storage.py

import io

import torch
from ._utils import _type, _cuda
from torch.types import Storage
from typing import Any, TypeVar, Type, Union, cast
import copy
import collections
from functools import lru_cache
try:
    import numpy as np
    HAS_NUMPY = True
except ModuleNotFoundError:
    np = None  # type: ignore[assignment]

T = TypeVar('T', bound='Union[_StorageBase, _TypedStorage]')
class _StorageBase(object):
    _cdata: Any
    is_sparse: bool = False
    is_sparse_csr: bool = False
    device: torch.device

    def __init__(self, *args, **kwargs): ...  # noqa: E704
    def __len__(self) -> int: ...  # noqa: E704
    def __getitem__(self, idx): ...  # noqa: E704
    def copy_(self, source: T, non_blocking: bool = None) -> T: ...  # noqa: E704
    def nbytes(self) -> int: ...  # noqa: E704

    def size(self) -> int:
        return self.nbytes()

    def type(self, dtype: str = None, non_blocking: bool = False) -> T: ...  # noqa: E704
    def cuda(self, device=None, non_blocking=False, **kwargs) -> T: ...  # noqa: E704
    def element_size(self) -> int: ...  # noqa: E704
    def get_device(self) -> int: ...  # noqa: E704
    def data_ptr(self) -> int: ...  # noqa: E704

    # Defined in torch/csrc/generic/StorageSharing.cpp
    def _share_filename_cpu_(self, *args, **kwargs): ...  # noqa: E704
    def _share_fd_cpu_(self, *args, **kwargs): ...  # noqa: E704
    @classmethod
    def _new_using_filename_cpu(cls: Type[T], size: int) -> T: ...  # noqa: E704
    @classmethod
    def _new_using_fd_cpu(cls: Type[T], size: int) -> T: ...  # noqa: E704
    @classmethod
    def from_buffer(cls, *args, **kwargs) -> T: ...  # noqa: E704
    @classmethod
    def _new_shared_filename_cpu(cls, manager, obj, size, *, device=None, dtype=None) -> T: ...  # noqa: E704
    @classmethod
    def _release_ipc_counter_cuda(cls, *args, **kwargs) -> T: ...  # noqa: E704
    @classmethod
    def _new_with_weak_ptr(cls, *args, **kwargs) -> T: ...  # noqa: E704
    def _shared_decref(self) -> T: ...  # noqa: E704
    def _write_file(self, *args, **kwargs): ...  # noqa: E704
    def resize_(self, size: int): ...  # noqa: E704
    def _weak_ref(self, *args, **kwargs) -> T: ...  # noqa: E704
    def is_pinned(self) -> bool: ...  # noqa: E704
    def _set_from_file(self, *args, **kwargs): ...  # noqa: E704
    def _set_cdata(self, *args, **kwargs): ...  # noqa: E704
    def _share_cuda_(self, *args, **kwargs): ...  # noqa: E704
    def is_shared(self) -> bool: ...  # noqa: E704
    @classmethod
    def _new_shared_cuda(cls, *args, **kwargs) -> T: ...  # noqa: E704
    def _shared_incref(self, *args, **kwargs): ...  # noqa: E704
    @classmethod
    def _free_weak_ref(cls, *args, **kwargs): ...  # noqa: E704
    @property
    def is_cuda(self): ...  # noqa: E704

    def __str__(self):
        info_str = (
            f'[{torch.typename(self)}(device={self.device}) '
            f'of size {len(self)}]')
        if self.device.type == 'meta':
            return '...\n' + info_str
        else:
            data_str = ' ' + '\n '.join(str(self[i]) for i in range(self.size()))
            return data_str + '\n' + info_str

    def __repr__(self):
        return str(self)

    def __iter__(self):
        return iter(map(lambda i: self[i], range(self.size())))

    def __copy__(self):
        return self.clone()

    def __deepcopy__(self, memo):
        memo = memo.setdefault('torch', {})
        if self._cdata in memo:
            return memo[self._cdata]
        new_storage = self.clone()
        memo[self._cdata] = new_storage
        return new_storage

    def __reduce__(self):
        b = io.BytesIO()
        torch.save(self, b, _use_new_zipfile_serialization=False)
        return (_load_from_bytes, (b.getvalue(),))

    def __sizeof__(self):
        return super(_StorageBase, self).__sizeof__() + self.size()

