ColossalAI/colossalai/context/parallel_context.py

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

import os
import random
from typing import Union

import numpy as np
import torch
import torch.distributed as dist

from colossalai.constants import ALLOWED_MODES, INITIALIZER_MAPPING
from colossalai.context.config import Config
from colossalai.registry import DIST_GROUP_INITIALIZER
from ._utils import set_parallel_size
from .parallel_mode import ParallelMode
from .random import add_seed, get_seeds, set_mode


class ParallelContext:
    """This class provides interface functions for users to get the parallel context, 
    such as the global rank, the local rank, the world size, etc. of each device.

    :param args: The distributed arguments in the system
    :type args: dict
    """

    def __init__(self, args=None):
        # distributed settings
        self._global_ranks = dict()
        self._local_ranks = dict()
        self._world_sizes = dict()
        self._groups = dict()
        self._ranks_in_group = dict()

        # load config from file
        self._dist_args = args
        self._config = None

        # default 3D parallel args, will be overwritten during process group intialization
        self.world_size = 1
        self.data_parallel_size = 1
        self.pipeline_parallel_size = 1
        self.tensor_parallel_size = 1

    @property
    def config(self):
        return self._config

    def load_config(self, config: Union[dict, str]):
        """Loads the configuration from either a dict or a file.

        :param config: Either a dict containing the configuration information or the filename
            of a file containing the configuration information
        :type config: dict or str
        :raises TypeError: Raises a TypeError if `config` is neither a dict or a str
        """
        if isinstance(config, str):
            self._config = Config.from_file(config)
        elif isinstance(config, dict):
            self._config = Config(config)
        else:
            raise TypeError("Invalid type for config, only dictionary or string is supported")

    def set_dist_args(self, args):
        """Sets the distributed arguments.

        :param args: The distributed arguments in the system
        :type args: dict
        """
        self._dist_args = args

    @staticmethod
    def _check_parallel_mode(parallel_mode: ParallelMode):
        assert isinstance(parallel_mode, ParallelMode)

    def get_global_rank(self):
        """Returns the global rank of the current device.

        :return: The global rank of the current device
        :rtype: int
        """
        return self._global_ranks[ParallelMode.GLOBAL]

    def add_global_rank(self, parallel_mode: ParallelMode, rank: int):
        """Adds the global rank of the current device for `parallel_mode` to the context.

        :param parallel_mode: The parallel mode for the rank
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :param rank: The rank to be added
        :type rank: int
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        """
        self._check_parallel_mode(parallel_mode)
        self._global_ranks[parallel_mode] = rank

    def get_local_rank(self, parallel_mode: ParallelMode):
        """Returns the local rank of the current device.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        :return: The local rank of the current device for `parallel_mode`
        :rtype: int
        """
        self._check_parallel_mode(parallel_mode)
        return self._local_ranks[parallel_mode]

    def add_local_rank(self, parallel_mode: ParallelMode, rank: int):
        """Adds the local rank of the current device for `parallel_mode` to the context.

        :param parallel_mode: The parallel mode for the rank
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :param rank: The rank to be added
        :type rank: int
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        """
        self._check_parallel_mode(parallel_mode)
        self._local_ranks[parallel_mode] = rank

    def get_next_global_rank(self, parallel_mode: ParallelMode):
        """Returns the global rank of the next device.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        :return: The global rank of the next device for `parallel_mode`
        :rtype: int
        """
        self._check_parallel_mode(parallel_mode)

        # get rank and world size
        local_rank = self.get_local_rank(parallel_mode)
        world_size = self.get_world_size(parallel_mode)
        ranks_in_group = self.get_ranks_in_group(parallel_mode)

        return ranks_in_group[(local_rank + 1) % world_size]

    def get_prev_global_rank(self, parallel_mode: ParallelMode):
        """Returns the global rank of the previous device.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        :return: The global rank of the previous device for `parallel_mode`
        :rtype: int
        """
        self._check_parallel_mode(parallel_mode)

        # get rank and world size
        local_rank = self.get_local_rank(parallel_mode)
        world_size = self.get_world_size(parallel_mode)
        ranks_in_group = self.get_ranks_in_group(parallel_mode)

        return ranks_in_group[(local_rank - 1) % world_size]

    def is_first_rank(self, parallel_mode: ParallelMode):
        """Returns a boolean value indicating whether the current device is the first one
        among its group for `parallel_mode`.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        :return: a boolean value indicating whether the current device is the first one
            among its group for `parallel_mode`
        :rtype: bool
        """
        rank = self.get_local_rank(parallel_mode)
        return rank == 0

