Source code for nemo_automodel.checkpoint._backports.filesystem

# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# taken from
# https://github.com/pytorch/pytorch/blob/c13e725edd8dd21406c629bf625f2d6c59ceedd1/torch/distributed/checkpoint/filesystem.py
# pylint: disable=missing-function-docstring, missing-class-docstring
import collections
import dataclasses
import io
import json
import operator
import os
import pickle
import queue
import threading
import uuid
import warnings
from abc import ABC, abstractmethod
from collections.abc import Generator, Iterable, Iterator, Sequence
from contextlib import contextmanager
from dataclasses import dataclass
from enum import Enum
from io import UnsupportedOperation
from pathlib import Path
from typing import IO, Any, Callable, Optional, Union, cast

import torch
from torch import Tensor
from torch._utils import _get_available_device_type, _get_device_module
from torch.distributed._shard._utils import narrow_tensor_by_index
from torch.distributed.checkpoint._extension import (
    ExtensionRegistry,
    StreamTransformExtension,
)
from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE, Metadata, StorageMeta
from torch.distributed.checkpoint.planner import (
    LoadItemType,
    LoadPlan,
    LoadPlanner,
    ReadItem,
    SavePlan,
    SavePlanner,
    WriteItem,
    WriteItemType,
)
from torch.distributed.checkpoint.staging import BlockingAsyncStager
from torch.distributed.checkpoint.storage import (
    StorageReader,
    StorageWriter,
    WriteResult,
)
from torch.distributed.checkpoint.utils import _create_file_view
from torch.futures import Future

# introduced as collections.abc.Buffer in Python 3.12
from typing_extensions import Buffer

from nemo_automodel.checkpoint._backports.hf_utils import (
    CUSTOM_METADATA_KEY,
    DCP_VERSION_KEY,
    HF_DCP_VERSION,
)


__all__ = [
    "FileSystemWriter",
    "FileSystemReader",
    "FileSystem",
    "FileSystemBase",
]

_metadata_fn: str = ".metadata"


[docs] class SerializationFormat(Enum): """Enumeration of supported on-disk checkpoint formats.""" TORCH_SAVE = "torch_save" SAFETENSORS = "safetensors"
[docs] @dataclass class _StorageInfo: """This is the per entry storage info.""" relative_path: str offset: int length: int transform_descriptors: Optional[Sequence[str]] = None
[docs] def __getstate__(self): return {k: v for k, v in self.__dict__.items() if v is not None}
[docs] @dataclass class _StoragePrefix: prefix: str
DEFAULT_SUFFIX = ".distcp"
[docs] def _generate_uuid() -> str: return str(uuid.uuid4())
[docs] class _TensorLoader(ABC):
[docs] @abstractmethod def add(self, size: int, obj: object) -> None: pass
[docs] @abstractmethod def start_loading(self) -> None: pass
[docs] @abstractmethod def values(self) -> Iterator[tuple[torch.Tensor, object]]: pass
[docs] class _SerialCpuLoader(_TensorLoader): def __init__(self, resolve_fun: Callable) -> None: self.resolve_fun = resolve_fun self.items: list[tuple[int, object]] = []
[docs] def add(self, size: int, obj: object) -> None: self.items.append((size, obj))
[docs] def start_loading(self) -> None: pass
[docs] def values(self) -> Iterator[tuple[torch.Tensor, object]]: for _, obj in self.items: tensor = self.resolve_fun(obj).detach() tensor = tensor.cpu() if tensor.storage().size() != tensor.numel(): tensor = tensor.clone() yield ( tensor, obj, )
[docs] class _OverlappingCpuLoader(_TensorLoader): def __init__( self, resolve_fun: Callable, stream: Optional[torch.Stream] = None, inflight_threshhold: int = 1_000_000, ) -> None: self.resolve_fun = resolve_fun self.items: list[tuple[int, object]] = [] self.inflight_threshhold = inflight_threshhold self.in_flight_data = 0 self.current_items: collections.deque = collections.deque() self.idx = 0 self.started = False self.device_type = stream.device_type if stream else _get_available_device_type() self.device_module = _get_device_module(self.device_type) self.stream = cast(torch.cuda.Stream, stream or self.device_module.current_stream()) if self.stream != self.device_module.current_stream(): self.stream.wait_stream(self.device_module.current_stream()) @property def _done(self) -> bool: return self.idx >= len(self.items)
