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 # 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 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 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): 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() 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')