    def is_last_rank(self, parallel_mode: ParallelMode):
        """Returns a boolean value indicating whether the current device is the last one
        among its group for `parallel_mode`.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        :return: a boolean value indicating whether the current device is the last one
            among its group for `parallel_mode`
        :rtype: bool
        """
        rank = self.get_local_rank(parallel_mode)
        world_size = self.get_world_size(parallel_mode)
        return rank == world_size - 1

    def get_world_size(self, parallel_mode: ParallelMode):
        """Returns the world size for `parallel_mode`.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        :return: The world size for `parallel_mode`
        :rtype: int
        """
        self._check_parallel_mode(parallel_mode)
        return self._world_sizes[parallel_mode]

    def add_world_size(self, parallel_mode: ParallelMode, world_size: int):
        """Adds world size for `parallel_mode`.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :param world_size: The world size to be added
        :type world_size: int
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        """
        self._check_parallel_mode(parallel_mode)
        self._world_sizes[parallel_mode] = world_size

    def get_group(self, parallel_mode: ParallelMode):
        """Returns the group of the current device for `parallel_mode`.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        :return: The group of the current device for `parallel_mode`
        :rtype: torch.distributed.ProcessGroup
        """
        self._check_parallel_mode(parallel_mode)
        return self._groups[parallel_mode]

    def add_group(self, parallel_mode: ParallelMode, group: dist.ProcessGroup):
        """Adds the group of the current device for `parallel_mode`.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :param group: The group to be added
        :type group: torch.distributed.ProcessGroup
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        """
        self._check_parallel_mode(parallel_mode)
        self._groups[parallel_mode] = group

    def get_ranks_in_group(self, parallel_mode: ParallelMode):
        """Returns the rank of the current device for `parallel_mode` in the group.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        :return: the rank of the current device for `parallel_mode` in the group
        :rtype: int
        """
        self._check_parallel_mode(parallel_mode)
        return self._ranks_in_group[parallel_mode]

    def add_ranks_in_group(self, parallel_mode: ParallelMode, ranks: list):
        """Adds the ranks of the current device for `parallel_mode` in the group.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :param ranks: List of ranks to be added
        :type ranks: list
        :raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
            of :class:`colossalai.context.ParallelMode`
        """
        self._check_parallel_mode(parallel_mode)
        self._ranks_in_group[parallel_mode] = ranks

    def init_global_dist(self, addr=None, port=None):
        """Initializes the global distributed environment.

        :param addr: The IP address of the current device
        :type addr: str, optional
        :param port: The port to be used in the system of the current device
        :type port: int, optional
        """
        # get config
        rank = self._dist_args.local_rank
        world_size = self._dist_args.world_size
        # default env config, overwrite by exporting
        # them in your bash script
        addr = os.getenv('MASTER_ADDR', 'localhost') if addr is None else addr
        port = os.getenv('MASTER_PORT', '8008') if port is None else port
        init_method = f'tcp://{addr}:{port}'

        dist.init_process_group(backend=self._dist_args.backend,
                                rank=rank,
                                world_size=world_size,
                                init_method=init_method)

        # None will give the default global process group for pytorch dist operations
        self._register_dist(rank, world_size, None,
                            list(range(world_size)), ParallelMode.GLOBAL)
        self._global_ranks[ParallelMode.GLOBAL] = rank

    def _register_dist(self, local_rank, world_size,
                       process_group, ranks_in_group, mode):
        self.add_local_rank(mode, local_rank)
        self.add_world_size(mode, world_size)
        self.add_group(mode, process_group)
        self.add_ranks_in_group(mode, ranks_in_group)

    def check_sanity(self):
        """Checks sanity of the parallel context.

        :raises AssertionError: Raises an AssertionError if the world size does not equal to the product
            of data paralle size, pipeline parallel size and tensor parallel size
        """
        dps = self.data_parallel_size
        pps = self.pipeline_parallel_size
        tps = self.tensor_parallel_size
        ws = self.world_size
        assert ws == dps * pps * tps, f"Expected the world size {ws} to be equal to data parallel size ({dps}) * pipeline parallel size ({pps}) * tensor parallel size ({tps})"

    def init_parallel_groups(self):
        """Initializes the parallel groups.

