ColossalAI/colossalai/context/process_group_initializer/initializer_model.py

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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch.distributed as dist
from colossalai.registry import DIST_GROUP_INITIALIZER
from .process_group_initializer import ProcessGroupInitializer
from ..parallel_mode import ParallelMode
@DIST_GROUP_INITIALIZER.register_module
class Initializer_Model(ProcessGroupInitializer):
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"""A ProcessGroupInitializer for model parallelism (model parallel group contains pipeline and tensor parallel
groups).
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Args:
rank (int): The rank of current process.
world_size (int): Size of whole communication world.
config (Config): Running configuration.
data_parallel_size (int): Size of data parallel.
pipeline_parallel_size (int): Size of pipeline parallel.
tensor_parallel_size (int): Size of tensor parallel.
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"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.model_parallel_size = self.tensor_parallel_size * self.pipeline_parallel_size
self.num_group = self.world_size // self.model_parallel_size
def init_dist_group(self):
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"""Initialize model parallel groups, and assign local_ranks and groups to each gpu.
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Returns:
Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
A Model parallelism's information tuple.
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"""
local_rank = None
ranks_in_group = None
process_group = None
cpu_group = None
group_world_size = None
mode = ParallelMode.MODEL
for i in range(self.num_group):
ranks = [i * self.model_parallel_size + j for j in range(self.model_parallel_size)]
group = dist.new_group(ranks)
group_cpu = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else group
if self.rank in ranks:
local_rank = ranks.index(self.rank)
group_world_size = len(ranks)
process_group = group
cpu_group = group_cpu
ranks_in_group = ranks
return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode