import torch
from functools import partial
import pytest
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.distributed import ReduceOp
from colossalai.core import global_context as gpc
from colossalai.initialize import launch
from colossalai.utils import free_port
from colossalai.testing import rerun_if_address_is_in_use
from colossalai.device.device_mesh import DeviceMesh
from colossalai.tensor.shape_consistency import CommSpec, CollectiveCommPattern
from colossalai.logging import disable_existing_loggers
from colossalai.tensor.sharding_spec import ShardingSpec


def check_all_gather(device_mesh, rank):
    # tensor to comm
    if rank in (0, 2):
        sharded_tensor_to_comm = torch.ones(2, 2).cuda()
    else:
        sharded_tensor_to_comm = torch.zeros(2, 2).cuda()

    # tensor to check
    tensor_to_check = torch.cat((torch.ones(2, 2), torch.zeros(2, 2)), 1).cuda()

    # test all gather
    dim_partition_dict = {1: [1]}

    # DistSpec:
    #     shard_sequence: R,S1
    #     device_mesh_shape: (2, 2)
    sharding_spec = ShardingSpec(device_mesh, tensor_to_check.shape, dim_partition_dict=dim_partition_dict)

    # CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1)
    comm_spec = CommSpec(CollectiveCommPattern.ALLGATHER, sharding_spec, gather_dim=1, logical_process_axis=1)
    comm_spec.covert_spec_to_action(sharded_tensor_to_comm)

    assert sharded_tensor_to_comm.equal(tensor_to_check)


def check_shard(device_mesh, rank):
    # tensor to comm
    sharded_tensor_to_comm_0 = torch.zeros(2, 2).cuda()
    sharded_tensor_to_comm_1 = torch.ones(2, 2).cuda()
    # tensor([[0., 0., 1., 1.],
    #         [0., 0., 1., 1.]])
    tensor_to_shard = torch.cat((sharded_tensor_to_comm_0, sharded_tensor_to_comm_1), 1)

    # test shard
    dim_partition_dict = {}

    # DistSpec:
    #     shard_sequence: R,R
    #     device_mesh_shape: (2, 2)
    sharding_spec = ShardingSpec(device_mesh, tensor_to_shard.shape, dim_partition_dict=dim_partition_dict)

    # CommSpec:(comm_pattern:shard, shard_dim:1, logical_process_axis:1)
    comm_spec = CommSpec(CollectiveCommPattern.SHARD, sharding_spec, shard_dim=1, logical_process_axis=1)
    comm_spec.covert_spec_to_action(tensor_to_shard)

    if rank in (0, 2):
        assert tensor_to_shard.equal(sharded_tensor_to_comm_0)
    if rank in (1, 3):
        assert tensor_to_shard.equal(sharded_tensor_to_comm_1)


def check_all_to_all(device_mesh, rank):
    # tensor to comm
    if rank in (0, 1):
        sharded_tensor_0 = torch.zeros(2, 1)
        sharded_tensor_1 = torch.ones(2, 1)
        # tensor([[0., 1.],
        #         [0., 1.]])
        tensor_to_comm = torch.cat((sharded_tensor_0, sharded_tensor_1), 1).cuda()
    if rank in (2, 3):
        sharded_tensor_0 = torch.ones(2, 1) * 2
        sharded_tensor_1 = torch.ones(2, 1) * 3
        # tensor([[2., 3.],
        #         [2., 3.]])
        tensor_to_comm = torch.cat((sharded_tensor_0, sharded_tensor_1), 1).cuda()

    if rank in (0, 1):
        # tensor([[0.],
        #         [0.],
        #         [2.],
        #         [2.]])
        tensor_to_check = torch.tensor([[0], [0], [2], [2]], dtype=tensor_to_comm.dtype).cuda()
    if rank in (2, 3):
        # tensor([[1.],
        #         [1.],
        #         [3.],
        #         [3.]])
        tensor_to_check = torch.tensor([[1], [1], [3], [3]], dtype=tensor_to_comm.dtype).cuda()

    # test shard
    dim_partition_dict = {0: [0]}

    # DistSpec:
    #     shard_sequence: S0,R
    #     device_mesh_shape: (2, 2)
    sharding_spec = ShardingSpec(device_mesh, torch.Size((4, 2)), dim_partition_dict=dim_partition_dict)

    # CommSpec:(comm_pattern:shard, shard_dim:1, logical_process_axis:1)
    comm_spec = CommSpec(CollectiveCommPattern.ALLTOALL,
                         sharding_spec,
                         gather_dim=0,
                         shard_dim=1,
                         logical_process_axis=0)
    comm_spec.covert_spec_to_action(tensor_to_comm)

    assert tensor_to_comm.equal(tensor_to_check)


def check_all_reduce(device_mesh, rank):
    # tensor to comm
    tensor_to_comm = torch.ones(2, 2).cuda() * rank

    # reduce through logical process axis 0
    # tensor to check
    if rank in (0, 2):
        # tensor([[2., 2.],
        #         [2., 2.]])
        tensor_to_check = torch.tensor([[2, 2], [2, 2]], dtype=tensor_to_comm.dtype).cuda()
    if rank in (1, 3):
        # tensor([[4., 4.],
        #         [4., 4.]])
        tensor_to_check = torch.tensor([[4, 4], [4, 4]], dtype=tensor_to_comm.dtype).cuda()

    dim_partition_dict = {}
    # DistSpec:
    #     shard_sequence: R,R
    #     device_mesh_shape: (2, 2)
    sharding_spec = ShardingSpec(device_mesh, tensor_to_comm.shape, dim_partition_dict=dim_partition_dict)

    # CommSpec:CommSpec:(comm_pattern:all_reduce, logical_process_axis:0)
    comm_spec = CommSpec(CollectiveCommPattern.ALLREDUCE, sharding_spec, logical_process_axis=0)
    comm_spec.covert_spec_to_action(tensor_to_comm)

    assert tensor_to_comm.equal(tensor_to_check)


def check_comm(rank, world_size, port):
    disable_existing_loggers()
    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')

    physical_mesh_id = torch.arange(0, 4)
    assert rank == gpc.get_global_rank()

    mesh_shape = (2, 2)
    # [[0, 1,
    #  [2, 3]]
    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
    # test all gather
    check_all_gather(device_mesh, rank)

    # test shard
    check_shard(device_mesh, rank)

    # test all to all
    check_all_to_all(device_mesh, rank)

    # test all reduce
    check_all_reduce(device_mesh, rank)
    gpc.destroy()


@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_comm_spec():
    world_size = 4
    run_func = partial(check_comm, world_size=world_size, port=free_port())
    mp.spawn(run_func, nprocs=world_size)


if __name__ == '__main__':
    test_comm_spec()