ColossalAI/colossalai/context/process_group_initializer/initializer_2p5d.py

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

import math

import torch.distributed as dist
from colossalai.context import Config
from colossalai.global_variables import tensor_parallel_env as env
from colossalai.registry import DIST_GROUP_INITIALIZER

from ..parallel_mode import ParallelMode
from .process_group_initializer import ProcessGroupInitializer


def _check_tesseract_env_var(tesseract_dim: int, tesseract_dep: int):
    # check global variable for TESSERACT
    env_tesseract_dim = env.tesseract_dim
    env_tesseract_dep = env.tesseract_dep

    if env_tesseract_dim and env_tesseract_dep:
        assert int(env_tesseract_dim) == tesseract_dim, \
            'TESSERACT_DIM has been set in the current environment and ' \
            'does not match with the value passed to this initialized'
        assert int(env_tesseract_dep) == tesseract_dep, \
            'TESSERACT_DEP has been set in the current environment and ' \
            'does not match with the value passed to this initialized'
    else:
        env.tesseract_dim = tesseract_dim
        env.tesseract_dep = tesseract_dep


# i row j col k dep
class Initializer_2p5D_ROW(ProcessGroupInitializer):
    """2p5d tensor parallel initialization among rows.

    :param tesseract_dim: The dimension of tesseract
    :param tesseract_dep: The dimension of depth
    :param args: Args used to initialize base class

    :type tesseract_dim: int
    :type tesseract_dep: int
    """

    def __init__(self, tesseract_dim: int, tesseract_dep: int, *args):
        super(Initializer_2p5D_ROW, self).__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dep = tesseract_dep
        self.tesseract_dim = tesseract_dim
        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"

    def init_dist_group(self):
        """Initialize 2p5D tensor row parallel groups, and assign local_ranks and groups to each gpu.

        :return: 2p5D tensor row parallelism's information
        :rtype: Tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
        """
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.PARALLEL_2P5D_ROW

        for h in range(self.num_group):
            for j in range(self.tesseract_dim):
                for k in range(self.tesseract_dep):
                    ranks = [
                        h * self.tensor_parallel_size + i + self.tesseract_dim * (j + self.tesseract_dim * k)
                        for i in range(self.tesseract_dim)
                    ]
                    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


class Initializer_2p5D_Col(ProcessGroupInitializer):
    """2p5d tensor parallel initialization among cols.

    :param tesseract_dim: The dimension of tesseract
    :param tesseract_dep: The dimension of depth
    :param args: Args used to initialize base class

    :type tesseract_dim: int
    :type tesseract_dep: int
    """

    def __init__(self, tesseract_dim: int, tesseract_dep: int, *args):
        super(Initializer_2p5D_Col, self).__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dep = tesseract_dep
        self.tesseract_dim = tesseract_dim
        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"

    def init_dist_group(self):
        """Initialize 2p5D tensor col parallel groups, and assign local_ranks and groups to each gpu.

        :return: 2p5D tensor col parallelism's information
        :rtype: Tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
        """
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.PARALLEL_2P5D_COL

        for h in range(self.num_group):
            for i in range(self.tesseract_dim):
                for k in range(self.tesseract_dep):
                    ranks = [
                        h * self.tensor_parallel_size + i + self.tesseract_dim * (j + self.tesseract_dim * k)
                        for j in range(self.tesseract_dim)
                    ]
                    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


class Initializer_2p5D_Dep(ProcessGroupInitializer):
    """2p5D tensor parallel initialization among depths.

    :param tesseract_dim: The dimension of tesseract
    :param tesseract_dep: The dimension of depth
    :param args: Args used to initialize base class

    :type tesseract_dim: int
    :type tesseract_dep: int
    """

    def __init__(self, tesseract_dim: int, tesseract_dep: int, *args):
        super(Initializer_2p5D_Dep, self).__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dep = tesseract_dep
        self.tesseract_dim = tesseract_dim
        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"

    def init_dist_group(self):
        """Initialize 2p5D tensor depth parallel groups, and assign local_ranks and groups to each gpu.

