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1229 lines
46 KiB
1229 lines
46 KiB
import io
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import torch
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from ._utils import _type, _cuda, _hpu
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from torch.types import Storage
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from typing import cast, Any, Dict as _Dict, Optional as _Optional, TypeVar, Type, Union
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import copy
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import collections
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from functools import lru_cache
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import warnings
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import threading
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import functools
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try:
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import numpy as np
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HAS_NUMPY = True
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except ModuleNotFoundError:
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np = None # type: ignore[assignment]
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_share_memory_lock = threading.Lock()
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_share_memory_map: _Dict[int, threading.RLock] = {}
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T = TypeVar('T', bound='Union[_StorageBase, TypedStorage]')
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class _StorageBase:
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_cdata: Any
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is_sparse: bool = False
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is_sparse_csr: bool = False
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device: torch.device
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def __init__(self, *args, **kwargs): ... # noqa: E704
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def __len__(self) -> int: ... # type: ignore[empty-body] # noqa: E704
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def __getitem__(self, idx): ... # noqa: E704
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def __setitem__(self, *args, **kwargs): ... # noqa: E704
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def copy_(self, source: T, non_blocking: _Optional[bool] = None) -> T: ... # type: ignore[empty-body] # noqa: E704
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def new(self) -> T: ... # type: ignore[empty-body, misc, type-var] # noqa: E704
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def nbytes(self) -> int: ... # type: ignore[empty-body] # noqa: E704
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def size(self) -> int:
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return self.nbytes()
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def type(self, dtype: _Optional[str] = None, non_blocking: bool = False) -> T: ... # type: ignore[empty-body, misc, type-var] # noqa: E704
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def cuda(self, device=None, non_blocking=False, **kwargs) -> T: ... # type: ignore[empty-body, misc, type-var] # noqa: E704
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def hpu(self, device=None, non_blocking=False, **kwargs) -> T: ... # type: ignore[empty-body, misc, type-var] # noqa: E704
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def element_size(self) -> int: ... # type: ignore[empty-body, type-var] # noqa: E704
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def get_device(self) -> int:
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return self.device.index
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def data_ptr(self) -> int: ... # type: ignore[empty-body] # noqa: E704
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def resizable(self) -> bool: ... # type: ignore[empty-body] # noqa: E704
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# Defined in torch/csrc/generic/StorageSharing.cpp
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def _share_filename_cpu_(self, *args, **kwargs): ... # noqa: E704
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def _share_fd_cpu_(self, *args, **kwargs): ... # noqa: E704
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@classmethod
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def _new_using_filename_cpu(cls: Type[T], size: int) -> T: ... # type: ignore[empty-body] # noqa: E704
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@classmethod
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def _new_using_fd_cpu(cls: Type[T], size: int) -> T: ... # type: ignore[empty-body] # noqa: E704
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@classmethod
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def from_buffer(cls: Type[T], *args, **kwargs) -> T: ... # type: ignore[empty-body] # noqa: E704
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@classmethod
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def _new_shared_filename_cpu(cls: Type[T], manager, obj, size, *, device=None, dtype=None) -> T: ... # type: ignore[empty-body] # noqa: E704
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@classmethod
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def _release_ipc_counter_cuda(cls: Type[T], *args, **kwargs) -> T: ... # type: ignore[empty-body] # noqa: E704
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@classmethod
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def _new_with_weak_ptr(cls: Type[T], *args, **kwargs) -> T: ... # type: ignore[empty-body] # noqa: E704
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def _shared_decref(self) -> T: ... # type: ignore[empty-body, misc, type-var] # noqa: E704
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def _write_file(self, *args, **kwargs): ... # noqa: E704
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def resize_(self, size: int): ... # noqa: E704
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def _weak_ref(self, *args, **kwargs) -> T: ... # type: ignore[empty-body, misc, type-var] # noqa: E704
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def _set_from_file(self, *args, **kwargs): ... # noqa: E704
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def _set_cdata(self, *args, **kwargs): ... # noqa: E704
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def _share_cuda_(self, *args, **kwargs): ... # noqa: E704
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def is_shared(self) -> bool: ... # type: ignore[empty-body] # noqa: E704
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@classmethod
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def _new_shared_cuda(cls: Type[T], *args, **kwargs) -> T: ... # type: ignore[empty-body] # noqa: E704
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def _shared_incref(self, *args, **kwargs): ... # noqa: E704
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@classmethod
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def _free_weak_ref(cls, *args, **kwargs): ... # noqa: E704
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@property
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def is_cuda(self): ... # noqa: E704
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@property
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def is_hpu(self): ... # noqa: E704
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@classmethod
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def from_file(cls, filename, shared, nbytes) -> T: ... # type: ignore[empty-body, misc, type-var] # noqa: E704
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@classmethod
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def _expired(cls, *args, **kwargs) -> T: ... # type: ignore[empty-body, misc, type-var] # noqa: E704
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def _byteswap(self, *args, **kwargs): ... # noqa: E704
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def _get_filename(self, *args, **kwargs) -> _Optional[str]: ... # type: ignore[empty-body, misc] # noqa: E704
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def __str__(self):
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info_str = (
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f'[{torch.typename(self)}(device={self.device}) '
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f'of size {len(self)}]')