        :raises AssertionError: Raises an AssertionError if the field paralle is not present in the config file
        """

        # get rank and world size
        rank = self.get_global_rank()
        world_size = self.get_world_size(ParallelMode.GLOBAL)
        self.world_size = world_size

        assert hasattr(self.config, 'parallel'), 'Expected the field parallel to be present in the config file'

        # set parallel size as attributes for global context
        parallel_config = self.config.parallel
        set_parallel_size(self, parallel_config, 'pipeline',
                          'pipeline_parallel_size')
        set_parallel_size(self, parallel_config, 'tensor',
                          'tensor_parallel_size')

        # the user should not set the data parallel size manually
        # instead, it should be calculated based on other parallel config
        self.data_parallel_size = self.world_size // (self.pipeline_parallel_size * self.tensor_parallel_size)

        # get the tensor parallel mode and check
        tensor_parallel_mode = parallel_config['tensor'].get('mode', None)
        assert tensor_parallel_mode in ALLOWED_MODES, f"mode in the parallel config must be set to one of {ALLOWED_MODES}"
        self.check_sanity()

        pg_init = []
        # LSG: init data parallel process group for compatibility with other parallel module such as zero
        pg_init.append(dict(type=INITIALIZER_MAPPING['data']))

        if self.pipeline_parallel_size > 1:
            pg_init.append(dict(type=INITIALIZER_MAPPING['pipeline']))
        pg_init.append(dict(type=INITIALIZER_MAPPING['tensor']))

        # init specific tensor parallel group
        if tensor_parallel_mode is not None:
            tensor_parallel_cfg = parallel_config['tensor'].copy()

            # remove duplicate parameters
            tensor_parallel_cfg.pop('mode')
            tensor_parallel_cfg.pop('size')

            # add this config to initialize later
            pg_init.append(dict(type=INITIALIZER_MAPPING[tensor_parallel_mode.lower()], **tensor_parallel_cfg))

        # run initialization of different process groups
        for initializer_cfg in pg_init:
            cfg = initializer_cfg.copy()
            initializer_type = cfg.pop('type')
            initializer = DIST_GROUP_INITIALIZER.get_module(initializer_type)(
                rank, world_size, self.config,
                self.data_parallel_size,
                self.pipeline_parallel_size,
                self.tensor_parallel_size,
                **cfg)
            parallel_setting = initializer.init_dist_group()
            if isinstance(parallel_setting, list):
                for args in parallel_setting:
                    self._register_dist(*args)
            else:
                self._register_dist(*parallel_setting)

    def is_initialized(self, parallel_mode: ParallelMode):
        """Returns a boolean value indicating whether `parallel_mode` is initialized
        in the current system.

        :param parallel_mode: The chosen parallel mode
        :type parallel_mode: :class:`colossalai.context.ParallelMode`
        :return: a boolean value indicating whether `parallel_mode` is initialized
            in the current system
        :rtype: bool
        """
        return parallel_mode in self._groups

    def destroy(self):
        """Destroys the current distributed parallel environment.
        """
        for mode, group in self._groups.items():
            if mode is not ParallelMode.GLOBAL:
                dist.destroy_process_group(group)
        # destroy global process group
        dist.destroy_process_group()

    def set_device(self):
        """Sets distributed processes to be bound to devices.
        """
        devices_per_node = torch.cuda.device_count()
        global_rank = self.get_global_rank()
        device = global_rank % devices_per_node
        torch.cuda.set_device(device)
        print(f'process rank {global_rank} is bound to device {device}')

    def set_seed(self):
        """Sets seeds for all random libraries.
        """
        if hasattr(self.config, 'seed'):
            seed = getattr(self.config, 'seed')
        else:
            seed = 2  # default seed

        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)

        global_rank = self.get_global_rank()

        if torch.cuda.is_available():
            # create random seed for different parallel modes
            # data parallel seed are kept the same
            parallel_seed = seed
            add_seed(ParallelMode.DATA, parallel_seed)

            # model parallel seeds are different across ranks
            pipeline_offset = self._local_ranks.get(ParallelMode.PIPELINE, 0)

            # add seed for data parallel and tensor parallel only
            if self.is_initialized(ParallelMode.TENSOR):
                tp_rank = self.get_local_rank(ParallelMode.TENSOR)
                # 100 is only to increase the diff in seeds between pipeline stages
                tp_rank_with_offset = tp_rank + pipeline_offset * 1024
                tp_seed = seed + tp_rank_with_offset
                add_seed(ParallelMode.TENSOR, tp_seed)

            set_mode(ParallelMode.DATA)
            seeds = get_seeds()
            seed_str = ', '.join([f'{k}: {v}' for k, v in seeds.items()])

            print(f"initialized seed on rank {global_rank}, "
                  f"numpy: {seed}, python random: {seed}, {seed_str},"
                  f"the default parallel seed is {ParallelMode.DATA}.", flush=True)
        else:
            print(f"initialized seed on rank {global_rank}, "
                  f"numpy: {seed}, python random: {seed}, pytorch: {seed}", flush=True)
            print('WARNING: CUDA is not available, thus CUDA RNG cannot be used to track CUDA random number states',
                  flush=True)