ep_size):
    """
        Create expert and data parallel groups.

        Note: Caller of this function is responsible to check if the groups already exist.

        Example - E + D parallel
        world_size = 16
        expert_parallel_size = 2 # number of experts in same group
        expert_data_parallel_group = [0,2,4,6,8,10,12,14], [1,3,5,7,9,11,13,15] - all reduce is only on MoE params
        expert_parallel_group = [0, 1], [2,3], [4,5], [6,7], [8,9] - no all reduce, but all to all
        data_parallel_group = [0,1,...,15] - all reduce is only on non-MoE
    """
    assert torch.distributed.is_initialized()

    log_dist(f'Creating expert and data parallel groups with size {ep_size}', ranks=[0])
    world_size = torch.distributed.get_world_size()
    rank = torch.distributed.get_rank()

    expert_parallel_size_ = min(ep_size, world_size)
    _ensure_divisibility(world_size, expert_parallel_size_)

    group_name = f"ep_size_{expert_parallel_size_}"

    # Build the expert data parallel groups.
    global _EXPERT_DATA_PARALLEL_GROUP

    # Only create group if it does not already exist
    if group_name not in _EXPERT_DATA_PARALLEL_GROUP:
        for i in range(expert_parallel_size_):
            ranks = range(i, world_size, expert_parallel_size_)
            group = torch.distributed.new_group(ranks)
            log_dist(
                f'Creating expert data parallel process group named {group_name} with ranks: {list(ranks)}',
                [0])
            if i == (rank % expert_parallel_size_):
                _EXPERT_DATA_PARALLEL_GROUP[group_name] = group

    # Build the expert parallel groups.
    global _EXPERT_PARALLEL_GROUP

    # Only create group if it does not already exist
    if group_name not in _EXPERT_PARALLEL_GROUP:
        for i in range(world_size // expert_parallel_size_):
            ranks = range(i * expert_parallel_size_, (i + 1) * expert_parallel_size_)
            group = torch.distributed.new_group(ranks)
            log_dist(
                f'creating expert parallel process group named {group_name} with ranks: {list(ranks)}',
                [0])
            if i == (rank // expert_parallel_size_):
                _EXPERT_PARALLEL_GROUP[group_name] = group


def _get_expert_parallel_ranks(world_size, model_parallel_size_, expert_parallel_size_):
    """Generate expert parallel and expert data parallel group ranks list.

        Example - E + M + D parallel
        world_size = 16
        model_degree = 2
        expert_degree = 4 # number of experts in same group
        mp_group = [0, 1], [2,3], [4,5] ...
        data_parallel_group =[0,2,4,6,8,10, 12,14],                 [1,3,5,7,9,11,13,15]
        expert_parallel_group = [0,2,4,6], [8,10,12,14]             [1,3,5,7], [9,11,13,15]
        expert_data_parallel_group = [0,8],[2,10],[4,12],[6,14],    [1,9],[3,11],[5,13],[7,15]

    Args:
        world_size (int): Distributed world size.
        model_parallel_size_ (int): Model parallel group size.
        expert_parallel_size_ (int): Expert parallel group size.

    Returns:
        Expert parallel group ranks and Expert data parallel group ranks list.
    """
    _ensure_divisibility(world_size, model_parallel_size_)
    dp_world_size = world_size // model_parallel_size_
    _ensure_divisibility(dp_world_size, expert_parallel_size_)

    # Generate data parallel groups
    data_parallel_groups = []
    dp_group_size = model_parallel_size_
    for i in range(dp_group_size):
        data_parallel_groups.append(list(range(i, world_size, dp_group_size)))

    expert_parallel_groups = []
    expert_data_parallel_groups = []
    for dp_ranks in data_parallel_groups:
        # partition of expert parallel groups, e.g. [0,2,4,6], [8,10,12,14]
        part_ep_groups = []
        for i in range(0, dp_world_size, expert_parallel_size_):
            part_ep_groups.append(dp_ranks[i:i + expert_parallel_size_])
        expert_parallel_groups.extend(part_ep_groups)

        # zip part_ep_groups get expert data parallel ranks, e.g [0,8],[2,10],[4,12],[6,14]
        for expert_dp_ranks in zip(*part_ep_groups):
            expert_data_parallel_groups.append(list(expert_dp_ranks))

    return expert_parallel_groups, expert_data_parallel_groups


def _create_expert_data_and_model_parallel(expert_parallel_size_, mpu):
    """
        Create expert and data parallel groups based on MPU (model parallel) group.

        Note: Caller of this function is responsible to check if the groups already exist.

        Example - E + M + D parallel
        world_size = 16
        model_degree = 2
        expert_degree = 4 # number of experts in same group
        mp_group = [0, 1], [2,3], [4,5] ...
        data_parallel_group =[0,2,4,6,8,10, 12,14],                 [1,3,5,7,9,11,13,15]
        expert_parallel_group = [0,2,4,6], [8,10,12,14]             [1,3,5,7], [9,11,13,15]
        expert_data_parallel_group = [0,8],[2,10],[4,12],[6,14],    [1,9],[3,11],[5,13],[7,15]
    """
    assert torch.distributed.is_initialized(), "torch distributed is not initialized"
    model_parallel_size_ = mpu.get_model_parallel_world_size()

    world_size = torch.distributed.get_world_size()
    rank = torch.distributed.get_rank()
    dp_world_size = mpu.get_data_parallel_world_size()
    dp_rank = mpu.get_data_parallel_rank()

    log_dist(
        f"Creating deepspeed groups with model parallel size {model_parallel_size_}, expert parallel size {expert_parallel_size_}, world size {world_size}, dp world size {dp_world_size}",
        [0])

    global _EXPERT_PARALLEL_GROUP, _EXPERT_DATA_PARALLEL_GROUP

    # Get world size and rank. Ensure some consistencies.
    _DATA_PARALLEL_GROUP = mpu.get_data_parallel_group()
    _MODEL_PARALLEL_GROUP = mpu.get_model_parallel_group()

    expert_parallel_size_ = min(expert_parallel_size_, dp_world_size)
    _ensure_divisibility(world_size, expert_parallel_size_)

    group_name = f"ep_size_{expert_parallel_size_}"

