Delete checkpoint.py

checkpoint.py

@@ -1,1671 +0,0 @@
-import gc
-import io
-import logging
-import pickle
-import shutil
-import traceback
-from abc import ABCMeta, abstractmethod
-from collections import defaultdict
-from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
-from contextlib import contextmanager
-from copy import deepcopy
-from dataclasses import dataclass, field, replace
-from functools import reduce
-from multiprocessing import shared_memory
-from pathlib import Path
-from typing import Any, Dict, Generator, List, Optional, Set, Tuple, cast
-
-import numpy as np
-import torch
-import torch.distributed.checkpoint as dist_cp
-import torch.multiprocessing as mp
-from packaging import version
-from torch.distributed import _remote_device
-from torch.distributed._shard._utils import narrow_tensor_by_index
-from torch.distributed._shard.metadata import ShardMetadata
-from torch.distributed._shard.sharded_tensor import ShardedTensor
-from torch.distributed.checkpoint.filesystem import WriteResult, _StorageInfo
-from torch.distributed.checkpoint.metadata import Metadata, MetadataIndex
-from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
-from torch.distributed.checkpoint.planner import LoadItemType, ReadItem
-from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
-from torch.distributed.fsdp import StateDictType
-from torch.distributed.fsdp.api import (
-    FullOptimStateDictConfig,
-    FullStateDictConfig,
-    ShardedOptimStateDictConfig,
-    ShardedStateDictConfig,
-)
-from torch.futures import Future
-
-try:
-    from torch.distributed.fsdp.flat_param import FlatParamHandle  # type: ignore
-except ModuleNotFoundError:
-    from torch.distributed.fsdp._flat_param import FlatParamHandle  # type: ignore
-
-from . import util
-
-from .aliases import PathOrStr
-from .config import BaseConfig, ShardedCheckpointerType, TrainConfig
-from .exceptions import OLMoCheckpointError
-from .optim import Optimizer, fix_optim_state_dict
-from .safetensors_util import safetensors_file_to_state_dict
-from .torch_util import (
-    barrier,
-    gc_cuda,
-    get_fs_local_rank,
-    get_global_rank,
-    get_world_size,
-)
-from .util import (
-    _get_s3_client,
-    default_thread_count,
-    dir_is_empty,
-    get_bytes_range,
-    get_progress_bar,
-    resource_path,
-    upload,
-    wait_for,
-)
-
-__all__ = [
-    "save_fsdp_model_and_optim_state",
-    "load_fsdp_model_and_optim_state",
-    "load_fsdp_optim_state",
-    "save_state_dict",
-    "load_state_dict",
-    "load_model_state",
-    "RemoteFileSystemWriter",
-    "RemoteFileSystemReader",
-    "Checkpointer",
-    "FullCheckpointer",
-    "TorchNewStyleShardedCheckpointer",
-    "TorchLegacyShardedCheckpointer",
-    "LocalShardedCheckpointer",
-    "build_sharded_checkpointer",
-]
-
-
-log = logging.getLogger(__name__)
-
-MODEL_AND_OPTIM_FOLDER = "model_and_optim"
-
-
-def save_fsdp_model_and_optim_state(
-    checkpoint_dir: PathOrStr,
-    fsdp_model: FSDP,
-    optim: Optimizer,
-    *,
-    upload_to: Optional[str] = None,
-    save_overwrite: bool = False,
-):
-    """
-    Use this to save a state dict for an FSDP model and its optimizer via :module:`torch.distributed.checkpoint`
-    functions. This should be used during distributed training and should be called by all ranks.
-
-    :param checkpoint_dir: The directory to save to.
-    :param fsdp_model: The FSDP model.
-    :param optim: The FSDP model's optimizer.
-    :param upload_to: Optional, a remote "directory" to upload the checkpoint files to.
-    :param save_overwrite: Overwrite existing files.
-
-    :raises FileExistsError: If a model and optim checkpoint already exists in ``checkpoint_dir`` and ``save_overwrite=False``.
-    """
-    checkpoint_dir = Path(checkpoint_dir)
-    target_dir = checkpoint_dir / MODEL_AND_OPTIM_FOLDER
-    if save_overwrite:
-        if get_fs_local_rank() == 0:
-            shutil.rmtree(target_dir, ignore_errors=True)
-    elif not dir_is_empty(target_dir):
-        raise FileExistsError(target_dir)
-    barrier()
-    if get_fs_local_rank() == 0:
-        target_dir.mkdir(exist_ok=True, parents=True)
-    barrier()
-    with FSDP.state_dict_type(
-        fsdp_model,
-        state_dict_type=StateDictType.SHARDED_STATE_DICT,
-        state_dict_config=ShardedStateDictConfig(offload_to_cpu=True),
-        optim_state_dict_config=ShardedOptimStateDictConfig(offload_to_cpu=True),
-    ):
-        model_and_optim_state = {
-            "model": fsdp_model.state_dict(),
-            "optim": FSDP.optim_state_dict(fsdp_model, optim),
-        }
-        dist_cp.save_state_dict(
-            model_and_optim_state,
-            RemoteFileSystemWriter(
-                target_dir,
-                upload_to=None if upload_to is None else f"{upload_to.rstrip('/')}/{MODEL_AND_OPTIM_FOLDER}",
-                save_overwrite=save_overwrite,
-            ),
-        )
-
-
-def load_fsdp_model_and_optim_state(
-    checkpoint_dir: PathOrStr,
-    fsdp_model: FSDP,
-    optim: Optimizer,
-    *,
-    local_cache: Optional[PathOrStr] = None,
-    load_optimizer_state: bool = True,
-):
-    """
-    Use this to load a state dict for an FSDP model and its optimizer via :module:`torch.distributed.checkpoint`
-    functions. This should be used during distributed training and should be called by all ranks.
-
-    :param checkpoint_dir: The checkpoint directory to load from. This can be a local or remote directory.
-    :param fsdp_model: The FSDP model.
-    :param optim: The FSDP model's optimizer.
-    :param local_cache: A local cache of the checkpoint directory. Use this when the ``checkpoint_dir`` is a
-        remote "directory" but there might be a cached version of the same artifacts.
-    :param load_optimizer_state: Set to ``False`` to skip loading the optimizer state.
-
-    :raises FileNotFoundError: If the ``checkpoint_dir`` doesn't contain a model and optimizer checkpoint.
-    """
-    load_path = str(checkpoint_dir).rstrip("/")
-    local_cache = None if local_cache is None else Path(local_cache)
-    with FSDP.state_dict_type(
-        fsdp_model,
-        state_dict_type=StateDictType.SHARDED_STATE_DICT,
-        state_dict_config=ShardedStateDictConfig(offload_to_cpu=True),
-        optim_state_dict_config=ShardedOptimStateDictConfig(offload_to_cpu=True),
-    ):
-        # Load the model state dict in place.
-        log.info("Loading model state...")
-        model_state = {"model": fsdp_model.state_dict()}
-        dist_cp.load_state_dict(
-            model_state,
-            RemoteFileSystemReader(
-                f"{load_path}/{MODEL_AND_OPTIM_FOLDER}",
-                local_cache=None if local_cache is None else local_cache / MODEL_AND_OPTIM_FOLDER,
-            ),
-        )
-        fsdp_model.load_state_dict(model_state["model"])
-
-        if not load_optimizer_state:
-            return
-
-        # Load optim state dict in place.
-        log.info("Loading sharded optimizer state...")
-        optim_state = load_sharded_optimizer_state_dict(
-            model_state_dict=model_state["model"],
-            optimizer_key="optim",
-            storage_reader=RemoteFileSystemReader(
-                f"{load_path}/{MODEL_AND_OPTIM_FOLDER}",
-                local_cache=None if local_cache is None else local_cache / MODEL_AND_OPTIM_FOLDER,
-            ),
-        )
-        del model_state
-        gc_cuda()
-        load_fsdp_optim_state(fsdp_model, optim, optim_state["optim"])
-
-
-def load_fsdp_optim_state(fsdp_model: FSDP, optim: Optimizer, optim_state: Dict[str, Any]):
-    log.info("Flattening sharded optimizer state...")
-    # NOTE: Careful! The order of the these arguments has changed from 2.0 to 2.1... ¯\_(ツ)_/¯
-    if version.parse(torch.__version__) < version.parse("2.1.0"):
-        flattened_osd = FSDP.optim_state_dict_to_load(optim_state, fsdp_model, optim)  # type: ignore
-    else:
-        flattened_osd = FSDP.optim_state_dict_to_load(fsdp_model, optim, optim_state)  # type: ignore
-    del optim_state
-    gc.collect()
-    log.info("Loading flattened optimizer state...")
