ColossalAI/colossalai/context/process_group_initializer/initializer_sequence.py

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch.distributed as dist

from colossalai.registry import DIST_GROUP_INITIALIZER
from .initializer_tensor import Initializer_Tensor
from .process_group_initializer import ProcessGroupInitializer
from ..parallel_mode import ParallelMode


@DIST_GROUP_INITIALIZER.register_module
class Initializer_Sequence_DP(ProcessGroupInitializer):
    """A ProcessGroupInitializer for sequence parallelism all-reduce.

    In Sequence Parallelism, each GPU holds the full copy of model weights,
    thus, gradient all-reduce occurs across all processes in the same pipeline stage

    :param args: Args used to initialize ProcessGroupInitializer
    :param kwargs: Kwargs used to initialize ProcessGroupInitializer
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.dp_size = self.world_size // self.pipeline_parallel_size
        self.num_group = self.pipeline_parallel_size

    def init_dist_group(self):
        """Initialize Sequence Parallel process groups used for gradient all-reduce.

        :return: (local_rank, group_world_size, process_group, ranks_in_group, mode)
        :rtype: Tuple
        """
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.SEQUENCE_DP

        for i in range(self.num_group):
            ranks = [i * self.dp_size + j for j in range(self.dp_size)]
            group = dist.new_group(ranks)

            if self.rank in ranks:
                local_rank = ranks.index(self.rank)
                group_world_size = len(ranks)
                process_group = group
                ranks_in_group = ranks
        return local_rank, group_world_size, process_group, ranks_in_group, mode


@DIST_GROUP_INITIALIZER.register_module
class Initializer_Sequence(ProcessGroupInitializer):
    """A ProcessGroupInitializer for sequence parallelism.

    :param args: Args used to initialize ProcessGroupInitializer
    :param kwargs: Kwargs used to initialize ProcessGroupInitializer
    """
    def __init__(self,
                 *args, **kwargs):
        super().__init__(*args, **kwargs)
        # reuse tensor parallel initializer code
        self._sequence_initializer = Initializer_Tensor(*args, **kwargs)
        self._sequence_dp_initializer = Initializer_Sequence_DP(*args, **kwargs)

    def init_dist_group(self):
        """Initialize Sequence parallel process groups and assign local_ranks and groups to each gpu.

        Sequence parallelism requires 2 process groups. The first is for model forward where several processes
        exchange paritial query, key and value embedding to compute self attention values. The second is for
        all-reduce to synchronize the model parameters.

        :return: Sequence parallelism's information
        :rtype: list of Tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
        """

        parallel_setting = []

        local_rank, group_world_size, process_group, ranks_in_group, mode = self._sequence_initializer.init_dist_group()
        # change mode to sequence
        mode = ParallelMode.SEQUENCE

        parallel_setting.append((local_rank, group_world_size, process_group, ranks_in_group, mode))
        parallel_setting.append(self._sequence_dp_initializer.init_dist_group())
        return parallel_setting