[docs] def _drain(self) -> list[tuple[torch.Tensor, object]]: drained = [] if self.in_flight_data >= self.inflight_threshhold: self.stream.synchronize() while self.in_flight_data >= self.inflight_threshhold: val = self.current_items.popleft() self.in_flight_data -= val[0].numel() * val[0].element_size() drained.append(val) return drained
[docs] def _refill(self) -> None: with self.device_module.stream(self.stream): while not self._done and self.in_flight_data < self.inflight_threshhold: _, obj = self.items[self.idx] self.idx += 1 tensor = self.resolve_fun(obj).detach() if tensor.device.type == self.device_type: tensor = tensor.to(device="cpu", non_blocking=True) elif tensor.device == torch.device("cpu"): if tensor.untyped_storage().size() != tensor.numel() * tensor.itemsize: # this forces the tensor to be both contiguous and with minimal storage tensor = tensor.clone() self.current_items.append( ( tensor, obj, ) ) self.in_flight_data += tensor.numel() * tensor.element_size()
[docs] def _finish(self) -> Iterable[tuple[torch.Tensor, object]]: assert self._done if len(self.current_items) > 0: self.stream.synchronize() return self.current_items
[docs] def add(self, size: int, obj: object) -> None: if self.started: raise RuntimeError("cannot add items after loading started") self.items.append((size, obj))
[docs] def start_loading(self) -> None: if self.started: return self.started = True self.items.sort(key=operator.itemgetter(0)) self._refill()
[docs] def values(self) -> Iterator[tuple[torch.Tensor, object]]: self.start_loading() while not self._done: drained = self._drain() self._refill() yield from drained yield from self._finish()
[docs] class _StorageWriterTransforms: """ This is experimental, and will likely move elsewhere in the future. It lives here to minimize changes while we are still learning and gathering feedback. """ def __init__(self, extensions: Optional[Sequence[StreamTransformExtension]] = None) -> None: """ If the extensions arg is None, this means the implementation should provide whatever defaults it chooses. An empty sequence indicates no extensions should be used. At this time, the default extensions sequence is empty. """ self.extensions = () if extensions is None else extensions
[docs] def transform_save_stream(self, write_item: WriteItem, raw_stream: io.IOBase) -> tuple[IO[bytes], list[str]]: # In order to avoid leaking fds, transformers' close must # cascade to wrapped streams, but since this function can # append to the raw stream, we can't close the actual stream. # So, we use this to put a wrapper around the raw stream's # close() to make it a noop, and it gets closed once all files # are appended. class NoCloseWriter(io.IOBase): def __init__(self, raw: io.IOBase): self.raw = raw def writeable(self) -> bool: return True def write(self, b: Buffer) -> int: return self.raw.write(b) def close(self): self.flush() self.raw.flush() # but not close. transform_to = cast(IO[bytes], NoCloseWriter(raw_stream)) for ex in self.extensions: transform_to = ex.transform_to(transform_to) return (transform_to, [ex.get_descriptor() for ex in reversed(self.extensions)])
[docs] def _item_size(item: WriteItem) -> int: size = 1 assert item.tensor_data is not None # can't use math.prod as PT needs to support older python for s in item.tensor_data.size: size *= s dtype = item.tensor_data.properties.dtype return size * torch._utils._element_size(dtype)
[docs] def _split_by_size_and_type(bins: int, items: list[WriteItem]) -> list[list[WriteItem]]: if bins == 1: return [items] bytes_w = [wi for wi in items if wi.type == WriteItemType.BYTE_IO] tensor_w = [wi for wi in items if wi.type != WriteItemType.BYTE_IO] buckets: list[list[WriteItem]] = [[] for _ in range(bins)] bucket_sizes = [0 for _ in range(bins)] tensor_w.sort(key=_item_size, reverse=True) for i, wi in enumerate(bytes_w): buckets[i % bins].append(wi) for wi in tensor_w: # TODO replace with headq idx = min(enumerate(bucket_sizes), key=operator.itemgetter(1))[0] buckets[idx].append(wi) bucket_sizes[idx] += _item_size(wi) return buckets
