mirror of https://github.com/hpcaitech/ColossalAI
579 lines
23 KiB
Python
579 lines
23 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import random
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import socket
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from collections import Counter
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from threading import local
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from typing import Union
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import numpy as np
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import torch
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import torch.distributed as dist
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from colossalai.constants import ALLOWED_MODES, INITIALIZER_MAPPING
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from colossalai.context.config import Config
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from colossalai.context.singleton_meta import SingletonMeta
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from colossalai.global_variables import tensor_parallel_env as env
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from colossalai.logging import get_dist_logger
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from colossalai.registry import DIST_GROUP_INITIALIZER
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from .parallel_mode import ParallelMode
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from .random import add_seed, get_seeds, set_mode
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class ParallelContext(metaclass=SingletonMeta):
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"""This class provides interface functions for users to get the parallel context,
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such as the global rank, the local rank, the world size, etc. of each device.
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Note:
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The parallel_mode used in this class should be concluded in ``ParallelMode``.
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More details about ``ParallelMode`` could be found in
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`parallel_mode <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/context/parallel_mode.py>`_.
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"""
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def __init__(self):
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# distributed settings
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self._global_ranks = dict()
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self._local_ranks = dict()
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self._world_sizes = dict()
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self._groups = dict()
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self._cpu_groups = dict()
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self._ranks_in_group = dict()
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# load config from file
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self._config = None
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# default 3D parallel args, will be overwritten during process group initialization
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self.world_size = 1
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self.data_parallel_size = 1
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self.pipeline_parallel_size = 1
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self.tensor_parallel_size = 1
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self.num_processes_on_current_node = -1
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self.virtual_pipeline_parallel_size = None
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self.virtual_pipeline_parallel_rank = None
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# logging
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self._verbose = False
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self._logger = get_dist_logger()
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@property
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def config(self):
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return self._config
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@property
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def verbose(self):
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return self._verbose
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@verbose.setter
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def verbose(self, verbose_: bool):
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self._verbose = verbose_
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def load_config(self, config: Union[dict, str]):
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"""Loads the configuration from either a dict or a file.
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Args:
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config (dict or str): Either a dict containing the configuration information or the filename
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of a file containing the configuration information.
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Raises:
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TypeError: Raises a TypeError if `config` is neither a dict nor a str.
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"""
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if isinstance(config, str):
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self._config = Config.from_file(config)
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elif isinstance(config, dict):
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self._config = Config(config)
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else:
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raise TypeError("Invalid type for config, only dictionary or string is supported")
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def detect_num_processes_on_current_node(self):
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hostname = socket.gethostname()
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hostname_list = [None for _ in range(self.get_world_size(ParallelMode.GLOBAL))]
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dist.all_gather_object(hostname_list, hostname, group=self.get_group(ParallelMode.GLOBAL))
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counter = Counter(hostname_list)
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self.num_processes_on_current_node = counter[hostname]
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@staticmethod
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def _check_parallel_mode(parallel_mode: ParallelMode):
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assert isinstance(parallel_mode, ParallelMode), \
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f'expected the argument parallel_mode to be of enum ParallelMode, but got {type(parallel_mode)}'
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def get_global_rank(self):
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"""Returns the global rank of the current device.
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Returns:
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int: The global rank of the current device
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"""
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return self._global_ranks[ParallelMode.GLOBAL]
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def add_global_rank(self, parallel_mode: ParallelMode, rank: int):
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"""Adds the global rank of the current device for `parallel_mode` to the context.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode for the rank.
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rank (int): The rank to be added
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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"""
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self._check_parallel_mode(parallel_mode)
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self._global_ranks[parallel_mode] = rank
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def get_local_rank(self, parallel_mode: ParallelMode):
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"""Returns the local rank of the current device.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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Returns:
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int: The local rank of the current device for `parallel_mode`.
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"""
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self._check_parallel_mode(parallel_mode)
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return self._local_ranks[parallel_mode]
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def _add_local_rank(self, parallel_mode: ParallelMode, rank: int):
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"""Adds the local rank of the current device for `parallel_mode` to the context.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode for the rank.
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rank (int): The rank to be added.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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"""
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self._check_parallel_mode(parallel_mode)
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self._local_ranks[parallel_mode] = rank
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def get_next_global_rank(self, parallel_mode: ParallelMode):
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"""Returns the global rank of the next device.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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Returns:
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int: The global rank of the next device for `parallel_mode`.
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"""
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self._check_parallel_mode(parallel_mode)
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# get rank and world size
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local_rank = self.get_local_rank(parallel_mode)
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world_size = self.get_world_size(parallel_mode)
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ranks_in_group = self.get_ranks_in_group(parallel_mode)
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return ranks_in_group[(local_rank + 1) % world_size]
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def get_prev_global_rank(self, parallel_mode: ParallelMode):
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"""Returns the global rank of the previous device.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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Returns:
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int: The global rank of the previous device for `parallel_mode`.