-    # Put optim state on CPU since `Optimizer.load_state_dict()` will create a deepcopy of the whole state dict,
-    # which takes up unnecessary GPU memory.
-    for state in flattened_osd["state"].values():
-        for k in state.keys():
-            v = state[k]
-            if isinstance(v, torch.Tensor):
-                state[k] = v.to(device="cpu")
-    gc_cuda()
-    optim.load_state_dict(fix_optim_state_dict(optim, flattened_osd))
-
-
-def save_state_dict(
-    checkpoint_dir: PathOrStr,
-    fname: str,
-    state_dict: Dict[str, Any],
-    *,
-    upload_to: Optional[str] = None,
-    save_overwrite: bool = False,
-    synchronize: bool = True,
-):
-    """
-    Save a regular state dict to the file ``fname`` within ``checkpoint_dir`` using :func:`torch.save()`.
-    This can be used during distributed training or not. If during distributed training the ``fname`` should be unique
-    for each rank.
-
-    :param checkpoint_dir: The directory to save to.
-    :param fname: The target file within ``checkpoint_dir`` to save to. This should be a path relative to the ``checkpoint_dir``.
-    :param state_dict: The state dict to save.
-    :param upload_to: Optional, a remote "directory" to upload the file to.
-    :param save_overwrite: Overwrite existing files.
-    :param synchronize: If ``False``, don't do any distributed synchronization. Use this when only calling
-        this function from a single rank.
-
-    :raises FileExistsError: If the ``fname`` already exists within ``checkpoint_dir`` and ``save_overwrite=False``.
-    """
-    checkpoint_dir = Path(checkpoint_dir)
-    target_path = checkpoint_dir / fname
-    if save_overwrite:
-        target_path.unlink(missing_ok=True)
-    elif target_path.is_file():
-        raise FileExistsError(target_path)
-    if synchronize:
-        barrier()
-    target_path.parent.mkdir(exist_ok=True, parents=True)
-    if synchronize:
-        barrier()
-    torch.save(state_dict, target_path)
-    if upload_to is not None:
-        upload_target = f"{upload_to.rstrip('/')}/{fname}"
-        log.info(f"Uploading {target_path} to {upload_target}...")
-        upload(target_path, upload_target, save_overwrite=save_overwrite)
-
-
-def load_state_dict(
-    checkpoint_dir: PathOrStr,
-    fname: str,
-    *,
-    local_cache: Optional[PathOrStr] = None,
-    map_location: Optional[str] = None,
-):
-    """
-    Load a regular state dict from the file ``fname`` within ``checkpoint_dir`` using :func:`torch.load()`.
-    This can be used during distributed training or not.
-
-    :param checkpoint_dir: A local or remote checkpoint directory.
-    :param fname: The target file within the ``checkpoint_dir``. This should be a path relative to the ``checkpoint_dir``.
-    :param local_cache: A local cache of the checkpoint directory. Use this when the ``checkpoint_dir`` is a
-        remote "directory" but there might be a cached version of the same artifacts.
-
-    :raises FileNotFoundError: If ``fname`` doesn't exist in the ``checkpoint_dir`` or the local cache.
-    """
-    if fname.endswith(".pt"):
-        # Try safetensors version first.
-        try:
-            path = resource_path(
-                str(checkpoint_dir).rstrip("/"), fname[:-2] + "safetensors", local_cache=local_cache
-            )
-            return safetensors_file_to_state_dict(path, map_location=map_location)
-        except FileNotFoundError:
-            pass
-
-    path = resource_path(str(checkpoint_dir).rstrip("/"), fname, local_cache=local_cache)
-    return torch.load(path, map_location=map_location)
-
-
-def load_model_state(checkpoint_dir: PathOrStr, model: torch.nn.Module):
-    """
-    Load model state from a distributed FSDP model checkpoint created from :func:`save_fsdp_model_and_optim_state()`.
-    Note that ``model`` should not be wrapped with FSDP.
-    """
-    state_dict = {"model": model.state_dict()}
-    dist_cp.load_state_dict(
-        state_dict,
-        RemoteFileSystemReader(f"{str(checkpoint_dir).rstrip('/')}/{MODEL_AND_OPTIM_FOLDER}"),
-        no_dist=True,
-    )
-    model.load_state_dict(state_dict["model"])
-
-
-class RemoteFileSystemWriter(dist_cp.FileSystemWriter):
-    """
-    A subclass of :class:`~torch.distributed.checkpoint.FileSystemWriter` that can upload files
-    directly to a cloud bucket when ``upload_to`` is specified.
-    """
-
-    def __init__(
-        self,
-        path: PathOrStr,
-        single_file_per_rank: bool = True,
-        sync_files: bool = True,
-        thread_count: Optional[int] = None,
-        per_thread_copy_ahead: int = 10_000_000,
-        upload_to: Optional[str] = None,
-        save_overwrite: bool = False,
-    ) -> None:
-        if thread_count is not None and thread_count <= 0:
-            raise ValueError("thread count must be at least 1")
-        super().__init__(
-            path,
-            single_file_per_rank=single_file_per_rank,
-            sync_files=sync_files,
-            # NOTE: we default to 1 thread here instead of whatever `default_thread_count()`
-            # returns because uploading big checkpoint files with multiple threads causes
-            # boto3 to fail in weird ways.
-            thread_count=thread_count or 1,
-            per_thread_copy_ahead=per_thread_copy_ahead,
-        )
-        self.upload_to = None if upload_to is None else upload_to.rstrip("/")
-        self.save_overwrite = save_overwrite
-
-    def write_data(
-        self,
-        plan: dist_cp.SavePlan,
-        planner: dist_cp.SavePlanner,
-    ) -> Future[List[WriteResult]]:
-        fut = super().write_data(plan, planner)
-        if self.upload_to is not None:
-            files_to_upload = set()
-            for write_result in fut.wait():
-                files_to_upload.add(write_result.storage_data.relative_path)
-
-            # Create the global S3 client up front to work around a threading issue in boto.
-            if self.upload_to.startswith("s3://"):
-                _get_s3_client("s3")
-            elif self.upload_to.startswith("r2://"):
-                _get_s3_client("r2")
-
-            with ThreadPoolExecutor(max_workers=self.thread_count) as executor:
-                futures = []
-                for fname in files_to_upload:
-                    source = self.path / fname
-                    target = f"{self.upload_to}/{fname}"
-                    log.info(f"Uploading {source} to {target}...")
-                    futures.append(executor.submit(upload, source, target, save_overwrite=self.save_overwrite))
-                for f in as_completed(futures):
-                    try:
-                        f.result()
-                    except BaseException:
-                        # NOTE: we might get an error here that can't be pickled, which causes a different failure
-                        # later when PyTorch tries to reduce that error across ranks. So here we just make
-                        # sure we're raising a simple error type that can be pickled.
-                        raise OLMoCheckpointError(f"Original error:\n{traceback.format_exc()}")
-        return fut
-
-    def finish(self, metadata: Metadata, results: List[List[WriteResult]]) -> None:
-        super().finish(metadata, results)
-        if self.upload_to is not None:
-            source = self.path / ".metadata"
-            target = f"{self.upload_to}/.metadata"
-            log.info(f"Uploading {source} to {target}...")
-            upload(source, target, save_overwrite=self.save_overwrite)
-
-
-class RemoteFileSystemReader(dist_cp.StorageReader):
-    """
-    A :class:`~torch.distributed.checkpoint.StorageReader` based on :class:`~torch.distributed.checkpoint.FileSystemReader`
-    that can read data directly from cloud storage as well as a local directory.
-    """
-
-    def __init__(
-        self, path: PathOrStr, *, local_cache: Optional[PathOrStr] = None, thread_count: Optional[int] = None
-    ):
-        super().__init__()
-        if thread_count is not None and thread_count <= 0:
-            raise ValueError("thread count must be at least 1")
-        self.path = str(path).rstrip("/")
-        self.cache = None if local_cache is None else Path(local_cache)
-        self.thread_count = thread_count or default_thread_count()
-        self.storage_data: Dict[MetadataIndex, _StorageInfo] = dict()
-        self._metadata: Optional[Metadata] = None
-
-    def _get_bytes(self, relative_path: str, offset: int, length: int) -> bytes:
-        if self.cache is not None and (path := self.cache / relative_path).is_file():
-            return get_bytes_range(path, offset, length)
-        else:
-            return get_bytes_range(f"{self.path}/{relative_path}", offset, length)
-
-    def _get_content_for_read(self, read_item: ReadItem) -> Tuple[ReadItem, bytes]:
-        sinfo = self.storage_data[read_item.storage_index]
-        content = self._get_bytes(sinfo.relative_path, sinfo.offset, sinfo.length)
-        return (read_item, content)
-
-    def read_data(self, plan: dist_cp.LoadPlan, planner: dist_cp.LoadPlanner) -> Future[None]:
-        # Create the global S3 client up front to work around a threading issue in boto.