    # Only create groups if they don't already exist
    # Need to check conditions outside the group creation loop because of the way torch.dist group creation works
    if group_name not in _EXPERT_DATA_PARALLEL_GROUP and group_name not in _EXPERT_PARALLEL_GROUP:
        expert_parallel_groups, expert_data_parallel_groups = _get_expert_parallel_ranks(
            world_size, model_parallel_size_, expert_parallel_size_)
        for ranks in expert_parallel_groups:
            group = torch.distributed.new_group(ranks)
            if rank in list(ranks):
                _EXPERT_PARALLEL_GROUP[group_name] = group

        for ranks in expert_data_parallel_groups:
            group = torch.distributed.new_group(ranks)
            if rank in list(ranks):
                _EXPERT_DATA_PARALLEL_GROUP[group_name] = group


def _get_max_expert_size():
    """Get the maximum ep_size from all the created groups."""
    assert _EXPERT_PARALLEL_GROUP is not None, "Warning! Process group not initialized"
    keylist = []
    for key in _EXPERT_PARALLEL_GROUP.keys():
        # index 2 is ep_size in the group name: ep_size_<ep_size>
        index = 2
        keylist.append(int(key.split('_')[index]))
    return max(keylist) if len(keylist) > 0 else None


def _get_max_expert_size_name():
    """Get the name of the group with max. ep_size"""
    return f'ep_size_{_get_max_expert_size()}'


def _get_max_expert_parallel_group():
    """Get the max expert parallel size."""
    return _get_expert_parallel_group(_get_max_expert_size_name())


def _get_expert_parallel_group(group_name):
    """Get the expert parallel group the caller rank belongs to."""
    assert group_name in _EXPERT_PARALLEL_GROUP, \
        'expert parallel group is not initialized'
    return _EXPERT_PARALLEL_GROUP[group_name]


def _get_expert_parallel_group_dict():
    """Get the expert parallel group dict."""
    return _EXPERT_PARALLEL_GROUP


def _get_expert_data_parallel_group(group_name):
    """Get the expert data parallel group the caller rank belongs to."""
    assert group_name in _EXPERT_DATA_PARALLEL_GROUP, \
        'expert data parallel group is not initialized'
    return _EXPERT_DATA_PARALLEL_GROUP[group_name]


def _get_expert_data_parallel_group_dict():
    """Get the expert data parallel group dict."""
    return _EXPERT_DATA_PARALLEL_GROUP


def _clone_world_group():
    """Create a clone of the world group
        Note: We need to clone the torch.distributed world group because we
        use _get_global_rank() utility function in DeepSpeed at many places.
        As that function does not work on torch.distributed.group.WORLD, we
        need to keep a clone of it.
    """
    assert torch.distributed.is_initialized(), "torch.distributed is not initialized"
    global _WORLD_GROUP
    if _WORLD_GROUP is None:
        # If not cloned already, clone the world group
        _WORLD_GROUP = torch.distributed.new_group(
            ranks=range(torch.distributed.get_world_size()))
    return _WORLD_GROUP


def _get_data_parallel_group():
    """Get the data parallel group the caller rank belongs to."""
    assert torch.distributed.is_initialized(), \
        'torch.distributed is not initialized'
    global mpu
    if mpu is not None:
        return mpu.get_data_parallel_group()
    # Return the clone of torch.distributed world group
    return _clone_world_group()


def _get_broadcast_src_rank():
    return _get_global_rank(_get_data_parallel_group(), 0)


def _get_expert_broadcast_src_rank(group_name):
    return _get_global_rank(_get_expert_data_parallel_group(group_name), 0)


def _get_expert_parallel_world_size(group_name):
    """Return world size for the expert parallel group."""
    return torch.distributed.get_world_size(group=_get_expert_parallel_group(group_name))


def _get_expert_data_parallel_world_size(group_name):
    """Return world size for the expert data parallel group."""
    return torch.distributed.get_world_size(
        group=_get_expert_data_parallel_group(group_name))


def _get_expert_parallel_rank(group_name):
    """Return my rank for the expert parallel group."""
    return torch.distributed.get_rank(group=_get_expert_parallel_group(group_name))


def _get_expert_parallel_src_rank(group_name):
    """Calculate the global rank corresponding to a local rank zero
    in the expert parallel group."""
    global_rank = torch.distributed.get_rank()
    local_world_size = _get_expert_parallel_world_size(group_name)
    return (global_rank // local_world_size) * local_world_size


def _get_expert_data_parallel_rank(group_name):
    """Return my rank for the expert data parallel group."""
    return torch.distributed.get_rank(group=_get_expert_data_parallel_group(group_name))


def _get_data_parallel_world_size():
    """Return world size for the data parallel group."""
    global mpu
    if mpu is not None:
        return mpu.get_data_parallel_world_size()
    return torch.distributed.get_world_size(group=_get_data_parallel_group())


def _get_model_parallel_world_size():
    """Return world size for the model parallel group."""
    global mpu
    if mpu is not None:
        return mpu.get_model_parallel_world_size()
    return 1


def _get_data_parallel_rank():
    """Return my rank for the data parallel group."""
    global mpu
    if mpu is not None:
        return mpu.get_data_parallel_rank()
    return torch.distributed.get_rank(group=_get_data_parallel_group())
