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Source code for torch.serialization

# mypy: allow-untyped-defs
import copyreg
import difflib
import functools
import io
import os
import pickle
import shutil
import struct
import sys
import tarfile
import tempfile
import warnings
from contextlib import closing, contextmanager
from enum import Enum
from typing import (
    Any,
    BinaryIO,
    Callable,
    cast,
    Dict,
    IO,
    List,
    Optional,
    Tuple,
    Type,
    Union,
)
from typing_extensions import TypeAlias, TypeGuard  # Python 3.10+

import torch
import torch._weights_only_unpickler as _weights_only_unpickler
from torch._sources import get_source_lines_and_file
from torch._utils import _import_dotted_name
from torch.storage import _get_dtype_from_pickle_storage_type
from torch.types import Storage


__all__ = [
    "SourceChangeWarning",
    "mkdtemp",
    "register_package",
    "check_module_version_greater_or_equal",
    "validate_cuda_device",
    "validate_hpu_device",
    "location_tag",
    "default_restore_location",
    "normalize_storage_type",
    "storage_to_tensor_type",
    "save",
    "load",
    "StorageType",
    "LoadEndianness",
    "get_default_load_endianness",
    "set_default_load_endianness",
    "clear_safe_globals",
    "get_safe_globals",
    "add_safe_globals",
]


DEFAULT_PROTOCOL = 2

LONG_SIZE = struct.Struct("=l").size
INT_SIZE = struct.Struct("=i").size
SHORT_SIZE = struct.Struct("=h").size

MAGIC_NUMBER = 0x1950A86A20F9469CFC6C
PROTOCOL_VERSION = 1001
STORAGE_KEY_SEPARATOR = ","

FILE_LIKE: TypeAlias = Union[str, os.PathLike, BinaryIO, IO[bytes]]
MAP_LOCATION: TypeAlias = Optional[
    Union[Callable[[Storage, str], Storage], torch.device, str, Dict[str, str]]
]
STORAGE: TypeAlias = Union[Storage, torch.storage.TypedStorage, torch.UntypedStorage]

IS_WINDOWS = sys.platform == "win32"

if not IS_WINDOWS:
    from mmap import MAP_PRIVATE, MAP_SHARED
else:
    MAP_SHARED, MAP_PRIVATE = None, None  # type: ignore[assignment]


class SourceChangeWarning(Warning):
    pass


@contextmanager
def mkdtemp():
    path = tempfile.mkdtemp()
    try:
        yield path
    finally:
        shutil.rmtree(path)


_package_registry: List[
    Tuple[
        int,
        Callable[[STORAGE], Optional[str]],
        Callable[[STORAGE, str], Optional[STORAGE]],
    ]
] = []


