ColossalAI/colossalai/tensor/shape_consistency.py

import torch
from dataclasses import dataclass
from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
from colossalai.tensor.utils import all_gather_simulator, all_to_all_simulator, shard_simulator
from enum import Enum
from copy import deepcopy
from typing import Dict, List, Optional, Tuple, Union
from colossalai.context.singleton_meta import SingletonMeta
import torch.distributed as dist
import math
from functools import reduce
import operator
from torch.distributed import ReduceOp

__all__ = [
    'CollectiveCommPattern', 'CommSpec', 'ShapeConsistencyManager', 'ShapeConsistencyOptions',
    'set_shape_consistency_options'
]


class CollectiveCommPattern(Enum):
    ALLGATHER = 'all_gather'
    ALLTOALL = 'all_to_all'
    SHARD = 'shard'
    ALLREDUCE = 'all_reduce'


class CommSpec:
    '''
    Communication spec is used to record the communication action. It has two main functions:
    1. Compute the communication cost which will be used in auto parallel solver.
    2. Convert the communication spec to real action which will be used in runtime.
    It contains comm_pattern to determine the
    communication method, sharding_spec to determine the communication size, gather_dim and shard_dim 
    to determine the buffer shape, and logical_process_axis

    Argument:
        comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec.
        sharding_spec(ShardingSpec): This is sharding spec of the tensor which will join the communication action.
        gather_dim(int, Optional): The gather_dim of the tensor will be gathered.
        shard_dim(int, Optional): The shard_dim of the tensor will be sharded.
        logical_process_axis(Union(int, List[int]), Optional): The mesh_dim to implement the communication action.
    '''

    def __init__(self, comm_pattern, sharding_spec, gather_dim=None, shard_dim=None, logical_process_axis=None):
        self.comm_pattern = comm_pattern
        self.sharding_spec = sharding_spec
        self.gather_dim = gather_dim
        self.shard_dim = shard_dim
        self.logical_process_axis = logical_process_axis
        if isinstance(self.logical_process_axis, list):
            self.device_mesh = self.sharding_spec.device_mesh.flatten_device_mesh
            self.logical_process_axis = 0
        else:
            self.device_mesh = self.sharding_spec.device_mesh

    def __repr__(self):
        res_list = ["CommSpec:("]
        if self.comm_pattern == CollectiveCommPattern.ALLGATHER:
            res_list.append(f"comm_pattern:all_gather, ")
            res_list.append(f"gather_dim:{self.gather_dim}, ")
            res_list.append(f"logical_process_axis:{self.logical_process_axis})")
        elif self.comm_pattern == CollectiveCommPattern.ALLTOALL:
            res_list.append(f"comm_pattern:all2all, ")
            res_list.append(f"gather_dim:{self.gather_dim}, ")
            res_list.append(f"shard_dim:{self.shard_dim}, ")
            res_list.append(f"logical_process_axis: {self.logical_process_axis})")
        elif self.comm_pattern == CollectiveCommPattern.SHARD:
            res_list.append(f"comm_pattern:shard, ")
            res_list.append(f"shard_dim:{self.shard_dim}, ")
            res_list.append(f"logical_process_axis:{self.logical_process_axis})")
        elif self.comm_pattern == CollectiveCommPattern.ALLREDUCE:
            res_list.append(f"comm_pattern:all_reduce, ")
            res_list.append(f"logical_process_axis:{self.logical_process_axis})")

        return ''.join(res_list)

    def get_comm_cost(self):
        '''
        For all_gather, all2all, and all_reduce operation, the formula provided in DeviceMesh with alpha-beta model is used to
        compute the communication cost.
        For shard operation, it is an on-chip operation, so the communication cost is zero. 
        '''
        comm_size = reduce(operator.mul, self.sharding_spec.get_sharded_shape_per_device(), 1)
        if self.comm_pattern == CollectiveCommPattern.ALLGATHER:
            return self.device_mesh.all_gather_cost(comm_size, self.logical_process_axis)
        if self.comm_pattern == CollectiveCommPattern.ALLTOALL:
            return self.device_mesh.all_to_all_cost(comm_size, self.logical_process_axis)
        if self.comm_pattern == CollectiveCommPattern.ALLREDUCE:
            return self.device_mesh.all_reduce_cost(comm_size, self.logical_process_axis)
        if self.comm_pattern == CollectiveCommPattern.SHARD:
            # give a tiny cost to shard
            return 10
        raise RuntimeError(f"Could not find a matching CollectiveCommPattern for {self.comm_pattern}.")

    def covert_spec_to_action(self, tensor):
        '''
        Convert CommSpec into runtime action, implement real collection communication to target tensor.
        The collection communication action is directed by the CommSpec.

