ColossalAI/colossalai/tensor/comm_spec.py

import operator
from enum import Enum
from functools import reduce

import torch
import torch.distributed as dist
from torch.distributed import ReduceOp

__all__ = [
    'CollectiveCommPattern',
    'CommSpec',
]


def _all_gather(tensor, comm_spec):
    '''
    Implement all gather operation on device mesh based on information provided by comm_spec.
    '''
    process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis]
    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(comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis])
            ]
            dist.all_gather(tensor_list, tensor, group=process_group)
            output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous()
            return output


def _split(tensor, comm_spec):
    '''
    Implement shard operation on device mesh based on information provided by comm_spec.
    '''
    process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis]
    for rank_list, _ in process_groups_list:
        if dist.get_rank() in rank_list:
            dim = comm_spec.shard_dim
            length = tensor.shape[comm_spec.shard_dim] // len(rank_list)
            start = length * rank_list.index(dist.get_rank())
            output = torch.narrow(tensor, dim, start, length).contiguous()
            return output


def _all_to_all(tensor, comm_spec):
    '''
    Implement all to all operation on device mesh based on information provided by comm_spec.
    '''
    process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis]
    for rank_list, process_group in process_groups_list:
        if dist.get_rank() in rank_list:
            new_shape = list(tensor.shape)
            new_shape[comm_spec.shard_dim] = new_shape[comm_spec.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 = comm_spec.shard_dim
            length = tensor.shape[comm_spec.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)
            output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous()
            return output


def _all_reduce(tensor, comm_spec, async_op=False):
    '''
    Implement all reduce operation on device mesh based on information provided by comm_spec.
    '''
    process_groups_list = comm_spec.device_mesh.process_groups_dict[comm_spec.logical_process_axis]
    for rank_list, process_group in process_groups_list:
        if dist.get_rank() in rank_list:
            if not tensor.is_contiguous():
                tensor = tensor.contiguous()
            dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op)
            return tensor


class _ReduceGrad(torch.autograd.Function):
    """
    A customized communication operation which forward is an identity operation,
    backward is all_reduce operation.

    Args:
        input_: input matrix.
        comm_spec: comm_spec will give information like process group, rank list, etc.
    """

    @staticmethod
    def symbolic(graph, input_):
        return input_

    @staticmethod
    def forward(ctx, input_, comm_spec):
        ctx.comm_spec = comm_spec
        return input_

    @staticmethod
    def backward(ctx, grad_output):
        return _all_reduce(grad_output, ctx.comm_spec), None


class _ReduceInput(torch.autograd.Function):
    """
    A customized communication operation which forward is all_reduce operation,
    backward is an identity operation.

    Args:
        input_: input matrix.
        comm_spec: comm_spec will give information like process group, rank list, etc.
    """

    @staticmethod
    def symbolic(graph, input_):
        return _all_reduce(input_)

    @staticmethod
    def forward(ctx, input_, comm_spec):
        return _all_reduce(input_, comm_spec)

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output, None


class _SplitForwardGatherBackward(torch.autograd.Function):
    """
    A customized communication operation which forward is split operation,
    backward is an all gather operation.

    Args:
        input_: input matrix.
        comm_spec: comm_spec will give information like process group, rank list, etc.
    """

    @staticmethod
    def symbolic(graph, input_):
        return _split(input_)

    @staticmethod
    def forward(ctx, input_, comm_spec):
        ctx.comm_spec = comm_spec
        return _split(input_, comm_spec)

    @staticmethod
    def backward(ctx, grad_output):
        return _all_gather(grad_output, ctx.comm_spec), None


class _GatherForwardSplitBackward(torch.autograd.Function):
    """
    A customized communication operation which forward is an all gather operation,
    backward is split operation.

    Args:
        input_: input matrix.
        comm_spec: comm_spec will give information like process group, rank list, etc.
    """

    @staticmethod
    def symbolic(graph, input_):
        return _all_gather(input_)

    @staticmethod
    def forward(ctx, input_, comm_spec):
        ctx.comm_spec = comm_spec
        return _all_gather(input_, comm_spec)

    @staticmethod
    def backward(ctx, grad_output):
        return _split(grad_output, ctx.comm_spec), None


class _AllToAll(torch.autograd.Function):
    """
    A customized communication operation which forward is an all to all operation,
    backward is an all to all operation.