-        if isinstance(self.path, str):
-            if self.path.startswith("s3://"):
-                _get_s3_client("s3")
-            elif self.path.startswith("r2://"):
-                _get_s3_client("r2")
-
-        with ThreadPoolExecutor(max_workers=self.thread_count) as executor:
-            read_item_content_futures = []
-            for read_item in plan.items:
-                read_item_content_futures.append(executor.submit(self._get_content_for_read, read_item))
-            read_item_content_results = []
-            for f in as_completed(read_item_content_futures):
-                try:
-                    read_item_content_results.append(f.result())
-                except BaseException:
-                    # NOTE: we might get an error here that can't be pickled, which causes a different failure
-                    # later when PyTorch tries to reduce that error across ranks. So here we just make
-                    # sure we're raising a simple error type that can be pickled.
-                    raise OLMoCheckpointError(f"Original error:\n{traceback.format_exc()}")
-
-        # Modified from `FileSystemReader.read_data()`
-        for read_item, content in read_item_content_results:
-            bytes = io.BytesIO(content)
-            bytes.seek(0)
-            if read_item.type == LoadItemType.BYTE_IO:
-                planner.load_bytes(read_item, bytes)
-            else:
-                tensor = cast(torch.Tensor, torch.load(bytes, map_location="cpu"))
-                tensor = narrow_tensor_by_index(tensor, read_item.storage_offsets, read_item.lengths)
-                target_tensor = planner.resolve_tensor(read_item).detach()
-
-                assert (
-                    target_tensor.size() == tensor.size()
-                ), f"req {read_item.storage_index} mismatch sizes {target_tensor.size()} vs {tensor.size()}"
-                target_tensor.copy_(tensor)
-                planner.commit_tensor(read_item, target_tensor)
-
-        fut: Future = Future()
-        fut.set_result(None)
-        return fut
-
-    def read_metadata(self) -> Metadata:
-        if self._metadata is None:
-            with resource_path(self.path, ".metadata", local_cache=self.cache).open("rb") as metadata_file:
-                self._metadata = pickle.load(metadata_file)
-        return self._metadata
-
-    def set_up_storage_reader(self, metadata: Metadata, is_coordinator: bool) -> None:
-        del is_coordinator
-        self.storage_data = metadata.storage_data
-        assert self.storage_data is not None
-
-    def prepare_local_plan(self, plan: dist_cp.LoadPlan) -> dist_cp.LoadPlan:
-        return plan
-
-    def prepare_global_plan(self, global_plan: List[dist_cp.LoadPlan]) -> List[dist_cp.LoadPlan]:
-        return global_plan
-
-
-class Checkpointer(metaclass=ABCMeta):
-    def __init__(self, cfg: TrainConfig, thread_count: Optional[int] = None):
-        self.cfg = cfg
-        self.thread_count = thread_count or default_thread_count()
-
-    @abstractmethod
-    def save_checkpoint(
-        self,
-        dir: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        train_state: Dict[str, Any],
-        *,
-        upload_to: Optional[str] = None,
-    ) -> None:
-        raise NotImplementedError
-
-    @abstractmethod
-    def restore_checkpoint(
-        self,
-        load_path: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-    ) -> Dict[str, Any]:
-        """
-        Restores a checkpoint to the model and optimizer. Returns the remaining trainer state.
-        """
-        raise NotImplementedError
-
-    def unshard_checkpoint(
-        self,
-        load_path: PathOrStr,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-        load_trainer_state: bool = True,
-        device: Optional[torch.device] = None,
-    ) -> Tuple[Dict[str, torch.Tensor], Optional[Dict[str, Any]], Optional[Dict[str, Any]]]:
-        """
-        Unshard a checkpoint.
-
-        Note this is not marked abstract because child classes are not required to implemented this.
-        """
-        del load_path, local_cache, load_optimizer_state, load_trainer_state, device
-        raise NotImplementedError
-
-    @contextmanager
-    def _temporary_wd(self, dir: PathOrStr) -> Generator[Path, None, None]:
-        # Make sure checkpoint directory doesn't exist unless it's okay to overwrite it.
-        checkpoint_dir = Path(dir)
-        if not dir_is_empty(checkpoint_dir):
-            if self.cfg.save_overwrite:
-                if get_fs_local_rank() == 0:
-                    shutil.rmtree(checkpoint_dir, ignore_errors=True)
-            else:
-                raise FileExistsError(checkpoint_dir)
-        # No need to mkdir here since we'll directly replace the temporary directory with
-        # this directory below.
-        barrier()
-
-        # Prepare temporary directory. We don't have to be as careful here, we can
-        # just remove it if it already exists.
-        checkpoint_dir_tmp = checkpoint_dir.with_name(checkpoint_dir.name + "-tmp")
-        if get_fs_local_rank() == 0:
-            shutil.rmtree(checkpoint_dir_tmp, ignore_errors=True)
-            checkpoint_dir_tmp.mkdir(exist_ok=True, parents=True)
-
-        barrier()
-
-        # Yield temporary directory for `.save_checkpoint()` to use.
-        yield checkpoint_dir_tmp
-
-        barrier()
-
-        # Finally if all went well replace the temporary directory with the actual
-        # checkpoint directory.
-        if get_fs_local_rank() == 0:
-            # Replace temp directory with target checkpoint directory.
-            try:
-                checkpoint_dir_tmp.replace(checkpoint_dir)
-            except FileNotFoundError:
-                # Caught when another (file-system) local rank 0 has already replaced the tmp directory.
-                # This can happen when nodes are saving to a common NFS drive but otherwise have distinct
-                # file-systems.
-                if not checkpoint_dir.exists():
-                    raise
-
-        # In the cases where we're using a shared NFS drive between ranks to save checkpoints,
-        # replacing the temp directory with the final directory from rank 0 might not be immediately
-        # realized in the file systems of the other ranks.
-        # So we wait here across all ranks until that final checkpoint directory is visible.
-        wait_for(lambda: checkpoint_dir.exists(), "Waiting for checkpoint directory", timeout=10.0)
-
-        barrier()
-
-    def _save_config(self, dir: PathOrStr, *, upload_to: Optional[str] = None) -> None:
-        if get_global_rank() == 0:
-            log.info("Saving config...")
-            self.cfg.save(config_path := Path(dir) / "config.yaml")
-            if upload_to is not None:
-                upload_target = f"{upload_to}/config.yaml"
-                log.info(f"Uploading {config_path} to {upload_target}")
-                upload(config_path, upload_target, save_overwrite=self.cfg.save_overwrite)
-
-
-class FullCheckpointer(Checkpointer):
-    """
-    A :class:`Checkpointer` that saves a single full model and optimizer state dictionary.
-    """
-
-    def save_checkpoint(
-        self,
-        dir: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        trainer_state: Dict[str, Any],
-        *,
-        upload_to: Optional[str] = None,
-    ) -> None:
-        with self._temporary_wd(dir) as checkpoint_dir:
-            with FSDP.state_dict_type(
-                fsdp_model,
-                state_dict_type=StateDictType.FULL_STATE_DICT,
-                state_dict_config=FullStateDictConfig(rank0_only=True, offload_to_cpu=True),
-                optim_state_dict_config=FullOptimStateDictConfig(rank0_only=True, offload_to_cpu=True),
-            ):
-                # We'll write the model and optimizer state dicts individually to reduce (CPU) memory consumption.
-                # First the model state.
-                model_state_dict = fsdp_model.state_dict()
-                if get_global_rank() == 0:
-                    log.info("Saving model state...")
-                    save_state_dict(
-                        checkpoint_dir,
-                        "model.pt",
-                        model_state_dict,
-                        upload_to=upload_to,
-                        save_overwrite=self.cfg.save_overwrite,
-                        synchronize=False,
-                    )
-                del model_state_dict
-                barrier()
-
-                # Then the optimizer state.
-                optim_state_dict = FSDP.optim_state_dict(fsdp_model, optim)
-                if get_global_rank() == 0:
-                    log.info("Saving optim state...")
-                    save_state_dict(
-                        checkpoint_dir,
-                        "optim.pt",
-                        optim_state_dict,
-                        upload_to=upload_to,
-                        save_overwrite=self.cfg.save_overwrite,
-                        synchronize=False,
-                    )
-                del optim_state_dict
-                barrier()
-
-            # Save trainer state.