        Argument:
            tensor(torch.Tensor): Tensor stored in each device, which could be different in different ranks.
        '''
        process_groups_list = self.device_mesh.process_groups_dict[self.logical_process_axis]

        if self.comm_pattern == CollectiveCommPattern.ALLGATHER:
            for rank_list, process_group in process_groups_list:
                if dist.get_rank() in rank_list:
                    tensor_list = [
                        torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device)
                        for _ in range(self.device_mesh.mesh_shape[self.logical_process_axis])
                    ]
                    tensor = tensor
                    group = process_group
                    dist.all_gather(tensor_list, tensor, group=group)
                    tensor.data = torch.cat(tuple(tensor_list), self.gather_dim)

        elif self.comm_pattern == CollectiveCommPattern.SHARD:
            for rank_list, process_group in process_groups_list:
                if dist.get_rank() in rank_list:
                    tensor = tensor
                    dim = self.shard_dim
                    length = tensor.shape[self.shard_dim] // len(rank_list)
                    start = length * rank_list.index(dist.get_rank())
                    tensor.data = torch.narrow(tensor, dim, start, length)

        elif self.comm_pattern == CollectiveCommPattern.ALLTOALL:
            for rank_list, process_group in process_groups_list:
                if dist.get_rank() in rank_list:
                    new_shape = list(tensor.shape)
                    new_shape[self.shard_dim] = new_shape[self.shard_dim] // len(rank_list)
                    new_shape = torch.Size(new_shape)
                    output_tensor_list = [
                        torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list))
                    ]
                    dim = self.shard_dim
                    length = tensor.shape[self.shard_dim] // len(rank_list)
                    input_tensor_list = [
                        torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list))
                    ]
                    group = process_group
                    dist.all_to_all(output_tensor_list, input_tensor_list, group)
                    tensor.data = torch.cat(tuple(output_tensor_list), self.gather_dim)

        elif self.comm_pattern == CollectiveCommPattern.ALLREDUCE:
            # For the consistency of collective communication operation, we temporally do not
            # allow all_reduce two different mesh dimensions in the same time.
            # e.g.: MatMul[(R, S01), (S01, R)] -> Partial(R, R),
            # all_reduce(Partial, logical_pg=(0, 1)) is NOT allowed, instead
            # we need to do this in two steps:
            # 1. all_reduce(Partial, logical_pg=1)
            # 2. all_reduce(Partial, logical_pg=0)
            for rank_list, process_group in process_groups_list:
                if dist.get_rank() in rank_list:
                    dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group)
                    tensor.data = tensor

        else:
            tensor.data = tensor


@dataclass
class ShapeConsistencyOptions:
    """
    ShapeConsistencyOptions is a dataclass which specifies the preferences for shape consistency.
    """
    # TODO: shape consistency option is not implemented yet
    pass


def set_shape_consistency_options(options: ShapeConsistencyOptions):
    """
    Configure the shape consistency manager via function call.
    """
    manager = ShapeConsistencyManager()
    manager.options = options


class ShapeConsistencyManager(metaclass=SingletonMeta):

    def __init__(self):
        self._options = None
        self.total_communication_cost = 0
        self.total_transform_steps = 0
        self.cached_spec_pairs_transform_path = {}

    @property
    def options(self):
        return self._options

    @options.setter
    def options(self, options_: ShapeConsistencyOptions):
        assert isinstance(options_, ShapeConsistencyOptions)
        self._options = options_

    def get_all_all_gather_spec(self, source_spec, orig_cost):
        '''
        Get all valid sharding specs from source_spec with single all-gather operation, and 
        accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
        For the all-gather operation, we just care about the S dimension.
        
        Argument:
            source_spec(ShardingSpec): the ShardingSpec of the source_spec.
            orig_cost(float): the original communication cost before this operation.