class LoadEndianness(Enum):
    NATIVE = 1
    LITTLE = 2
    BIG = 3


_default_load_endian: Optional[LoadEndianness] = None


[docs]def get_default_load_endianness() -> Optional[LoadEndianness]: """ Get fallback byte order for loading files If byteorder mark is not present in saved checkpoint, this byte order is used as fallback. By default, it's "native" byte order. Returns: default_load_endian: Optional[LoadEndianness] """ return _default_load_endian
[docs]def set_default_load_endianness(endianness): """ Set fallback byte order for loading files If byteorder mark is not present in saved checkpoint, this byte order is used as fallback. By default, it's "native" byte order. Args: endianness: the new fallback byte order """ global _default_load_endian if not isinstance(endianness, LoadEndianness) and endianness is not None: raise TypeError("Invalid argument type in function set_default_load_endianness") _default_load_endian = endianness
_default_mmap_options: int = MAP_PRIVATE
[docs]def get_default_mmap_options() -> int: """ Get default mmap options for :func:`torch.load` with ``mmap=True``. Defaults to ``mmap.MAP_PRIVATE``. Returns: default_mmap_options: int """ return _default_mmap_options
[docs]def set_default_mmap_options(flags: int): """ Set default mmap options for :func:`torch.load` with ``mmap=True`` to flags. For now, only either ``mmap.MAP_PRIVATE`` or ``mmap.MAP_SHARED`` are supported. Please open an issue if you need any other option to be added here. .. note:: This feature is currently not supported for Windows. Args: flags: ``mmap.MAP_PRIVATE`` or ``mmap.MAP_SHARED`` """ global _default_mmap_options if IS_WINDOWS: raise RuntimeError( "Changing the default mmap options is currently not supported for Windows" ) if flags != MAP_PRIVATE and flags != MAP_SHARED: raise ValueError( "Invalid argument in function set_default_mmap_options, " f"expected mmap.MAP_PRIVATE or mmap.MAP_SHARED, but got {flags}" ) _default_mmap_options = flags
[docs]def clear_safe_globals() -> None: """ Clears the list of globals that are safe for ``weights_only`` load. """ _weights_only_unpickler._clear_safe_globals()
[docs]def get_safe_globals() -> List[Any]: """ Returns the list of user-added globals that are safe for ``weights_only`` load. """ return _weights_only_unpickler._get_safe_globals()
[docs]def add_safe_globals(safe_globals: List[Any]) -> None: """ Marks the given globals as safe for ``weights_only`` load. Args: safe_globals (List[Any]): list of globals to mark as safe """ _weights_only_unpickler._add_safe_globals(safe_globals)
def _is_zipfile(f) -> bool: # This is a stricter implementation than zipfile.is_zipfile(). # zipfile.is_zipfile() is True if the magic number appears anywhere in the # binary. Since we expect the files here to be generated by torch.save or # torch.jit.save, it's safe to only check the start bytes and avoid # collisions and assume the zip has only 1 file. # See bugs.python.org/issue28494. start = f.tell() # Read the first few bytes and match against the ZIP file signature local_header_magic_number = b"PK\x03\x04" read_bytes = f.read(len(local_header_magic_number)) f.seek(start) return read_bytes == local_header_magic_number
[docs]def register_package( priority: int, tagger: Callable[[STORAGE], Optional[str]], deserializer: Callable[[STORAGE, str], Optional[STORAGE]], ): """ Registers callables for tagging and deserializing storage objects with an associated priority. Tagging associates a device with a storage object at save time while deserializing moves a storage object to an appropriate device at load time. :attr:`tagger` and :attr:`deserializer` are run in the order given by their :attr:`priority` until a tagger/deserializer returns a value that is not `None`. To override the deserialization behavior for a device in the global registry, one can register a tagger with a higher priority than the existing tagger. This function can also be used to register a tagger and deserializer for new devices. Args: priority: Indicates the priority associated with the tagger and deserializer, where a lower value indicates higher priority. tagger: Callable that takes in a storage object and returns its tagged device as a string or None. deserializer: Callable that takes in storage object and a device string and returns a storage object on the appropriate device or None. Returns: `None` Example: >>> def ipu_tag(obj): >>> if obj.device.type == 'ipu': >>> return 'ipu' >>> def ipu_deserialize(obj, location): >>> if location.startswith('ipu'): >>> ipu = getattr(torch, "ipu", None) >>> assert ipu is not None, "IPU device module is not loaded" >>> assert torch.ipu.is_available(), "ipu is not available" >>> return obj.ipu(location) >>> torch.serialization.register_package(11, ipu_tag, ipu_deserialize) """ queue_elem = (priority, tagger, deserializer) _package_registry.append(queue_elem) _package_registry.sort()
def check_module_version_greater_or_equal( module, req_version_tuple, error_if_malformed=True ): """ Check if a module's version satisfies requirements Usually, a module's version string will be like 'x.y.z', which would be represented as a tuple (x, y, z), but sometimes it could be an unexpected format. If the version string does not match the given tuple's format up to the length of the tuple, then error and exit or emit a warning. Args: module: the module to check the version of req_version_tuple: tuple (usually of ints) representing the required version error_if_malformed: whether we should exit if module version string is malformed Returns: requirement_is_met: bool """ try: version_strs = module.__version__.split(".") # Cast module version fields to match the types of the required version module_version = tuple( type(req_field)(version_strs[idx]) for idx, req_field in enumerate(req_version_tuple) ) requirement_is_met = module_version >= req_version_tuple except Exception as e: message = ( f"'{module.__name__}' module version string is malformed '{module.