    def clone(self):
        """Returns a copy of this storage"""
        return type(self)(self.nbytes(), device=self.device).copy_(self)

    def tolist(self):
        """Returns a list containing the elements of this storage"""
        return list(self)

    def cpu(self):
        """Returns a CPU copy of this storage if it's not already on the CPU"""
        if self.device.type != 'cpu':
            return torch._UntypedStorage(self.size()).copy_(self, False)
        else:
            return self

    def mps(self):
        """Returns a CPU copy of this storage if it's not already on the CPU"""
        if self.device.type != 'mps':
            return torch._UntypedStorage(self.size(), device="mps").copy_(self, False)
        else:
            return self

    def _to(self, dtype):
        if not isinstance(dtype, torch.dtype):
            raise TypeError(f"Argument 'dtype' must be torch.dtype, not {type(dtype)}")
        storage = torch.tensor([], dtype=torch.uint8, device=self.device).set_(cast(Storage, self)).to(dtype).storage()
        if storage.data_ptr() == self.data_ptr():
            storage = storage.clone()
        return storage

    def double(self):
        """Casts this storage to double type"""
        return self._to(torch.double)

    def float(self):
        """Casts this storage to float type"""
        return self._to(torch.float)

    def half(self):
        """Casts this storage to half type"""
        return self._to(torch.half)

    def long(self):
        """Casts this storage to long type"""
        return self._to(torch.long)

    def int(self):
        """Casts this storage to int type"""
        return self._to(torch.int)

    def short(self):
        """Casts this storage to short type"""
        return self._to(torch.short)

    def char(self):
        """Casts this storage to char type"""
        return self._to(torch.int8)

    def byte(self):
        """Casts this storage to byte type"""
        return self._to(torch.uint8)

    def bool(self):
        """Casts this storage to bool type"""
        return self._to(torch.bool)

    def bfloat16(self):
        """Casts this storage to bfloat16 type"""
        return self._to(torch.bfloat16)

    def complex_double(self):
        """Casts this storage to complex double type"""
        return self._to(torch.cdouble)

    def complex_float(self):
        """Casts this storage to complex float type"""
        return self._to(torch.cfloat)

    def pin_memory(self):
        """Copies the storage to pinned memory, if it's not already pinned."""
        if self.is_cuda:
            raise TypeError(f"cannot pin '{self.type()}' only CPU memory can be pinned")
        import torch.cuda
        allocator = torch.cuda.memory._host_allocator()  # type: ignore[attr-defined]
        return type(self)(self.size(), allocator=allocator).copy_(self)

    def share_memory_(self):
        """Moves the storage to shared memory.

        This is a no-op for storages already in shared memory and for CUDA
        storages, which do not need to be moved for sharing across processes.
        Storages in shared memory cannot be resized.

        Returns: self
        """
        from torch.multiprocessing import get_sharing_strategy
        if self.is_cuda:
            pass  # CUDA doesn't use POSIX shared memory
        elif get_sharing_strategy() == 'file_system':
            self._share_filename_cpu_()
        else:
            self._share_fd_cpu_()
        return self

    @classmethod
    def _new_shared(cls, size, *, device='cpu'):
        """Creates a new storage in shared memory with the same data type"""
        from torch.multiprocessing import get_sharing_strategy
        device = torch.device(device)
        if device.type == 'cuda':
            return cls(size, device=device)
        elif get_sharing_strategy() == 'file_system':
            return cls._new_using_filename_cpu(size)
        else:
            return cls._new_using_fd_cpu(size)

    def _untyped(self):
        return self


class _UntypedStorage(torch._C.StorageBase, _StorageBase):
    def __getitem__(self, *args, **kwargs):
        if self.device.type == 'meta':
            raise NotImplementedError("Not available for 'meta' device type")
        return super().__getitem__(*args, **kwargs)

    @property
    def is_cuda(self):
        return self.device.type == 'cuda'

def _load_from_bytes(b):
    return torch.load(io.BytesIO(b))


_StorageBase.type = _type  # type: ignore[assignment]
_StorageBase.cuda = _cuda  # type: ignore[assignment]