        Return:
            valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-gather operation.

        Example:
            dim_partition_dict = {0: [0], 1: [1]}
            # DistSpec:
            #     shard_sequence: S0,S1,R
            #     device_mesh_shape: (4, 4)
            sharding_spec = ShardingSpec(device_mesh, entire_shape, dim_partition_dict)
            shape_consistency_manager = ShapeConsistencyManager()
            rst_dict = shape_consistency_manager.get_all_all_gather_spec(sharding_spec, 0)
            print(rst_dict)
        
        Output:
            {DistSpec: 
            shard_sequence: R,S1,R 
            device_mesh_shape: (4, 4): 0, DistSpec: 
            shard_sequence: S0,R,R 
            device_mesh_shape: (4, 4): 0}
        '''
        valid_spec_dict = {}
        comm_pattern = CollectiveCommPattern.ALLGATHER
        for target_pair in source_spec.dim_partition_dict.items():
            shard_list = all_gather_simulator(target_pair)
            index = target_pair[0]
            new_dim_partition_dict = deepcopy(source_spec.dim_partition_dict)

            # We won't add empty list into dim_partition_dict
            # The key will be popped if the related shard_list is empty
            if shard_list:
                new_dim_partition_dict[index] = shard_list
            else:
                new_dim_partition_dict.pop(index)

            # generate the CommSpec to record the action of source_sharding_spec->new_sharding_spec
            gather_dim = index
            logical_process_axis = target_pair[1][-1]
            comm_spec = CommSpec(comm_pattern,
                                 sharding_spec=source_spec,
                                 gather_dim=gather_dim,
                                 logical_process_axis=logical_process_axis)

            # compute the communication cost with CommSpec
            cost = comm_spec.get_comm_cost()

            # generate new sharding spec
            new_sharding_spec = ShardingSpec(source_spec.device_mesh,
                                             source_spec.entire_shape,
                                             dim_partition_dict=new_dim_partition_dict)
            valid_spec_dict[new_sharding_spec] = (comm_spec, orig_cost + cost)
        return valid_spec_dict

    def get_all_all_to_all_spec(self, source_spec, orig_cost):
        '''
        Get all valid sharding specs from source_spec with single all-to-all operation, and 
        accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
        For the all-to-all operation, we just care about the pairs containing S dimension.
        
        Argument:
            source_spec(ShardingSpec): the ShardingSpec of the source_spec.
            orig_cost(float): the original communication cost before this operation.

        Return:
            valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-to-all operation.

        Example:
            dim_partition_dict = {0: [0], 1: [1]}
            # DistSpec:
            #     shard_sequence: S0,S1,R
            #     device_mesh_shape: (4, 4)
            sharding_spec = ShardingSpec(device_mesh, entire_shape, dim_partition_dict)
            shape_consistency_manager = ShapeConsistencyManager()
            rst_dict = shape_consistency_manager.get_all_all_to_all_spec(sharding_spec, 0)
            print(rst_dict)
        
        Output:
            {DistSpec: 
            shard_sequence: S01,R,R 
            device_mesh_shape: (4, 4): 0, DistSpec: 
            shard_sequence: R,S1,S0 
            device_mesh_shape: (4, 4): 0, DistSpec: 
            shard_sequence: S0,R,S1 
            device_mesh_shape: (4, 4): 0}
        '''
        valid_spec_dict = {}
        comm_pattern = CollectiveCommPattern.ALLTOALL
        tensor_dims = len(source_spec.entire_shape)
        for f_index in range(tensor_dims - 1):
            for b_index in range(f_index + 1, tensor_dims):
                # skip (R, R) cases
                if f_index not in source_spec.dim_partition_dict and b_index not in source_spec.dim_partition_dict:
                    continue
                else:
                    if f_index in source_spec.dim_partition_dict:
                        # skip (S01, R) -> (R, S01) is NOT allowed
                        if len(source_spec.dim_partition_dict[f_index]) >= 2:
                            continue
                        f_target_pair = (f_index, deepcopy(source_spec.dim_partition_dict[f_index]))
                    else:
                        f_target_pair = (f_index, [])
                    if b_index in source_spec.dim_partition_dict:
                        # skip (R, S01) -> (S01, R) is NOT allowed
                        if len(source_spec.dim_partition_dict[b_index]) >= 2:
                            continue
                        b_target_pair = (b_index, deepcopy(source_spec.dim_partition_dict[b_index]))
                    else:
                        b_target_pair = (b_index, [])