-            if get_global_rank() == 0:
-                log.info("Saving trainer state...")
-                save_state_dict(
-                    checkpoint_dir,
-                    "train.pt",
-                    trainer_state,
-                    upload_to=upload_to,
-                    save_overwrite=self.cfg.save_overwrite,
-                    synchronize=False,
-                )
-            # Save config.
-            self._save_config(checkpoint_dir, upload_to=upload_to)
-
-    def restore_checkpoint(
-        self,
-        load_path: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-    ) -> Dict[str, Any]:
-        with FSDP.state_dict_type(
-            fsdp_model,
-            state_dict_type=StateDictType.FULL_STATE_DICT,
-            state_dict_config=FullStateDictConfig(rank0_only=False, offload_to_cpu=True),
-            optim_state_dict_config=FullOptimStateDictConfig(rank0_only=False, offload_to_cpu=True),
-        ):
-            with torch.no_grad():
-                # fill everything with NaN, so we can check afterwards that every parameter has been restored
-                for module_name, module in fsdp_model.named_modules():
-                    if not isinstance(module, FSDP):
-                        continue
-                    for param in module.params:
-                        param.fill_(torch.nan)
-
-                # restore params from checkpoint
-                state_dict_to_load = load_state_dict(
-                    load_path, "model.pt", local_cache=local_cache, map_location="cpu"
-                )
-                (
-                    state_dict_to_load,
-                    og_keys_to_new,
-                ) = fsdp_model._fsdp_wrapped_module._make_state_dict_compatible(state_dict_to_load)
-
-                for module_name, module in fsdp_model.named_modules():
-                    if not isinstance(module, FSDP):
-                        continue
-                    for param in module.params:
-                        assert param._is_flat_param
-                        for fqn, spi in zip(param._fqns, param._shard_param_infos):
-                            if not spi.in_shard:
-                                continue
-                            key = f"{module_name}.{fqn}"
-                            key = key.replace("_fsdp_wrapped_module.", "")
-                            key = key.lstrip(".")
-                            t = state_dict_to_load[key]
-                            t = t.flatten()
-                            param[spi.offset_in_shard : spi.offset_in_shard + spi.numel_in_shard].copy_(
-                                t[spi.intra_param_start_idx : spi.intra_param_end_idx + 1]
-                            )
-
-                # make sure that every parameter has been restored
-                for module_name, module in fsdp_model.named_modules():
-                    if not isinstance(module, FSDP):
-                        continue
-                    for param in module.params:
-                        if torch.isnan(param).any():
-                            raise ValueError(
-                                f"Module '{module_name}' contains NaNs, this is likely a bug restoring from full checkpoints"
-                            )
-
-            # Load optimizer state.
-            if load_optimizer_state:
-                optim_state_dict_to_load = load_state_dict(
-                    load_path, "optim.pt", local_cache=local_cache, map_location="cpu"
-                )
-                optim_state_dict_to_load = self._make_optim_state_dict_compatible(
-                    optim_state_dict_to_load,
-                    og_keys_to_new,
-                )
-                load_fsdp_optim_state(fsdp_model, optim, optim_state_dict_to_load)
-                del optim_state_dict_to_load
-
-            # Load other state.
-            try:
-                trainer_state = load_state_dict(load_path, "train.pt", local_cache=local_cache)
-            except FileNotFoundError:
-                # for backwards compatibility
-                trainer_state = load_state_dict(load_path, "other.pt", local_cache=local_cache)
-        barrier()
-        return trainer_state
-
-    def _make_optim_state_dict_compatible(
-        self, optim_state_dict: Dict[str, Any], og_keys_to_new: Dict[str, Set[str]]
-    ) -> Dict[str, Any]:
-        # This state dict comes in two forms: one where the state keys are integers and one where the
-        # keys are fully qualified parameter names. The latter case is easier to deal with here so we
-        # first transform the integer key form into the FQN key form.
-        if isinstance(optim_state_dict["param_groups"][0]["params"][0], int):
-            id_to_fqn: Dict[int, str] = {}
-            for group in optim_state_dict["param_groups"]:
-                new_param_names = []
-                for fqn, id in zip(group["param_names"], group["params"]):
-                    fqn = fqn.replace("_fsdp_wrapped_module.", "")
-                    id_to_fqn[id] = fqn
-                    new_param_names.append(fqn)
-                group["param_names"] = new_param_names
-                group["params"] = new_param_names
-            for id in list(optim_state_dict["state"].keys()):
-                optim_state_dict["state"][id_to_fqn[id]] = optim_state_dict["state"].pop(id)
-        else:
-            # Otherwise we still want to clean up the param names to remove the "_fsdp_wrapped_module." prefix.
-            for group in optim_state_dict["param_groups"]:
-                group["param_names"] = [fqn.replace("_fsdp_wrapped_module.", "") for fqn in group["param_names"]]
-                group["params"] = [fqn.replace("_fsdp_wrapped_module.", "") for fqn in group["params"]]
-                assert group["param_names"] == group["params"]
-            for key in list(optim_state_dict["state"].keys()):
-                optim_state_dict["state"][key.replace("_fsdp_wrapped_module.", "")] = optim_state_dict[
-                    "state"
-                ].pop(key)
-
-        # Now we can transform the state dict by renaming parameters according to `og_keys_to_new`.
-        # First fix param names in the state.
-        for og_key, new_keys in og_keys_to_new.items():
-            og_state = optim_state_dict["state"].pop(og_key, None)
-            if og_state is None:
-                continue
-            for i, new_key in enumerate(new_keys):
-                if i == len(new_keys) - 1:
-                    optim_state_dict["state"][new_key] = og_state
-                else:
-                    optim_state_dict["state"][new_key] = deepcopy(og_state)
-        # Now fix param names in the param groups.
-        for group in optim_state_dict["param_groups"]:
-            og_names = group["params"]
-            new_names = []
-            for og_key in og_names:
-                for new_key in og_keys_to_new[og_key]:
-                    new_names.append(new_key)
-            group["params"] = new_names
-            group["param_names"] = new_names
-
-        return optim_state_dict
-
-    def load_checkpoint(
-        self,
-        load_path: PathOrStr,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-        device: Optional[torch.device] = None,
-    ) -> Tuple[Dict[str, torch.Tensor], Optional[Dict[str, Any]]]:
-        device = device if device is not None else torch.device("cpu")
-        model_state = load_state_dict(load_path, "model.pt", local_cache=local_cache, map_location=device)  # type: ignore
-        optim_state = None
-        if load_optimizer_state:
-            optim_state = load_state_dict(load_path, "optim.pt", local_cache=local_cache, map_location=device)  # type: ignore
-        return model_state, optim_state
-
-
-class TorchNewStyleShardedCheckpointer(Checkpointer):
-    """
-    A sharded :class:`Checkpointer` that uses PyTorch's new distributed checkpointing functionality.
-    """
-
-    def save_checkpoint(
-        self,
-        dir: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        trainer_state: Dict[str, Any],
-        *,
-        upload_to: Optional[str] = None,
-    ) -> None:
-        with self._temporary_wd(dir) as checkpoint_dir:
-            # Save model and optim state.
-            save_fsdp_model_and_optim_state(
-                checkpoint_dir,
-                fsdp_model,
-                optim,
-                upload_to=upload_to,
-                save_overwrite=self.cfg.save_overwrite,
-            )
-
-            # Save trainer state.
-            log.info("Saving trainer state...")
-            save_state_dict(
-                checkpoint_dir,
-                f"train/rank{get_global_rank()}.pt",
-                trainer_state,
-                upload_to=upload_to,
-                save_overwrite=self.cfg.save_overwrite,
-            )
-
-            # Save config.
-            self._save_config(checkpoint_dir, upload_to=upload_to)
-
-    def restore_checkpoint(
-        self,
-        load_path: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-    ) -> Dict[str, Any]:
-        # Load model and optimizer state in place.
-        log.info("Loading model and optimizer state...")
-        load_fsdp_model_and_optim_state(
-            load_path,
-            fsdp_model,
-            optim,
-            local_cache=local_cache,
-            load_optimizer_state=load_optimizer_state,
-        )
-
-        # Load trainer state dict.
-        log.info("Loading trainer state...")
-        try:
-            trainer_state = load_state_dict(
-                load_path, f"train/rank{get_global_rank()}.pt", local_cache=local_cache
-            )
-        except FileNotFoundError:
-            # Fall back to rank 0 train state.
-            # This can happen when we're restoring a checkpoint with a different world size.