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if self.device.type == 'meta':
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return '...\n' + info_str
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else:
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data_str = ' ' + '\n '.join(str(self[i]) for i in range(self.size()))
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return data_str + '\n' + info_str
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def __repr__(self):
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return str(self)
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def __iter__(self):
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return iter(self[i] for i in range(self.size()))
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def __copy__(self):
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return self.clone()
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def __deepcopy__(self, memo):
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memo = memo.setdefault('torch', {})
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if self._cdata in memo:
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return memo[self._cdata]
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new_storage = self.clone()
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memo[self._cdata] = new_storage
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return new_storage
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def __reduce__(self):
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b = io.BytesIO()
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torch.save(self, b, _use_new_zipfile_serialization=False)
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return (_load_from_bytes, (b.getvalue(),))
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def __sizeof__(self):
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return super().__sizeof__() + self.size()
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def clone(self):
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"""Return a copy of this storage."""
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return type(self)(self.nbytes(), device=self.device).copy_(self)
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def tolist(self):
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"""Return a list containing the elements of this storage."""
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return list(self)
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def cpu(self):
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"""Return a CPU copy of this storage if it's not already on the CPU."""
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if self.device.type != 'cpu':
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return torch.UntypedStorage(self.size()).copy_(self, False)
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else:
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return self
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def mps(self):
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"""Return a MPS copy of this storage if it's not already on the MPS."""
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if self.device.type != 'mps':
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return torch.UntypedStorage(self.size(), device="mps").copy_(self, False)
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else:
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return self
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def _to(self, dtype):
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if not isinstance(dtype, torch.dtype):
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raise TypeError(f"Argument 'dtype' must be torch.dtype, not {type(dtype)}")
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storage = torch.tensor([], dtype=torch.uint8, device=self.device).set_(cast(Storage, self)).to(dtype)._typed_storage()
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if storage.data_ptr() == self.data_ptr():
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storage = storage.clone()
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return storage
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def double(self):
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"""Casts this storage to double type."""
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return self._to(torch.double)
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def float(self):
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"""Casts this storage to float type."""
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return self._to(torch.float)
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def half(self):
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"""Casts this storage to half type."""
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return self._to(torch.half)
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def long(self):
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"""Casts this storage to long type."""
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return self._to(torch.long)
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def int(self):
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"""Casts this storage to int type."""
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return self._to(torch.int)
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def short(self):
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"""Casts this storage to short type."""
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return self._to(torch.short)
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def char(self):
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"""Casts this storage to char type."""
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return self._to(torch.int8)
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def byte(self):
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"""Casts this storage to byte type."""
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return self._to(torch.uint8)
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def bool(self):
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"""Casts this storage to bool type."""
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return self._to(torch.bool)
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def bfloat16(self):
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"""Casts this storage to bfloat16 type."""
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return self._to(torch.bfloat16)
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def complex_double(self):
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"""Casts this storage to complex double type."""
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return self._to(torch.cdouble)
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def complex_float(self):
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"""Casts this storage to complex float type."""
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return self._to(torch.cfloat)
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def float8_e5m2(self):
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"""Casts this storage to float8_e5m2 type"""
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return self._to(torch.float8_e5m2)
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def float8_e4m3fn(self):
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"""Casts this storage to float8_e4m3fn type"""
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return self._to(torch.float8_e4m3fn)
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def float8_e5m2fnuz(self):
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"""Casts this storage to float8_e5m2fnuz type"""
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return self._to(torch.float8_e5m2fnuz)
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def float8_e4m3fnuz(self):
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"""Casts this storage to float8_e4m3fnuz type"""
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return self._to(torch.float8_e4m3fnuz)
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def is_pinned(self, device: Union[str, torch.device] = 'cuda'):
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r"""Determine whether the CPU storage is already pinned on device.
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Args:
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device (str or torch.device): The device to pin memory on. Default: ``'cuda'``.
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Returns:
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A boolean variable.
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"""
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return torch.tensor([], dtype=torch.uint8, device=self.device).set_(
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cast(Storage, self)).is_pinned(device)
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def pin_memory(self, device: Union[str, torch.device] = 'cuda'):
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r"""Copy the CPU storage to pinned memory, if it's not already pinned.
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Args:
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device (str or torch.device): The device to pin memory on. Default: ``'cuda'``.
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Returns:
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A pinned CPU storage.
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"""
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if self.device.type != 'cpu':
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raise TypeError(f"cannot pin '{self.type()}' only CPU memory can be pinned")
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pinned_tensor = torch.tensor([], dtype=torch.uint8, device=self.device).set_(
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cast(Storage, self)).pin_memory(device)
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return pinned_tensor.untyped_storage()
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def share_memory_(self):
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"""See :meth:`torch.UntypedStorage.share_memory_`"""
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from torch.multiprocessing import get_sharing_strategy
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if self.device.type in ["cuda", torch._C._get_privateuse1_backend_name()]:
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pass # CUDA or PrivateUse1 doesn't use POSIX shared memory
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elif get_sharing_strategy() == 'file_system':
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self._share_filename_cpu_()
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else:
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self._share_fd_cpu_()
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return self
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@classmethod
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def _new_shared(cls, size, *, device='cpu'):
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"""Create a new storage in shared memory with the same data type."""
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from torch.multiprocessing import get_sharing_strategy
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device = torch.device(device)
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if device.type in ["cuda", torch._C._get_privateuse1_backend_name(), "hpu"]:
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return cls(size, device=device)
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elif get_sharing_strategy() == 'file_system':
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return cls._new_using_filename_cpu(size)
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else:
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return cls._new_using_fd_cpu(size)
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def untyped(self):
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return self
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def byteswap(self, dtype):
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"""Swap bytes in underlying data."""
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elem_size = torch._utils._element_size(dtype)
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# for complex types, don't swap first and second numbers
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if dtype.is_complex:
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elem_size = max(int(elem_size / 2), 1)
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self._byteswap(elem_size)
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def _share_memory_lock_protected(fn):
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@functools.wraps(fn)
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def wrapper(self, *args, **kwargs):
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to_free = None
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to_wait = None
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with _share_memory_lock:
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key = self._cdata
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if key in _share_memory_map:
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to_wait = _share_memory_map[key]
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else:
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_share_memory_map[key] = threading.RLock()
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_share_memory_map[key].acquire()
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to_free = key
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# If we're already in the process of sharing the storage, wait
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# for it to be done.
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if to_wait is not None:
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with to_wait:
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pass
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try:
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return fn(self, *args, **kwargs)
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finally:
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# If we acquired the storage lock here and we're done working on it
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# we can now release it and free the entry.
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if to_free is not None:
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# Ensure that the cdata from the storage didn't change and only
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# the data_ptr did.
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assert self._cdata == to_free
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with _share_memory_lock:
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_share_memory_map[to_free].release()
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del _share_memory_map[to_free]
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return wrapper
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class UntypedStorage(torch._C.StorageBase, _StorageBase):
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def __getitem__(self, *args, **kwargs):
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if self.device.type == 'meta':
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raise NotImplementedError("Not available for 'meta' device type")
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return super().__getitem__(*args, **kwargs)
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@property
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def is_cuda(self):
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return self.device.type == 'cuda'
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@property
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def is_hpu(self):
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return self.device.type == 'hpu'
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@property
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def filename(self) -> _Optional[str]:
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"""Returns the file name associated with this storage if the storage was memory mapped from a file.
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or ``None`` if the storage was not created by memory mapping a file."""
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return self._get_filename()
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@_share_memory_lock_protected
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def share_memory_(self, *args, **kwargs):
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"""
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Moves the storage to shared memory.
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This is a no-op for storages already in shared memory and for CUDA
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storages, which do not need to be moved for sharing across processes.
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Storages in shared memory cannot be resized.
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Note that to mitigate issues like `this <https://github.com/pytorch/pytorch/issues/95606>`_
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it is thread safe to call this function from multiple threads on the same object.
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It is NOT thread safe though to call any other function on self without proper
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synchronization. Please see :doc:`/notes/multiprocessing` for more details.
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.. note::
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When all references to a storage in shared memory are deleted, the associated shared memory
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object will also be deleted. PyTorch has a special cleanup process to ensure that this happens
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even if the current process exits unexpectedly.
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It is worth noting the difference between :meth:`share_memory_` and :meth:`from_file` with ``shared = True``
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#. ``share_memory_`` uses `shm_open(3) <https://man7.org/linux/man-pages/man3/shm_open.3.html>`_ to create a
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POSIX shared memory object while :meth:`from_file` uses
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`open(2) <https://man7.org/linux/man-pages/man2/open.2.html>`_ to open the filename passed by the user.
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#. Both use an `mmap(2) call <https://man7.org/linux/man-pages/man2/mmap.2.html>`_ with ``MAP_SHARED``
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to map the file/object into the current virtual address space
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#. ``share_memory_`` will call ``shm_unlink(3)`` on the object after mapping it to make sure the shared memory
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object is freed when no process has the object open. ``torch.from_file(shared=True)`` does not unlink the
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file. This file is persistent and will remain until it is deleted by the user.
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Returns:
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``self``
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"""
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return super().share_memory_(*args, **kwargs)
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@_share_memory_lock_protected
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def _share_fd_cpu_(self, *args, **kwargs):
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return super()._share_fd_cpu_(*args, **kwargs)
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@_share_memory_lock_protected
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def _share_filename_cpu_(self, *args, **kwargs):
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return super()._share_filename_cpu_(*args, **kwargs)
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|
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def _load_from_bytes(b):
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return torch.load(io.BytesIO(b))
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|
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|
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_StorageBase.type = _type # type: ignore[assignment]
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_StorageBase.cuda = _cuda # type: ignore[assignment]
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_StorageBase.hpu = _hpu # type: ignore[assignment]
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|
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|
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@lru_cache(maxsize=None)
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def _dtype_to_storage_type_map():
|
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# NOTE: We should no longer add dtypes to this map. This map
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# is only used for BC/FC with older PyTorch versions. Going forward,
|
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# new dtypes of TypedStorage should not translate to a legacy
|
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# <type>Storage class. Instead, new dtypes of TypedStorage should
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# be serialized as an UntypedStorage paired with a torch.dtype
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return {
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torch.double: 'DoubleStorage',
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torch.float: 'FloatStorage',
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torch.half: 'HalfStorage',
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torch.long: 'LongStorage',
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torch.int: 'IntStorage',
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torch.int16: 'ShortStorage',
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torch.int8: 'CharStorage',
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torch.uint8: 'ByteStorage',
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torch.bool: 'BoolStorage',
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torch.bfloat16: 'BFloat16Storage',
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torch.cdouble: 'ComplexDoubleStorage',
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torch.cfloat: 'ComplexFloatStorage',
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torch.qint8: 'QInt8Storage',
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torch.qint32: 'QInt32Storage',
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torch.quint8: 'QUInt8Storage',
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torch.quint4x2: 'QUInt4x2Storage',
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torch.quint2x4: 'QUInt2x4Storage',
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}
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|
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@lru_cache(maxsize=None)
|
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def _storage_type_to_dtype_map():
|
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dtype_map = {
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val: key for key, val in _dtype_to_storage_type_map().items()}
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return dtype_map
