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256 lines
11 KiB
256 lines
11 KiB
3 years ago
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import math
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import os
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import torch.distributed as dist
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from colossalai.constants import TESSERACT_DIM, TESSERACT_DEP
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from colossalai.context import Config
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from colossalai.core import global_context as gpc
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from colossalai.registry import DIST_GROUP_INITIALIZER
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from .process_group_initializer import ProcessGroupInitializer
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from ..parallel_mode import ParallelMode
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def _check_tesseract_env_var(tesseract_dim: int,
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tesseract_dep: int):
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# check environment variable for TESSERACT
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env_tesseract_dim = os.environ.get(TESSERACT_DIM, None)
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env_tesseract_dep = os.environ.get(TESSERACT_DEP, None)
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if env_tesseract_dim and env_tesseract_dep:
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assert int(env_tesseract_dim) == tesseract_dim, \
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'TESSERACT_DIM has been set in the current environment and ' \
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'does not match with the value passed to this initialized'
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assert int(env_tesseract_dep) == tesseract_dep, \
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'TESSERACT_DEP has been set in the current environment and ' \
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'does not match with the value passed to this initialized'
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else:
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os.environ[TESSERACT_DIM] = str(tesseract_dim)
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os.environ[TESSERACT_DEP] = str(tesseract_dep)
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# i row j col k dep
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class Initializer_2p5D_ROW(ProcessGroupInitializer):
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'''2p5d tensor parallel initialization among rows.
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'''
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def __init__(self,
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tesseract_dim: int,
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tesseract_dep: int,
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*args):
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super(Initializer_2p5D_ROW, self).__init__(*args)
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self.tensor_parallel_size = gpc.tensor_parallel_size
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dep = tesseract_dep
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self.tesseract_dim = tesseract_dim
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assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
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"Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"
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def init_dist_group(self):
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'''Initialize 2p5D tensor row parallel groups, and assign local_ranks and groups to each gpu.
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:return: 2p5D tensor row parallelism's information
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:rtype: tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
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'''
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local_rank = None
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ranks_in_group = None
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process_group = None
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group_world_size = None
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mode = ParallelMode.PARALLEL_2P5D_ROW
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for h in range(self.num_group):
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for j in range(self.tesseract_dim):
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for k in range(self.tesseract_dep):
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ranks = [h * self.tensor_parallel_size + i + self.tesseract_dim * (
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j + self.tesseract_dim * k) for i in range(self.tesseract_dim)]
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group = dist.new_group(ranks)
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if self.rank in ranks:
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local_rank = ranks.index(self.rank)
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group_world_size = len(ranks)
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process_group = group
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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, ranks_in_group, mode
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class Initializer_2p5D_Col(ProcessGroupInitializer):
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'''2p5d tensor parallel initialization among cols.
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'''
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def __init__(self,
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tesseract_dim: int,
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tesseract_dep: int,
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*args):
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super(Initializer_2p5D_Col, self).__init__(*args)
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self.tensor_parallel_size = gpc.tensor_parallel_size
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dep = tesseract_dep
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self.tesseract_dim = tesseract_dim
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assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
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"Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"
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def init_dist_group(self):
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'''Initialize 2p5D tensor col parallel groups, and assign local_ranks and groups to each gpu.
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:return: 2p5D tensor col parallelism's information
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:rtype: tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
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'''
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local_rank = None
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ranks_in_group = None
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process_group = None
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group_world_size = None
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mode = ParallelMode.PARALLEL_2P5D_COL
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for h in range(self.num_group):
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for i in range(self.tesseract_dim):
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for k in range(self.tesseract_dep):
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ranks = [h * self.tensor_parallel_size + i + self.tesseract_dim * (
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j + self.tesseract_dim * k) for j in range(self.tesseract_dim)]
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group = dist.new_group(ranks)
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if self.rank in ranks:
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local_rank = ranks.index(self.rank)
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group_world_size = len(ranks)
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process_group = group
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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, ranks_in_group, mode
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class Initializer_2p5D_Dep(ProcessGroupInitializer):
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'''2p5D tensor parallel initialization among depths.
