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from typing import Optional
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
from videosys.utils.utils import set_seed
PARALLEL_MANAGER = None
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
self.cp_group: ProcessGroup = self.get_group_along_axis(cp_axis)
self.cp_rank = dist.get_rank(self.cp_group)
self.sp_size = sp_size
self.sp_group: ProcessGroup = self.get_group_along_axis(sp_axis)
self.sp_rank = dist.get_rank(self.sp_group)
self.enable_sp = sp_size > 1
logger.info(f"Init parallel manager with dp_size: {dp_size}, cp_size: {cp_size}, sp_size: {sp_size}")
def set_parallel_manager(dp_size, cp_size, sp_size):
global PARALLEL_MANAGER
PARALLEL_MANAGER = ParallelManager(dp_size, cp_size, sp_size)
def get_data_parallel_group():
return PARALLEL_MANAGER.dp_group
def get_data_parallel_size():
return PARALLEL_MANAGER.dp_size
def get_data_parallel_rank():
return PARALLEL_MANAGER.dp_rank
def get_sequence_parallel_group():
return PARALLEL_MANAGER.sp_group
def get_sequence_parallel_size():
return PARALLEL_MANAGER.sp_size
def get_sequence_parallel_rank():
return PARALLEL_MANAGER.sp_rank
def get_cfg_parallel_group():
return PARALLEL_MANAGER.cp_group
def get_cfg_parallel_size():
return PARALLEL_MANAGER.cp_size
def enable_sequence_parallel():
if PARALLEL_MANAGER is None:
return False
return PARALLEL_MANAGER.enable_sp
def get_parallel_manager():
return PARALLEL_MANAGER
def initialize(
rank=0,
world_size=1,
init_method=None,
seed: Optional[int] = None,
sp_size: Optional[int] = None,
enable_cp: bool = True,
):
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
# init sequence parallel
if sp_size is None:
sp_size = dist.get_world_size()
dp_size = 1
else:
assert dist.get_world_size() % sp_size == 0, f"world_size {dist.get_world_size()} must be divisible by sp_size"
dp_size = dist.get_world_size() // sp_size
# update cfg parallel
if enable_cp and sp_size % 2 == 0:
sp_size = sp_size // 2
cp_size = 2
else:
cp_size = 1
set_parallel_manager(dp_size, cp_size, sp_size)
if seed is not None:
set_seed(seed + get_data_parallel_rank())
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