Source code for hfai.pl.strategies.hfreduce_bind_numa

import logging
from .strategy_utils import bind_numa, check_numa, unwrap_lightning_module_hfai
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
from hfai.nn.parallel import DistributedDataParallel

log = logging.getLogger(__name__)


class HFReduceStrategyBindNuma(DDPStrategy):
    """
    这是一个可以绑定 numa、使用 hfreduce 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="hfreduce_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 = "hfreduce_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()

    def _setup_model(self, model):
        """Wraps the model into a :class:`~torch.nn.parallel.distributed.DistributedDataParallel` module."""
        device_ids = self.determine_ddp_device_ids()
        print(f"setting up DDP model with device ids: {device_ids}, kwargs: {self._ddp_kwargs}")
        return DistributedDataParallel(module=model, device_ids=device_ids, **self._ddp_kwargs)

    @property
    def lightning_module(self) -> "pytorch_lightning.LightningModule":
        return unwrap_lightning_module_hfai(self._model)


Registry.register("hfreduce_bind_numa", HFReduceStrategyBindNuma, description="HFReduce strategy with `bind_numa`")