#!/usr/bin/env python
# -*- encoding: utf-8 -*-

import torch
import torch.distributed as dist
from torch import Tensor

from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.utils import get_current_device


def all_gather(tensor: Tensor, dim: int,
               parallel_mode: ParallelMode) -> Tensor:
    """Gathers all tensors from the parallel group and concatenates them in a 
    specific dimension.
    
    :param tensor: Tensor to be gathered
    :param dim: The dimension concatenating in
    :param parallel_mode: Parallel group mode used in this communication
    :type tensor: Tensor
    :type dim: int
    :type parallel_mode: ParallelMode
    :return: The tensor generated by all-gather
    :rtype: Tensor
    """
    depth = gpc.get_world_size(parallel_mode)
    temp = tensor.clone()
    shape = list(temp.shape)
    shape[dim] *= depth
    out = torch.empty(shape, dtype=temp.dtype, device=get_current_device())
    out = list(torch.chunk(out, depth, dim=dim))
    out = [val.contiguous() for val in out]
    dist.all_gather(out, temp, group=gpc.get_group(parallel_mode))
    out = torch.cat(out, dim=dim)
    return out


def reduce_scatter(tensor: Tensor, dim: int,
                   parallel_mode: ParallelMode) -> Tensor:
    """Reduces all tensors then scatters it in a specific dimension to all 
    members in the parallel group.
    
    :param tensor: Tensor to be reduced and scattered
    :param dim: The dimension scattering in
    :param parallel_mode: Parallel group mode used in this communication
    :type tensor: Tensor
    :type dim: int
    :type parallel_mode: ParallelMode
    :return: The tensor generated by reduce-scatter
    :rtype: Tensor
    """
    depth = gpc.get_world_size(parallel_mode)
    temp = list(torch.chunk(tensor, depth, dim=dim))
    temp = [val.contiguous() for val in temp]
    out = torch.empty(temp[0].shape,
                      dtype=temp[0].dtype,
                      device=get_current_device())
    dist.reduce_scatter(output=out,
                        input_list=temp,
                        group=gpc.get_group(parallel_mode))
    return out


def scatter(tensor: Tensor, src: int, dim: int,
            parallel_mode: ParallelMode) -> Tensor:
    """Scatters in a specific dimension from source rank to all ranks in 
    the parallel group.
    
    :param tensor: Tensor to be scattered
    :param dim: The dimension scattering in
    :param parallel_mode: Parallel group mode used in this communication
    :type tensor: Tensor
    :type dim: int
    :type parallel_mode: ParallelMode
    :return: The tensor generated by scatter
    :rtype: Tensor
    """
    depth = gpc.get_world_size(parallel_mode)
    temp = tensor.clone()
    dist.broadcast(temp, src=src, group=gpc.get_group(parallel_mode))
    rank = gpc.get_local_rank(parallel_mode)
    out = torch.chunk(temp, depth, dim=dim)[rank].contiguous()
    return out