__version__}' and cannot be compared" f" with tuple {str(req_version_tuple)}" ) if error_if_malformed: raise RuntimeError(message) from e else: warnings.warn(message + ", but continuing assuming that requirement is met") requirement_is_met = True return requirement_is_met def _cpu_tag(obj): if obj.device.type == "cpu": return "cpu" def _mps_tag(obj): if obj.device.type == "mps": return "mps" def _meta_tag(obj): if obj.device.type == "meta": return "meta" def _backend_tag(backend_name, obj): if backend_name == "privateuse1": backend_name = torch._C._get_privateuse1_backend_name() if obj.device.type == backend_name: if obj.device.index is None: return backend_name else: return backend_name + ":" + str(obj.device.index) def _cpu_deserialize(obj, location): if location == "cpu": return obj def _mps_deserialize(obj, location): if location.startswith("mps"): return obj.mps() def _meta_deserialize(obj, location): if location == "meta": return torch.UntypedStorage(obj.nbytes(), device="meta") def _validate_device(location, backend_name): """ Check whether the device index of specified backend is valid In case of privateuse1 backend, your must first register a device_module for privateuse1 using torch._register_device_module. Implement the following methods in device_module like cuda: device_module._utils._get_device_index(location, True), device_module.device_count(). Args: location: string of device backend_name: the backend name or the name of privateuse1, which can be renamed Returns: device_index: int """ if not hasattr(torch, backend_name): raise RuntimeError( f"The {backend_name.upper()} device module is not registered. " "If you are running on a CPU-only machine, " "please use torch.load with map_location=torch.device('cpu') " "to map your storages to the CPU." ) device_module = getattr(torch, backend_name) if hasattr(device_module, "_utils") and hasattr( device_module._utils, "_get_device_index" ): device_index = device_module._utils._get_device_index(location, True) device = torch.device(backend_name, device_index) else: device = torch.device(location) device_index = device.index if device.index else 0 if hasattr(device_module, "is_available") and not device_module.is_available(): raise RuntimeError( f"Attempting to deserialize object on a {backend_name.upper()} " f"device but torch.{backend_name}.is_available() is False. " "If you are running on a CPU-only machine, " "please use torch.load with map_location=torch.device('cpu') " "to map your storages to the CPU." ) if hasattr(device_module, "device_count"): device_count = device_module.device_count() if device_index >= device_count: raise RuntimeError( f"Attempting to deserialize object on {backend_name.upper()} device " f"{device_index} but torch.{backend_name}.device_count() is {device_count}. " "Please use torch.load with map_location to map your storages " "to an existing device." ) return device def validate_cuda_device(location): return _validate_device(location, "cuda").index def validate_hpu_device(location): return _validate_device(location, "hpu").index def _deserialize(backend_name, obj, location): if backend_name == "privateuse1": backend_name = torch._C._get_privateuse1_backend_name() if location.startswith(backend_name): device = _validate_device(location, backend_name) return obj.to(device=device) register_package(10, _cpu_tag, _cpu_deserialize) register_package( 20, functools.partial(_backend_tag, "cuda"), functools.partial(_deserialize, "cuda") ) register_package(21, _mps_tag, _mps_deserialize) register_package(22, _meta_tag, _meta_deserialize) register_package( 23, functools.partial(_backend_tag, "privateuse1"), functools.partial(_deserialize, "privateuse1"), ) register_package( 24, functools.partial(_backend_tag, "hpu"), functools.partial(_deserialize, "hpu") ) register_package( 25, functools.partial(_backend_tag, "xpu"), functools.partial(_deserialize, "xpu") ) def location_tag( storage: Union[Storage, torch.storage.TypedStorage, torch.UntypedStorage] ): for _, tagger, _ in _package_registry: location = tagger(storage) if location: return location raise RuntimeError( "don't know how to determine data location of " + torch.typename(storage) ) def default_restore_location(storage, location): """ Restores `storage` using a deserializer function registered for the `location`. This function looks in the registry for deserializer functions that match the `location`. If found, it attempts to use them, in priority order, to restore `storage` until one returns a not `None` result. If no deserializer can be found in the registry, or all found fail to bear a result, it raises a `RuntimeError`. Args: storage (STORAGE): the storage object to restore location (str): the location tag associated with the storage object Returns: storage: Optional[STORAGE] Raises: RuntimeError: If no deserializer matching `location` is found in the registry or if all matching ones return `None`. """ for _, _, fn in _package_registry: result = fn(storage, location) if result is not None: return result raise RuntimeError( "don't know how to restore data location of " + torch.typename(storage) + " (tagged with " + location + ")" ) def normalize_storage_type(storage_type): return getattr(torch, storage_type.__name__) def storage_to_tensor_type(storage): storage_type = type(storage) module = _import_dotted_name(storage_type.__module__) return getattr(module, storage_type.__name__.replace("Storage", "Tensor")) def _is_path(name_or_buffer) -> TypeGuard[Union[str, os.PathLike]]: return isinstance(name_or_buffer, (str, os.PathLike)) class _opener: def __init__(self, file_like): self.file_like = file_like def __enter__(self): return self.file_like def __exit__(self, *args): pass class _open_file(_opener): def __init__(self, name, mode): super().__init__(open(name, mode)) def __exit__(self, *args): self.file_like.close() class _open_buffer_reader(_opener): def __init__(self, buffer): super().__init__(buffer) _check_seekable(buffer) class _open_buffer_writer(_opener): def __exit__(self, *args): self.file_like.flush() def _open_file_like(name_or_buffer, mode): if _is_path(name_or_buffer): return _open_file(name_or_buffer, mode) else: if "w" in mode: return _open_buffer_writer(name_or_buffer) elif "r" in mode: return _open_buffer_reader(name_or_buffer) else: raise RuntimeError(f"Expected 'r' or 'w' in mode but got {mode}") class _open_zipfile_reader(_opener): def __init__(self, name_or_buffer) -> None: super().__init__(torch._C.PyTorchFileReader(name_or_buffer)) class _open_zipfile_writer_file(_opener): def __init__(self, name) -> None: self.file_stream = None self.name = str(name) try: self.name.encode("ascii") except UnicodeEncodeError: # PyTorchFileWriter only supports ascii filename. # For filenames with non-ascii characters, we rely on Python # for writing out the file. self.file_stream = io.FileIO(self.name, mode="w") super().__init__(torch._C.PyTorchFileWriter(self.file_stream)) else: super().__init__(torch._C.PyTorchFileWriter(self.name)) def __exit__(self, *args) -> None: self.file_like.write_end_of_file() if self.file_stream is not None: self.file_stream.close() class _open_zipfile_writer_buffer(_opener): def __init__(self, buffer) -> None: if not callable(getattr(buffer, "write", None)): msg = f"Buffer of {str(type(buffer)).strip('<>')} has no callable attribute 'write'" if not hasattr(buffer, "write"): raise AttributeError(msg) raise TypeError(msg) self.buffer = buffer super().__init__(torch._C.PyTorchFileWriter(buffer)) def __exit__(self, *args) -> None: self.file_like.write_end_of_file() self.buffer.flush() def _open_zipfile_writer(name_or_buffer): container: Type[_opener] if _is_path(name_or_buffer): container = _open_zipfile_writer_file else: container = _open_zipfile_writer_buffer return container(name_or_buffer) def _is_compressed_file(f) -> bool: compress_modules = ["gzip"] try: return f.