ColossalAI/colossalai/context/process_group_initializer/initializer_pipeline.py

56 lines
2.2 KiB
Python

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from torch import 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_Pipeline(ProcessGroupInitializer):
"""A ProcessGroupInitializer for pipeline parallelism.
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
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.data_group_size = self.world_size // self.data_parallel_size
self.pipeline_stage_size = self.data_group_size // self.pipeline_parallel_size
def init_dist_group(self):
"""Initialize pipeline parallel groups, and assign local_ranks and groups to each gpu.
Returns:
List[Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode)]:
A Pipeline parallelism's information in list of tuples.
"""
dist_settings = list()
for i in range(self.data_parallel_size):
for j in range(self.pipeline_stage_size):
pipe_ranks = list(
range(i * self.data_group_size + j,
(i + 1) * self.data_group_size,
self.pipeline_stage_size))
pipe_group_size = len(pipe_ranks)
pipe_group = dist.new_group(pipe_ranks)
if self.rank in pipe_ranks:
local_rank = pipe_ranks.index(self.rank)
group_world_size = pipe_group_size
process_group = pipe_group
ranks_in_group = pipe_ranks
dist_settings.append(
tuple((local_rank, group_world_size,
process_group, ranks_in_group,
ParallelMode.PIPELINE)))
return dist_settings