#!/usr/bin/env python # -*- encoding: utf-8 -*- import math import os import torch.distributed as dist from colossalai.constants import TESSERACT_DIM, TESSERACT_DEP from colossalai.context import Config from colossalai.core import global_context as gpc from colossalai.registry import DIST_GROUP_INITIALIZER from .process_group_initializer import ProcessGroupInitializer from ..parallel_mode import ParallelMode def _check_tesseract_env_var(tesseract_dim: int, tesseract_dep: int): # check environment variable for TESSERACT env_tesseract_dim = os.environ.get(TESSERACT_DIM, None) env_tesseract_dep = os.environ.get(TESSERACT_DEP, None) 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: os.environ[TESSERACT_DIM] = str(tesseract_dim) os.environ[TESSERACT_DEP] = str(tesseract_dep) # i row j col k dep class Initializer_2p5D_ROW(ProcessGroupInitializer): '''2p5d tensor parallel initialization among rows. ''' def __init__(self, tesseract_dim: int, tesseract_dep: int, *args): super(Initializer_2p5D_ROW, self).__init__(*args) self.tensor_parallel_size = gpc.tensor_parallel_size 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. ''' def __init__(self, tesseract_dim: int, tesseract_dep: int, *args): super(Initializer_2p5D_Col, self).__init__(*args) self.tensor_parallel_size = gpc.tensor_parallel_size 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. ''' def __init__(self, tesseract_dim: int, tesseract_dep: int, *args): super(Initializer_2p5D_Dep, self).__init__(*args) self.tensor_parallel_size = gpc.tensor_parallel_size 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. ''' def __init__(self, tesseract_dim: int, tesseract_dep: int, *args): super(Initializer_2p5D_XZ, self).__init__(*args) self.tensor_parallel_size = gpc.tensor_parallel_size 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. """ def __init__(self, rank: int, world_size: int, config: Config, data_parallel_size: int, pipeline_parlalel_size: int, tensor_parallel_size: int, depth: int ): args = (rank, world_size, config, data_parallel_size, pipeline_parlalel_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 = [] parallel_setting.append(self.col_initializer.init_dist_group()) parallel_setting.append(self.row_initializer.init_dist_group()) parallel_setting.append(self.dep_initializer.init_dist_group()) parallel_setting.append(self.xz_initializer.init_dist_group()) return parallel_setting