| import torch.distributed as dist |
| from torch.distributed import ProcessGroup |
| import os |
|
|
| class DistributedEnv: |
| _vae_group = None |
| _local_rank = None |
| _world_size = None |
|
|
| @classmethod |
| def initialize(cls, vae_group: ProcessGroup): |
| if vae_group is None: |
| cls._vae_group = dist.group.WORLD |
| else: |
| cls._vae_group = vae_group |
| cls._local_rank = int(os.environ.get('LOCAL_RANK', 0)) |
| cls._rank_mapping = None |
| cls._init_rank_mapping() |
| |
| @classmethod |
| def get_vae_group(cls) -> ProcessGroup: |
| if cls._vae_group is None: |
| raise RuntimeError("DistributedEnv not initialized. Call initialize() first.") |
| return cls._vae_group |
|
|
| @classmethod |
| def get_global_rank(cls) -> int: |
| return dist.get_rank() |
| |
| @classmethod |
| def _init_rank_mapping(cls): |
| """Initialize the mapping between group ranks and global ranks""" |
| if cls._rank_mapping is None: |
| |
| ranks = [None] * cls.get_group_world_size() |
| dist.all_gather_object(ranks, cls.get_global_rank(), group=cls.get_vae_group()) |
| cls._rank_mapping = ranks |
|
|
| @classmethod |
| def get_global_rank_from_group_rank(cls, group_rank: int) -> int: |
| """Convert a rank in VAE group to global rank using cached mapping. |
| |
| Args: |
| group_rank: The rank in VAE group |
| |
| Returns: |
| The corresponding global rank |
| |
| Raises: |
| RuntimeError: If the group_rank is invalid |
| """ |
| if cls._rank_mapping is None: |
| cls._init_rank_mapping() |
| |
| if group_rank < 0 or group_rank >= cls.get_group_world_size(): |
| raise RuntimeError(f"Invalid group rank: {group_rank}. Must be in range [0, {cls.get_group_world_size()-1}]") |
| |
| return cls._rank_mapping[group_rank] |
| |
| @classmethod |
| def get_rank_in_vae_group(cls) -> int: |
| return dist.get_rank(cls.get_vae_group()) |
|
|
| @classmethod |
| def get_group_world_size(cls) -> int: |
| return dist.get_world_size(cls.get_vae_group()) |
|
|
| @classmethod |
| def get_local_rank(cls) -> int: |
| return cls._local_rank |
|
|