@lru_cache(maxsize=None)
def _dtype_to_storage_type_map():
    # NOTE: We should no longer add dtypes to this map. This map
    # is only used for BC/FC with older PyTorch versions. Going forward,
    # new dtypes of _TypedStorage should not translate to a legacy
    # <type>Storage class. Instead, new dtypes of _TypedStorage should
    # be serialized as an _UntypedStorage paired with a torch.dtype
    return {
        torch.double: 'DoubleStorage',
        torch.float: 'FloatStorage',
        torch.half: 'HalfStorage',
        torch.long: 'LongStorage',
        torch.int: 'IntStorage',
        torch.int16: 'ShortStorage',
        torch.int8: 'CharStorage',
        torch.uint8: 'ByteStorage',
        torch.bool: 'BoolStorage',
        torch.bfloat16: 'BFloat16Storage',
        torch.cdouble: 'ComplexDoubleStorage',
        torch.cfloat: 'ComplexFloatStorage',
        torch.qint8: 'QInt8Storage',
        torch.qint32: 'QInt32Storage',
        torch.quint8: 'QUInt8Storage',
        torch.quint4x2: 'QUInt4x2Storage',
        torch.quint2x4: 'QUInt2x4Storage',
    }

@lru_cache(maxsize=None)
def _storage_type_to_dtype_map():
    dtype_map = {
        val: key for key, val in _dtype_to_storage_type_map().items()}
    return dtype_map

def _get_storage_from_sequence(sequence, dtype, device):
    if dtype in [torch.quint8, torch.quint4x2, torch.quint2x4, torch.qint32, torch.qint8]:
        interpret_dtypes = {
            torch.quint8: torch.uint8,
            torch.quint4x2: torch.uint8,
            torch.quint2x4: torch.uint8,
            torch.qint32: torch.int32,
            torch.qint8: torch.int8
        }
        tmp_tensor = torch.tensor(
            sequence,
            dtype=interpret_dtypes[dtype],
            device=device)

    else:
        tmp_tensor = torch.tensor(
            sequence,
            dtype=dtype,
            device=device)

    return tmp_tensor.storage()._untyped()

def _isint(x):
    if HAS_NUMPY:
        return isinstance(x, (int, np.integer))
    else:
        return isinstance(x, int)

class _TypedStorage:
    is_sparse = False

    dtype: torch.dtype

    def fill_(self, value):
        self[0:len(self)] = value
        return self

    def __new__(cls, *args, wrap_storage=None, dtype=None, device=None):
        if cls == torch.storage._LegacyStorage:
            raise RuntimeError("Only child classes of _LegacyStorage can be instantiated")

        if cls == _TypedStorage:
            return super().__new__(cls)

        else:
            arg_error_msg = (
                f'{cls}.__new__ received an invalid combination '
                f'of arguments. Expected one of:\n'
                ' * no arguments\n'
                ' * (int size)\n'
                ' * (Sequence data)\n'
                ' * (*, _UntypedStorage wrap_storage)')

            if device is not None:
                raise RuntimeError(
                    arg_error_msg +
                    "\nKeyword argument 'device' cannot be specified")

            if dtype is not None:
                raise RuntimeError(
                    arg_error_msg +
                    "\nKeyword argument 'dtype' cannot be specified")

            if wrap_storage is None:
                if len(args) > 1:
                    raise RuntimeError(
                        arg_error_msg +
                        "\nToo many positional arguments")

                if len(args) == 1 and not _isint(args[0]) and not isinstance(args[0], collections.abc.Sequence):
                    raise TypeError(
                        arg_error_msg +
                        f"\nArgument type not recognized: {type(args[0])}")

                return _TypedStorage(
                    *args,
                    dtype=cls.dtype,
                    device='cuda' if eval(cls.__module__) is torch.cuda else 'cpu')

            else:
                if len(args) != 0:
                    raise RuntimeError(
                        arg_error_msg +
                        "\nNo positional arguments should be given when using "
                        "'wrap_storage'")

                if not isinstance(wrap_storage, torch._UntypedStorage):
                    raise TypeError(
                        arg_error_msg +
                        f"\nArgument 'wrap_storage' must be _UntypedStorage, but got {type(wrap_storage)}")

                cls_device = 'cuda' if cls.__module__ == 'torch.cuda' else 'cpu'

                if wrap_storage.device.type != cls_device:
                    raise RuntimeError(
                        arg_error_msg +
                        f"\nDevice of 'wrap_storage' must be {cls_device}"
                        f", but got {wrap_storage.device.type}")