[docs] def _write_item( transforms: _StorageWriterTransforms, stream: io.IOBase, data: Union[io.BytesIO, torch.Tensor], write_item: WriteItem, storage_key: str, serialization_format: SerializationFormat, ) -> WriteResult: offset = stream.tell() (transform_to, transform_descriptors) = transforms.transform_save_stream(write_item, stream) if write_item.type == WriteItemType.BYTE_IO: assert isinstance(data, io.BytesIO) transform_to.write(data.getbuffer()) else: assert isinstance(data, torch.Tensor) assert data.device == torch.device("cpu") if serialization_format == SerializationFormat.TORCH_SAVE: torch.save(data, transform_to) transform_to.close() if serialization_format == SerializationFormat.TORCH_SAVE or isinstance(data, io.BytesIO): length = stream.tell() - offset else: length = data.numel() * data.element_size() # For consistency with earlier versions, leave this field out of the # metadata if there are no extensions. info_transform_descriptors = None if len(transform_descriptors) == 0 else transform_descriptors return WriteResult( index=write_item.index, size_in_bytes=length, storage_data=_StorageInfo( storage_key, offset, length, transform_descriptors=info_transform_descriptors, ), )
[docs] def _write_files_from_queue( create_stream: Callable, file_queue: queue.Queue, result_queue: queue.Queue, planner: SavePlanner, transforms: _StorageWriterTransforms, inflight_threshhold: int, use_fsync: bool, thread_count: int, serialization_format: SerializationFormat, ) -> None: # Convert incoming enum (could be from torch.distributed.checkpoint) to our local # SerializationFormat so that identity checks inside torch\'s _write_item succeed. if not isinstance(serialization_format, SerializationFormat): try: serialization_format = SerializationFormat[serialization_format.name] # type: ignore[arg-type] except Exception: # pragma: no cover – fallback for enum value conversion serialization_format = SerializationFormat(serialization_format.value) # type: ignore[arg-type] try: while True: file_name, storage_key, write_items = file_queue.get_nowait() loader: _TensorLoader custom_backend_name = torch._C._get_privateuse1_backend_name() custom_device_mod = getattr(torch, custom_backend_name, None) # TODO: Using the OverlappingCpuLoader with multiple threads creates significant # performance degredation, observed as being related to cuda stream syncs. We # should try to fix this and use _OverlappingCpuLoader for all threaded cases if ( thread_count == 1 and (torch.cuda.is_available() or (custom_device_mod and custom_device_mod.is_available())) and inflight_threshhold > 0 ): loader = _OverlappingCpuLoader( planner.resolve_data, inflight_threshhold=inflight_threshhold, ) else: loader = _SerialCpuLoader( planner.resolve_data, ) tensor_w = [wi for wi in write_items if wi.type != WriteItemType.BYTE_IO] for write_item in tensor_w: loader.add(_item_size(write_item), write_item) loader.start_loading() bytes_w = [wi for wi in write_items if wi.type == WriteItemType.BYTE_IO] write_results = [] with create_stream(file_name, "wb") as stream: for write_item in bytes_w: data = planner.resolve_data(write_item) write_results.append( _write_item( transforms, stream, data, write_item, storage_key, serialization_format, ) ) tensor_dict = {} metadata_dict = {} for tensor, write_item in loader.values(): assert tensor.is_cpu write_results.append( _write_item( transforms, stream, tensor, write_item, storage_key, serialization_format, ) ) tensor_dict[write_item.index.fqn] = tensor metadata_dict[write_item.index.fqn] = {"saved_offsets": write_item.tensor_data.chunk.offsets} if serialization_format == SerializationFormat.SAFETENSORS: from safetensors.torch import save # type: ignore[import-not-found] stream.write( save( tensor_dict, metadata={ CUSTOM_METADATA_KEY: json.dumps(metadata_dict), DCP_VERSION_KEY: str(HF_DCP_VERSION), "format": "pt", }, ) ) if use_fsync: try: os.fsync(stream.fileno()) except (AttributeError, UnsupportedOperation): os.sync() stream.close() result_queue.put(write_results) except queue.Empty: pass
[docs] class FileSystemBase(ABC):
[docs] @contextmanager @abstractmethod def create_stream(self, path: Union[str, os.PathLike], mode: str) -> Generator[io.IOBase, None, None]: ...