                # skip (S1, S0) -> S10
                if f_target_pair[1] and b_target_pair[1] and f_target_pair[1][0] >= b_target_pair[1][0]:
                    continue
                f_shard_list, b_shard_list = all_to_all_simulator(f_target_pair, b_target_pair)
                f_index = f_target_pair[0]
                b_index = b_target_pair[0]

                # generate the CommSpec to record the action of source_sharding_spec->new_sharding_spec
                if len(f_shard_list) < len(f_target_pair[1]):
                    gather_dim = f_index
                    shard_dim = b_index
                    logical_process_axis = f_target_pair[1][-1]
                else:
                    gather_dim = b_index
                    shard_dim = f_index
                    logical_process_axis = b_target_pair[1][-1]
                comm_spec = CommSpec(comm_pattern,
                                     sharding_spec=source_spec,
                                     gather_dim=gather_dim,
                                     shard_dim=shard_dim,
                                     logical_process_axis=logical_process_axis)

                # compute the communication cost with CommSpec
                cost = comm_spec.get_comm_cost()
                new_dim_partition_dict = deepcopy(source_spec.dim_partition_dict)

                # We won't add empty list into dim_partition_dict
                # The key will be popped if the related shard_list is empty
                if f_shard_list:
                    new_dim_partition_dict[f_index] = f_shard_list
                else:
                    new_dim_partition_dict.pop(f_index)
                if b_shard_list:
                    new_dim_partition_dict[b_index] = b_shard_list
                else:
                    new_dim_partition_dict.pop(b_index)

                # generate new sharding spec
                new_sharding_spec = ShardingSpec(source_spec.device_mesh,
                                                 source_spec.entire_shape,
                                                 dim_partition_dict=new_dim_partition_dict)
                valid_spec_dict[new_sharding_spec] = (comm_spec, orig_cost + cost)
        return valid_spec_dict

    def get_all_shard_spec(self, source_spec, orig_cost):
        '''
        Get all valid sharding specs from source_spec with single shard operation, and 
        accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
        For the sharding operation, we just care about legal sharding dimensions.
        
        Argument:
            source_spec(ShardingSpec): the ShardingSpec of the source_spec.
            orig_cost(float): the original communication cost before this operation.

        Return:
            valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-to-all operation.

        Example:
            dim_partition_dict = {0: [0]}
            # DistSpec:
            #     shard_sequence: S0,R,R
            #     device_mesh_shape: (4, 4)
            sharding_spec = ShardingSpec(device_mesh, entire_shape, dim_partition_dict)
            shape_consistency_manager = ShapeConsistencyManager()
            rst_dict = shape_consistency_manager.get_all_shard_spec(sharding_spec, 0)
            print(rst_dict)
        
        Output:
            {DistSpec: 
            shard_sequence: S01,R,R 
            device_mesh_shape: (4, 4): 0, DistSpec: 
            shard_sequence: S0,S1,R 
            device_mesh_shape: (4, 4): 0, DistSpec: 
            shard_sequence: S0,R,S1 
            device_mesh_shape: (4, 4): 0}
        '''
        valid_spec_dict = {}
        comm_pattern = CollectiveCommPattern.SHARD

        # legal sharding dims means the mesh_id is still available to use.
        legal_sharding_dims = [i for i in range(len(source_spec.device_mesh.mesh_shape))]
        for dim, shard_list in source_spec.dim_partition_dict.items():
            for element in shard_list:
                legal_sharding_dims.remove(element)
        if len(legal_sharding_dims) == 0:
            return valid_spec_dict

        tensor_dims = len(source_spec.entire_shape)
        for index in range(tensor_dims):
            if index not in source_spec.dim_partition_dict:
                shard_list_list = shard_simulator((index, []), legal_sharding_dims)
            else:
                shard_list_list = shard_simulator((index, source_spec.dim_partition_dict[index]), legal_sharding_dims)
            if not shard_list_list:
                continue
            for shard_list in shard_list_list:
                new_dim_partition_dict = deepcopy(source_spec.dim_partition_dict)
                new_dim_partition_dict[index] = shard_list