        :return: 2p5D tensor depth parallelism's information
        :rtype: Tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
        """
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.PARALLEL_2P5D_DEP

        for h in range(self.num_group):
            for i in range(self.tesseract_dim):
                for j in range(self.tesseract_dim):
                    ranks = [
                        h * self.tensor_parallel_size + i + self.tesseract_dim * (j + self.tesseract_dim * k)
                        for k in range(self.tesseract_dep)
                    ]
                    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


# i row j col k dep
class Initializer_2p5D_XZ(ProcessGroupInitializer):
    """2p5d tensor parallel initialization among cols times dep.

    :param tesseract_dim: The dimension of tesseract
    :param tesseract_dep: The dimension of depth
    :param args: Args used to initialize base class

    :type tesseract_dim: int
    :type tesseract_dep: int
    """

    def __init__(self, tesseract_dim: int, tesseract_dep: int, *args):
        super(Initializer_2p5D_XZ, self).__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dep = tesseract_dep
        self.tesseract_dim = tesseract_dim
        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"

    def init_dist_group(self):
        """Initialize 2p5D tensor colXdepth parallel groups, and assign local_ranks and groups to each gpu.

        :return: 2p5D tensor colXdepth parallelism's information
        :rtype: Tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
        """
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.PARALLEL_2P5D_XZ

        for h in range(self.num_group):
            for i in range(self.tesseract_dim):
                ranks = [
                    h * self.tensor_parallel_size + i + self.tesseract_dim * (j + self.tesseract_dim * k)
                    for k in range(self.tesseract_dep)
                    for j in range(self.tesseract_dim)
                ]
                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_2p5D(ProcessGroupInitializer):
    """
    Serve as the single entry point to Tesseract parallel initialization.

    :param rank: The rank of current process
    :param world_size: Size of whole communication world
    :param config: Running configuration
    :param data_parallel_size: Size of data parallel
    :param pipeline_parallel_size: Size of pipeline parallel
    :param tensor_parallel_size: Size of tensor parallel
    :param depth: The depth of 2p5d parallel
    :type rank: int
    :type world_size: int
    :type config: Config
    :type data_parallel_size: int
    :type pipeline_parallel_size: int
    :type tensor_parallel_size: int
    :type depth: int
    """

    def __init__(self, rank: int, world_size: int, config: Config, data_parallel_size: int, pipeline_parallel_size: int,
                 tensor_parallel_size: int, depth: int):
        args = (rank, world_size, config, data_parallel_size, pipeline_parallel_size, tensor_parallel_size)
        super().__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dim = int(math.sqrt(self.tensor_parallel_size / depth))
        self.tesseract_dep = depth

        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "2.5D tesseract dim should equal to (tensor parallel size / tesseract dep) ^ 0.5"
        _check_tesseract_env_var(self.tesseract_dim, self.tesseract_dep)

        self.col_initializer = Initializer_2p5D_Col(self.tesseract_dim, self.tesseract_dep, *args)
        self.row_initializer = Initializer_2p5D_ROW(self.tesseract_dim, self.tesseract_dep, *args)
        self.dep_initializer = Initializer_2p5D_Dep(self.tesseract_dim, self.tesseract_dep, *args)
        self.xz_initializer = Initializer_2p5D_XZ(self.tesseract_dim, self.tesseract_dep, *args)

    def init_dist_group(self):
        """Initialize 2p5D tensor row, col, depth, and colXdepth parallel groups, and assign local_ranks and groups to each gpu.
        :return: Whole 2p5D tensor parallelism's information
        :rtype: list of Tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
        """
        parallel_setting = [
            self.col_initializer.init_dist_group(),
            self.row_initializer.init_dist_group(),
            self.dep_initializer.init_dist_group(),
            self.xz_initializer.init_dist_group()
        ]
        return parallel_setting