[docs] @abstractmethod def concat_path(self, path: Union[str, os.PathLike], suffix: str) -> Union[str, os.PathLike]: ...
[docs] @abstractmethod def rename(self, path: Union[str, os.PathLike], new_path: Union[str, os.PathLike]) -> None: ...
[docs] @abstractmethod def init_path(self, path: Union[str, os.PathLike]) -> Union[str, os.PathLike]: ...
[docs] @abstractmethod def mkdir(self, path: Union[str, os.PathLike]) -> None: ...
[docs] @classmethod @abstractmethod def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool: ...
[docs] @abstractmethod def exists(self, path: Union[str, os.PathLike]) -> bool: ...
[docs] @abstractmethod def rm_file(self, path: Union[str, os.PathLike]) -> None: ...
[docs] class FileSystem(FileSystemBase):
[docs] @contextmanager def create_stream(self, path: Union[str, os.PathLike], mode: str) -> Generator[io.IOBase, None, None]: if not isinstance(path, Path): path = Path(path) with path.open(mode) as stream: yield cast(io.IOBase, stream)
[docs] def concat_path(self, path: Union[str, os.PathLike], suffix: str) -> Union[str, os.PathLike]: if not isinstance(path, Path): path = Path(path) return path / suffix
[docs] def init_path(self, path: Union[str, os.PathLike]) -> Union[str, os.PathLike]: if not isinstance(path, Path): path = Path(path) return path
[docs] def rename(self, path: Union[str, os.PathLike], new_path: Union[str, os.PathLike]) -> None: if not isinstance(path, Path): path = Path(path) path.rename(cast(Path, new_path))
[docs] def mkdir(self, path: Union[str, os.PathLike]) -> None: if not isinstance(path, Path): path = Path(path) path.mkdir(parents=True, exist_ok=True)
[docs] @classmethod def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool: if isinstance(checkpoint_id, Path): return True if "://" in str(checkpoint_id): return False for p in Path(checkpoint_id).parents: if p.exists() and os.access(str(p), os.W_OK): return True return False
[docs] def exists(self, path: Union[str, os.PathLike]) -> bool: if not isinstance(path, Path): path = Path(path) return path.exists()
[docs] def rm_file(self, path: Union[str, os.PathLike]) -> None: if not isinstance(path, Path): path = Path(path) path.unlink()
[docs] def ls(self, path: Union[str, os.PathLike]) -> list[str]: if not isinstance(path, Path): path = Path(path) return [str(p) for p in path.iterdir()]
[docs] class _FileSystemWriter(StorageWriter): """ Basic implementation of StorageWriter using file IO. This implementation makes the following assumptions and simplifications: * The checkpoint path is an empty or non-existing directory. * File creation is atomic The checkpoint consist of one file per write request plus a `.metadata` file with the serialized metadata. """ def __init__( self, path: Union[str, os.PathLike], single_file_per_rank: bool = True, sync_files: bool = True, thread_count: int = 1, per_thread_copy_ahead: int = 10_000_000, overwrite: bool = True, _extensions: Optional[Sequence[StreamTransformExtension]] = None, serialization_format: SerializationFormat = SerializationFormat.TORCH_SAVE, *args: Any, **kwargs: Any, ) -> None: """ Initialize the writer pointing to `path`. Args: path: directory where the checkpoint will be written to. single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True. sync_files : force files to be synced to permanent storage. Default to True. thread_count: Number of IO threads to use to write. Default to 1. per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb. overwrite: Whether to allow overwriting existing checkpoints. Defaults to True. _extensions: Extensions to apply to output streams (EXPERIMENTAL) N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure. """ # Torch DCP's StorageWriter base-class defines no custom __init__, however due to # the complex monkey-patching we perform the ``super()`` resolution can fail when # multiple copies of the class hierarchy coexist (e.g., when Torch imports its # modules before NeMo-Automodel aliases them). In such rare cases ``super`` may # raise a ``TypeError`` even though there is effectively nothing to initialize # in the parent class. Swallow the error and proceed – this is safe because the # parent class has no state to set up. try: super().