Source code for torch.distributed.checkpoint.storage

import abc
import os
from dataclasses import dataclass
from typing import Any, List, Union

from torch.futures import Future

from .metadata import Metadata, MetadataIndex
from .planner import LoadPlan, LoadPlanner, SavePlan, SavePlanner

__all__ = ["WriteResult", "StorageWriter", "StorageReader"]


@dataclass(frozen=True)
class WriteResult:
    index: MetadataIndex

    size_in_bytes: int
    storage_data: Any


class StorageWriter(abc.ABC):
    """
    Interface used by ``save_state_dict`` to write to storage.

    One StorageWriter instance acts as both the coordinator and the follower
    in a distributed checkpoint. As part of initialization, each instance
    is told its role.

    A subclass should expect the following sequence of calls.

    0) (all ranks) set checkpoint_id if users pass a valid checkpoint_id.
    1) (all ranks) set_up_storage_writer()
    2) (all ranks) prepare_local_plan()
    3) (coordinator) prepare_global_plan()
    4) (all ranks) write_data()
    5) (coordinator) finish()
    """

    @abc.abstractmethod
    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
        """
        Calls to indicates a brand new checkpoint write is going to happen.
        A checkpoint_id may be present if users set the checkpoint_id for
        this checkpoint write. The meaning of the checkpiont_id is
        storage-dependent. It can be a path to a folder/file or a key for
        a key-value storage.

        Args:
            checkpoint_id (Union[str, os.PathLike, None]):
                The ID of this checkpoint instance. The meaning of the checkpoint_id
                depends on the storage. It can be a path to a folder or to a file.
                It can also be a key if the storage is a key-value store.
                (Default: ``None``)
        """
        ...

    @abc.abstractmethod
    def set_up_storage_writer(self, is_coordinator: bool) -> None:
        """
        Initialize this instance.

        Args:
            is_coordinator (bool): Whether this instance is responsible for coordinating
              the checkpoint.
        """
        pass

    @abc.abstractmethod
    def prepare_local_plan(self, plan: SavePlan) -> SavePlan:
        """
        Perform storage-specific local planning.

        While this method can produce a completely different plan, the recommended
        way is to store storage specific data in SavePlan::storage_data.

        Args:
            plan (SavePlan): The local plan from the ``SavePlanner`` in use.

        Returns:
            A transformed ``SavePlan`` after storage local planning
        """
        pass

    @abc.abstractmethod
    def prepare_global_plan(self, plans: List[SavePlan]) -> List[SavePlan]:
        """
        Perform centralized planning of storage.

        This method is only called on the coordinator instance.

        While this method can produce a completely different plan, the preferred
        way is to store storage specific data in SavePlan::storage_data.

        Args:
            plans: A list of ``SavePlan`` instances, one for each rank.

        Returns:
            A list of transformed ``SavePlan`` after storage global planning
        """
        pass

    @abc.abstractmethod
    def write_data(
        self, plan: SavePlan, planner: SavePlanner
    ) -> Future[List[WriteResult]]:
        """
        Write all items from ``plan`` using ``planner`` to resolve the data.

        A subclass should call ``SavePlanner::resolve_data`` on each item
        from the plan to get access to the underlying object to write.

        Subclasses should lazily call `resolve_data` as it can allocate memory.
        In case of tensors, make following assumptions:

        - They might be on any device, including not matching the one on ``WriteItem::tensor_data``
        - They might be views or not contiguous. Only the projection needs to be saved.

        Args:
            plan (SavePlan): The save plan to execute.
            planner (SavePlanner): Planner object to be used to resolve items to data.

        Returns:
            A future that completes to a list of WriteResult
        """
        pass

    @abc.abstractmethod
    def finish(self, metadata: Metadata, results: List[List[WriteResult]]) -> None:
        """
        Write the metadata and marks the current checkpoint as successful.

