Source code for hfai.pl.strategies.ddp_bind_numa

import logging
from .strategy_utils import bind_numa, check_numa
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from pytorch_lightning.strategies.ddp import DDPStrategy
from pytorch_lightning.strategies.strategy_registry import StrategyRegistry as Registry

log = logging.getLogger(__name__)


class DDPStrategyBindNuma(DDPStrategy):
    """
    这是一个可以绑定 numa 的 ddp strategy, 支持 ``1.6.0 <= pytorch_lightning.__version__ <= 1.7.6``

    Examples:

    .. code-block:: python

        from hfai.pl import HFAIEnvironment

        trainer = pytorch_lightning.Trainer(
            max_epochs=3,
            gpus=8,
            strategy="ddp_bind_numa", # hfai 支持 ddp_bind_numa, ddp_spawn_bind_numa, hfreduce_bind_numa, hfreduce_spawn_bind_numa
            plugins=[HFAIEnvironment()] # 定义 Hfai 环境并作为插件输入
        )

        model_module = ToyNetModule()

        trainer.fit(
            model_module
        )
    """

    strategy_name = "ddp_bind_numa"

    def set_world_ranks(self) -> None:
        if self.cluster_environment is None:
            return
        bind_numa(self.cluster_environment)  # add numa bind
        assert check_numa(self.cluster_environment)  # check if bind success
        self.cluster_environment.set_global_rank(self.node_rank * self.num_processes + self.local_rank)
        self.cluster_environment.set_world_size(self.num_nodes * self.num_processes)
        rank_zero_only.rank = self.cluster_environment.global_rank()


Registry.register("ddp_bind_numa", DDPStrategyBindNuma, description="DDP strategy with `bind_numa`")