Source code for torch.distributed.checkpoint.filesystem

from abc import ABC, abstractmethod
import queue
import threading
import collections

from dataclasses import dataclass
import os
import dataclasses
import io
import pickle
from typing import List, Union, Dict, cast

import torch
from torch import Tensor
from torch.futures import Future
from pathlib import Path

from .metadata import (
from .storage import (

from .planner import (

from .utils import _create_file_view

from torch.distributed._shard._utils import narrow_tensor_by_index
from torch._utils import _get_device_module

__all__ = [

class _StorageInfo:
    This is the per entry storage info

    relative_path: str
    offset: int
    length: int

class _StoragePrefix:
    prefix: str

DEFAULT_SUFFIX = ".distcp"

def _trim(tensor: torch.Tensor) -> torch.Tensor:
    tensor = tensor.detach().cpu()
    if tensor._typed_storage()._size() != tensor.numel():
        tensor = tensor.clone()
    return tensor

def _result_from_write_item(
    item: WriteItem, size_in_bytes, storage_data
) -> WriteResult:
    return WriteResult(
        index=item.index, size_in_bytes=size_in_bytes, storage_data=storage_data

class _TensorLoader(ABC):
    def add(self, size, obj):

    def start_loading(self):

    def values(self):

class _SerialCpuLoader(_TensorLoader):
    def __init__(self, resolve_fun):
        self.resolve_fun = resolve_fun
        self.items = []

    def add(self, size, obj):
        self.items.append((size, obj))

    def start_loading(self):

    def values(self):
        for _, obj in self.items:
            tensor = self.resolve_fun(obj).detach()
            tensor = tensor.cpu()
            if != tensor.numel():
                tensor = tensor.clone()
            yield (

class _OverlappingCpuLoader(_TensorLoader):
    def __init__(self, resolve_fun, stream=None, inflight_threshhold=1_000_000):
        self.resolve_fun = resolve_fun
        self.items = []
        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 torch.device("cuda").type
        self.device_module = _get_device_module(self.device_type) = stream or self.device_module.current_stream()
        if != self.device_module.current_stream():

    def _done(self):
        return self.idx >= len(self.items)

    def _drain(self):
        drained = []
        if self.in_flight_data >= self.inflight_threshhold:
        while self.in_flight_data >= self.inflight_threshhold:
            val = self.current_items.popleft()
            self.in_flight_data -= val[0].numel() * val[0].element_size()
        return drained

    def _refill(self):
            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 ="cpu", non_blocking=True)
                elif tensor.device == torch.device("cpu"):
                    if != tensor.numel():
                        # this forces the tensor to be both contiguous and with minimal storage
                        tensor = tensor.clone()

                self.in_flight_data += tensor.numel() * tensor.element_size()

    def _finish(self):
        assert self._done
        if len(self.current_items) > 0:
        return self.current_items

    def add(self, size, obj):
        if self.started:
            raise RuntimeError("cannot add items after loading started")
        self.items.append((size, obj))

    def start_loading(self):
        if self.started:
        self.started = True
        self.items.sort(key=lambda x: x[0])

    def values(self):
        while not self._done:
            drained = self._drain()
            yield from drained

        yield from self._finish()

def _item_size(item: WriteItem) -> int:
    size = 1
    assert item.tensor_data is not None
    # can't use as PT needs to support older python
    for s in item.tensor_data.size:
        size *= s

    dtype =
    return size * torch._utils._element_size(dtype)

def _split_by_size_and_type(
    bins, 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=lambda x: x[1])[0]
        bucket_sizes[idx] += _item_size(wi)

    return buckets

def _write_item(stream, data, write_item, storage_key):
    offset = stream.tell()

    if write_item.type == WriteItemType.BYTE_IO:
        assert isinstance(data, io.BytesIO)
        assert isinstance(data, torch.Tensor)
        assert data.device == torch.device("cpu"), stream)
    length = stream.tell() - offset