__module__ in compress_modules except AttributeError: return False def _should_read_directly(f): """ Checks if f is a file that should be read directly. It should be read directly if it is backed by a real file (has a fileno) and is not a a compressed file (e.g. gzip) """ if _is_compressed_file(f): return False try: return f.fileno() >= 0 except io.UnsupportedOperation: return False except AttributeError: return False def _check_seekable(f) -> bool: def raise_err_msg(patterns, e): for p in patterns: if p in str(e): msg = ( str(e) + ". You can only torch.load from a file that is seekable." + " Please pre-load the data into a buffer like io.BytesIO and" + " try to load from it instead." ) raise type(e)(msg) raise e try: f.seek(f.tell()) return True except (io.UnsupportedOperation, AttributeError) as e: raise_err_msg(["seek", "tell"], e) return False def _check_dill_version(pickle_module) -> None: """Checks if using dill as the pickle module, and if so, checks if it is the correct version. If dill version is lower than 0.3.1, a ValueError is raised. Args: pickle_module: module used for pickling metadata and objects """ if pickle_module is not None and pickle_module.__name__ == "dill": required_dill_version = (0, 3, 1) if not check_module_version_greater_or_equal( pickle_module, required_dill_version, False ): raise ValueError( ( "'torch' supports dill >= {}, but you have dill {}." " Please upgrade dill or switch to 'pickle'" ).format( ".".join([str(num) for num in required_dill_version]), pickle_module.__version__, ) ) def _check_save_filelike(f): if not _is_path(f) and not hasattr(f, "write"): raise AttributeError( "expected 'f' to be string, path, or a file-like object with " "a 'write' attribute" )
[docs]def save( obj: object, f: FILE_LIKE, pickle_module: Any = pickle, pickle_protocol: int = DEFAULT_PROTOCOL, _use_new_zipfile_serialization: bool = True, _disable_byteorder_record: bool = False, ) -> None: # Reference: https://github.com/pytorch/pytorch/issues/54354 # The first line of this docstring overrides the one Sphinx generates for the # documentation. We need it so that Sphinx doesn't leak `pickle`s path from # the build environment (e.g. `<module 'pickle' from '/leaked/path'). """save(obj, f, pickle_module=pickle, pickle_protocol=DEFAULT_PROTOCOL, _use_new_zipfile_serialization=True) Saves an object to a disk file. See also: :ref:`saving-loading-tensors` Args: obj: saved object f: a file-like object (has to implement write and flush) or a string or os.PathLike object containing a file name pickle_module: module used for pickling metadata and objects pickle_protocol: can be specified to override the default protocol .. note:: A common PyTorch convention is to save tensors using .pt file extension. .. note:: PyTorch preserves storage sharing across serialization. See :ref:`preserve-storage-sharing` for more details. .. note:: The 1.6 release of PyTorch switched ``torch.save`` to use a new zipfile-based file format. ``torch.load`` still retains the ability to load files in the old format. If for any reason you want ``torch.save`` to use the old format, pass the kwarg ``_use_new_zipfile_serialization=False``. Example: >>> # xdoctest: +SKIP("makes cwd dirty") >>> # Save to file >>> x = torch.tensor([0, 1, 2, 3, 4]) >>> torch.save(x, 'tensor.pt') >>> # Save to io.BytesIO buffer >>> buffer = io.BytesIO() >>> torch.save(x, buffer) """ torch._C._log_api_usage_once("torch.save") _check_dill_version(pickle_module) _check_save_filelike(f) if _use_new_zipfile_serialization: with _open_zipfile_writer(f) as opened_zipfile: _save( obj, opened_zipfile, pickle_module, pickle_protocol, _disable_byteorder_record, ) return else: with _open_file_like(f, "wb") as opened_file: _legacy_save(obj, opened_file, pickle_module, pickle_protocol)
def _legacy_save(obj, f, pickle_module, pickle_protocol) -> None: import torch.nn as nn serialized_container_types = {} serialized_storages = {} # Since loading storages that view the same data with different dtypes is # not supported, we need to keep track of the dtype associated with each # storage data_ptr and throw an error if the dtype is ever different. # TODO: This feature could be added in the future storage_dtypes: Dict[int, torch.dtype] = {} def persistent_id(obj: Any) -> Optional[Tuple]: # FIXME: the docs say that persistent_id should only return a string # but torch store returns tuples. This works only in the binary protocol # see # https://docs.python.org/2/library/pickle.html#pickling-and-unpickling-external-objects # https://github.com/python/cpython/blob/master/Lib/pickle.py#L527-L537 if isinstance(obj, type) and issubclass(obj, nn.Module): if obj in serialized_container_types: return None serialized_container_types[obj] = True source_file = source = None try: source_lines, _, source_file = get_source_lines_and_file(obj) source = "".join(source_lines) except ( Exception ): # saving the source is optional, so we can ignore any errors warnings.warn( "Couldn't retrieve source code for container of " "type " + obj.__name__ + ". It won't be checked " "for correctness upon loading." ) return ("module", obj, source_file, source) if isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj): storage: torch.UntypedStorage if isinstance(obj, torch.storage.TypedStorage): # TODO: Once we decide to break serialization FC, this case # can be deleted storage = obj._untyped_storage storage_dtype = obj.dtype storage_type_str = obj._pickle_storage_type() storage_type = getattr(torch, storage_type_str) dtype = obj.dtype storage_numel = obj._size() elif isinstance(obj, torch.UntypedStorage): storage = obj storage_dtype = torch.uint8 storage_type = normalize_storage_type(type(obj)) dtype = torch.uint8 storage_numel = storage.nbytes() else: raise TypeError(f"type not recognized: {type(obj)}") # If storage is allocated, ensure that any other saved storages # pointing to the same data all have the same dtype. If storage is # not allocated, don't perform this check if storage.data_ptr() != 0: if storage.data_ptr() in storage_dtypes: if storage_dtype != storage_dtypes[storage.data_ptr()]: raise RuntimeError( "Cannot save multiple tensors or storages that " "view the same data as different types" ) else: storage_dtypes[storage.data_ptr()] = storage_dtype view_metadata: Optional[Tuple[str, int, int]] # Offset is always 0, but we keep it for backwards compatibility # with the old serialization format (which supported storage views) offset = 0 storage_key = str(storage._cdata) location = location_tag(storage) # TODO: There's an issue here with FC. It might be impossible to # solve, but it's worth noting. Imagine we save a list `[storage, # tensor]`, where `tensor.storage()` is the same as `storage`, and # `tensor.element_size() > 1`. Let's say that `tensor.dtype == # torch.float`. The storage will be serialized with element size # of 1, since we're choosing to serialize the first occurance of # a duplicate storage. Since this legacy serialization format saves # the numel of the storage, rather than nbytes directly, we'll be # effectively saving nbytes in this case. We'll be able to load it # and the tensor