-            trainer_state = load_state_dict(load_path, "train/rank0.pt", local_cache=local_cache)
-        barrier()
-        return trainer_state
-
-
-class TorchLegacyShardedCheckpointer(Checkpointer):
-    """
-    A sharded :class:`Checkpointer` that just uses `torch.save()` with extra logic for handling FSDP model
-    and optim state.
-
-    The world size must be kept consistent when using this checkpointer.
-    """
-
-    def save_checkpoint(
-        self,
-        dir: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        trainer_state: Dict[str, Any],
-        *,
-        upload_to: Optional[str] = None,
-    ) -> None:
-        with self._temporary_wd(dir) as checkpoint_dir:
-            with FSDP.state_dict_type(
-                fsdp_model,
-                state_dict_type=StateDictType.SHARDED_STATE_DICT,
-                state_dict_config=ShardedStateDictConfig(offload_to_cpu=True),
-                optim_state_dict_config=ShardedOptimStateDictConfig(offload_to_cpu=True),
-            ):
-                state_dict = {
-                    "model": fsdp_model.state_dict(),
-                    "optim": FSDP.optim_state_dict(fsdp_model, optim),
-                    **trainer_state,
-                }
-                save_state_dict(
-                    checkpoint_dir,
-                    f"rank{get_global_rank()}.pt",
-                    state_dict,
-                    upload_to=upload_to,
-                    save_overwrite=self.cfg.save_overwrite,
-                )
-
-            # Save config.
-            self._save_config(checkpoint_dir, upload_to=upload_to)
-
-    def restore_checkpoint(
-        self,
-        load_path: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-    ) -> Dict[str, Any]:
-        with FSDP.state_dict_type(
-            fsdp_model,
-            state_dict_type=StateDictType.SHARDED_STATE_DICT,
-            state_dict_config=ShardedStateDictConfig(offload_to_cpu=True),
-            optim_state_dict_config=ShardedOptimStateDictConfig(offload_to_cpu=True),
-        ):
-            # Deserialize state dict.
-            state_dict = load_state_dict(
-                load_path, f"rank{get_global_rank()}.pt", local_cache=local_cache, map_location="cpu"
-            )
-
-            # Load model and optimizer state.
-            log.info("Loading model state...")
-            fsdp_model.load_state_dict(state_dict["model"])
-            del state_dict["model"]
-            if load_optimizer_state:
-                log.info("Loading optimizer state...")
-                load_fsdp_optim_state(fsdp_model, optim, state_dict["optim"])
-            del state_dict["optim"]
-
-        barrier()
-        return state_dict
-
-    def unshard_checkpoint(
-        self,
-        load_path: PathOrStr,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-        load_trainer_state: bool = True,
-        device: Optional[torch.device] = None,
-    ) -> Tuple[Dict[str, torch.Tensor], Optional[Dict[str, Any]], Optional[Dict[str, Any]]]:
-        assert local_cache is None, "this method currently only supports local files"
-        full_state_dict = self._unshard(load_path, device or torch.device("cpu"), skip_keys={"rng"})
-        model_state = full_state_dict.pop("model")
-        optim_state = full_state_dict.pop("optim")
-        return (
-            model_state,
-            optim_state if load_optimizer_state else None,
-            full_state_dict if load_trainer_state else None,
-        )
-
-    def _copy_sharded_tensors_to_shared_mem(self, state: Dict, world_size: int, rank: int, key: Tuple):
-        key = tuple() if key is None else key
-        if isinstance(state, (list, tuple, set)):
-            for i, sub_state in enumerate(state):
-                self._copy_sharded_tensors_to_shared_mem(sub_state, world_size, rank, key + (i,))
-        elif isinstance(state, dict):
-            for name in state.keys():
-                self._copy_sharded_tensors_to_shared_mem(state[name], world_size, rank, key + (name,))
-        elif isinstance(state, ShardedTensor):
-            self._copy_sharded_tensor_to_shared_mem(state, world_size, rank, key)
-            return
-        else:
-            return
-
-    def _get_shard_placement_and_rank_sizes(
-        self, shards_metadata: List[ShardMetadata], world_size: int
-    ) -> Tuple[Dict[ShardMetadata, Tuple[int, int]], List[int]]:
-        def shard_size(shard_md):
-            return reduce((lambda x, y: x * y), shard_md.shard_sizes)  # type: ignore[attr-defined]
-
-        rank_sizes = [0 for _ in range(world_size)]
-        shard_placement: Dict[ShardMetadata, Tuple[int, int]] = {}
-        for shard_md in shards_metadata:
-            shard_rank = cast(_remote_device, shard_md.placement).rank()
-            assert shard_rank is not None
-            if shard_rank >= world_size:
-                raise RuntimeError(f"Shard rank {shard_rank} exceeds world size {world_size}")
-
-            shard_placement[shard_md] = (shard_rank, rank_sizes[shard_rank])
-            rank_sizes[shard_rank] += shard_size(shard_md)
-
-        return shard_placement, rank_sizes
-
-    def _copy_sharded_tensor_to_shared_mem(
-        self, sharded_tensor: ShardedTensor, world_size: int, rank: int, key: Tuple
-    ) -> Any:
-        shard0_md = sharded_tensor.metadata()
-        shard_placement, rank_sizes = self._get_shard_placement_and_rank_sizes(
-            shard0_md.shards_metadata, world_size
-        )
-
-        rank_size = rank_sizes[rank]
-        assert rank_size >= 0
-        if rank_size == 0:
-            return
-
-        assert shard0_md.tensor_properties.dtype == torch.float32, "Expected sharded tensor to be fp32"
-        numpy_type = np.float32
-
-        sharded_memory_name = "-".join(key + (str(rank),))
-
-        shm = shared_memory.SharedMemory(
-            create=True, size=rank_size * np.dtype(numpy_type).itemsize, name=sharded_memory_name
-        )
-        np_arr = np.ndarray((rank_size,), dtype=numpy_type, buffer=shm.buf)
-
-        for local_shard in sharded_tensor.local_shards():
-            shard_rank = cast(_remote_device, local_shard.metadata.placement).rank()
-            assert shard_rank == rank
-
-            src = local_shard.tensor.flatten()
-            shard_offset = shard_placement[local_shard.metadata][1]
-
-            np_arr[shard_offset : shard_offset + src.numel()] = src.numpy()
-
-        shm.close()
-
-    def _copy_sharded_data_to_shared_mem(self, world_size: int, shard_filepath: Path):
-        shard_number = int(shard_filepath.name[4:-3])
-        log.info("Starting unsharding shard number %d to shared memory", shard_number)
-
-        with self._patch_sharded_tensor_load():
-            shard = torch.load(shard_filepath, map_location="cpu")
-            log.debug("Done loading shard number %d", shard_number)
-
-        self._copy_sharded_tensors_to_shared_mem(
-            shard, world_size, shard_number, (str(shard_filepath.parent).replace("/", "_"),)
-        )
-        log.info("Done unsharding shard number %d to shared memory", shard_number)
-
-    def _unshard_using_sharded_mem(
-        self, state: Any, world_size: int, device: torch.device, shard_dir: PathOrStr
-    ) -> Any:
-        return self._unshard_state_using_shared_mem(state, world_size, device, (str(shard_dir).replace("/", "_"),))
-
-    def _unshard_state_using_shared_mem(
-        self, state: Any, world_size: int, device: torch.device, key: Tuple
-    ) -> Any:
-        if isinstance(state, (list, tuple, set)):
-            return state.__class__(
-                self._unshard_state_using_shared_mem(sub_state, world_size, device, key + (i,))
-                for i, sub_state in enumerate(state)
-            )
-        elif isinstance(state, dict):
-            return {
-                name: self._unshard_state_using_shared_mem(state[name], world_size, device, key + (name,))
-                for name in state.keys()
-            }
-        elif isinstance(state, ShardedTensor):
-            return self._unshard_tensor_using_shared_mem(state, world_size, device, key)
-        elif isinstance(state, torch.Tensor):
-            return state.to(device=device)
-        else:
-            return state
-
-    def _unshard_tensor_using_shared_mem(
-        self, sharded_tensor: ShardedTensor, world_size: int, device: torch.device, key: Tuple
-    ) -> torch.Tensor:
-        shard0_md = sharded_tensor.metadata()
-
-        def shard_size(shard_md):
-            return reduce((lambda x, y: x * y), shard_md.shard_sizes)  # type: ignore[attr-defined]
-
-        shard_placement, rank_sizes = self._get_shard_placement_and_rank_sizes(
-            shard0_md.shards_metadata, world_size
-        )
-
-        assert shard0_md.tensor_properties.dtype == torch.float32, "Expected sharded tensor to be fp32"
-        numpy_type = np.float32
-
-        out = torch.empty(
-            *sharded_tensor.metadata().size, dtype=sharded_tensor.metadata().tensor_properties.dtype, device=device
-        )
-        dims = len(sharded_tensor.metadata().size)
-        for shard_md, (rank, rank_offset) in shard_placement.items():
-            if rank >= world_size:
-                raise RuntimeError(f"Shard rank {rank} exceeds world size {world_size}")
-
-            sharded_memory_name = "-".join(key + (str(rank),))
-            shm = shared_memory.SharedMemory(name=sharded_memory_name)
-
-            rank_size = rank_sizes[rank]
-            assert rank_size >= 0
-            if rank_size == 0:
-                continue
-
-            np_arr = np.ndarray((rank_size,), dtype=numpy_type, buffer=shm.buf)
-
-            tensor = torch.from_numpy(np_arr)[rank_offset : rank_offset + shard_size(shard_md)]
-            tensor = tensor.view(shard_md.shard_sizes)
-
-            out_narrow_view = out
-            for dim in range(dims):
-                out_narrow_view = out_narrow_view.narrow(
-                    dim,
-                    shard_md.shard_offsets[dim],
-                    shard_md.shard_sizes[dim],
-                )
-
-            out_narrow_view.copy_(tensor)
-
-            shm.close()
-            shm.unlink()
-
-        return out
-
-    @contextmanager
-    def _patch_sharded_tensor_load(self):
-        """
-        Monkeypatch for torch's ShardedTensor, so we can unpickle without having torch.distributed set up.
