ColossalAI/colossalai/legacy/context/process_group_initializer/initializer_1d.py

58 lines
2.1 KiB
Python

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch.distributed as dist
from colossalai.legacy.global_variables import tensor_parallel_env as env
from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
from ..parallel_mode import ParallelMode
from .process_group_initializer import ProcessGroupInitializer
@DIST_GROUP_INITIALIZER.register_module
class Initializer_1D(ProcessGroupInitializer):
"""A ProcessGroupInitializer for 1d tensor 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.num_group = self.world_size // self.tensor_parallel_size
def init_dist_group(self):
"""Initialize 1D tensor parallel groups, and assign local_ranks and groups to each gpu.
Returns:
Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
1D tensor parallelism's information in a tuple.
"""
local_rank = None
ranks_in_group = None
process_group = None
cpu_group = None
group_world_size = None
mode = ParallelMode.PARALLEL_1D
env.parallel_input_1d = False
for i in range(self.num_group):
ranks = [i * self.tensor_parallel_size + j for j in range(self.tensor_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