back up with no problems in _this_ and future # versions of pytorch, but in older versions, here's the problem: # the storage will be loaded up as a UntypedStorage, and then the # FloatTensor will loaded and the UntypedStorage will be assigned to # it. Since the storage dtype does not match the tensor dtype, this # will cause an error. If we reverse the list, like `[tensor, # storage]`, then we will save the `tensor.storage()` as a faked # `FloatStorage`, and the saved size will be the correct # dtype-specific numel count that old versions expect. `tensor` # will be able to load up properly in old versions, pointing to # a FloatStorage. However, `storage` is still being translated to # a UntypedStorage, and it will try to resolve to the same # FloatStorage that `tensor` contains. This will also cause an # error. It doesn't seem like there's any way around this. # Probably, we just cannot maintain FC for the legacy format if the # saved list contains both a tensor and a storage that point to the # same data. We should still be able to maintain FC for lists of # just tensors, as long as all views share the same dtype as the # tensor they are viewing. if storage_key not in serialized_storages: serialized_storages[storage_key] = (storage, dtype) is_view = storage._cdata != storage._cdata if is_view: view_metadata = (str(storage._cdata), offset, storage.nbytes()) else: view_metadata = None res = ( "storage", storage_type, storage_key, location, storage_numel, view_metadata, ) return res return None sys_info = dict( protocol_version=PROTOCOL_VERSION, little_endian=sys.byteorder == "little", type_sizes=dict( short=SHORT_SIZE, int=INT_SIZE, long=LONG_SIZE, ), ) pickle_module.dump(MAGIC_NUMBER, f, protocol=pickle_protocol) pickle_module.dump(PROTOCOL_VERSION, f, protocol=pickle_protocol) pickle_module.dump(sys_info, f, protocol=pickle_protocol) pickler = pickle_module.Pickler(f, protocol=pickle_protocol) pickler.persistent_id = persistent_id pickler.dump(obj) serialized_storage_keys = sorted(serialized_storages.keys()) pickle_module.dump(serialized_storage_keys, f, protocol=pickle_protocol) f.flush() for key in serialized_storage_keys: storage, dtype = serialized_storages[key] storage._write_file( f, _should_read_directly(f), True, torch._utils._element_size(dtype) ) def _save(obj, zip_file, pickle_module, pickle_protocol, _disable_byteorder_record): serialized_storages = {} id_map: Dict[int, str] = {} # Since loading storages that view the same data with different dtypes is # not supported, we need to keep track of the dtype associated with each # storage data_ptr and throw an error if the dtype is ever different. # TODO: This feature could be added in the future storage_dtypes: Dict[int, torch.dtype] = {} def persistent_id(obj): # FIXME: the docs say that persistent_id should only return a string # but torch store returns tuples. This works only in the binary protocol # see # https://docs.python.org/2/library/pickle.html#pickling-and-unpickling-external-objects # https://github.com/python/cpython/blob/master/Lib/pickle.py#L527-L537 if isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj): if isinstance(obj, torch.storage.TypedStorage): # TODO: Once we decide to break serialization FC, this case # can be deleted storage = obj._untyped_storage storage_dtype = obj.dtype storage_type_str = obj._pickle_storage_type() storage_type = getattr(torch, storage_type_str) storage_numel = obj._size() else: storage = obj storage_dtype = torch.uint8 storage_type = normalize_storage_type(type(obj)) storage_numel = storage.nbytes() # If storage is allocated, ensure that any other saved storages # pointing to the same data all have the same dtype. If storage is # not allocated, don't perform this check if storage.data_ptr() != 0: if storage.data_ptr() in storage_dtypes: if storage_dtype != storage_dtypes[storage.data_ptr()]: raise RuntimeError( "Cannot save multiple tensors or storages that " "view the same data as different types" ) else: storage_dtypes[storage.data_ptr()] = storage_dtype storage_key = id_map.setdefault(storage._cdata, str(len(id_map))) location = location_tag(storage) serialized_storages[storage_key] = storage return ("storage", storage_type, storage_key, location, storage_numel) return None # Write the pickle data for `obj` data_buf = io.BytesIO() pickler = pickle_module.Pickler(data_buf, protocol=pickle_protocol) pickler.persistent_id = persistent_id pickler.dump(obj) data_value = data_buf.getvalue() zip_file.write_record("data.pkl", data_value, len(data_value)) # Write byte order marker if not _disable_byteorder_record: if sys.byteorder not in ["little", "big"]: raise ValueError("Unknown endianness type: " + sys.byteorder) zip_file.write_record("byteorder", sys.byteorder, len(sys.byteorder)) # Write each tensor to a file named tensor/the_tensor_key in the zip archive for key in sorted(serialized_storages.keys()): name = f"data/{key}" storage = serialized_storages[key] # given that we copy things around anyway, we might use storage.cpu() # this means to that to get tensors serialized, you need to implement # .cpu() on the underlying Storage if storage.device.type != "cpu": storage = storage.cpu() # Now that it is on the CPU we can directly copy it into the zip file num_bytes = storage.nbytes() zip_file.write_record(name, storage, num_bytes)
[docs]def load( f: FILE_LIKE, map_location: MAP_LOCATION = None, pickle_module: Any = None, *, weights_only: bool = False, mmap: Optional[bool] = None, **pickle_load_args: Any, ) -> Any: # Reference: https://github.com/pytorch/pytorch/issues/54354 # The first line of this docstring overrides the one Sphinx generates for the # documentation. We need it so that Sphinx doesn't leak `pickle`s path from # the build environment (e.g. `<module 'pickle' from '/leaked/path'). """load(f, map_location=None, pickle_module=pickle, *, weights_only=False, mmap=None, **pickle_load_args) Loads an object saved with :func:`torch.save` from a file. :func:`torch.load` uses Python's unpickling facilities but treats storages, which underlie tensors, specially. They are first deserialized on the CPU and are then moved to the device they were saved from. If this fails (e.g. because the run time system doesn't have certain devices), an exception is raised. However, storages can be dynamically remapped to an alternative set of devices using the :attr:`map_location` argument. If :attr:`map_location` is a callable, it will be called once for each serialized storage with two arguments: storage and location. The storage argument will be the initial deserialization of the storage, residing on the CPU. Each serialized storage has a location tag associated with it which identifies the device it was saved from, and this tag is the second argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. :attr:`map_location` should return either ``None`` or a storage. If :attr:`map_location` returns a storage, it will be used as the final deserialized object, already moved to the right device. Otherwise, :func:`torch.load` will fall back to the default behavior, as if :attr:`map_location` wasn't specified. If :attr:`map_location` is a :class:`torch.device` object or a string containing a device tag, it indicates the location where all tensors should be loaded. Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags appearing in the file (keys), to ones that specify where to put the storages (values). User extensions can register their own location tags and tagging and deserialization methods using :func:`torch.serialization.register_package`. Args: f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), or a string or os.PathLike object containing a file name map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage locations pickle_module: module used for unpickling metadata and objects (has to match the :attr:`pickle_module` used to serialize file) weights_only: Indicates whether unpickler should be restricted to loading only tensors, primitive types, dictionaries and any types added via :func:`torch.serialization.add_safe_globals`. mmap: Indicates whether the file should be mmaped rather than loading all the storages into memory. Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they are moved to the location that they were tagged with when saving, or specified by ``map_location``. This second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the tensor storages from disk to CPU memory in the first step, ``f`` is mmaped. pickle_load_args: (Python 3 only) optional keyword arguments passed over to :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., :attr:`errors=...`. .. warning:: :func:`torch.load()` unless `weights_only` parameter is set to `True`, uses ``pickle`` module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Never load data that could have come from an untrusted source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. .. note:: When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. .. note:: By default, we decode byte strings as ``utf-8``. This is to avoid a common error case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` when loading files saved by Python 2 in Python 3. If this default is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them as byte arrays which can be decoded later with ``byte_array.decode(...)``. Example: >>> # xdoctest: +SKIP("undefined filepaths") >>> torch.load('tensors.pt', weights_only=True) # Load all tensors onto the CPU >>> torch.load('tensors.pt', map_location=torch.device('cpu'), weights_only=True) # Load all tensors onto the CPU, using a function >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage, weights_only=True) # Load all tensors onto GPU 1 >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1), weights_only=True) # Map tensors from GPU 1 to GPU 0 >>> torch.load('tensors.pt', map_location={'cuda:1': 'cuda:0'}, weights_only=True) # Load tensor from io.BytesIO object # Loading from a buffer setting weights_only=False, warning this can be unsafe >>> with open('tensor.pt', 'rb') as f: ... buffer = io.BytesIO(f.read()) >>> torch.load(buffer, weights_only=False) # Load a module with 'ascii' encoding for unpickling # Loading from a module setting weights_only=False, warning this can be unsafe >>> torch.load('module.pt', encoding='ascii', weights_only=False) """ torch._C._log_api_usage_once("torch.load") UNSAFE_MESSAGE = ( "Weights only load failed. Re-running `torch.load` with `weights_only` set to `False`" " will likely succeed, but it can result in arbitrary code execution." " Do it only if you get the file from a trusted source. Alternatively, to load" " with `weights_only` please check the recommended steps in the following error message." " WeightsUnpickler error: " ) # Add ability to force safe only weight loads via environment variable if os.getenv("TORCH_FORCE_WEIGHTS_ONLY_LOAD", "0").lower() in [ "1", "y", "yes", "true", ]: weights_only = True if weights_only: if pickle_module is not None: raise RuntimeError( "Can not safely load weights when explicit pickle_module is specified" ) else: if pickle_module is None: pickle_module = pickle # make flipping default BC-compatible if mmap is None: mmap = False _check_dill_version(pickle_module) if "encoding" not in pickle_load_args.keys(): pickle_load_args["encoding"] = "utf-8" with _open_file_like(f, "rb") as opened_file: if _is_zipfile(opened_file): # The zipfile reader is going to advance the current file position. # If we want to actually tail call to torch.jit.load, we need to # reset back to the original position. orig_position = opened_file.tell() overall_storage = None with _open_zipfile_reader(opened_file) as opened_zipfile: if _is_torchscript_zip(opened_zipfile): warnings.warn( "'torch.load' received a zip file that looks like a TorchScript archive" " dispatching to 'torch.jit.load' (call 'torch.jit.load' directly to" " silence this warning)", UserWarning, ) opened_file.seek(orig_position) return torch.jit.load(opened_file, map_location=map_location) if mmap: if not _is_path(f): raise ValueError( "f must be a file path in order to use the mmap argument" ) size = os.path.getsize(f) if not IS_WINDOWS: shared = get_default_mmap_options() == MAP_SHARED else: shared = False overall_storage = torch.UntypedStorage.from_file( os.fspath(f), shared, size ) if weights_only: try: return _load( opened_zipfile, map_location, _weights_only_unpickler, overall_storage=overall_storage, **pickle_load_args, ) except RuntimeError as e: raise pickle.UnpicklingError(UNSAFE_MESSAGE + str(e)) from None return _load( opened_zipfile, map_location, pickle_module, overall_storage=overall_storage, **pickle_load_args, ) if mmap: f_name = "" if not isinstance(f, str) else f"{f}, " raise RuntimeError( "mmap can only be used with files saved with " f"`torch.save({f_name}_use_new_zipfile_serialization=True), " "please torch.save your checkpoint with this option in order to use mmap." ) if weights_only: try: return _legacy_load( opened_file, map_location, _weights_only_unpickler, **pickle_load_args, ) except RuntimeError as e: raise pickle.UnpicklingError(UNSAFE_MESSAGE + str(e)) from None return _legacy_load( opened_file, map_location, pickle_module, **pickle_load_args )