                # generate the CommSpec to record the action of source_sharding_spec->new_sharding_spec
                shard_dim = index
                logical_process_axis = shard_list[-1]
                comm_spec = CommSpec(comm_pattern,
                                     sharding_spec=source_spec,
                                     shard_dim=shard_dim,
                                     logical_process_axis=logical_process_axis)

                # compute the communication cost with CommSpec
                cost = comm_spec.get_comm_cost()

                # generate new sharding spec
                new_sharding_spec = ShardingSpec(source_spec.device_mesh,
                                                 source_spec.entire_shape,
                                                 dim_partition_dict=new_dim_partition_dict)
                valid_spec_dict[new_sharding_spec] = (comm_spec, orig_cost + cost)
        return valid_spec_dict

    def get_all_one_step_transform_spec(self, source_spec, orig_cost):
        '''
        Get all valid sharding specs from source_spec with one step transform, and 
        accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
        Note:
            all-gather will eliminate a sharding dimension, all-to-all will keep sharding dimension same as before,
            and shard will add a sharding dimension. Therefore, the result of above operations are mutual exclusive,
            we could safely put them together.
        
        Argument:
            source_spec(ShardingSpec): the ShardingSpec of the source_spec.
            orig_cost(float): the original communication cost before this operation.

        Return:
            valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-to-all operation.
        '''
        valid_spec_dict = {}
        valid_spec_dict.update(self.get_all_all_gather_spec(source_spec, orig_cost))
        valid_spec_dict.update(self.get_all_all_to_all_spec(source_spec, orig_cost))
        valid_spec_dict.update(self.get_all_shard_spec(source_spec, orig_cost))
        return valid_spec_dict

    def shape_consistency(self, source_spec, target_spec):
        '''
        This method will find a path to transform source_spec to target_spec with
        a greedy algorithm.
        The basic idea is:
        Step1:
            Generate all one-step transform sequences from source_spec.
        Step2:
            Pick the 'best' sharding spec following the heuristic function.
        Step3:
            Repeat above steps until the source spec transform to target spec.

        During finding the transform path, commucation cost will be accumulated, and it
        will be finally used in auto parallel solver. 

        Additionally, to avoid repeating the path search in runtime, we cached all solved path
        in auto parallel strategy building time, which could handle most of cases in runtime.

        Argument:
            source_spec(ShardingSpec): ShardingSpec of the source activation.
            target_spec(ShardingSpec): ShardingSpec of the target activation.

        Return:
            transform_path(List[ShardingSpec]): The transform path from source_spec to target_spec,
                                                it contains the source_spec and target_spec.
            comm_action_sequence(List[CommSpec]): Keep the communication operations to complete the shape consistency in order.
            total_cost(float): total cost to complete shape consistency transform.

        Example:
            dim_partition_source = {1: [0, 1]}
            dim_partition_target = {0: [0, 1]}
            # DistSpec: 
            #     shard_sequence: R,S01,R 
            #     device_mesh_shape: (4, 4)
            sharding_spec_source = ShardingSpec(device_mesh, entire_shape, dim_partition_source)
            # DistSpec: 
            #     shard_sequence: S01,R,R 
            #     device_mesh_shape: (4, 4)
            sharding_spec_target = ShardingSpec(device_mesh, entire_shape, dim_partition_target)
            transform_path, comm_action_sequence, total_cost = shape_consistency_manager.shape_consistency(sharding_spec_source, sharding_spec_target)
            print(f'transform_path: {transform_path}')
            print(f'comm_action_sequence: {comm_action_sequence}')
            print(f'total_cost: {total_cost}')
    
        output:
            transform_path: [DistSpec: 
                    shard_sequence: R,S01,R 
                    device_mesh_shape: (4, 4), DistSpec: 
                    shard_sequence: R,S0,R 
                    device_mesh_shape: (4, 4), DistSpec: 
                    shard_sequence: S0,R,R 
                    device_mesh_shape: (4, 4), DistSpec: 
                    shard_sequence: S01,R,R 
                    device_mesh_shape: (4, 4)]
            comm_action_sequence: [CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1), 
                                   CommSpec:(comm_pattern:all2all, gather_dim:1, shard_dim:0, logical_process_axis: 0),
                                   CommSpec:(comm_pattern:shard, shard_dim:0, logical_process_axis:1)]
            total_cost: 12294.402000000002
        '''
        MAX_TRANSFORM_STEPS = 20
        total_cost = 0
        total_steps = 0
        transform_path = []
        comm_action_sequence = []
        spec_pairs = (str(source_spec.sharding_sequence), str(target_spec.sharding_sequence))
        self.cached_spec_pairs_transform_path[spec_pairs] = (None, None)

        # We do nothing if the sharding spec is all the same.
        if source_spec.sharding_sequence_difference(target_spec) == 0:
            self.cached_spec_pairs_transform_path[spec_pairs] = (transform_path, comm_action_sequence)
            return (transform_path, comm_action_sequence, total_cost)

        temp_sharding_spec = source_spec
        transform_path.append(temp_sharding_spec)
        # To avoid dead loop, the loop will break after MAX_TRANSFORM_STEPS transforms
        while total_steps <= MAX_TRANSFORM_STEPS:
            valid_transform_spec_dict = self.get_all_one_step_transform_spec(temp_sharding_spec, total_cost)
            best_difference_score = math.inf

            for sharding_spec, info_pairs in valid_transform_spec_dict.items():
                comm_spec, cost = info_pairs
                spec_difference = sharding_spec.sharding_sequence_difference(target_spec)

                if spec_difference == 0:
                    total_cost += cost
                    transform_path.append(sharding_spec)
                    comm_action_sequence.append(comm_spec)
                    self.cached_spec_pairs_transform_path[spec_pairs] = (transform_path, comm_action_sequence)
                    return (transform_path, comm_action_sequence, total_cost)

                if spec_difference < best_difference_score:
                    temp_sharding_spec = sharding_spec
                    temp_cost = cost
                    temp_comm_spec = comm_spec
                    best_difference_score = spec_difference

            transform_path.append(temp_sharding_spec)
            comm_action_sequence.append(temp_comm_spec)
            total_cost += temp_cost
            total_steps += 1

        raise RuntimeError(f"Could not find a valid transform path with in {MAX_TRANSFORM_STEPS} steps.")

    def apply(self, tensor_with_sharding_spec, target_spec):
        '''
        Apply target_spec to tensor with source sharding spec, the transform path is generated by the 
        shape_consistency method.
        
        Argument:
            tensor_with_sharding_spec (torch.Tensor): a tensor with source sharding spec to be transformed to the target spec.
            target_spec (ShardingSpec): The tensor transform processes will be directed by the target_spec.
        
        Example:
            physical_mesh_id = torch.arange(0, 4)
            mesh_shape = (2, 2)
            # [[0, 1,
            #  [2, 3]]
            device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
            entire_shape = torch.Size((4, 2))
            shape_consistency_manager = ShapeConsistencyManager()
            dim_partition_source = {0: [0]}
            dim_partition_target = {1: [0]}

            # DistSpec:
            #     shard_sequence: S0,R
            #     device_mesh_shape: (2, 2)
            sharding_spec_source = ShardingSpec(device_mesh, entire_shape, dim_partition_source)
            
            # DistSpec:
            #     shard_sequence: R,S0
            #     device_mesh_shape: (2, 2)
            sharding_spec_target = ShardingSpec(device_mesh, entire_shape, dim_partition_target)

            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()

            tensor_to_comm.sharding_spec = sharding_spec_source
            shape_consistency_manager.apply(tensor_to_comm, sharding_spec_target)
            print(tensor_to_comm)
        
        Output in rank0 and rank2:
            tensor([[0.],
                    [0.],
                    [2.],
                    [2.]])
        
        Output in rank1 and rank3:
            tensor([[1.],
                    [1.],
                    [3.],
                    [3.]])
        '''
        _, comm_action_sequence, _ = self.shape_consistency(tensor_with_sharding_spec.sharding_spec, target_spec)
        for comm_spec in comm_action_sequence:
            comm_spec.covert_spec_to_action(tensor_with_sharding_spec)
        tensor_with_sharding_spec.sharding_spec = target_spec