-        """
-
-        def _rebuild_from_type_v2_monkey(func, new_type, args, state):
-            ret = func(*args)
-            if type(ret) is not new_type:
-                ret = ret.as_subclass(new_type)
-
-            # Shortcut the construction of ShardedTensor
-            # This is in the top 5 of my worst hacks.
-            if isinstance(ret, ShardedTensor):
-                ret._local_shards, ret._metadata, _, ret._sharding_spec, ret._init_rrefs = state
-                return ret
-
-            # The rest of this function ought to be in the top 5 of somebody else's worst hacks.
-            # Tensor does define __setstate__ even though it doesn't define
-            # __getstate__. So only use __setstate__ if it is NOT the one defined
-            # on Tensor
-            if getattr(ret.__class__, "__setstate__", torch.Tensor.__setstate__) is not torch.Tensor.__setstate__:
-                ret.__setstate__(state)
-            else:
-                ret = torch._utils._set_obj_state(ret, state)
-            return ret
-
-        original_rebuild_from_type_v2 = torch._tensor._rebuild_from_type_v2
-        try:
-            torch._tensor._rebuild_from_type_v2 = _rebuild_from_type_v2_monkey
-            yield
-        finally:
-            torch._tensor._rebuild_from_type_v2 = original_rebuild_from_type_v2
-
-    def _unshard(self, input_dir: PathOrStr, device: torch.device, skip_keys: Optional[Set[str]] = None):
-        """
-        The current unsharding implementation consists of:
-
-        1. Loading each shard on a separate process and copying their sharded tensors to shared memory.
-        2. Loading 1 shard on the main process as a base unsharded object.
-        3. Using the sharded tensors in shared memory to populate the base unsharded object.
-
-        This implementation replaced a prior implementation that instead loaded
-        all shards using threads, because that implementation turned out to
-        be extremely slow (e.g. 6+ hours) sometimes when the world size was 1024.
-        The current implementation is slower than the old one in many scenarios,
-        but is significantly faster in the above mentioned case (e.g. 30 minutes)
-        if there are enough CPUs.
-        """
-
-        input_dir = Path(input_dir)
-        skip_keys = skip_keys or set()
-
-        shard_filepaths = list(input_dir.glob("rank*.pt"))
-        world_size = len(shard_filepaths)
-        if world_size == 0:
-            raise RuntimeError("No shards found for unsharding")
-
-        log.info("Number of shards: %d", world_size)
-        shard_size_gb = shard_filepaths[0].stat().st_size / (1024 * 1024 * 1024)
-        min_ram_required_estimate_gb = shard_size_gb * world_size
-        log.info(
-            "Shards are %.2fGB each, at least %.2fGB RAM is required", shard_size_gb, min_ram_required_estimate_gb
-        )
-
-        log.info("Copying sharded tensors to shared memory using multiple processes")
-        # Copy sharded data to shared memory using multiple processes, so this process can load
-        # from memory rather than disk. We spawn a new process instead of forking since shared memory
-        # appears to get deleted when forked processes end for some reason.
-        executor = ProcessPoolExecutor(
-            mp_context=mp.get_context("spawn"), initializer=util.prepare_cli_environment
-        )
-        futures = []
-        for shard_filepath in shard_filepaths:
-            shard_rank = int(shard_filepath.name[4:-3])
-
-            if shard_rank >= world_size:
-                raise RuntimeError(
-                    f"Shard rank {shard_rank} of file {shard_filepath} exceeds world size {world_size}"
-                )
-
-            futures.append(executor.submit(self._copy_sharded_data_to_shared_mem, world_size, shard_filepath))
-
-        for f in as_completed(futures):
-            f.result()
-        executor.shutdown()
-
-        log.info("Loading a shard on the main process to be unsharded state")
-        with self._patch_sharded_tensor_load():
-            state = torch.load(shard_filepaths[0], map_location="cpu")
-
-        for key in skip_keys:
-            if key in state:
-                del state[key]
-
-        log.info("Unsharding from %d shards ...", world_size)
-        return self._unshard_using_sharded_mem(state, world_size, device, input_dir)
-
-
-@dataclass
-class _LocalShardedCheckpointerMetadata(BaseConfig):
-    world_size: int = field(default_factory=get_world_size)
-
-
-@dataclass
-class _FlatParamShard:
-    full_shape: torch.Size
-    shard_offsets: Tuple[int, int]
-    shard_data: Optional[torch.Tensor]
-
-    def copy_into(self, full_tensor: torch.Tensor) -> None:
-        assert self.shard_data is not None
-        full_tensor_shard_view = full_tensor.view(-1)[self.shard_offsets[0] : self.shard_offsets[1] + 1]
-        assert self.shard_data.shape == full_tensor_shard_view.shape
-        full_tensor_shard_view.copy_(self.shard_data)
-
-
-class LocalShardedCheckpointer(Checkpointer):
-    """
-    A sharded :class:`Checkpointer` that directly saves the local FSDP flat params data.
-    The optimizer state is saved directly with `torch.save()` without reformatting via FSDP methods.
-
-    The world size must be kept consistent when using this checkpointer. However, you can easily
-    reconstruct a full unsharded model and/or optimizer state dictionary from a single Python process
-    using :meth:`unshard_checkpoint()` (no distributed initialization required).
-    """
-
-    # These correspond to metadata attributes on `torch.distributed.fsdp.flat_param.FlatParameter`.
-    _FLAT_PARAM_METADATA_TO_SAVE = (
-        "_fqns",
-        "_shard_param_offsets",
-        "_shard_indices",
-        "_numels",
-        "_numels_with_padding",
-        "_shapes",
-        "_shard_numel_padded",
-        "_shard_param_infos",
-    )
-
-    def _fsdp_modules(self, fsdp_model: FSDP) -> List[Tuple[str, FSDP]]:
-        """
-        Returns a list of FSDP modules with their FQN.
-        """
-        modules = []
-        for name, module in fsdp_model.named_modules():
-            if isinstance(module, FSDP):
-                modules.append((name, module))
-        return modules
-
-    def _prepare_fsdp_model(self, fsdp_model: FSDP) -> None:
-        from torch.distributed.fsdp._runtime_utils import _lazy_init
-
-        # TODO (epwalsh): I'm not sure if this is necessary, but this is what PyTorch does before saving/loading
-        # an FSDP state dict through the built-in methods.
-        if torch.cuda.is_available():
-            torch.cuda.synchronize()
-        _lazy_init(fsdp_model, fsdp_model)
-
-    def _fsdp_handles(self, fsdp_model: FSDP) -> List[FlatParamHandle]:
-        if version.parse(torch.__version__) < version.parse("2.1.0"):
-            return fsdp_model._handles  # type: ignore
-        elif version.parse(torch.__version__) < version.parse("2.3.0"):
-            # Handle could be None if the FSDP wrapper doesn't manage any parameters.
-            if hasattr(fsdp_model, "_handle") and fsdp_model._handle is not None:
-                return [fsdp_model._handle]  # type: ignore
-            else:
-                return []
-        else:
-            # Need to verify FSDP internals with newer versions.
