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storage.py
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storage.py
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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
T = TypeVar('T', bound='Union[_StorageBase, TypedStorage]')
class _StorageBase(object):
_cdata: Any
is_cuda: bool = False
is_sparse: 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) -> 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_(self): ... # noqa: E704
def _share_fd_(self): ... # noqa: E704
@classmethod
def _new_using_filename(cls: Type[T], size: int) -> T: ... # noqa: E704
@classmethod
def _new_using_fd(cls: Type[T], size: int) -> T: ... # noqa: E704
def __str__(self):
content = ' ' + '\n '.join(str(self[i]) for i in range(len(self)))
return content + f'\n[{torch.typename(self)} 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"""
device = self.get_device() if self.is_cuda else -1
with torch.cuda.device(device):
return type(self)(self.nbytes()).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"""
return _type(self, getattr(torch, self.__class__.__name__))
def _to(self, 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._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_()
else:
self._share_fd_()
return self
@classmethod
def _new_shared(cls, size):
"""Creates a new storage in shared memory with the same data type"""
from torch.multiprocessing import get_sharing_strategy
if cls.is_cuda:
return cls(size)
elif get_sharing_strategy() == 'file_system':
return cls._new_using_filename(size)
else:
return cls._new_using_fd(size)
def _untyped(self):
return self
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():
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
class TypedStorage:
is_sparse = False
def fill_(self, value):
self[0:len(self)] = value
return self
def __init__(self, *args, **kwargs):
arg_error_msg = (
f'{type(self)} constructor received an invalid combination '
f'of arguments - got args={tuple(type(arg) for arg in args)}, '
f'kwargs={ {key: type(val) for key, val in kwargs.items()} }, but '
'expected one of:\n'
' * no arguments\n'
' * (int size)\n'
' * (Sequence data)\n')
if type(self) == TypedStorage:
arg_error_msg += ' * (wrap_storage=<UntypedStorage>, dtype=<torch.dtype>)'
else:
arg_error_msg += ' * (wrap_storage=<UntypedStorage>)'
if 'wrap_storage' in kwargs:
assert len(args) == 0, (
"No positional arguments should be given when using "
"'wrap_storage'")
if type(self) == TypedStorage:
assert 'dtype' in kwargs, (
"When using 'wrap_storage', 'dtype' also must be specified")
assert len(kwargs) == 2, (
"Only 'wrap_storage' and 'dtype' should be given, but got: "
f"{kwargs}")
dtype = kwargs['dtype']
assert isinstance(dtype, torch.dtype)
self.dtype = dtype
else:
assert hasattr(self, 'dtype')
assert len(kwargs) == 1, (
f"Only 'wrap_storage' should be given, but got: {kwargs.keys()}")
dtype = self.dtype
storage = kwargs['wrap_storage']
if not isinstance(storage, (torch.UntypedStorage, torch.cuda.UntypedStorage)):
raise TypeError(arg_error_msg)
if type(self) != TypedStorage and storage.__module__ != self.__module__:
raise TypeError((
arg_error_msg +
f'\n`storage` `module {storage.__module__}` does not match '
f'module of {type(self)}'))
self._storage = storage
else:
assert type(self) != TypedStorage, (
"Calling __init__ this way is only supported in TypedStorage's "
"child classes. TypedStorage can only be directly instantiated "
"when kwargs 'wrap_storage' and 'dtype' are given.")
assert len(kwargs) == 0, "invalid keyword arguments"
def isint(x):
try:
int(x)
except TypeError:
return False
return True
if len(args) == 0:
self._storage = eval(self.__module__).UntypedStorage()
elif len(args) == 1 and isint(args[0]):
self._storage = eval(self.__module__).UntypedStorage(int(args[0]) * self.element_size())
elif len(args) == 1 and isinstance(args[0], collections.abc.Sequence):
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_tensor = torch.tensor(
args[0],
dtype=interpret_dtypes[self.dtype],
device='cuda' if eval(self.__module__) is torch.cuda else 'cpu')
else:
tmp_tensor = torch.tensor(
args[0],
dtype=self.dtype,
device='cuda' if eval(self.__module__) is torch.cuda else 'cpu')
self._storage = tmp_tensor.storage()._untyped()
else:
raise TypeError(arg_error_msg)
@property
def is_cuda(self):
return self._storage.device.type == 'cuda'
def _untyped(self):
return self._storage
def _new_wrapped_storage(self, untyped_storage):
module = eval(untyped_storage.__module__)
assert type(untyped_storage) == module.UntypedStorage
if type(self) == TypedStorage:
return TypedStorage(wrap_storage=untyped_storage, dtype=self.dtype)
else:
# NOTE: We need to use the module of untyped_storage in case self's
# module is different, e.g. if self is on CPU and untyped_storage
# is on CUDA, and vice versa
return getattr(module, type(self).__name__)(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()
def copy_(self, source: T, non_blocking=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:
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:
cuda_storage = 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()))
if type(self) == TypedStorage:
return data_str + (
f'\n[{torch.typename(self)} with dtype {self.dtype} '
f'of size {len(self)}]')
else:
return data_str + f'\n[{torch.typename(self)} 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
@classmethod
def _new_shared(cls, size):
"""Creates a new storage in shared memory with the same data type"""
module = eval(cls.__module__)
untyped_storage = module.UntypedStorage._new_shared(size * cls().element_size())
return cls(wrap_storage=untyped_storage)
@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 eval(cls.__module__).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, **kwargs):
if cls == TypedStorage:
raise RuntimeError(
'from_buffer: only supported for subclasses of TypedStorage')
if 'dtype' in kwargs or len(args) == 5:
raise RuntimeError((
"from_buffer: 'dtype' can only be specified in "
"UntypedStorage.from_buffer"))
kwargs['dtype'] = cls().dtype
untyped_storage = eval(cls.__module__).UntypedStorage.from_buffer(*args, **kwargs)
return cls(wrap_storage=untyped_storage)
def _to(self, 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):
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 eval(cls.__module__).UntypedStorage._new_shared_cuda(*args, **kwargs)
@classmethod
def _new_with_weak_ptr(cls, *args, **kwargs):
return eval(cls.__module__).UntypedStorage._new_with_weak_ptr(*args, **kwargs)
def _share_filename_(self, *args, **kwargs):
manager_handle, storage_handle, size = self._storage._share_filename_(*args, **kwargs)
return manager_handle, storage_handle, size // self.element_size()
@classmethod
def _new_shared_filename(cls, manager, obj, size):
bytes_size = size * torch._utils._element_size(cls.dtype)
return cls(wrap_storage=eval(cls.__module__).UntypedStorage._new_shared_filename(manager, obj, bytes_size))
def _shared_decref(self):
self._storage._shared_decref()
return self
@classmethod
def _release_ipc_counter(cls, *args, **kwargs):
return eval(cls.__module__).UntypedStorage._release_ipc_counter(*args, **kwargs)
def _shared_incref(self, *args, **kwargs):
return self._storage._shared_incref(*args, **kwargs)
def _share_fd_(self, *args, **kwargs):
fd, size = self._storage._share_fd_(*args, **kwargs)
return fd, size // self.element_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')