TeaCache/videosys/core/parallel_mgr.py
2024-12-16 17:27:04 +08:00

52 lines
1.6 KiB
Python

import torch
import torch.distributed as dist
from colossalai.cluster.process_group_mesh import ProcessGroupMesh
from torch.distributed import ProcessGroup
from videosys.utils.logging import init_dist_logger, logger
class ParallelManager(ProcessGroupMesh):
def __init__(self, dp_size, cp_size, sp_size):
super().__init__(dp_size, cp_size, sp_size)
dp_axis, cp_axis, sp_axis = 0, 1, 2
self.dp_size = dp_size
self.dp_group: ProcessGroup = self.get_group_along_axis(dp_axis)
self.dp_rank = dist.get_rank(self.dp_group)
self.cp_size = cp_size
if cp_size > 1:
self.cp_group: ProcessGroup = self.get_group_along_axis(cp_axis)
self.cp_rank = dist.get_rank(self.cp_group)
else:
self.cp_group = None
self.cp_rank = None
self.sp_size = sp_size
if sp_size > 1:
self.sp_group: ProcessGroup = self.get_group_along_axis(sp_axis)
self.sp_rank = dist.get_rank(self.sp_group)
else:
self.sp_group = None
self.sp_rank = None
logger.info(f"Init parallel manager with dp_size: {dp_size}, cp_size: {cp_size}, sp_size: {sp_size}")
def initialize(
rank=0,
world_size=1,
init_method=None,
):
if not dist.is_initialized():
try:
dist.destroy_process_group()
except Exception:
pass
dist.init_process_group(backend="nccl", init_method=init_method, world_size=world_size, rank=rank)
torch.cuda.set_device(rank)
init_dist_logger()
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True