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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
import warnings
try:
    import numpy as np
    HAS_NUMPY = True
except ModuleNotFoundError:
    np = None  # type: ignore[assignment]

T = TypeVar('T', bound='Union[_StorageBase, TypedStorage]')
class _StorageBase:
    _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
    @classmethod
    def from_file(cls, filename, shared, nbytes) -> T: ...  # noqa: E704
    @classmethod
    def _expired(cls, *args, **kwargs) -> T: ...  # 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().__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)._typed_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


[docs]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._typed_storage()._untyped_storage def _isint(x): if HAS_NUMPY: return isinstance(x, (int, np.integer)) else: return isinstance(x, int) def _warn_typed_storage_removal(stacklevel=2): message = ( "TypedStorage is deprecated. It will be removed in the future and " "UntypedStorage will be the only storage class. This should only matter " "to you if you are using storages directly. To access UntypedStorage " "directly, use tensor.untyped_storage() instead of tensor.storage()" ) warnings.warn(message, UserWarning, stacklevel=stacklevel + 1)
[docs]class TypedStorage: is_sparse = False dtype: torch.dtype @property def _dtype(self): return self.dtype
[docs] def fill_(self, value): _warn_typed_storage_removal() self._setitem(slice(0, self._size()), value) return self
def __new__(cls, *args, wrap_storage=None, dtype=None, device=None, _internal=False): if not _internal: _warn_typed_storage_removal() 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 cls.__module__ == 'torch.cuda' else 'cpu', _internal=True) 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, _internal=True) def __init__(self, *args, device=None, dtype=None, wrap_storage=None, _internal=False): if not _internal: _warn_typed_storage_removal() 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._untyped_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._untyped_storage = torch.UntypedStorage(device=device) elif len(args) == 1: if _isint(args[0]): self._untyped_storage = torch.UntypedStorage(int(args[0]) * self._element_size(), device=device) elif isinstance(args[0], collections.abc.Sequence): self._untyped_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): _warn_typed_storage_removal() return self._untyped_storage.device.type == 'cuda'
[docs] def untyped(self): """Returns the internal :class:`torch.UntypedStorage`""" _warn_typed_storage_removal() return self._untyped_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, _internal=True) else: return type(self)(wrap_storage=untyped_storage) def __len__(self): _warn_typed_storage_removal() return self._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): _warn_typed_storage_removal() return self._setitem(idx, value) 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._untyped_storage.device) tmp_tensor.set_(TypedStorage( wrap_storage=self._untyped_storage, dtype=tmp_dtype, _internal=True)) else: tmp_tensor = torch.tensor([], dtype=self.dtype, device=self._untyped_storage.device).set_(self) tmp_tensor[idx] = value def __getitem__(self, idx): _warn_typed_storage_removal() return self._getitem(idx) def _getitem(self, idx): if self._untyped_storage.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._untyped_storage, dtype=interpret_dtypes[self.dtype], _internal=True)._getitem(idx) idx_wrapped = self._maybe_wrap_index(idx) tmp_tensor = torch.tensor([], dtype=self.dtype, device=self._untyped_storage.device).set_(self) return tmp_tensor[idx_wrapped].item()
[docs] def copy_(self, source: T, non_blocking: bool = None): _warn_typed_storage_removal() if isinstance(source, TypedStorage): self._untyped_storage.copy_(source._untyped_storage, non_blocking) else: self._untyped_storage.copy_(source, non_blocking) return self
[docs] def nbytes(self): _warn_typed_storage_removal() return self._nbytes()
# For internal use only, to avoid deprecation warning def _nbytes(self): return self._untyped_storage.nbytes()
[docs] def type(self, dtype: str = None, non_blocking: bool = False) -> Union[T, str]: _warn_typed_storage_removal() 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._untyped_storage.type(dtype, non_blocking)
[docs] def cuda(self, device=None, non_blocking=False, **kwargs) -> T: _warn_typed_storage_removal() 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._untyped_storage.cuda(device, non_blocking, **kwargs) return self._new_wrapped_storage(cuda_storage)
[docs] def element_size(self): _warn_typed_storage_removal() return self._element_size()
# For internal use only, to avoid deprecation warning def _element_size(self): return torch._utils._element_size(self.dtype)
[docs] def get_device(self) -> int: _warn_typed_storage_removal() return self._untyped_storage.get_device()
def __str__(self): _warn_typed_storage_removal() 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): _warn_typed_storage_removal() return str(self) def __iter__(self): _warn_typed_storage_removal() return iter(map(lambda i: self[i], range(self.size()))) def __copy__(self): _warn_typed_storage_removal() return self._new_wrapped_storage(copy.copy(self._untyped_storage)) def __deepcopy__(self, memo): _warn_typed_storage_removal() return self._deepcopy(memo) # For internal use only, to avoid deprecation warning def _deepcopy(self, memo): return self._new_wrapped_storage(copy.deepcopy(self._untyped_storage, memo)) def __sizeof__(self): _warn_typed_storage_removal() return super().__sizeof__() + self.nbytes()