                return _TypedStorage(
                    *args,
                    wrap_storage=wrap_storage,
                    dtype=cls.dtype)

    def __init__(self, *args, device=None, dtype=None, wrap_storage=None):
        arg_error_msg = (
            '_TypedStorage.__init__ received an invalid combination '
            'of arguments. Expected one of:\n'
            ' * (*, torch.device device, torch.dtype dtype)\n'
            ' * (int size, *, torch.device device, torch.dtype dtype)\n'
            ' * (Sequence data, *, torch.device device, torch.dtype dtype)\n'
            ' * (*, _UntypedStorage wrap_storage, torch.dtype dtype)')

        if wrap_storage is not None:
            if len(args) != 0:
                raise RuntimeError(
                    arg_error_msg +
                    "\nNo positional arguments should be given when using "
                    "'wrap_storage'")

            if dtype is None:
                raise RuntimeError(
                    arg_error_msg +
                    "\nArgument 'dtype' must be specified")

            if not isinstance(dtype, torch.dtype):
                raise TypeError(
                    arg_error_msg +
                    f"\nArgument 'dtype' must be torch.dtype, not {type(dtype)}")

            if device is not None:
                raise RuntimeError(
                    arg_error_msg +
                    "\nArgument 'device' should not be specified when 'wrap_storage' is given")

            self.dtype = dtype

            if not isinstance(wrap_storage, torch._UntypedStorage):
                raise TypeError(
                    arg_error_msg +
                    f"\nArgument 'wrap_storage' must be _UntypedStorage, but got {type(wrap_storage)}")

            self._storage = wrap_storage

        else:
            self.dtype = torch.get_default_dtype() if dtype is None else dtype
            device = torch.device('cpu' if device is None else device)

            if self.dtype in [torch.quint8, torch.quint4x2, torch.quint2x4, torch.qint32, torch.qint8]:
                if device.type == 'cuda':
                    raise RuntimeError("Cannot create CUDA storage with quantized dtype")

            if len(args) == 0:
                self._storage = torch._UntypedStorage(device=device)

            elif len(args) == 1:
                if _isint(args[0]):
                    self._storage = torch._UntypedStorage(int(args[0]) * self.element_size(), device=device)
                elif isinstance(args[0], collections.abc.Sequence):
                    self._storage = _get_storage_from_sequence(args[0], self.dtype, device)
                else:
                    raise TypeError(
                        arg_error_msg +
                        f"\nArgument type not recognized: {type(args[0])}")

            else:
                raise RuntimeError(
                    arg_error_msg +
                    "\nToo many positional arguments")


    @property
    def is_cuda(self):
        return self.device.type == 'cuda'

    def _untyped(self):
        return self._storage

    def _new_wrapped_storage(self, untyped_storage):
        assert type(untyped_storage) == torch._UntypedStorage

        if type(self) == _TypedStorage:
            return _TypedStorage(wrap_storage=untyped_storage, dtype=self.dtype)
        else:
            return type(self)(wrap_storage=untyped_storage)

    def __len__(self):
        return self._storage.nbytes() // self.element_size()

    def _maybe_wrap_index(self, idx, is_stop=False):
        if idx is None:
            if is_stop:
                return self.size()
            else:
                return 0

        else:
            if type(idx) != int:
                raise TypeError(
                    f"can't index a {type(self)} with {type(idx)}")
            if is_stop:
                if (idx > self.size()) or (idx < -self.size()):
                    raise IndexError(
                        f'index {idx} out of range for storage of size {self.size()}')
                if idx > 0:
                    return idx
                else:
                    return idx % self.size()
            else:
                if (idx >= self.size()) or (idx < -self.size()):
                    raise IndexError(
                        f'index {idx} out of range for storage of size {self.size()}')
                return idx % self.size()

    def __setitem__(self, idx, value):
        if not isinstance(idx, (int, slice)):
            raise RuntimeError(f"can't index a {type(self)} with {type(idx)}")
        if torch.is_storage(value):
            raise RuntimeError(f'cannot set item with value type {type(value)}')
        if self.dtype in [torch.quint8, torch.quint4x2, torch.quint2x4, torch.qint32, torch.qint8]:
            interpret_dtypes = {
                torch.quint8: torch.uint8,
                torch.quint4x2: torch.uint8,
                torch.quint2x4: torch.uint8,
                torch.qint32: torch.int32,
                torch.qint8: torch.int8
            }
            tmp_dtype = interpret_dtypes[self.dtype]
            tmp_tensor = torch.tensor([], dtype=tmp_dtype, device=self.device).set_(_TypedStorage(
                wrap_storage=self._storage,
                dtype=tmp_dtype))
        else:
            tmp_tensor = torch.tensor([], dtype=self.dtype, device=self.device).set_(self)

        tmp_tensor[idx] = value

    def __getitem__(self, idx):
        if self.device.type == 'meta':
            raise NotImplementedError("Not available for 'meta' device type")

