Source code for torch.serialization

import difflib
import os
import io
import shutil
import struct
import sys
import torch
import tarfile
import tempfile
import warnings
from contextlib import closing, contextmanager
from ._utils import _import_dotted_name
from ._six import string_classes as _string_classes
from torch._utils_internal import get_source_lines_and_file
from torch.types import Storage
from typing import Any, BinaryIO, cast, Dict, Optional, Type, Tuple, Union
import copyreg
import pickle
import pathlib


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

MAGIC_NUMBER = 0x1950a86a20f9469cfc6c

class SourceChangeWarning(Warning):

def mkdtemp():
    path = tempfile.mkdtemp()
    yield path

_package_registry = []

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 or
    #, it's safe to only check the start bytes and avoid
    # collisions and assume the zip has only 1 file.
    # See

    # Read the first 4 bytes of the file
    read_bytes = []
    start = f.tell()

    byte =
    while byte != "":
        if len(read_bytes) == 4:
        byte =

    local_header_magic_number = [b'P', b'K', b'\x03', b'\x04']
    return read_bytes == local_header_magic_number

def register_package(priority, tagger, deserializer):
    queue_elem = (priority, tagger, deserializer)

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.

        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

        requirement_is_met: bool
        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 = (
            "'%s' module version string is malformed '%s' and cannot be compared"
            " with tuple %s"
        ) % (
            module.__name__, module.__version__, str(req_version_tuple)
        if error_if_malformed:
            raise RuntimeError(message)
            warnings.warn(message + ', but continuing assuming that requirement is met')
            requirement_is_met = True

    return requirement_is_met

def _cpu_tag(obj):
    if type(obj).__module__ == 'torch':
        return 'cpu'

def _cuda_tag(obj):
    if type(obj).__module__ == 'torch.cuda':
        return 'cuda:' + str(obj.get_device())

def _cpu_deserialize(obj, location):
    if location == 'cpu':
        return obj

def validate_cuda_device(location):
    device = torch.cuda._utils._get_device_index(location, True)

    if not torch.cuda.is_available():
        raise RuntimeError('Attempting to deserialize object on a CUDA '
                           'device but torch.cuda.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 device >= torch.cuda.device_count():
        raise RuntimeError('Attempting to deserialize object on CUDA device '
                           '{device} but torch.cuda.device_count() is {device_count}. Please use '
                           'torch.load with map_location to map your storages '
                           'to an existing device.'.format(
                               device=device, device_count=torch.cuda.device_count()))
    return device

def _cuda_deserialize(obj, location):
    if location.startswith('cuda'):
        device = validate_cuda_device(location)
        if getattr(obj, "_torch_load_uninitialized", False):
            storage_type = getattr(torch.cuda, type(obj).__name__)
            with torch.cuda.device(device):
                return storage_type(obj.size())
            return obj.cuda(device)

register_package(10, _cpu_tag, _cpu_deserialize)
register_package(20, _cuda_tag, _cuda_deserialize)

def location_tag(storage: Storage):
    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):
    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):
    return isinstance(name_or_buffer, str) or \
        (sys.version_info[0] == 3 and isinstance(name_or_buffer, pathlib.Path))

class _opener(object):
    def __init__(self, file_like):
        self.file_like = file_like

    def __enter__(self):
        return self.file_like

    def __exit__(self, *args):

class _open_file(_opener):
    def __init__(self, name, mode):
        super(_open_file, self).__init__(open(name, mode))

    def __exit__(self, *args):

class _open_buffer_reader(_opener):
    def __init__(self, buffer):
        super(_open_buffer_reader, self).__init__(buffer)

class _open_buffer_writer(_opener):
    def __exit__(self, *args):

def _open_file_like(name_or_buffer, mode):
    if _is_path(name_or_buffer):
        return _open_file(name_or_buffer, mode)
        if 'w' in mode:
            return _open_buffer_writer(name_or_buffer)
        elif 'r' in mode:
            return _open_buffer_reader(name_or_buffer)
            raise RuntimeError("Expected 'r' or 'w' in mode but got {}".format(mode))

class _open_zipfile_reader(_opener):
    def __init__(self, name_or_buffer) -> None:
        super(_open_zipfile_reader, self).__init__(torch._C.PyTorchFileReader(name_or_buffer))