__init__() except TypeError: # Fallback for inconsistent MRO across patched modules pass self.fs = FileSystem() self.path = self.fs.init_path(path) self.single_file_per_rank = single_file_per_rank self.sync_files = sync_files self.thread_count = thread_count self.per_thread_copy_ahead = per_thread_copy_ahead self.save_id = _generate_uuid() self.overwrite = overwrite self.transforms = _StorageWriterTransforms(_extensions) self.serialization_format = serialization_format
[docs] def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None: if checkpoint_id: self.path = self.fs.init_path(checkpoint_id) self.save_id = _generate_uuid()
[docs] def set_up_storage_writer(self, is_coordinator: bool) -> None: pass
[docs] def prepare_local_plan(self, plan: SavePlan) -> SavePlan: self.fs.mkdir(self.path) if self.fs.exists(self.metadata_path): if self.overwrite: warnings.warn( f"Detected an existing checkpoint in {self.metadata_path}, overwriting since {self.overwrite=}." " Past version 2.5 of PyTorch, `overwrite` will default to False. Set this variable to True to" " maintain this functionality or False to raise when an existing checkpoint is found." ) else: raise RuntimeError(f"Checkpoint already exists and {self.overwrite=}.") return plan
[docs] def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]: new_plans = [dataclasses.replace(plan, storage_data=_StoragePrefix(f"__{i}_")) for i, plan in enumerate(plans)] return new_plans
[docs] def write_data( self, plan: SavePlan, planner: SavePlanner, ) -> Future[list[WriteResult]]: storage_plan: _StoragePrefix = plan.storage_data file_count = 0 def gen_file(): nonlocal file_count file_name = f"{storage_plan.prefix}{file_count}{DEFAULT_SUFFIX}" file_count += 1 return file_name file_queue: queue.Queue = queue.Queue() if self.single_file_per_rank: for bucket in _split_by_size_and_type(self.thread_count, plan.items): file_name = gen_file() path = self.fs.concat_path(self.path, file_name) file_queue.put((path, file_name, bucket)) else: for item in plan.items: file_name = gen_file() path = self.fs.concat_path(self.path, file_name) file_queue.put((path, file_name, [item])) return self._write_data(planner, file_queue)
[docs] def _write_data( self, planner: SavePlanner, file_queue: queue.Queue, ) -> Future[list[WriteResult]]: result_queue: queue.Queue = queue.Queue() threads = [] for _ in range(1, self.thread_count): t = threading.Thread( target=_write_files_from_queue, args=( self.fs.create_stream, file_queue, result_queue, planner, self.transforms, self.per_thread_copy_ahead, self.sync_files, self.thread_count, self.serialization_format, ), ) t.start() threads.append(t) _write_files_from_queue( create_stream=self.fs.create_stream, file_queue=file_queue, result_queue=result_queue, planner=planner, transforms=self.transforms, inflight_threshhold=self.per_thread_copy_ahead, use_fsync=self.sync_files, thread_count=self.thread_count, serialization_format=self.serialization_format, ) for t in threads: t.join() res = [] try: while True: res += result_queue.get_nowait() except queue.Empty: fut: Future[list[WriteResult]] = Future() fut.set_result(res) return fut