-            raise NotImplementedError
-
-    @torch.no_grad()
-    def _get_flat_param_state_to_save(self, fsdp_model: FSDP) -> Dict[str, Any]:
-        self._prepare_fsdp_model(fsdp_model)
-        module_data = []
-        for module_fqn, fsdp_module in self._fsdp_modules(fsdp_model):
-            handle_data = []
-            for handle in self._fsdp_handles(fsdp_module):
-                data: Dict[str, Any] = {}
-                # This is a `FlatParameter` instance.
-                # See `torch.distributed.fsdp.flat_param` for the API.
-                flat_param = handle.flat_param
-                data["flat_param.data"] = flat_param.detach()
-                for key in self._FLAT_PARAM_METADATA_TO_SAVE:
-                    if hasattr(flat_param, key):
-                        data[f"flat_param.{key}"] = getattr(flat_param, key)
-                handle_data.append(data)
-            module_data.append({"handles": handle_data, "name": module_fqn})
-        return {"modules": module_data}
-
-    @torch.no_grad()
-    def _load_flat_param_state(self, fsdp_model: FSDP, model_state: Dict[str, Any]):
-        """Load the state produced from `self._get_flat_param_state_to_save()`."""
-        self._prepare_fsdp_model(fsdp_model)
-        fsdp_modules = self._fsdp_modules(fsdp_model)
-        assert len(model_state["modules"]) == len(fsdp_modules)
-        for (_, fsdp_module), module_data in zip(fsdp_modules, model_state["modules"]):
-            handles = self._fsdp_handles(fsdp_module)
-            assert len(handles) == len(module_data["handles"])
-            for handle, data in zip(handles, module_data["handles"]):
-                flat_param = handle.flat_param
-                # Make sure metadata matches.
-                for key in self._FLAT_PARAM_METADATA_TO_SAVE:
-                    if hasattr(flat_param, key):
-                        assert getattr(flat_param, key) == data[f"flat_param.{key}"]
-                # Load the flat sharded data.
-                flat_param.copy_(data["flat_param.data"])
-
-    def _save_metadata(self, dir: PathOrStr, *, upload_to: Optional[str] = None) -> None:
-        if get_fs_local_rank() == 0:
-            log.info("Saving metadata...")
-            metadata = _LocalShardedCheckpointerMetadata()
-            metadata.save(metadata_path := Path(dir) / "metadata.yaml")
-            if upload_to is not None and get_global_rank() == 0:
-                upload_target = f"{upload_to}/metadata.yaml"
-                log.info(f"Uploading {metadata_path} to {upload_target}")
-                upload(metadata_path, upload_target, save_overwrite=self.cfg.save_overwrite)
-
-    def _load_metadata(
-        self, load_path: PathOrStr, *, local_cache: Optional[PathOrStr] = None
-    ) -> _LocalShardedCheckpointerMetadata:
-        metadata_path = resource_path(load_path, "metadata.yaml", local_cache=local_cache)
-        return _LocalShardedCheckpointerMetadata.load(metadata_path)
-
-    def save_checkpoint(
-        self,
-        dir: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        trainer_state: Dict[str, Any],
-        *,
-        upload_to: Optional[str] = None,
-    ) -> None:
-        with self._temporary_wd(dir) as checkpoint_dir:
-            # Gather local FSDP flat params data to save.
-            # We also save some flat param metadata like the corresponding fully qualified names (fqns)
-            # of each original parameter so we can validate that the sharding is the same when loading
-            # one of these checkpoints.
-            log.info("Saving local FSDP flat params data...")
-            save_state_dict(
-                checkpoint_dir,
-                f"model/rank{get_global_rank()}.pt",
-                self._get_flat_param_state_to_save(fsdp_model),
-                upload_to=upload_to,
-                save_overwrite=self.cfg.save_overwrite,
-            )
-
-            # Save optimizer state.
-            log.info("Saving local optimizer state...")
-            save_state_dict(
-                checkpoint_dir,
-                f"optim/rank{get_global_rank()}.pt",
-                optim.state_dict(),
-                upload_to=upload_to,
-                save_overwrite=self.cfg.save_overwrite,
-            )
-
-            # Save trainer state.
-            log.info("Saving trainer state...")
-            save_state_dict(
-                checkpoint_dir,
-                f"train/rank{get_global_rank()}.pt",
-                trainer_state,
-                upload_to=upload_to,
-                save_overwrite=self.cfg.save_overwrite,
-            )
-
-            # Save metadata.
-            self._save_metadata(checkpoint_dir, upload_to=upload_to)
-
-            # Save config. We do this last b/c the presence of a config in a remote checkpoint
-            # "directory" indicates that the folder is valid, as a opposed to a partially
-            # uploaded checkpoint directory that failed before completing.
-            self._save_config(checkpoint_dir, upload_to=upload_to)
-
-    def restore_checkpoint(
-        self,
-        load_path: PathOrStr,
-        fsdp_model: FSDP,
-        optim: Optimizer,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-    ) -> Dict[str, Any]:
-        # Load metadata and make sure checkpoint is compatible.
-        metadata = self._load_metadata(load_path, local_cache=local_cache)
-        assert metadata.world_size == get_world_size()
-
-        # Load local FSDP flat param data.
-        log.info("Loading local FSDP flat params data...")
-        model_state = load_state_dict(
-            load_path, f"model/rank{get_global_rank()}.pt", local_cache=local_cache, map_location="cpu"
-        )
-        self._load_flat_param_state(fsdp_model, model_state)
-        del model_state
-
-        # Load local optim state.
-        if load_optimizer_state:
-            log.info("Loading local optimizer state...")
-            optim_state = load_state_dict(
-                load_path, f"optim/rank{get_global_rank()}.pt", local_cache=local_cache, map_location="cpu"
-            )
-            # HACK/TODO (epwalsh): When we use adaptive clipping we track the 'grad_norm_exp_avg' for every param
-            # in every rank, and keep this in the optimizer state. But this causes issues when loading the
-            # state since torch sees the state is non-empty for some params which would normally be empty,
-            # and then assumes it should have all of the other state tensors for that param, which is doesn't.
-            # So for now we just remove 'grad_norm_exp_avg' everywhere from the state, which resets that metric.
-            # Not the end of the world but there's probably a better way around this without resetting
-            # the metric.
-            for param_id in list(optim_state["state"].keys()):
-                state = optim_state["state"][param_id]
-                if "grad_norm_exp_avg" in state:
-                    del state["grad_norm_exp_avg"]
-                if len(state) == 0:
-                    del optim_state["state"][param_id]
-            optim.load_state_dict(optim_state)
-            del optim_state
-
-        # Load local trainer state.
-        log.info("Loading local trainer state...")
-        trainer_state = load_state_dict(load_path, f"train/rank{get_global_rank()}.pt", local_cache=local_cache)
-        barrier()
-        return trainer_state
-
-    def _iter_flat_param_shards(
-        self, model_state: Dict[str, Any]
-    ) -> Generator[Tuple[str, _FlatParamShard], None, None]:
-        for module_data in model_state["modules"]:
-            module_prefix = module_data["name"].replace("_fsdp_wrapped_module.", "")
-            for handle in module_data["handles"]:
-                flat_data = handle["flat_param.data"]
-                if (num_padding := handle["flat_param._shard_numel_padded"]) > 0:
-                    # If there's padding in the flat param it should be on the right.
-                    assert (flat_data[-num_padding:] == 0).all()
-                # NOTE: this changes depending on the torch version, but we don't do a version
-                # check since we might be trying to unshard an old checkpoint that was stored
-                # with a different torch version than we're currently running with.