        # NOTE: Before _TypedStorage existed, indexing with a slice used to be
        # possible for <type>Storage objects. However, it would return
        # a storage view, which would be a hassle to implement in _TypedStorage,
        # so it was disabled
        if isinstance(idx, slice):
            raise RuntimeError('slices are only supported in _UntypedStorage.__getitem__')
        elif not isinstance(idx, int):
            raise RuntimeError(f"can't index a {type(self)} with {type(idx)}")

        if self.dtype in [torch.quint8, torch.quint4x2, torch.quint2x4, torch.qint32, torch.qint8]:
            interpret_dtypes = {
                torch.quint8: torch.uint8,
                torch.quint4x2: torch.uint8,
                torch.quint2x4: torch.uint8,
                torch.qint32: torch.int32,
                torch.qint8: torch.int8
            }
            return _TypedStorage(
                wrap_storage=self._storage,
                dtype=interpret_dtypes[self.dtype])[idx]

        idx_wrapped = self._maybe_wrap_index(idx)
        tmp_tensor = torch.tensor([], dtype=self.dtype, device=self.device).set_(self)
        return tmp_tensor[idx_wrapped].item()

    def copy_(self, source: T, non_blocking: bool = None):
        self._storage.copy_(source._untyped(), non_blocking)
        return self

    def nbytes(self):
        return self._storage.nbytes()

    def type(self, dtype: str = None, non_blocking: bool = False) -> Union[T, str]:
        if dtype is None:
            legacy_class = self._get_legacy_storage_class()

            if legacy_class is not None:
                return legacy_class.__module__ + '.' + legacy_class.__name__

            return '.'.join([self.__module__, type(self).__name__])

        else:
            return self._storage.type(dtype, non_blocking)

    def cuda(self, device=None, non_blocking=False, **kwargs) -> T:
        if self.dtype in [torch.quint8, torch.quint4x2, torch.quint2x4, torch.qint32, torch.qint8]:
            raise RuntimeError("Cannot create CUDA storage with quantized dtype")
        cuda_storage: torch._UntypedStorage = self._storage.cuda(device, non_blocking, **kwargs)
        return self._new_wrapped_storage(cuda_storage)

    def element_size(self):
        return torch._utils._element_size(self.dtype)

    def get_device(self) -> int:
        return self._storage.get_device()

    def __str__(self):
        info_str = (
            f'[{torch.typename(self)}(dtype={self.dtype}, '
            f'device={self.device}) of size {len(self)}]')
        if self.device.type == 'meta':
            return '...\n' + info_str
        else:
            data_str = ' ' + '\n '.join(str(self[i]) for i in range(self.size()))
            return data_str + '\n' + info_str

    def __repr__(self):
        return str(self)

    def __iter__(self):
        return iter(map(lambda i: self[i], range(self.size())))

    def __copy__(self):
        return self._new_wrapped_storage(copy.copy(self._storage))

    def __deepcopy__(self, memo):
        return self._new_wrapped_storage(copy.deepcopy(self._storage, memo))

    def __sizeof__(self):
        return super(_TypedStorage, self).__sizeof__() + self.nbytes()

    def clone(self):
        """Returns a copy of this storage"""
        return self._new_wrapped_storage(self._storage.clone())

    def tolist(self):
        """Returns a list containing the elements of this storage"""
        return list(self)

    def cpu(self):
        """Returns a CPU copy of this storage if it's not already on the CPU"""
        return self._new_wrapped_storage(self._storage.cpu())

    def pin_memory(self):
        """Coppies the  storage to pinned memory, if it's not already pinned."""
        return self._new_wrapped_storage(self._storage.pin_memory())

    def share_memory_(self):
        """Moves the storage to shared memory.