[docs] def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None: storage_md = {} for wr_list in results: storage_md.update({wr.index: wr.storage_data for wr in wr_list}) metadata.storage_data = storage_md metadata.storage_meta = self.storage_meta() tmp_path = cast(Path, self.fs.concat_path(self.path, f"{_metadata_fn}.tmp")) with self.fs.create_stream(tmp_path, "wb") as metadata_file: pickle.dump(metadata, metadata_file) if self.sync_files: try: os.fsync(metadata_file.fileno()) except (AttributeError, UnsupportedOperation): os.sync() # delete in-case other checkpoints were present. if self.fs.exists(self.metadata_path): self.fs.rm_file(self.metadata_path) self.fs.rename(tmp_path, self.metadata_path)
[docs] def storage_meta(self) -> Optional[StorageMeta]: return StorageMeta(checkpoint_id=self.checkpoint_id, save_id=self.save_id)
@property def metadata_path(self) -> Union[str, os.PathLike]: return cast(Path, self.fs.concat_path(self.path, _metadata_fn)) @property def checkpoint_id(self) -> Union[str, os.PathLike]: """ return the checkpoint_id that will be used to save the checkpoint. """ return self.path
[docs] @classmethod def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool: return FileSystem.validate_checkpoint_id(checkpoint_id)
[docs] class _StorageReaderTransforms: """ This is experimental, and will likely move elsewhere in the future. It lives here to minimize changes while we are still learning and gathering feedback. """ def __init__(self, extension_registry: Optional[ExtensionRegistry] = None) -> None: self.extension_registry = ExtensionRegistry() if extension_registry is None else extension_registry
[docs] def transform_load_stream( self, read_item: ReadItem, transform_descriptors: Sequence[str], raw_stream: IO[bytes], ) -> IO[bytes]: extensions = self.extension_registry.from_descriptor_list(transform_descriptors) transform_from = raw_stream for ex in extensions: if isinstance(ex, StreamTransformExtension): transform_from = ex.transform_from(transform_from) return transform_from
[docs] class FileSystemReader(StorageReader): def __init__( self, path: Union[str, os.PathLike], _extension_registry: Optional[ExtensionRegistry] = None, # EXPERIMENTAL ) -> None: super().__init__() self.fs = FileSystem() self.path = self.fs.init_path(path) self.storage_data: dict[Any, Any] = {} self.load_id = _generate_uuid() self.transforms = _StorageReaderTransforms(_extension_registry)
[docs] def _slice_file(self, file, sinfo: _StorageInfo) -> IO[bytes]: return cast(IO[bytes], _create_file_view(file, sinfo.offset, sinfo.length))
[docs] def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None: self.storage_data = {} if checkpoint_id: self.path = self.fs.init_path(checkpoint_id) self.load_id = _generate_uuid()
[docs] def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]: # group requests by file per_file: dict[str, list[ReadItem]] = {} for read_item in plan.items: item_md: _StorageInfo = self.storage_data[read_item.storage_index] path = item_md.relative_path per_file.setdefault(path, []).append(read_item) for relative_path, reqs in per_file.items(): new_path = self.fs.concat_path(self.path, relative_path) with self.fs.create_stream(new_path, "rb") as stream: # TODO sort by offset and cache the reading for req in reqs: item_md = self.storage_data[req.storage_index] file_slice = self._slice_file(stream, item_md) transform_from = self.transforms.transform_load_stream( req, # This field wasn't present in older # implementations so provide a fallback. item_md.transform_descriptors or (), file_slice, ) if req.type == LoadItemType.BYTE_IO: read_bytes = io.BytesIO(transform_from.read(-1)) read_bytes.seek(0) planner.load_bytes(req, read_bytes) else: if transform_from.seekable(): seekable = transform_from else: # torch.load requires a seekable input, so read the transform # stream now and store the output if needed seekable = io.BytesIO(transform_from.read(-1)) seekable.seek(0) tensor = cast( Tensor, torch.load( seekable, map_location="cpu", weights_only=True, ), ) tensor = narrow_tensor_by_index(tensor, req.storage_offsets, req.lengths) target_tensor = planner.resolve_tensor(req).detach() assert target_tensor.size() == tensor.size(), ( f"req {req.storage_index} mismatch sizes {target_tensor.size()} vs {tensor.size()}" ) target_tensor.copy_(tensor) planner.commit_tensor(req, target_tensor) fut: Future = Future() fut.set_result(None) return fut