class _open_zipfile_writer_file(_opener):
    def __init__(self, name) -> None:
        super(_open_zipfile_writer_file, self).__init__(torch._C.PyTorchFileWriter(str(name)))

    def __exit__(self, *args) -> None:

class _open_zipfile_writer_buffer(_opener):
    def __init__(self, buffer) -> None:
        self.buffer = buffer
        super(_open_zipfile_writer_buffer, self).__init__(torch._C.PyTorchFileWriter(buffer))

    def __exit__(self, *args) -> None:

def _open_zipfile_writer(name_or_buffer):
    container: Type[_opener]
    if _is_path(name_or_buffer):
        container = _open_zipfile_writer_file
        container = _open_zipfile_writer_buffer
    return container(name_or_buffer)

def _is_compressed_file(f) -> bool:
    compress_modules = ['gzip']
        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
        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

        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.

        pickle_module: module used for pickling metadata and objects

    if 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 >= %s, but you have dill %s."
                " Please upgrade dill or switch to 'pickle'"
            ) % (
                '.'.join([str(num) for num in required_dill_version]),

[docs]def save(obj, f: Union[str, os.PathLike, BinaryIO], pickle_module=pickle, pickle_protocol=DEFAULT_PROTOCOL, _use_new_zipfile_serialization=True) -> None: """Saves an object to a disk file. See also: `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 `preserve-storage-sharing` for more details. .. note:: The 1.6 release of PyTorch switched ```` 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 ```` to use the old format, pass the kwarg ``_use_new_zipfile_serialization=False``. Example: >>> # Save to file >>> x = torch.tensor([0, 1, 2, 3, 4]) >>>, '') >>> # Save to io.BytesIO buffer >>> buffer = io.BytesIO() >>>, buffer) """ _check_dill_version(pickle_module) with _open_file_like(f, 'wb') as opened_file: if _use_new_zipfile_serialization: with _open_zipfile_writer(opened_file) as opened_zipfile: _save(obj, opened_zipfile, pickle_module, pickle_protocol) return _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 = {} 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 # # 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) elif torch.is_storage(obj): view_metadata: Optional[Tuple[str, int, int]] obj = cast(Storage, obj) storage_type = normalize_storage_type(type(obj)) # Offset is always 0, but we keep it for backwards compatibility # with the old serialization format (which supported storage views) offset = 0 obj_key = str(obj._cdata) location = location_tag(obj) serialized_storages[obj_key] = obj is_view = obj._cdata != obj._cdata if is_view: view_metadata = (str(obj._cdata), offset, obj.size()) else: view_metadata = None return ('storage', storage_type, obj_key, location, obj.size(), view_metadata) 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: serialized_storages[key]._write_file(f, _should_read_directly(f), True) def _save(obj, zip_file, pickle_module, pickle_protocol): serialized_storages = {} 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 # # if torch.is_storage(obj): storage_type = normalize_storage_type(type(obj)) obj_key = str(obj._cdata) location = location_tag(obj) serialized_storages[obj_key] = obj return ('storage', storage_type, obj_key, location, obj.size()) 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 each tensor to a file named tensor/the_tensor_key in the zip archive for key in sorted(serialized_storages.keys()): name = 'data/{}'.format(key) storage = serialized_storages[key] if storage.device.type == 'cpu': # If it's on the CPU we can directly copy it into the zip file num_bytes = storage.size() * storage.element_size() zip_file.write_record(name, storage.data_ptr(), num_bytes) else: # Copy to a buffer, then serialize that buf = io.BytesIO() storage._write_file(buf, _should_read_directly(buf)) buf_value = buf.getvalue() zip_file.write_record(name, buf_value, len(buf_value))
[docs]def load(f, map_location=None, pickle_module=pickle, **pickle_load_args): """Loads an object saved with :func:`` 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) 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()` 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, 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: >>> torch.load('') # Load all tensors onto the CPU >>> torch.load('', map_location=torch.device('cpu')) # Load all tensors onto the CPU, using a function >>> torch.load('', map_location=lambda storage, loc: storage) # Load all tensors onto GPU 1 >>> torch.load('', map_location=lambda storage, loc: storage.cuda(1)) # Map tensors from GPU 1 to GPU 0 >>> torch.load('', map_location={'cuda:1':'cuda:0'}) # Load tensor from io.BytesIO object >>> with open('', 'rb') as f: buffer = io.BytesIO( >>> torch.load(buffer) # Load a module with 'ascii' encoding