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'''
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def __init__(self,
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tesseract_dim: int,
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tesseract_dep: int,
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*args):
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super(Initializer_2p5D_Dep, self).__init__(*args)
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self.tensor_parallel_size = gpc.tensor_parallel_size
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dep = tesseract_dep
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self.tesseract_dim = tesseract_dim
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assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
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"Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"
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def init_dist_group(self):
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'''Initialize 2p5D tensor depth parallel groups, and assign local_ranks and groups to each gpu.
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:return: 2p5D tensor depth parallelism's information
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:rtype: tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
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'''
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local_rank = None
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ranks_in_group = None
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process_group = None
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group_world_size = None
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mode = ParallelMode.PARALLEL_2P5D_DEP
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for h in range(self.num_group):
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for i in range(self.tesseract_dim):
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for j in range(self.tesseract_dim):
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ranks = [h * self.tensor_parallel_size + i + self.tesseract_dim * (
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j + self.tesseract_dim * k) for k in range(self.tesseract_dep)]
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group = dist.new_group(ranks)
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if self.rank in ranks:
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local_rank = ranks.index(self.rank)
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group_world_size = len(ranks)
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process_group = group
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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, ranks_in_group, mode
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# i row j col k dep
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class Initializer_2p5D_XZ(ProcessGroupInitializer):
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'''2p5d tensor parallel initialization among cols times dep.
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'''
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def __init__(self,
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tesseract_dim: int,
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tesseract_dep: int,
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*args):
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super(Initializer_2p5D_XZ, self).__init__(*args)
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self.tensor_parallel_size = gpc.tensor_parallel_size
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dep = tesseract_dep
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self.tesseract_dim = tesseract_dim
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assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
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"Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"
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def init_dist_group(self):
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'''Initialize 2p5D tensor colXdepth parallel groups, and assign local_ranks and groups to each gpu.
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:return: 2p5D tensor colXdepth parallelism's information
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:rtype: tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
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'''
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local_rank = None
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ranks_in_group = None
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process_group = None
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group_world_size = None
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mode = ParallelMode.PARALLEL_2P5D_XZ
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for h in range(self.num_group):
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for i in range(self.tesseract_dim):
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ranks = [h * self.tensor_parallel_size + i + self.tesseract_dim * (
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j + self.tesseract_dim * k) for k in range(self.tesseract_dep) for j in
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range(self.tesseract_dim)]
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group = dist.new_group(ranks)
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if self.rank in ranks:
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local_rank = ranks.index(self.rank)
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group_world_size = len(ranks)
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process_group = group
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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, ranks_in_group, mode
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@DIST_GROUP_INITIALIZER.register_module
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class Initializer_2p5D(ProcessGroupInitializer):
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"""
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Serve as the single entry point to Tesseract parallel initialization.
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"""
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def __init__(self,
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rank: int,
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world_size: int,
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config: Config,
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data_parallel_size: int,
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pipeline_parlalel_size: int,
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tensor_parallel_size: int,
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depth: int
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):
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args = (rank, world_size, config, data_parallel_size, pipeline_parlalel_size, tensor_parallel_size)
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super().__init__(*args)
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dim = int(math.sqrt(self.tensor_parallel_size / depth))
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self.tesseract_dep = depth
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assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
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"2.5D tesseract dim should equal to (tensor parallel size / tesseract dep) ^ 0.5"
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_check_tesseract_env_var(self.tesseract_dim, self.tesseract_dep)
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self.col_initializer = Initializer_2p5D_Col(self.tesseract_dim, self.tesseract_dep, *args)
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self.row_initializer = Initializer_2p5D_ROW(self.tesseract_dim, self.tesseract_dep, *args)
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self.dep_initializer = Initializer_2p5D_Dep(self.tesseract_dim, self.tesseract_dep, *args)
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self.xz_initializer = Initializer_2p5D_XZ(self.tesseract_dim, self.tesseract_dep, *args)
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def init_dist_group(self):
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'''Initialize 2p5D tensor row, col, depth, and colXdepth parallel groups, and assign local_ranks and groups to each gpu.
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:return: Whole 2p5D tensor parallelism's information
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:rtype: list of tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
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'''
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parallel_setting = []
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parallel_setting.append(self.col_initializer.init_dist_group())
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parallel_setting.append(self.row_initializer.init_dist_group())
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parallel_setting.append(self.dep_initializer.init_dist_group())
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parallel_setting.append(self.xz_initializer.init_dist_group())
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return parallel_setting
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