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"""
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self._check_parallel_mode(parallel_mode)
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# get rank and world size
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local_rank = self.get_local_rank(parallel_mode)
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world_size = self.get_world_size(parallel_mode)
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ranks_in_group = self.get_ranks_in_group(parallel_mode)
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return ranks_in_group[(local_rank - 1) % world_size]
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def is_first_rank(self, parallel_mode: ParallelMode):
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"""Returns a boolean value indicating whether the current device is the first one
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among its group for `parallel_mode`.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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Returns:
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bool: a boolean value indicating whether the current device is the first one
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among its group for `parallel_mode`.
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"""
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rank = self.get_local_rank(parallel_mode)
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return rank == 0
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def is_last_rank(self, parallel_mode: ParallelMode):
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"""Returns a boolean value indicating whether the current device is the last one
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among its group for `parallel_mode`.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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Returns:
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bool: a boolean value indicating whether the current device is the first one
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among its group for `parallel_mode`.
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"""
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rank = self.get_local_rank(parallel_mode)
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world_size = self.get_world_size(parallel_mode)
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return rank == world_size - 1
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def is_pipeline_first_stage(self, ignore_virtual=False):
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if not ignore_virtual:
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if self.virtual_pipeline_parallel_size is not None and self.virtual_pipeline_parallel_rank != 0:
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return False
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return self.is_first_rank(ParallelMode.PIPELINE)
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def is_pipeline_last_stage(self, ignore_virtual=False):
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if not ignore_virtual:
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if self.virtual_pipeline_parallel_size \
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is not None and self.virtual_pipeline_parallel_rank != self.virtual_pipeline_parallel_size - 1:
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return False
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return self.is_last_rank(ParallelMode.PIPELINE)
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def get_world_size(self, parallel_mode: ParallelMode):
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"""Returns the world size for `parallel_mode`.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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Returns:
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int: The world size for `parallel_mode`.
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"""
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self._check_parallel_mode(parallel_mode)
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return self._world_sizes[parallel_mode]
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def _add_world_size(self, parallel_mode: ParallelMode, world_size: int):
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"""Adds world size for `parallel_mode`.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The parallel mode corresponding to the process group
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world_size (int): The world size to be added
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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"""
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self._check_parallel_mode(parallel_mode)
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self._world_sizes[parallel_mode] = world_size
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def get_group(self, parallel_mode: ParallelMode):
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"""Returns the group of the current device for `parallel_mode`.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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Returns:
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torch.distributed.ProcessGroup: The group of the current device for `parallel_mode`.
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"""
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self._check_parallel_mode(parallel_mode)
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return self._groups[parallel_mode]
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def _add_group(self, parallel_mode: ParallelMode, group: dist.ProcessGroup):
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"""Adds the group of the current device for `parallel_mode`.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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group (torch.distributed.ProcessGroup): The group to be added
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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"""
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self._check_parallel_mode(parallel_mode)
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self._groups[parallel_mode] = group
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def get_cpu_group(self, parallel_mode: ParallelMode):
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"""Returns the Gloo group of the current device for `parallel_mode`.
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:param parallel_mode: The chosen parallel mode
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:type parallel_mode: :class:`colossalai.context.ParallelMode`
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:raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`
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:return: The group of the current device for `parallel_mode`
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:rtype: torch.distributed.ProcessGroup
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"""
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self._check_parallel_mode(parallel_mode)
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return self._cpu_groups[parallel_mode]
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def _add_cpu_group(self, parallel_mode: ParallelMode, group: dist.ProcessGroup):
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"""Adds the Gloo group of the current device for `parallel_mode`.
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:param parallel_mode: The chosen parallel mode
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:type parallel_mode: :class:`colossalai.context.ParallelMode`
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:param group: The group to be added
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:type group: torch.distributed.ProcessGroup
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:raises AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`
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"""
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self._check_parallel_mode(parallel_mode)
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self._cpu_groups[parallel_mode] = group
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def get_ranks_in_group(self, parallel_mode: ParallelMode):
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"""Returns the rank of the current device for `parallel_mode` in the group.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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Returns:
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int: The rank of the current device for `parallel_mode` in the group.
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"""
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self._check_parallel_mode(parallel_mode)
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return self._ranks_in_group[parallel_mode]
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def _add_ranks_in_group(self, parallel_mode: ParallelMode, ranks: list):
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"""Adds the ranks of the current device for `parallel_mode` in the group.
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Args:
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parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
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ranks (list): List of ranks to be added
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Raises:
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AssertionError: Raises an AssertionError if `parallel_mode` is not an instance
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of :class:`colossalai.context.ParallelMode`.
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"""
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self._check_parallel_mode(parallel_mode)
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self._ranks_in_group[parallel_mode] = ranks
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def init_global_dist(self, rank: int, world_size: int, backend: str, host: str, port: int):
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"""Initializes the global distributed environment
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Args:
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rank (int): rank for the default process group.
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world_size (int): world size of the default process group.
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backend (str): backend for ``torch.distributed``
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host (str): the master address for distributed training.
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port (str): the master port for distributed training
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"""
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# initialize the default process group
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init_method = f'tcp://[{host}]:{port}'
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dist.init_process_group(rank=rank, world_size=world_size, backend=backend, init_method=init_method)
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# None will give the default global process group for pytorch dist operations
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ranks = list(range(world_size))
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cpu_group = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else None
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self._register_dist(rank, world_size, dist.GroupMember.WORLD, cpu_group, ranks, ParallelMode.GLOBAL)
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self.add_global_rank(ParallelMode.GLOBAL, rank)
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def _register_dist(self, local_rank, world_size, process_group, cpu_group, ranks_in_group, mode):
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self._add_local_rank(mode, local_rank)
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self._add_world_size(mode, world_size)
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self._add_group(mode, process_group)
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self._add_cpu_group(mode, cpu_group)
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self._add_ranks_in_group(mode, ranks_in_group)
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def check_sanity(self):
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"""Checks sanity of the parallel context.
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Raises:
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AssertionError: Raises an AssertionError if the world size does not equal to the product
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of data parallel size, pipeline parallel size and tensor parallel size.
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"""
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dps = self.data_parallel_size
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pps = self.pipeline_parallel_size
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tps = self.tensor_parallel_size
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ws = self.world_size
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assert ws == dps * pps * \
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tps, f"Expected the world size {ws} to be equal to data" \
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f" parallel size ({dps}) * pipeline parallel size " \
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f"({pps}) * tensor parallel size ({tps})"
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def _set_parallel_size_from_config(self, config: dict, key: str, attr_name: str):
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if key in config:
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ele = config[key]
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if isinstance(ele, int):
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setattr(self, attr_name, ele)
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elif isinstance(ele, dict):
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setattr(self, attr_name, ele['size'])
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else:
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raise NotImplementedError(
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f'{"Parallel configuration does not support this kind of argument, please use int or dict"}')
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def init_parallel_groups(self):
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"""Initializes the parallel groups.
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Raises:
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AssertionError: Raises an AssertionError if the field parallel is not present in the config file.
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"""
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# get rank and world size
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rank = self.get_global_rank()
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world_size = self.get_world_size(ParallelMode.GLOBAL)
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self.world_size = world_size
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# set parallel size as attributes for global context
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parallel_config = self.config.get('parallel', None)
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if parallel_config is not None:
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self._set_parallel_size_from_config(parallel_config, 'pipeline', 'pipeline_parallel_size')
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self._set_parallel_size_from_config(parallel_config, 'tensor', 'tensor_parallel_size')
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# the user should not set the data parallel size manually
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# instead, it should be calculated based on other parallel config
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self.data_parallel_size = self.world_size // (self.pipeline_parallel_size * self.tensor_parallel_size)
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# get the tensor parallel mode and check
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tensor_parallel_mode = None
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if parallel_config is not None and 'tensor' in \
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parallel_config and 'mode' in parallel_config['tensor']:
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tensor_parallel_mode = parallel_config['tensor']['mode']
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assert tensor_parallel_mode in ALLOWED_MODES, \
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f"mode in the parallel config must be set to one of {ALLOWED_MODES}"
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env.mode = tensor_parallel_mode
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self.check_sanity()
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pg_init = []
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# LSG: init data parallel process group for compatibility with other parallel module such as zero
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pg_init.append(dict(type=INITIALIZER_MAPPING['data']))
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# LSG: init model parallel process group for compatibility with amp and clip grad
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pg_init.append(dict(type=INITIALIZER_MAPPING['model']))
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if self.pipeline_parallel_size > 1:
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pg_init.append(dict(type=INITIALIZER_MAPPING['pipeline']))
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pg_init.append(dict(type=INITIALIZER_MAPPING['tensor']))
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# init specific tensor parallel group
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if tensor_parallel_mode is not None:
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tensor_parallel_cfg = parallel_config['tensor'].copy()
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# remove duplicate parameters
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tensor_parallel_cfg.pop('mode')
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tensor_parallel_cfg.pop('size')
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# add this config to initialize later
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pg_init.append(dict(type=INITIALIZER_MAPPING[tensor_parallel_mode.lower()], **tensor_parallel_cfg))
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# run initialization of different process groups
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for initializer_cfg in pg_init:
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cfg = initializer_cfg.copy()
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initializer_type = cfg.pop('type')
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initializer = DIST_GROUP_INITIALIZER.get_module(initializer_type)(rank, world_size, self.config,
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self.data_parallel_size,
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|
self.pipeline_parallel_size,
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|
self.tensor_parallel_size, **cfg)
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|
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.
|
|
|
|
Args:
|
|
parallel_mode (:class:`colossalai.context.ParallelMode`): The chosen parallel mode.
|
|
|
|
Returns:
|
|
bool: a boolean value indicating whether `parallel_mode` is initialized in the current system.
|
|
"""
|
|
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()
|
|
self._groups.clear()
|
|
|
|
def set_device(self, device_ordinal: int = None):
|
|
"""Sets distributed processes to be bound to devices.
|
|
|
|
Args:
|
|
device_ordinal (int, optional): the device id to be bound to
|
|
"""
|
|
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.
|
|
|
|
Args:
|
|
seed (int): seed for random states
|
|
"""
|
|
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
|
|
|
|
|
|
global_context = ParallelContext()
|