for unpickling >>> torch.load('', encoding='ascii') """ _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() 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) return torch.jit.load(opened_file) return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args) 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 _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) 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 =, 2) if file_size == 0: f.write(lines) elif file_size != len(lines) or != lines: raise IOError msg = ("Saved a reverse patch to " + file_name + ". " "Run `patch -p0 < " + file_name + "` to revert your " "changes.") except IOError: 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 = ("source code of class '{container_type}' has changed. {msg}" .format(container_type=torch.typename(container_type), msg=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(, 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 obj = storage_type._new_with_file(f) obj = restore_location(obj, location) deserialized_objects[key] = obj storage_views = pickle_module.load(f, **pickle_load_args) for target_cdata, root_cdata, offset, size in storage_views: root = deserialized_objects[root_cdata] deserialized_objects[target_cdata] = root[offset:offset + size] 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] tensor_type = storage_to_tensor_type(storage) ndim, = struct.unpack('<i', # skip next 4 bytes; legacy encoding treated ndim as 8 bytes size = struct.unpack('<{}q'.format(ndim), * ndim)) stride = struct.unpack('<{}q'.format(ndim), * ndim)) storage_offset, = struct.unpack('<q', tensor = tensor_type().set_(storage, storage_offset, size, stride) deserialized_objects[key] = tensor pickle_file = tar.extractfile('pickle') unpickler = pickle_module.Unpickler(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': data_type, root_key, location, size, view_metadata = data location = _maybe_decode_ascii(location) if root_key not in deserialized_objects: obj = data_type(size) obj._torch_load_uninitialized = True deserialized_objects[root_key] = restore_location(obj, location) storage = deserialized_objects[root_key] if view_metadata is not None: view_key, offset, view_size = view_metadata if view_key not in deserialized_objects: deserialized_objects[view_key] = storage[offset:offset + view_size] return deserialized_objects[view_key] else: return storage else: raise RuntimeError("Unknown saved id type: %s" % 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 and will throw an un-pickling error here raise RuntimeError( "{filename} is a zip archive (did you mean to use torch.jit.load()?)".format( # if not a tarfile, reset file offset and proceed 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. " "Received object of type \"{}\". Please update to Python 3.8.2 or newer to restore this " "functionality.".format(type(f))) 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("Invalid protocol version: %s" % protocol_version) _sys_info = pickle_module.load(f, **pickle_load_args) unpickler = pickle_module.Unpickler(f, **pickle_load_args) unpickler.persistent_load = persistent_load result = unpickler.load() deserialized_storage_keys = pickle_module.load(f, **pickle_load_args) offset = f.tell() if f_should_read_directly else None for key in deserialized_storage_keys: assert key in deserialized_objects deserialized_objects[key]._set_from_file(f, offset, f_should_read_directly) 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, _string_classes): 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 def _load(zip_file, map_location, pickle_module, **pickle_load_args): restore_location = _get_restore_location(map_location) loaded_storages = {} def load_tensor(data_type, size, key, location): name = 'data/{}'.format(key) dtype = data_type(0).dtype storage = zip_file.get_storage_from_record(name, size, dtype).storage() loaded_storages[key] = restore_location(storage, location) def persistent_load(saved_id): assert isinstance(saved_id, tuple) typename = _maybe_decode_ascii(saved_id[0]) data = saved_id[1:] assert typename == 'storage', \ "Unknown typename for persistent_load, expected 'storage' but got '{}'".format(typename) data_type, key, location, size = data if key not in loaded_storages: load_tensor(data_type, size, key, _maybe_decode_ascii(location)) storage = loaded_storages[key] return storage # Load the data (which may in turn use `persistent_load` to load tensors) data_file = io.BytesIO(zip_file.get_record('data.pkl')) unpickler = pickle_module.Unpickler(data_file, **pickle_load_args) unpickler.persistent_load = persistent_load result = unpickler.load() torch._utils._validate_loaded_sparse_tensors() return result def _is_torchscript_zip(zip_file): for file_name in zip_file.get_all_records(): parts = file_name.split(os.sep) if len(parts) > 1 and parts[1] == 'constants.pkl': return True return False


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