# Implementing the abstract function in StorageReader
[docs] def read_metadata(self) -> Metadata: path = self.fs.concat_path(self.path, ".metadata") with self.fs.create_stream(path, "rb") as metadata_file: metadata = pickle.load(metadata_file) if getattr(metadata, "storage_meta", None) is None: metadata.storage_meta = StorageMeta() metadata.storage_meta.load_id = self.load_id return metadata
[docs] def set_up_storage_reader(self, metadata: Metadata, is_coordinator: bool) -> None: self.storage_data = metadata.storage_data assert self.storage_data is not None
[docs] def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan: return plan
[docs] def prepare_global_plan(self, plans: list[LoadPlan]) -> list[LoadPlan]: return plans
@property def checkpoint_id(self) -> Union[str, os.PathLike]: """ return the checkpoint_id that will be used to load the checkpoint. """ return self.path
[docs] @classmethod def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool: return FileSystem.validate_checkpoint_id(checkpoint_id)
[docs] class FileSystemWriter(_FileSystemWriter, BlockingAsyncStager): """ Basic implementation of StorageWriter using file IO. This implementation makes the following assumptions and simplifications: * The checkpoint path is an empty or non-existing directory. * File creation is atomic The checkpoint consist of one file per write request plus a `.metadata` file with the serialized metadata. """ def __init__( self, path: Union[str, os.PathLike], single_file_per_rank: bool = True, sync_files: bool = True, thread_count: int = 1, per_thread_copy_ahead: int = 10_000_000, cache_staged_state_dict: bool = False, overwrite: bool = True, _extensions: Optional[Sequence[StreamTransformExtension]] = None, serialization_format: SerializationFormat = SerializationFormat.TORCH_SAVE, ) -> None: """ Initialize the writer pointing to `path`. Args: path: directory where the checkpoint will be written to. single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True. sync_files : force files to be synced to permanent storage. Default to True. thread_count: Number of IO threads to use to write. Default to 1. per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb. cache_staged_state_dict: Whether to cache the staged state_dict. This option decreases staging latency at the cost of increases memory usage. Additionally, if this parameter is set to True, it's the expectation that the stager is maintained and re-used for multiple dcp.async_save calls. Default to False. overwrite: Whether to allow overwriting existing checkpoints. Defaults to True. _extensions: Extensions to apply to output streams (EXPERIMENTAL) N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure. """ _FileSystemWriter.__init__( self, path=path, single_file_per_rank=single_file_per_rank, sync_files=sync_files, thread_count=thread_count, per_thread_copy_ahead=per_thread_copy_ahead, overwrite=overwrite, _extensions=_extensions, serialization_format=serialization_format, ) BlockingAsyncStager.__init__( self, cache_staged_state_dict=cache_staged_state_dict, )
[docs] def stage(self, state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE: """Override of AsyncStager.stage""" # in the async case, the state dict is already on CPU, so maintaining this # buffer makes no sense self.per_thread_copy_ahead = 0 return super().stage(state_dict)