    return _result_from_write_item(
        write_item, length, _StorageInfo(storage_key, offset, length)

def _write_files_from_queue(
    file_queue: queue.Queue,
    result_queue: queue.Queue,
    planner: SavePlanner,
    inflight_threshhold: int,
    use_fsync: bool,
        while True:
            file_name, storage_key, write_items = file_queue.get_nowait()
            loader: _TensorLoader

            if torch.cuda.is_available() and inflight_threshhold > 0:
                loader = _OverlappingCpuLoader(
                    lambda x: planner.resolve_data(x),
                loader = _SerialCpuLoader(
                    lambda x: planner.resolve_data(x),

            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)

            bytes_w = [
                wi for wi in write_items if wi.type == WriteItemType.BYTE_IO
            write_results = []

            with open(file_name, "wb") as stream:
                for write_item in bytes_w:
                    data = planner.resolve_data(write_item)
                        _write_item(stream, data, write_item, storage_key)

                for tensor, write_item in loader.values():
                    assert tensor.is_cpu
                        _write_item(stream, tensor, write_item, storage_key)

                if use_fsync:
    except queue.Empty:

[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, ) -> 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. N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure. """ super().__init__() self.path = 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 def set_up_storage_writer(self, is_coordinator: bool) -> None: pass def prepare_local_plan(self, plan: SavePlan) -> SavePlan: self.path.mkdir(parents=True, exist_ok=True) return plan def prepare_global_plan( self, global_plan: List[SavePlan] ) -> List[SavePlan]: new_plans = [ dataclasses.replace(plan, storage_data=_StoragePrefix(f"__{i}_")) for i, plan in enumerate(global_plan) ] return new_plans 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() file_queue.put((self.path / file_name, file_name, bucket)) else: for item in plan.items: file_name = gen_file() file_queue.put((self.path / file_name, file_name, [item])) result_queue: queue.Queue = queue.Queue() threads = [] for _ in range(1, self.thread_count): t = threading.Thread( target=_write_files_from_queue, args=( file_queue, result_queue, planner, self.per_thread_copy_ahead, self.sync_files, ), ) t.start() threads.append(t) _write_files_from_queue( file_queue=file_queue, result_queue=result_queue, planner=planner, inflight_threshhold=self.per_thread_copy_ahead, use_fsync=self.sync_files, ) for t in threads: t.join() res = [] try: while True: res += result_queue.get_nowait() except queue.Empty: pass fut: Future[List[WriteResult]] = Future() fut.set_result(res) return fut def finish( self, metadata: Metadata, results: List[List[WriteResult]] ) -> None: storage_md = dict() for wr_list in results: storage_md.update({wr.index: wr.storage_data for wr in wr_list}) metadata.storage_data = storage_md with (self.path / ".metadata.tmp").open("wb") as metadata_file: pickle.dump(metadata, metadata_file) os.fsync(metadata_file.fileno()) (self.path / ".metadata.tmp").rename(self.path / ".metadata")
[docs]class FileSystemReader(StorageReader): def __init__(self, path: Union[str, os.PathLike]) -> None: super().__init__() self.path = Path(path) self.storage_data: Dict[MetadataIndex, _StorageInfo] = dict() def _slice_file(self, file, sinfo: _StorageInfo): return _create_file_view(file, sinfo.offset, sinfo.length) def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]: # group requests by file per_file: Dict[str, List[ReadItem]] = dict() for read_item in plan.items: item_md = 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(): with (self.path / relative_path).open("rb") as file: # 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(file, item_md) if req.type == LoadItemType.BYTE_IO: bytes = io.BytesIO( planner.load_bytes(req, bytes) else: tensor = cast( Tensor, torch.load(file_slice, map_location="cpu") ) 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 def read_metadata(self) -> Metadata: with (self.path / ".metadata").open("rb") as metadata_file: return pickle.load(metadata_file) 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 def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan: return plan def prepare_global_plan( self, global_plan: List[LoadPlan] ) -> List[LoadPlan]: return global_plan


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