# 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]
@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]
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)
@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 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 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)