        This is a no-op for storages already in shared memory and for CUDA
        storages, which do not need to be moved for sharing across processes.
        Storages in shared memory cannot be resized.

        Returns: self
        """
        self._storage.share_memory_()
        return self

    def _new_shared(self, size, *, device=None):
        """Creates a new storage in shared memory with the same data type"""
        if device is None:
            device = 'cpu'
        device = torch.device(device)
        untyped_storage = torch._UntypedStorage._new_shared(size * self.element_size(), device=device)
        return _TypedStorage(
            wrap_storage=untyped_storage,
            dtype=self.dtype)

    @property
    def _cdata(self):
        return self._storage._cdata

    @property
    def device(self):
        return self._storage.device

    def size(self):
        return len(self)

    def pickle_storage_type(self):
        try:
            return _dtype_to_storage_type_map()[self.dtype]
        except KeyError:
            raise KeyError(f'dtype {self.dtype} is not recognized')

    def __reduce__(self):
        b = io.BytesIO()
        torch.save(self, b, _use_new_zipfile_serialization=False)
        return (_load_from_bytes, (b.getvalue(),))

    def data_ptr(self):
        return self._storage.data_ptr()

    def resize_(self, size):
        self._storage.resize_(size * self.element_size())

    @classmethod
    def _free_weak_ref(cls, *args, **kwargs):
        return _UntypedStorage._free_weak_ref(*args, **kwargs)

    def _weak_ref(self, *args, **kwargs):
        return self._storage._weak_ref(*args, **kwargs)

    @classmethod
    def from_buffer(cls, *args, dtype=None, device=None, **kwargs):
        if cls == _TypedStorage:
            dtype = torch.get_default_dtype() if dtype is None else dtype
            device = torch.device('cpu' if device is None else device)
            if device.type != 'cpu':
                raise RuntimeError(f'_TypedStorage.from_buffer: Not available for device {device.type}')
            untyped_storage: torch._UntypedStorage = torch._UntypedStorage.from_buffer(*args, dtype=dtype, **kwargs)

        else:
            if dtype is not None or len(args) == 5:
                raise RuntimeError((
                    "from_buffer: 'dtype' can only be specified in "
                    "_UntypedStorage.from_buffer and _TypedStorage.from_buffer"))
            if device is not None:
                raise RuntimeError((
                    "from_buffer: 'device' can only be specified in "
                    "_UntypedStorage.from_buffer and _TypedStorage.from_buffer"))

            dtype = cls.dtype
            untyped_storage = torch._UntypedStorage.from_buffer(*args, dtype=dtype, **kwargs)

        return _TypedStorage(wrap_storage=untyped_storage, dtype=dtype)

    def _to(self, dtype):
        if not isinstance(dtype, torch.dtype):
            raise TypeError(f"Argument 'dtype' must be torch.dtype, not {type(dtype)}")
        storage = torch.tensor([], dtype=self.dtype, device=self.device).set_(self).to(dtype).storage()
        if storage.data_ptr() == self.data_ptr():
            storage = storage.clone()
        return storage

    def double(self):
        """Casts this storage to double type"""
        return self._to(torch.double)

    def float(self):
        """Casts this storage to float type"""
        return self._to(torch.float)

    def half(self):
        """Casts this storage to half type"""
        return self._to(torch.half)

    def long(self):
        """Casts this storage to long type"""
        return self._to(torch.long)

    def int(self):
        """Casts this storage to int type"""
        return self._to(torch.int)

    def short(self):
        """Casts this storage to short type"""
        return self._to(torch.short)

    def char(self):
        """Casts this storage to char type"""
        return self._to(torch.int8)

    def byte(self):
        """Casts this storage to byte type"""
        return self._to(torch.uint8)

    def bool(self):
        """Casts this storage to bool type"""
        return self._to(torch.bool)

    def bfloat16(self):
        """Casts this storage to bfloat16 type"""
        return self._to(torch.bfloat16)

    def complex_double(self):
        """Casts this storage to complex double type"""
        return self._to(torch.cdouble)

    def complex_float(self):
        """Casts this storage to complex float type"""
        return self._to(torch.cfloat)

    @classmethod
    def from_file(cls, filename, shared, size):
        """
        from_file(filename, shared=False, size=0) -> Storage

        If `shared` is `True`, then memory is shared between all processes.
        All changes are written to the file. If `shared` is `False`, then the changes on
        the storage do not affect the file.