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|
|
def _get_storage_from_sequence(sequence, dtype, device):
|
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if dtype in [torch.quint8, torch.quint4x2, torch.quint2x4, torch.qint32, torch.qint8]:
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interpret_dtypes = {
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torch.quint8: torch.uint8,
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torch.quint4x2: torch.uint8,
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torch.quint2x4: torch.uint8,
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torch.qint32: torch.int32,
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torch.qint8: torch.int8
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}
|
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tmp_tensor = torch.tensor(
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sequence,
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dtype=interpret_dtypes[dtype],
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device=device)
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|
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else:
|
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tmp_tensor = torch.tensor(
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sequence,
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dtype=dtype,
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device=device)
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|
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return tmp_tensor._typed_storage()._untyped_storage
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|
|
def _isint(x):
|
|
if HAS_NUMPY:
|
|
return isinstance(x, (int, np.integer))
|
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else:
|
|
return isinstance(x, int)
|
|
|
|
_always_warn_typed_storage_removal = False
|
|
|
|
def _get_always_warn_typed_storage_removal():
|
|
return _always_warn_typed_storage_removal
|
|
|
|
def _set_always_warn_typed_storage_removal(always_warn):
|
|
global _always_warn_typed_storage_removal
|
|
assert isinstance(always_warn, bool)
|
|
_always_warn_typed_storage_removal = always_warn
|
|
|
|
def _warn_typed_storage_removal(stacklevel=2):
|
|
global _always_warn_typed_storage_removal
|
|
|
|
def is_first_time():
|
|
if not hasattr(_warn_typed_storage_removal, 'has_warned'):
|
|
return True
|
|
else:
|
|
return not _warn_typed_storage_removal.__dict__['has_warned']
|
|
|
|
if _get_always_warn_typed_storage_removal() or is_first_time():
|
|
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)
|
|
_warn_typed_storage_removal.__dict__['has_warned'] = True
|
|
|
|
def _reset_warn_typed_storage_removal():
|
|
_warn_typed_storage_removal.__dict__['has_warned'] = False
|
|
|
|
def _get_device_from_module(module: str):
|
|
last_part = module.rsplit(".", 1)[-1]
|
|
if last_part in ["cuda", torch._C._get_privateuse1_backend_name(), "hpu"]:
|
|
return last_part
|
|
else:
|
|
return "cpu"
|
|
|
|
class TypedStorage:
|
|
is_sparse = False
|
|
|
|
dtype: torch.dtype
|
|
|
|
@property
|
|
def _dtype(self):
|
|
return self.dtype
|
|
|
|
@property
|
|
def filename(self) -> _Optional[str]:
|
|
"""Returns the file name associated with this storage if the storage was memory mapped from a file.
|
|
or ``None`` if the storage was not created by memory mapping a file."""
|
|
return self._untyped_storage.filename
|
|
|
|
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=_get_device_from_module(cls.__module__),
|
|
_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 = _get_device_from_module(cls.__module__)
|
|
|
|
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'
|
|
|
|
@property
|
|
def is_hpu(self):
|
|
_warn_typed_storage_removal()
|
|
return self._untyped_storage.device.type == 'hpu'
|
|
|
|
def untyped(self):
|
|
"""Return 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)
|
|
from torch._subclasses.fake_tensor import unset_fake_temporarily
|
|
|
|
with unset_fake_temporarily():
|
|
tmp_tensor = torch.tensor([], dtype=self.dtype, device=self._untyped_storage.device).set_(self)
|
|
return tmp_tensor[idx_wrapped].item()
|
|
|
|
def copy_(self, source: T, non_blocking: _Optional[bool] = None):
|
|
_warn_typed_storage_removal()
|
|
if isinstance(source, TypedStorage):
|
|
self._untyped_storage.copy_(source._untyped_storage, non_blocking) # type: ignore[arg-type]
|
|
else:
|
|
self._untyped_storage.copy_(source, non_blocking) # type: ignore[arg-type]
|
|
return self
|
|
|
|
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()
|
|
|
|
def type(self, dtype: _Optional[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)
|
|
|
|
def cuda(self, device=None, non_blocking=False, **kwargs) -> T: # type: ignore[misc, type-var]
|
|
_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)
|
|
|
|
def hpu(self, device=None, non_blocking=False, **kwargs) -> T: # type: ignore[misc, type-var]
|
|
_warn_typed_storage_removal()
|
|
if self.dtype in [torch.quint8, torch.quint4x2, torch.quint2x4, torch.qint32, torch.qint8]:
|
|
raise RuntimeError("Cannot create HPU storage with quantized dtype")
|
|
hpu_storage: torch.UntypedStorage = self._untyped_storage.hpu(device, non_blocking, **kwargs)
|
|
return self._new_wrapped_storage(hpu_storage)
|
|
|
|
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)
|
|
|
|
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(self[i] for i in 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()
|
|
|
|
def clone(self):
|
|
"""Return a copy of this storage."""
|
|
_warn_typed_storage_removal()
|
|
return self._new_wrapped_storage(self._untyped_storage.clone())
|
|
|
|
def tolist(self):
|
|
"""Return a list containing the elements of this storage."""
|
|
_warn_typed_storage_removal()
|
|
return list(self)
|
|
|
|
def cpu(self):
|
|
"""Return 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())
|
|
|
|
def is_pinned(self, device: Union[str, torch.device] = 'cuda'):
|
|
r"""Determine whether the CPU TypedStorage is already pinned on device.
|
|
|
|
Args:
|
|
device (str or torch.device): The device to pin memory on. Default: ``'cuda'``
|
|
|
|
Returns:
|
|
A boolean variable.