        The actual format/schema used for serializing `metadata` is an
        implementation detail. The only requirement is that it's recoverable
        in to the same object graph.

        Args:
            metadata (Metadata): metadata for the new checkpoint
            results: A list of WriteResults from all ranks.

        Returns:
            None
        """
        pass

    @classmethod
    @abc.abstractmethod
    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
        """
        Check if the given checkpoint_id is supported by the stroage. This allow
        us to enable automatic storage selection.
        """
        ...


class StorageReader(abc.ABC):
    """
    Interface used by ``load_state_dict`` to read from storage.

    One StorageReader instance acts as both the coordinator and the follower
    in a distributed checkpoint. As part of initialization, each instance
    is told its role.

    A subclass should expected the following sequence of calls by ``load_state_dict``:

    0) (all ranks) set checkpoint_id if users pass a valid checkpoint_id.
    1) (all ranks) read_metadata()
    2) (all ranks) set_up_storage_reader()
    3) (all ranks) prepare_local_plan()
    4) (coordinator) prepare_global_plan()
    5) (all ranks) read_data()
    """

    @abc.abstractmethod
    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
        """
        Calls to indicates a brand new checkpoint read is going to happen.
        A checkpoint_id may be present if users set the checkpoint_id for
        this checkpoint read. The meaning of the checkpiont_id is
        storage-dependent. It can be a path to a folder/file or a key for
        a key-value storage.

        Args:
            checkpoint_id (Union[str, os.PathLike, None]):
                The ID of this checkpoint instance. The meaning of the checkpoint_id
                depends on the storage. It can be a path to a folder or to a file.
                It can also be a key if the storage is more like a key-value store.
                (Default: ``None``)
        """
        ...

    @abc.abstractmethod
    def read_metadata(self) -> Metadata:
        """
        Read the checkpoint metadata.

        Returns:
            The metadata object associated with the checkpoint being loaded.

        """
        pass

    @abc.abstractmethod
    def set_up_storage_reader(self, metadata: Metadata, is_coordinator: bool) -> None:
        """
        Initialize this instance.

        Args:
            metadata (Metadata): The metadata schema to use.
            is_coordinator (bool): Whether this instance is responsible for coordinating
              the checkpoint.
        """
        pass

    @abc.abstractmethod
    def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan:
        """
        Perform storage-specific local planning.

        While this method can produce a completely different plan, the recommended
        way is to store storage specific data in LoadPlan::storage_data.

        Args:
            plan (LoadPlan): The local plan from the ``LoadPlan`` in use.

        Returns:
            A transformed ``LoadPlan`` after storage local planning
        """
        pass

    @abc.abstractmethod
    def prepare_global_plan(self, plans: List[LoadPlan]) -> List[LoadPlan]:
        """
        Perform centralized planning of storage loading.

        This method is only called on the coordinator instance.

        While this method can produce a completely different plan, the preferred
        way is to store storage specific data in LoadPlan::storage_data.

        Args:
            plans: A list of ``LoadPlan`` instances, one for each rank.

        Returns:
            A list of transformed ``LoadPlan`` after storage global planning
        """
        pass

    @abc.abstractmethod
    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
        """
        Read all items from ``plan`` using ``planner`` to resolve the data.

        A subclass should call ``LoadPlanner::load_bytes`` to deserialize a BytesIO
        object into the right place.

        A subclass should call ``LoadPlanner::resolve_tensor`` to get access to the
        tensors that in should load data into.

        It's the StorageLayer responsibility to properly schedule any cross device copies
        required.

        Args:
            plan (LoadPlan): The local plan to execute on
            planner (LoadPlanner): The planner object to use to resolve items.

        Returns:
            A future that completes once all reads are finished.
        """
        pass

    @classmethod
    @abc.abstractmethod
    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
        """
        Check if the given checkpoint_id is supported by the stroage. This allow
        us to enable automatic storage selection.
        """
        ...