        `size` is the number of elements in the storage. If `shared` is `False`,
        then the file must contain at least `size * sizeof(Type)` bytes
        (`Type` is the type of storage). If `shared` is `True` the file will be
        created if needed.

        Args:
            filename (str): file name to map
            shared (bool): whether to share memory
            size (int): number of elements in the storage
        """
        if cls == _TypedStorage:
            raise RuntimeError('from_file can only be called on derived classes')
        untyped_storage = eval(cls.__module__)._UntypedStorage.from_file(
            filename,
            shared,
            size * torch._utils._element_size(cls.dtype))
        storage = cls(wrap_storage=untyped_storage)
        return storage

    @classmethod
    def _expired(cls, *args, **kwargs):
        return eval(cls.__module__)._UntypedStorage._expired(*args, **kwargs)

    def is_pinned(self):
        return self._storage.is_pinned()

    def _write_file(self, *args, **kwargs):
        return self._storage._write_file(*args, **kwargs)

    def _set_from_file(self, *args, **kwargs):
        return self._storage._set_from_file(*args, **kwargs)

    def _set_cdata(self, *args, **kwargs):
        return self._storage._set_cdata(*args, **kwargs)

    def _share_cuda_(self, *args, **kwargs):
        return self._storage._share_cuda_(*args, **kwargs)

    def is_shared(self):
        return self._storage.is_shared()

    @classmethod
    def _new_shared_cuda(cls, *args, **kwargs):
        return torch._UntypedStorage._new_shared_cuda(*args, **kwargs)

    def _share_filename_cpu_(self, *args, **kwargs):
        manager_handle, storage_handle, size = self._storage._share_filename_cpu_(*args, **kwargs)
        return manager_handle, storage_handle, size // self.element_size()

    def _shared_decref(self):
        self._storage._shared_decref()
        return self

    @classmethod
    def _release_ipc_counter(cls, *args, device=None, **kwargs):
        return torch._UntypedStorage._release_ipc_counter_cuda(*args, **kwargs)

    def _shared_incref(self, *args, **kwargs):
        return self._storage._shared_incref(*args, **kwargs)

    def _share_fd_cpu_(self, *args, **kwargs):
        fd, size = self._storage._share_fd_cpu_(*args, **kwargs)
        return fd, size // self.element_size()

    def _get_legacy_storage_class(self):
        if self.dtype not in _dtype_to_storage_type_map():
            return None

        storage_name = _dtype_to_storage_type_map()[self.dtype]

        if self.device.type not in ['cpu', 'cuda']:
            return None

        module = 'torch.' if self.device.type == 'cpu' else 'torch.cuda.'

        try:
            return eval(module + storage_name)
        except AttributeError:
            return None

_TypedStorage.type.__doc__ = _type.__doc__
_TypedStorage.cuda.__doc__ = _cuda.__doc__

class _LegacyStorageMeta(type):
    dtype: torch.dtype

    def __instancecheck__(cls, instance):
        if type(instance) == _TypedStorage:
            cls_device = 'cuda' if cls.__module__ == 'torch.cuda' else 'cpu'
            return (cls_device == instance.device.type) and (cls.dtype == instance.dtype)
        return False

class _LegacyStorage(_TypedStorage, metaclass=_LegacyStorageMeta):
    @classmethod
    def _new_shared(cls, size):
        """Creates a new storage in shared memory with the same data type"""
        untyped_storage = torch._UntypedStorage._new_shared(size * cls().element_size())
        return cls(wrap_storage=untyped_storage)

    @classmethod
    def _release_ipc_counter(cls, *args, **kwargs):
        return torch._UntypedStorage._release_ipc_counter_cuda(*args, **kwargs)

    @classmethod
    def _new_shared_filename(cls, manager, obj, size):
        bytes_size = size * torch._utils._element_size(cls.dtype)
        return cls(wrap_storage=torch._UntypedStorage._new_shared_filename_cpu(manager, obj, bytes_size))

def _get_dtype_from_pickle_storage_type(pickle_storage_type: str):
    try:
        return _storage_type_to_dtype_map()[pickle_storage_type]
    except KeyError:
        raise KeyError(
            f'pickle storage type "{pickle_storage_type}" is not recognized')