[docs] def clone(self): """Returns a copy of this storage""" _warn_typed_storage_removal() return self._new_wrapped_storage(self._untyped_storage.clone())
[docs] def tolist(self): """Returns a list containing the elements of this storage""" _warn_typed_storage_removal() return list(self)
[docs] def cpu(self): """Returns a CPU copy of this storage if it's not already on the CPU""" _warn_typed_storage_removal() return self._new_wrapped_storage(self._untyped_storage.cpu())
[docs] def pin_memory(self): """Coppies the storage to pinned memory, if it's not already pinned.""" _warn_typed_storage_removal() return self._new_wrapped_storage(self._untyped_storage.pin_memory())
[docs] 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 """ _warn_typed_storage_removal() return self._share_memory_()
# For internal use only, to avoid deprecation warning def _share_memory_(self): self._untyped_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, _internal=True) @property def _cdata(self): return self._untyped_storage._cdata @property def device(self): _warn_typed_storage_removal() return self._untyped_storage.device
[docs] def size(self): _warn_typed_storage_removal() return self._size()
# For internal use only, to avoid deprecation warning def _size(self): # NB: don't indirect through __len__, as that requires # an int to be returned return self._untyped_storage.nbytes() // self._element_size()
[docs] def pickle_storage_type(self): _warn_typed_storage_removal() return self._pickle_storage_type()
# For internal use only, to avoid deprecation warning def _pickle_storage_type(self): try: return _dtype_to_storage_type_map()[self.dtype] except KeyError as e: raise KeyError(f'dtype {self.dtype} is not recognized') from e def __reduce__(self): b = io.BytesIO() torch.save(self, b, _use_new_zipfile_serialization=False) return (_load_from_bytes, (b.getvalue(),))
[docs] def data_ptr(self): _warn_typed_storage_removal() return self._data_ptr()
# For internal use only, to avoid deprecation warning def _data_ptr(self): return self._untyped_storage.data_ptr()
[docs] def resize_(self, size): _warn_typed_storage_removal() self._resize_(size)
# For internal use only, to avoid deprecation warning def _resize_(self, size): self._untyped_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._untyped_storage._weak_ref(*args, **kwargs)
[docs] @classmethod def from_buffer(cls, *args, **kwargs): _warn_typed_storage_removal() return cls._from_buffer(*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, _internal=True) 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)._typed_storage() if storage.data_ptr() == self.data_ptr(): storage = storage.clone() return storage
[docs] def double(self): """Casts this storage to double type""" _warn_typed_storage_removal() return self._to(torch.double)
[docs] def float(self): """Casts this storage to float type""" _warn_typed_storage_removal() return self._to(torch.float)
[docs] def half(self): """Casts this storage to half type""" _warn_typed_storage_removal() return self._to(torch.half)
[docs] def long(self): """Casts this storage to long type""" _warn_typed_storage_removal() return self._to(torch.long)
[docs] def int(self): """Casts this storage to int type""" _warn_typed_storage_removal() return self._to(torch.int)
[docs] def short(self): """Casts this storage to short type""" _warn_typed_storage_removal() return self._to(torch.short)
[docs] def char(self): """Casts this storage to char type""" _warn_typed_storage_removal() return self._to(torch.int8)
[docs] def byte(self): """Casts this storage to byte type""" _warn_typed_storage_removal() return self._to(torch.uint8)
[docs] def bool(self): """Casts this storage to bool type""" _warn_typed_storage_removal() return self._to(torch.bool)
[docs] def bfloat16(self): """Casts this storage to bfloat16 type""" _warn_typed_storage_removal() return self._to(torch.bfloat16)
[docs] def complex_double(self): """Casts this storage to complex double type""" _warn_typed_storage_removal() return self._to(torch.cdouble)
[docs] def complex_float(self): """Casts this storage to complex float type""" _warn_typed_storage_removal() 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 """ _warn_typed_storage_removal() if cls == TypedStorage: raise RuntimeError('from_file can only be called on derived classes') untyped_storage: UntypedStorage = 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 UntypedStorage._expired(*args, **kwargs)
[docs] def is_pinned(self): _warn_typed_storage_removal() return self._untyped_storage.is_pinned()
def _write_file(self, *args, **kwargs): return self._untyped_storage._write_file(*args, **kwargs) def _set_from_file(self, *args, **kwargs): return self._untyped_storage._set_from_file(*args, **kwargs) def _set_cdata(self, *args, **kwargs): return self._untyped_storage._set_cdata(*args, **kwargs) def _share_cuda_(self, *args, **kwargs): return self._untyped_storage._share_cuda_(*args, **kwargs)
[docs] def is_shared(self): _warn_typed_storage_removal() return self._is_shared()
# For internal use only, to avoid deprecation warning def _is_shared(self): return self._untyped_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._untyped_storage._share_filename_cpu_(*args, **kwargs) return manager_handle, storage_handle, size // self._element_size() def _shared_decref(self): self._untyped_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._untyped_storage._shared_incref(*args, **kwargs) def _share_fd_cpu_(self, *args, **kwargs): fd, size = self._untyped_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 getattr(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 as e: raise KeyError( f'pickle storage type "{pickle_storage_type}" is not recognized') from e

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