# Register pickling support for layout instances such as # torch.sparse_coo, etc def _get_layout(name): """Get layout extension object from its string representation.""" cache = _get_layout.cache # type: ignore[attr-defined] if not cache: for v in torch.__dict__.values(): if isinstance(v, torch.layout): cache[str(v)] = v return cache[name] # There are yet not good way to type annotate function attributes https://github.com/python/mypy/issues/2087 _get_layout.cache = {} # type: ignore[attr-defined] copyreg.pickle(torch.layout, lambda obj: (_get_layout, (str(obj),))) def _legacy_load(f, map_location, pickle_module, **pickle_load_args): deserialized_objects: Dict[int, Any] = {} restore_location = _get_restore_location(map_location) class UnpicklerWrapper(pickle_module.Unpickler): # type: ignore[name-defined] def find_class(self, mod_name, name): if type(name) is str and "Storage" in name: try: return StorageType(name) except KeyError: pass return super().find_class(mod_name, name) def _check_container_source(container_type, source_file, original_source): try: current_source = "".join(get_source_lines_and_file(container_type)[0]) except Exception: # saving the source is optional, so we can ignore any errors warnings.warn( "Couldn't retrieve source code for container of " "type " + container_type.__name__ + ". It won't be checked " "for correctness upon loading." ) return if original_source != current_source: if container_type.dump_patches: file_name = container_type.__name__ + ".patch" diff = difflib.unified_diff( current_source.split("\n"), original_source.split("\n"), source_file, source_file, lineterm="", ) lines = "\n".join(diff) try: with open(file_name, "a+") as f: file_size = f.seek(0, 2) f.seek(0) if file_size == 0: f.write(lines) elif file_size != len(lines) or f.read() != lines: raise OSError msg = ( "Saved a reverse patch to " + file_name + ". " "Run `patch -p0 < " + file_name + "` to revert your " "changes." ) except OSError: msg = ( "Tried to save a patch, but couldn't create a " "writable file " + file_name + ". Make sure it " "doesn't exist and your working directory is " "writable." ) else: msg = ( "you can retrieve the original source code by " "accessing the object's source attribute or set " "`torch.nn.Module.dump_patches = True` and use the " "patch tool to revert the changes." ) msg = f"source code of class '{torch.typename(container_type)}' has changed. {msg}" warnings.warn(msg, SourceChangeWarning) def legacy_load(f): deserialized_objects: Dict[int, Any] = {} def persistent_load(saved_id): if isinstance(saved_id, tuple): # Ignore containers that don't have any sources saved if all(saved_id[1:]): _check_container_source(*saved_id) return saved_id[0] return deserialized_objects[int(saved_id)] with closing( tarfile.open(fileobj=f, mode="r:", format=tarfile.PAX_FORMAT) ) as tar, mkdtemp() as tmpdir: tar.extract("storages", path=tmpdir) with open(os.path.join(tmpdir, "storages"), "rb", 0) as f: num_storages = pickle_module.load(f, **pickle_load_args) for i in range(num_storages): args = pickle_module.load(f, **pickle_load_args) key, location, storage_type = args dtype = storage_type._dtype obj = cast(Storage, torch.UntypedStorage)._new_with_file( f, torch._utils._element_size(dtype) ) obj = restore_location(obj, location) # TODO: Once we decide to break serialization FC, we can # stop wrapping with TypedStorage deserialized_objects[key] = torch.storage.TypedStorage( wrap_storage=obj, dtype=dtype, _internal=True ) storage_views = pickle_module.load(f, **pickle_load_args) for target_cdata, root_cdata, offset, numel in storage_views: root = deserialized_objects[root_cdata] element_size = torch._utils._element_size(root.dtype) offset_bytes = offset * element_size # TODO: Once we decide to break serialization FC, we can # stop wrapping with TypedStorage deserialized_objects[target_cdata] = torch.storage.TypedStorage( wrap_storage=root._untyped_storage[ offset_bytes : offset_bytes + numel * element_size ], dtype=root.dtype, _internal=True, ) tar.extract("tensors", path=tmpdir) with open(os.path.join(tmpdir, "tensors"), "rb", 0) as f: num_tensors = pickle_module.load(f, **pickle_load_args) for _ in range(num_tensors): args = pickle_module.load(f, **pickle_load_args) key, storage_id, original_tensor_type = args storage = deserialized_objects[storage_id] (ndim,) = struct.unpack("<i", f.read(4)) # skip next 4 bytes; legacy encoding treated ndim as 8 bytes f.read(4) numel = struct.unpack(f"<{ndim}q", f.read(8 * ndim)) stride = struct.unpack(f"<{ndim}q", f.read(8 * ndim)) (storage_offset,) = struct.unpack("<q", f.read(8)) tensor = torch.empty((0,), dtype=storage.dtype).set_( storage._untyped_storage, storage_offset, numel, stride ) deserialized_objects[key] = tensor pickle_file = tar.extractfile("pickle") unpickler = UnpicklerWrapper(pickle_file, **pickle_load_args) unpickler.persistent_load = persistent_load result = unpickler.load() return result deserialized_objects = {} def persistent_load(saved_id): assert isinstance(saved_id, tuple) typename = _maybe_decode_ascii(saved_id[0]) data = saved_id[1:] if typename == "module": # Ignore containers that don't have any sources saved if all(data[1:]): _check_container_source(*data) return data[0] elif typename == "storage": storage_type, root_key, location, numel, view_metadata = data location = _maybe_decode_ascii(location) dtype = storage_type.dtype nbytes = numel * torch._utils._element_size(dtype) if root_key not in deserialized_objects: if torch._guards.active_fake_mode() is not None: obj = cast(Storage, torch.UntypedStorage(nbytes, device="meta")) else: obj = cast(Storage, torch.UntypedStorage(nbytes)) obj._torch_load_uninitialized = True obj = restore_location(obj, location) # TODO: Once we decide to break serialization FC, we can # stop wrapping with TypedStorage typed_storage = torch.storage.TypedStorage( wrap_storage=obj, dtype=dtype, _internal=True ) deserialized_objects[root_key] = typed_storage else: typed_storage = deserialized_objects[root_key] if typed_storage._data_ptr() == 0: typed_storage = torch.storage.TypedStorage( device=typed_storage._untyped_storage.device, dtype=dtype, _internal=True, ) if view_metadata is not None: view_key, offset, view_size = view_metadata offset_bytes = offset * torch._utils._element_size(dtype) view_size_bytes = view_size * torch._utils._element_size(dtype) if view_key not in deserialized_objects: # TODO: Once we decide to break serialization FC, we can # stop wrapping with TypedStorage deserialized_objects[view_key] = torch.storage.TypedStorage( wrap_storage=typed_storage._untyped_storage[ offset_bytes : offset_bytes + view_size_bytes ], dtype=dtype, _internal=True, ) res = deserialized_objects[view_key] else: res = typed_storage return res else: raise RuntimeError(f"Unknown saved id type: {saved_id[0]}") _check_seekable(f) f_should_read_directly = _should_read_directly(f) if f_should_read_directly and f.tell() == 0: # legacy_load requires that f has fileno() # only if offset is zero we can attempt the legacy tar file loader try: return legacy_load(f) except tarfile.TarError: if _is_zipfile(f): # .zip is used for torch.jit.save and will throw an un-pickling error here raise RuntimeError( f"{f.name} is a zip archive (did you mean to use torch.jit.load()?)" ) from None # if not a tarfile, reset file offset and proceed f.seek(0) if not hasattr(f, "readinto") and (3, 8, 0) <= sys.version_info < (3, 8, 2): raise RuntimeError( "torch.load does not work with file-like objects that do not implement readinto on Python 3.8.0 and 3.8.1. " f'Received object of type "{type(f)}". Please update to Python 3.8.2 or newer to restore this ' "functionality." ) magic_number = pickle_module.load(f, **pickle_load_args) if magic_number != MAGIC_NUMBER: raise RuntimeError("Invalid magic number; corrupt file?") protocol_version = pickle_module.load(f, **pickle_load_args) if protocol_version != PROTOCOL_VERSION: raise RuntimeError(f"Invalid protocol version: {protocol_version}") _sys_info = pickle_module.load(f, **pickle_load_args) unpickler = UnpicklerWrapper(f, **pickle_load_args) unpickler.persistent_load = persistent_load result = unpickler.load() deserialized_storage_keys = pickle_module.load(f, **pickle_load_args) if torch._guards.active_fake_mode() is None: offset = f.tell() if f_should_read_directly else None for key in deserialized_storage_keys: assert key in deserialized_objects typed_storage = deserialized_objects[key] typed_storage._untyped_storage._set_from_file( f, offset, f_should_read_directly, torch._utils._element_size(typed_storage.dtype), ) if offset is not None: offset = f.tell() torch._utils._validate_loaded_sparse_tensors() return result def _maybe_decode_ascii(bytes_str: Union[bytes, str]) -> str: # When using encoding='bytes' in Py3, some **internal** keys stored as # strings in Py2 are loaded as bytes. This function decodes them with # ascii encoding, one that Py3 uses by default. # # NOTE: This should only be used on internal keys (e.g., `typename` and # `location` in `persistent_load` below! if isinstance(bytes_str, bytes): return bytes_str.decode("ascii") return bytes_str def _get_restore_location(map_location): if map_location is None: restore_location = default_restore_location elif isinstance(map_location, dict): def restore_location(storage, location): location = map_location.get(location, location) return default_restore_location(storage, location) elif isinstance(map_location, (str, bytes)): def restore_location(storage, location): return default_restore_location(storage, map_location) elif isinstance(map_location, torch.device): def restore_location(storage, location): return default_restore_location(storage, str(map_location)) else: def restore_location(storage, location): result = map_location(storage, location) if result is None: result = default_restore_location(storage, location) return result return restore_location class StorageType: def __init__(self, name): self._dtype = _get_dtype_from_pickle_storage_type(name) @property def dtype(self): return self._dtype def __str__(self): return f"StorageType(dtype={self.dtype})" def _load( zip_file, map_location, pickle_module, pickle_file="data.pkl", overall_storage=None, **pickle_load_args, ): restore_location = _get_restore_location(map_location) loaded_storages = {} # check if byteswapping is needed byteordername = "byteorder" byteorderdata = None if zip_file.has_record(byteordername): byteorderdata = zip_file.get_record(byteordername) if byteorderdata not in [b"little", b"big"]: raise ValueError("Unknown endianness type: " + byteorderdata.decode()) elif ( get_default_load_endianness() == LoadEndianness.LITTLE or get_default_load_endianness() is None ): byteorderdata = b"little" elif get_default_load_endianness() == LoadEndianness.BIG: byteorderdata = b"big" elif get_default_load_endianness() == LoadEndianness.NATIVE: pass else: raise ValueError("Invalid load endianness type") if ( not zip_file.has_record(byteordername) and get_default_load_endianness() is None and sys.byteorder == "big" ): # Default behaviour was changed # See https://github.com/pytorch/pytorch/issues/101688 warnings.warn( "The default load endianness for checkpoints without a byteorder mark " "on big endian machines was changed from 'native' to 'little' endian, " "to avoid this behavior please use " "torch.serialization.set_default_load_endianness to set " "the desired default load endianness", UserWarning, ) def load_tensor(dtype, numel, key, location): name = f"data/{key}" if torch._guards.detect_fake_mode(None) is not None: nbytes = numel * torch._utils._element_size(dtype) storage = torch.UntypedStorage(nbytes, device="meta") elif overall_storage is not None: storage_offset = zip_file.get_record_offset(name) storage = overall_storage[storage_offset : storage_offset + numel] else: storage = ( zip_file.get_storage_from_record(name, numel, torch.UntypedStorage) ._typed_storage() ._untyped_storage ) # swap here if byteswapping is needed if byteorderdata is not None: if byteorderdata.decode() != sys.byteorder: storage.byteswap(dtype) # TODO: Once we decide to break serialization FC, we can # stop wrapping with TypedStorage typed_storage = torch.storage.TypedStorage( wrap_storage=restore_location(storage, location), dtype=dtype, _internal=True, ) if typed_storage._data_ptr() != 0: loaded_storages[key] = typed_storage return typed_storage def persistent_load(saved_id): assert isinstance(saved_id, tuple) typename = _maybe_decode_ascii(saved_id[0]) data = saved_id[1:] assert ( typename == "storage" ), f"Unknown typename for persistent_load, expected 'storage' but got '{typename}'" storage_type, key, location, numel = data if storage_type is torch.UntypedStorage: dtype = torch.uint8 else: dtype = storage_type.dtype if key in loaded_storages: typed_storage = loaded_storages[key] else: nbytes = numel * torch._utils._element_size(dtype) typed_storage = load_tensor( dtype, nbytes, key, _maybe_decode_ascii(location) ) return typed_storage load_module_mapping: Dict[str, str] = { # See https://github.com/pytorch/pytorch/pull/51633 "torch.tensor": "torch._tensor" } # Need to subclass Unpickler instead of directly monkey-patching the find_class method # because it's marked readonly in pickle. # The type: ignore is because mypy can't statically determine the type of this class. class UnpicklerWrapper(pickle_module.Unpickler): # type: ignore[name-defined] # from https://stackoverflow.com/questions/13398462/unpickling-python-objects-with-a-changed-module-path/13405732 # Lets us override the imports that pickle uses when unpickling an object. # This is useful for maintaining BC if we change a module path that tensor instantiation relies on. def find_class(self, mod_name, name): if type(name) is str and "Storage" in name: try: return StorageType(name) except KeyError: pass mod_name = load_module_mapping.get(mod_name, mod_name) return super().find_class(mod_name, name) # Load the data (which may in turn use `persistent_load` to load tensors) data_file = io.BytesIO(zip_file.get_record(pickle_file)) unpickler = UnpicklerWrapper(data_file, **pickle_load_args) unpickler.persistent_load = persistent_load # Needed for tensors where storage device and rebuild tensor device are # not connected (wrapper subclasses and tensors rebuilt using numpy) torch._utils._thread_local_state.map_location = map_location result = unpickler.load() del torch._utils._thread_local_state.map_location torch._utils._validate_loaded_sparse_tensors() torch._C._log_api_usage_metadata( "torch.load.metadata", {"serialization_id": zip_file.serialization_id()} ) return result def _is_torchscript_zip(zip_file): return "constants.pkl" in zip_file.get_all_records()

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