|
|
"""
|
|
_warn_typed_storage_removal()
|
|
return self._untyped_storage.is_pinned(device)
|
|
|
|
def pin_memory(self, device: Union[str, torch.device] = 'cuda'):
|
|
r"""Copy the CPU TypedStorage to pinned memory, if it's not already pinned.
|
|
|
|
Args:
|
|
device (str or torch.device): The device to pin memory on. Default: ``'cuda'``.
|
|
|
|
Returns:
|
|
A pinned CPU storage.
|
|
"""
|
|
_warn_typed_storage_removal()
|
|
return self._new_wrapped_storage(self._untyped_storage.pin_memory(device=device))
|
|
|
|
def share_memory_(self):
|
|
"""See :meth:`torch.UntypedStorage.share_memory_`"""
|
|
_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):
|
|
"""Create 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
|
|
|
|
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()
|
|
|
|
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(),))
|
|
|
|
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()
|
|
|
|
def resizable(self):
|
|
_warn_typed_storage_removal()
|
|
return self._untyped_storage.resizable()
|
|
|
|
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)
|
|
|
|
@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
|
|
|
|
def double(self):
|
|
"""Casts this storage to double type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.double)
|
|
|
|
def float(self):
|
|
"""Casts this storage to float type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.float)
|
|
|
|
def half(self):
|
|
"""Casts this storage to half type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.half)
|
|
|
|
def long(self):
|
|
"""Casts this storage to long type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.long)
|
|
|
|
def int(self):
|
|
"""Casts this storage to int type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.int)
|
|
|
|
def short(self):
|
|
"""Casts this storage to short type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.short)
|
|
|
|
def char(self):
|
|
"""Casts this storage to char type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.int8)
|
|
|
|
def byte(self):
|
|
"""Casts this storage to byte type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.uint8)
|
|
|
|
def bool(self):
|
|
"""Casts this storage to bool type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.bool)
|
|
|
|
def bfloat16(self):
|
|
"""Casts this storage to bfloat16 type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.bfloat16)
|
|
|
|
def complex_double(self):
|
|
"""Casts this storage to complex double type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.cdouble)
|
|
|
|
def complex_float(self):
|
|
"""Casts this storage to complex float type."""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.cfloat)
|
|
|
|
def float8_e5m2(self):
|
|
"""Casts this storage to float8_e5m2 type"""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.float8_e5m2)
|
|
|
|
def float8_e4m3fn(self):
|
|
"""Casts this storage to float8_e4m3fn type"""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.float8_e4m3fn)
|
|
|
|
def float8_e5m2fnuz(self):
|
|
"""Casts this storage to float8_e5m2fnuz type"""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.float8_e5m2fnuz)
|
|
|
|
def float8_e4m3fnuz(self):
|
|
"""Casts this storage to float8_e4m3fnuz type"""
|
|
_warn_typed_storage_removal()
|
|
return self._to(torch.float8_e4m3fnuz)
|
|
|
|
@classmethod
|
|
def from_file(cls, filename, shared, size):
|
|
"""from_file(filename, shared=False, size=0) -> Storage
|
|
|
|
Creates a CPU storage backed by a memory-mapped file.
|
|
|
|
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 (whether ``MAP_SHARED`` or ``MAP_PRIVATE`` is passed to the
|
|
underlying `mmap(2) call <https://man7.org/linux/man-pages/man2/mmap.2.html>`_)
|
|
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)
|
|
|
|
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)
|
|
|
|
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', "hpu", torch._C._get_privateuse1_backend_name()]:
|
|
return None
|
|
|
|
module = torch if self.device.type == 'cpu' else getattr(torch, self.device.type)
|
|
|
|
try:
|
|
return getattr(module, storage_name)
|
|
except AttributeError:
|
|
return None
|
|
|
|
TypedStorage.type.__doc__ = _type.__doc__
|
|
TypedStorage.cuda.__doc__ = _cuda.__doc__
|
|
TypedStorage.hpu.__doc__ = _hpu.__doc__
|
|
|
|
class _LegacyStorageMeta(type):
|
|
dtype: torch.dtype
|
|
|
|
def __instancecheck__(cls, instance):
|
|
if type(instance) == TypedStorage:
|
|
cls_device = _get_device_from_module(cls.__module__)
|
|
return (cls_device == instance.device.type) and (cls.dtype == instance.dtype)
|
|
return False
|
|
|
|
class _LegacyStorage(TypedStorage, metaclass=_LegacyStorageMeta):
|
|
@classmethod
|
|
def _new_shared(cls, size):
|
|
"""Create 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
|