#!/usr/bin/env python # -*- encoding: utf-8 -*- 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.global_variables import moe_env from colossalai.global_variables import tensor_parallel_env as env from colossalai.logging import get_dist_logger from colossalai.registry import DIST_GROUP_INITIALIZER 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. """ __instance = None @staticmethod def get_instance(): if ParallelContext.__instance is None: ParallelContext() return ParallelContext.__instance def __init__(self): # create a singleton instance if ParallelContext.__instance is not None: raise Exception( 'ParallelContext is a singleton class, you should get the instance by colossalai.core.global_context') else: ParallelContext.__instance = self # 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._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 self.virtual_pipeline_parallel_size = None self.virtual_pipeline_parallel_rank = None # logging self._verbose = False self._logger = get_dist_logger() @property def config(self): return self._config @property def verbose(self): return self._verbose @verbose.setter def verbose(self, verbose_: bool): self._verbose = verbose_ 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") @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 is_pipeline_first_stage(self, ignore_virtual=False): if not ignore_virtual: if self.virtual_pipeline_parallel_size is not None and self.virtual_pipeline_parallel_rank != 0: return False return self.is_first_rank(ParallelMode.PIPELINE) def is_pipeline_last_stage(self, ignore_virtual=False): if not ignore_virtual: if self.virtual_pipeline_parallel_size \ is not None and self.virtual_pipeline_parallel_rank != self.virtual_pipeline_parallel_size - 1: return False return self.is_last_rank(ParallelMode.PIPELINE) 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, rank: int, world_size: int, backend: str, host: str, port: int): """Initializes the global distributed environment :param rank: rank for the default process group :type rank: int :param world_size: world size of the default process group :type world_size: int :param host: the master address for distributed training :type host: str :param port: the master port for distributed training :type port: str :param backend: backend for torch.distributed :type backend: str """ # initialize the default process group init_method = f'tcp://{host}:{port}' dist.init_process_group(rank=rank, world_size=world_size, backend=backend, 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.add_global_rank(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" \ f" parallel size ({dps}) * pipeline parallel size " \ f"({pps}) * tensor parallel size ({tps})" def _set_parallel_size_from_config(self, config: dict, key: str, attr_name: str): if key in config: ele = config[key] if isinstance(ele, int): setattr(self, attr_name, ele) elif isinstance(ele, dict): setattr(self, attr_name, ele['size']) else: raise NotImplementedError( f'{"Parallel configuration does not support this kind of argument, please use int or dict"}') 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 # set parallel size as attributes for global context parallel_config = self.config.get('parallel', None) if parallel_config is not None: self._set_parallel_size_from_config(parallel_config, 'pipeline', 'pipeline_parallel_size') self._set_parallel_size_from_config(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 = None if parallel_config is not None and 'tensor' in \ parallel_config and 'mode' in parallel_config['tensor']: tensor_parallel_mode = parallel_config['tensor']['mode'] assert tensor_parallel_mode in ALLOWED_MODES, \ f"mode in the parallel config must be set to one of {ALLOWED_MODES}" env.mode = tensor_parallel_mode 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'])) # LSG: init model parallel process group for compatibility with amp and clip grad pg_init.append(dict(type=INITIALIZER_MAPPING['model'])) 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)) # initialization for moe environment if parallel_config is not None and 'moe' in parallel_config: param = parallel_config['moe'] assert 'size' in param, "Moe model parallel size should be given" moe_env.setup(param['size']) pg_init.append(dict(type=INITIALIZER_MAPPING['moe'])) # 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, device_ordinal: int = None): """Sets distributed processes to be bound to devices. :param device_ordinal: the device id to be bound to :type device_ordinal: int, optional """ global_rank = self.get_global_rank() if device_ordinal is None: devices_per_node = torch.cuda.device_count() device_ordinal = global_rank % devices_per_node torch.cuda.set_device(device_ordinal) if self._verbose: self._logger.info(f'process rank {global_rank} is bound to device {device_ordinal}') def set_seed(self, seed: int): """Sets seeds for all random libraries. :param seed: seed for random states :type seed: int """ 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()]) if self._verbose: self._logger.info(f"initialized seed on rank {global_rank}, " f"numpy: {seed}, python random: {seed}, {seed_str}," f"the default parallel seed is {ParallelMode.DATA}.") else: if self._verbose: self._logger.info( f"initialized seed on rank {global_rank}, " f"numpy: {seed}, python random: {seed}, pytorch: {seed}", ranks=[0]) self._logger.info( 'WARNING: CUDA is not available, thus CUDA RNG cannot be used to track CUDA random number states', ranks=[0]) def set_virtual_pipeline_parallel_size(self, size): self.virtual_pipeline_parallel_size = size def set_virtual_pipeline_parallel_rank(self, rank): self.virtual_pipeline_parallel_rank = rank