    Args:
        input_: input matrix.
        comm_spec: comm_spec will give information like process group, rank list, etc.
    """

    @staticmethod
    def symbolic(graph, input_):
        return _all_to_all(input_)

    @staticmethod
    def forward(ctx, input_, comm_spec):
        output = _all_to_all(input_, comm_spec)
        comm_spec_for_backward = CommSpec(comm_pattern=comm_spec.comm_pattern,
                                          sharding_spec=comm_spec.sharding_spec,
                                          gather_dim=comm_spec.shard_dim,
                                          shard_dim=comm_spec.gather_dim,
                                          logical_process_axis=comm_spec.logical_process_axis)
        ctx.comm_spec = comm_spec_for_backward
        return output

    @staticmethod
    def backward(ctx, grad_outputs):
        return _all_to_all(grad_outputs, ctx.comm_spec), None


def reduce_grad(input_, comm_spec):
    return _ReduceGrad.apply(input_, comm_spec)


def reduce_input(input_, comm_spec):
    return _ReduceInput.apply(input_, comm_spec)


def split_forward_gather_backward(input_, comm_spec):
    return _SplitForwardGatherBackward.apply(input_, comm_spec)


def gather_forward_split_backward(input_, comm_spec):
    return _GatherForwardSplitBackward.apply(input_, comm_spec)


def all_to_all(input_, comm_spec):
    return _AllToAll.apply(input_, comm_spec)


class CollectiveCommPattern(Enum):
    GATHER_FWD_SPLIT_BWD = 'gather_fwd_split_bwd'
    ALL2ALL_FWD_ALL2ALL_BWD = 'all2all_fwd_all2all_bwd'
    SPLIT_FWD_GATHER_BWD = 'split_fwd_gather_bwd'
    ALLREDUCE_FWD_IDENTITY_BWD = 'all_reduce_fwd_identity_bwd'
    IDENTITY_FWD_ALLREDUCE_BWD = 'identity_fwd_all_reduce_bwd'


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,
                 forward_only=False):
        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
        self.forward_only = forward_only
        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.GATHER_FWD_SPLIT_BWD:
            res_list.append(f"comm_pattern:GATHER_FWD_SPLIT_BWD, ")
            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.ALL2ALL_FWD_ALL2ALL_BWD:
            res_list.append(f"comm_pattern:ALL2ALL_FWD_ALL2ALL_BWD, ")
            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.SPLIT_FWD_GATHER_BWD:
            res_list.append(f"comm_pattern:SPLIT_FWD_GATHER_BWD, ")
            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_FWD_IDENTITY_BWD:
            res_list.append(f"comm_pattern:ALLREDUCE_FWD_IDENTITY_BWD, ")
            res_list.append(f"logical_process_axis:{self.logical_process_axis})")
        elif self.comm_pattern == CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD:
            res_list.append(f"comm_pattern:IDENTITY_FWD_ALLREDUCE_BWD, ")
            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)
        cost_dict = {}
        if self.comm_pattern == CollectiveCommPattern.GATHER_FWD_SPLIT_BWD:
            forward_communication_cost = self.device_mesh.all_gather_cost(comm_size, self.logical_process_axis)
            # give a tiny cost to shard
            backward_communication_cost = 10

        if self.comm_pattern == CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD:
            forward_communication_cost = self.device_mesh.all_to_all_cost(comm_size, self.logical_process_axis)
            # grad should have same shape as input tensor
            # all to all operation has same logical process axis as forward.
            backward_communication_cost = self.device_mesh.all_to_all_cost(comm_size, self.logical_process_axis)

        if self.comm_pattern == CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD:
            forward_communication_cost = self.device_mesh.all_reduce_cost(comm_size, self.logical_process_axis)
            backward_communication_cost = 0

        if self.comm_pattern == CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD:
            forward_communication_cost = 0
            backward_communication_cost = self.device_mesh.all_reduce_cost(comm_size, self.logical_process_axis)

        if self.comm_pattern == CollectiveCommPattern.SPLIT_FWD_GATHER_BWD:
            # give a tiny cost to shard
            forward_communication_cost = 10
            backward_communication_cost = self.device_mesh.all_gather_cost(comm_size, self.logical_process_axis)

        if self.forward_only:
            cost_dict["forward"] = forward_communication_cost
            cost_dict["backward"] = 0
            cost_dict["total"] = cost_dict["forward"] + cost_dict["backward"]
        else:
            cost_dict["forward"] = forward_communication_cost
            cost_dict["backward"] = backward_communication_cost
            cost_dict["total"] = cost_dict["forward"] + cost_dict["backward"]

        return cost_dict

    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.
        '''
        if self.comm_pattern in pattern_to_func_dict:
            tensor = pattern_to_func_dict[self.comm_pattern](tensor, self)
        else:
            tensor = tensor
        return tensor


pattern_to_func_dict = {
    CollectiveCommPattern.GATHER_FWD_SPLIT_BWD: gather_forward_split_backward,
    CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD: all_to_all,
    CollectiveCommPattern.SPLIT_FWD_GATHER_BWD: split_forward_gather_backward,
    CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD: reduce_input,
    CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD: reduce_grad,
}