Source code for torch.storage
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):
data_str = ' ' + '\n '.join(str(self[i]) for i in range(self.size()))
return data_str + (
f'\n[{torch.typename(self)}(device={self.device}) '
f'of size {len(self)}]')
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 _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):
pass
@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)
[docs]class _TypedStorage:
is_sparse = False
dtype: torch.dtype
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 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):
# 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()
[docs] def copy_(self, source: T, non_blocking: bool = None):
self._storage.copy_(source._untyped(), non_blocking)
return self
[docs] 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)
[docs] 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 __str__(self):
data_str = ' ' + '\n '.join(str(self[i]) for i in range(self.size()))
return data_str + (
f'\n[{torch.typename(self)}(dtype={self.dtype}, '
f'device={self.device}) of size {len(self)}]')
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()
[docs] def clone(self):
"""Returns a copy of this storage"""
return self._new_wrapped_storage(self._storage.clone())
[docs] def tolist(self):
"""Returns a list containing the elements of this storage"""
return list(self)
[docs] 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())
[docs] 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 _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
[docs] 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(),))
@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)
[docs] @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
[docs] def bfloat16(self):
"""Casts this storage to bfloat16 type"""
return self._to(torch.bfloat16)
[docs] def complex_double(self):
"""Casts this storage to complex double type"""
return self._to(torch.cdouble)
[docs] def complex_float(self):
"""Casts this storage to complex float type"""
return self._to(torch.cfloat)
[docs] @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 _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)
@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')