-                if "flat_param._shard_indices" in handle:
-                    # torch <=2.0.1
-                    param_start = handle["flat_param._shard_indices"][0]
-                    current_flat_index = 0
-                    for relative_fqn, full_shape, (offset_start, offset_end) in zip(
-                        handle["flat_param._fqns"][param_start:],
-                        handle["flat_param._shapes"][param_start:],
-                        handle["flat_param._shard_param_offsets"],
-                    ):
-                        root_fqn = relative_fqn if not module_prefix else f"{module_prefix}.{relative_fqn}"
-                        numel_shard = offset_end - offset_start + 1
-                        flat_param_shard = _FlatParamShard(
-                            full_shape=full_shape,
-                            shard_offsets=(offset_start, offset_end),
-                            shard_data=flat_data[current_flat_index : current_flat_index + numel_shard],
-                        )
-                        current_flat_index += numel_shard
-                        yield root_fqn, flat_param_shard
-                else:
-                    # torch >=2.1.0
-                    for relative_fqn, full_shape, shard_param_info in zip(
-                        handle["flat_param._fqns"],
-                        handle["flat_param._shapes"],
-                        handle["flat_param._shard_param_infos"],
-                    ):
-                        if not shard_param_info.in_shard:
-                            continue
-                        root_fqn = relative_fqn if not module_prefix else f"{module_prefix}.{relative_fqn}"
-                        flat_param_shard = _FlatParamShard(
-                            full_shape=full_shape,
-                            shard_offsets=(
-                                shard_param_info.intra_param_start_idx,
-                                shard_param_info.intra_param_end_idx,
-                            ),
-                            shard_data=flat_data[
-                                shard_param_info.offset_in_shard : shard_param_info.offset_in_shard
-                                + shard_param_info.numel_in_shard
-                            ],
-                        )
-                        yield root_fqn, flat_param_shard
-
-    def unshard_checkpoint(
-        self,
-        load_path: PathOrStr,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        load_optimizer_state: bool = True,
-        load_trainer_state: bool = True,
-        device: Optional[torch.device] = None,
-    ) -> Tuple[Dict[str, torch.Tensor], Optional[Dict[str, Any]], Optional[Dict[str, Any]]]:
-        device = device or torch.device("cpu")
-        metadata = self._load_metadata(load_path, local_cache=local_cache)
-
-        # Gather paths model state, potentially downloading them.
-        log.info("Gathering model state dicts...")
-        model_state_paths = self._gather_state_dict_paths(
-            load_path, "model", metadata.world_size, local_cache=local_cache
-        )
-
-        # Load model state dicts one-by-one, materializing and populating the full parameters as we go.
-        log.info("Materializing full parameters...")
-        full_model_state: Dict[str, torch.Tensor] = {}
-        # We keep a copy of the flat param metadata minus the actual tensors so we can reconstruct
-        # the full optimizer state below without having to reload the model state dicts.
-        flat_params_data: Dict[int, Dict[str, _FlatParamShard]] = defaultdict(dict)
-        for rank, path in enumerate(model_state_paths):
-            log.info(f"Loading shards from rank {rank}...")
-            model_state = torch.load(path, map_location="cpu")
-            for root_fqn, flat_param_shard in self._iter_flat_param_shards(model_state):
-                if root_fqn not in full_model_state:
-                    log.info(
-                        f"Materializing full parameter '{root_fqn}' with shape {flat_param_shard.full_shape}..."
-                    )
-                    assert flat_param_shard.shard_data is not None
-                    full_model_state[root_fqn] = torch.empty(
-                        flat_param_shard.full_shape, dtype=flat_param_shard.shard_data.dtype, device=device
-                    )
-                    # Fill with NaNs so we can validate that the whole parameter has been populated
-                    # afterwards.
-                    full_model_state[root_fqn].fill_(torch.nan)
-                # Copy over the local shard to the relevant part of the full parameter.
-                full_param = full_model_state[root_fqn]
-                log.info(f"Loading rank {rank} shard for '{root_fqn}'...")
-                flat_param_shard.copy_into(full_param)
-                flat_params_data[rank][root_fqn] = replace(flat_param_shard, shard_data=None)
-
-        log.info("Validating full parameters...")
-        for key, tensor in full_model_state.items():
-            if torch.isnan(tensor).any():
-                raise ValueError(f"Parameter '{key}' contains NaNs, this is likely a bug with the unsharder")
-
-        trainer_state: Optional[Dict[str, Any]] = None
-        if load_trainer_state:
-            trainer_state = load_state_dict(load_path, "train/rank0.pt", local_cache=local_cache)
-
-        if not load_optimizer_state:
-            return full_model_state, None, trainer_state
-
-        log.info("Gathering optim state dicts...")
-        optim_state_paths = self._gather_state_dict_paths(
-            load_path, "optim", metadata.world_size, local_cache=local_cache
-        )
-
-        log.info("Materializing full optim state...")
-        full_optim_state: Dict[str, Any] = {"state": defaultdict(dict)}
-        fqn_to_id: Dict[str, int] = {}
-        id_to_fqn: Dict[int, str] = {}
-        for rank, path in enumerate(optim_state_paths):
-            log.info(f"Loading sharded optim state from rank {rank}...")
-            optim_state = torch.load(path, map_location="cpu")
-
-            # Initialize param groups.
-            # We assume parameter groups are the same across all ranks.
-            # The only thing that differs across ranks is the state for each local sharded param.
-            if "param_groups" not in full_optim_state:
-                full_optim_state["param_groups"] = optim_state["param_groups"]
-            else:
-                assert full_optim_state["param_groups"] == optim_state["param_groups"]
-
-            # Generate mapping of parameter FQNs to optimizer param IDs and vice-versa.
-            if not fqn_to_id or not id_to_fqn:
-                for group in full_optim_state["param_groups"]:
-                    for fqn, id in zip(group["param_names"], group["params"]):
-                        fqn = fqn.replace("_fsdp_wrapped_module.", "")
-                        fqn_to_id[fqn] = id
-                        id_to_fqn[id] = fqn
-
-            # Iterate over local shard state and copy into the full state.
-            for id, shard_state in optim_state["state"].items():
-                fqn = id_to_fqn[id]
-                flat_param_shard = flat_params_data[rank].get(fqn)  # type: ignore[assignment]
-                full_state = full_optim_state["state"][id]
-                for key, shard_value in shard_state.items():
-                    assert isinstance(shard_value, torch.Tensor)
-                    if shard_value.shape == torch.Size([]):
-                        # Add singleton tensors directly to full state. These should be the same across
-                        # all ranks.
-                        assert key in ("step", "grad_norm_exp_avg")  # sanity check
-                        if key not in full_state:
-                            full_state[key] = shard_value.to(device)
-                        else:
-                            assert full_state[key] == shard_value
-                    else:
-                        # Otherwise we have a sharded param state.
-                        # If the corresponding full param state hasn't been materialized yet, do so now.
-                        assert flat_param_shard is not None, f"missing flat_params_data for {fqn} from rank {rank}"
-                        if key not in full_state:
-                            log.info(
-                                f"Materializing full state '{key}' for '{fqn}' with shape {flat_param_shard.full_shape}..."
-                            )
-                            full_state[key] = torch.empty(
-                                flat_param_shard.full_shape, dtype=shard_value.dtype, device=device
-                            )
-                        full_state_value = full_state[key]
-
-                        # Copy over the local shard state to the relevant part of the full parameter state.
-                        log.info(f"Loading rank {rank} shard state of '{key}' for '{fqn}'...")
-                        replace(flat_param_shard, shard_data=shard_value).copy_into(full_state_value)
-
-        # Lastly, clean up the parameter names in param groups.
-        for group in full_optim_state["param_groups"]:
-            group["param_names"] = [n.replace("_fsdp_wrapped_module.", "") for n in group["param_names"]]
-
-        return full_model_state, full_optim_state, trainer_state
-
-    def _get_state_dict_path(
-        self,
-        load_path: PathOrStr,
-        state_dict_type: str,
-        rank: int,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-        progress=None,
-    ) -> Tuple[int, Path]:
-        fname = f"{state_dict_type}/rank{rank}.pt"
-        return rank, resource_path(str(load_path).rstrip("/"), fname, local_cache=local_cache, progress=progress)
-
-    def _gather_state_dict_paths(
-        self,
-        load_path: PathOrStr,
-        state_dict_type: str,
-        world_size: int,
-        *,
-        local_cache: Optional[PathOrStr] = None,
-    ) -> List[Path]:
-        progress = get_progress_bar()
-        with ThreadPoolExecutor(max_workers=self.thread_count) as executor:
-            futures = []
-            for rank in range(world_size):
-                future = executor.submit(
-                    self._get_state_dict_path,
-                    load_path,
-                    state_dict_type,
-                    rank,
-                    local_cache=local_cache,
-                    progress=progress,
-                )
-                futures.append(future)
-
-            results: Dict[int, Path] = {}
-            for future in as_completed(futures):
-                rank, path = future.result()
-                results[rank] = path
-
-        return [results[rank] for rank in range(world_size)]
-
-
-def build_sharded_checkpointer(
-    cfg: TrainConfig, *, name: Optional[ShardedCheckpointerType] = None
-) -> Checkpointer:
-    name = name or cfg.sharded_checkpointer
-    if name == ShardedCheckpointerType.torch_new:
-        return TorchNewStyleShardedCheckpointer(cfg)
-    elif name == ShardedCheckpointerType.torch_legacy:
-        return TorchLegacyShardedCheckpointer(cfg)
-    elif name == ShardedCheckpointerType.local:
-        return LocalShardedCheckpointer(cfg)
-    else:
-        raise NotImplementedError(name)