# @lint-ignore-every PYTHON3COMPATIMPORTS
r"""
The torch package contains data structures for multi-dimensional
tensors and mathematical operations over these are defined.
Additionally, it provides many utilities for efficient serializing of
Tensors and arbitrary types, and other useful utilities.
It has a CUDA counterpart, that enables you to run your tensor computations
on an NVIDIA GPU with compute capability >= 3.0.
"""
import os
import sys
import platform
from ._utils import _import_dotted_name
from ._utils_internal import get_file_path, prepare_multiprocessing_environment
from .version import __version__ # noqa: F401
from ._six import string_classes as _string_classes
__all__ = [
'typename', 'is_tensor', 'is_storage', 'set_default_tensor_type',
'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
'no_grad', 'enable_grad', 'rand', 'randn',
'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
]
################################################################################
# Load the extension module
################################################################################
# Loading the extension with RTLD_GLOBAL option allows to not link extension
# modules against the _C shared object. Their missing THP symbols will be
# automatically filled by the dynamic loader.
import os as _dl_flags
# if we have numpy, it *must* be imported before the call to setdlopenflags()
# or there is risk that later c modules will segfault when importing numpy
try:
import numpy as _np # noqa: F401
except ImportError:
pass
if platform.system() == 'Windows':
# first get nvToolsExt PATH
def get_nvToolsExt_path():
NVTOOLEXT_HOME = _dl_flags.getenv('NVTOOLSEXT_PATH', 'C:\\Program Files\\NVIDIA Corporation\\NvToolsExt')
if _dl_flags.path.exists(NVTOOLEXT_HOME):
return _dl_flags.path.join(NVTOOLEXT_HOME, 'bin', 'x64')
else:
return ''
py_dll_path = _dl_flags.path.join(sys.exec_prefix, 'Library', 'bin')
th_dll_path = _dl_flags.path.join(_dl_flags.path.dirname(__file__), 'lib')
dll_paths = [th_dll_path, py_dll_path, get_nvToolsExt_path(), _dl_flags.environ['PATH']]
# then add the path to env
_dl_flags.environ['PATH'] = ';'.join(dll_paths)
else:
# first check if the os package has the required flags
if not hasattr(_dl_flags, 'RTLD_GLOBAL') or not hasattr(_dl_flags, 'RTLD_LAZY'):
try:
# next try if DLFCN exists
import DLFCN as _dl_flags
except ImportError:
# as a last attempt, use compile-time constants
import torch._dl as _dl_flags
old_flags = sys.getdlopenflags()
sys.setdlopenflags(_dl_flags.RTLD_GLOBAL | _dl_flags.RTLD_LAZY)
del _dl_flags
from torch._C import *
__all__ += [name for name in dir(_C)
if name[0] != '_' and
not name.endswith('Base')]
if platform.system() != 'Windows':
sys.setdlopenflags(old_flags)
del old_flags
################################################################################
# Define basic utilities
################################################################################
def typename(o):
if isinstance(o, torch.Tensor):
return o.type()
module = ''
class_name = ''
if hasattr(o, '__module__') and o.__module__ != 'builtins' \
and o.__module__ != '__builtin__' and o.__module__ is not None:
module = o.__module__ + '.'
if hasattr(o, '__qualname__'):
class_name = o.__qualname__
elif hasattr(o, '__name__'):
class_name = o.__name__
else:
class_name = o.__class__.__name__
return module + class_name
def is_tensor(obj):
r"""Returns True if `obj` is a PyTorch tensor.
Args:
obj (Object): Object to test
"""
return isinstance(obj, torch.Tensor)
def is_storage(obj):
r"""Returns True if `obj` is a PyTorch storage object.
Args:
obj (Object): Object to test
"""
return type(obj) in _storage_classes
def set_default_tensor_type(t):
r"""Sets the default ``torch.Tensor`` type to floating point tensor type
:attr:`t`. This type will also be used as default floating point type for
type inference in :func:`torch.tensor`.
The default floating point tensor type is initially ``torch.FloatTensor``.
Args:
t (type or string): the floating point tensor type or its name
Example::
>>> torch.tensor([1.2, 3]).dtype # initial default for floating point is torch.float32
torch.float32
>>> torch.set_default_tensor_type(torch.DoubleTensor)
>>> torch.tensor([1.2, 3]).dtype # a new floating point tensor
torch.float64
"""
if isinstance(t, _string_classes):
t = _import_dotted_name(t)
_C._set_default_tensor_type(t)
def set_default_dtype(d):
r"""Sets the default floating point dtype to :attr:`d`. This type will be
used as default floating point type for type inference in
:func:`torch.tensor`.
The default floating point dtype is initially ``torch.float32``.
Args:
d (:class:`torch.dtype`): the floating point dtype to make the default
Example::
>>> torch.tensor([1.2, 3]).dtype # initial default for floating point is torch.float32
torch.float32
>>> torch.set_default_dtype(torch.float64)
>>> torch.tensor([1.2, 3]).dtype # a new floating point tensor
torch.float64
"""
_C._set_default_dtype(d)
# If you edit these imports, please update torch/__init__.py.in as well
from .random import set_rng_state, get_rng_state, manual_seed, initial_seed, seed
from .serialization import save, load
from ._tensor_str import set_printoptions
################################################################################
# Define Storage and Tensor classes
################################################################################
from .tensor import Tensor
from .storage import _StorageBase
class DoubleStorage(_C.DoubleStorageBase, _StorageBase):
pass
class FloatStorage(_C.FloatStorageBase, _StorageBase):
pass
class HalfStorage(_C.HalfStorageBase, _StorageBase):
pass
class LongStorage(_C.LongStorageBase, _StorageBase):
pass
class IntStorage(_C.IntStorageBase, _StorageBase):
pass
class ShortStorage(_C.ShortStorageBase, _StorageBase):
pass
class CharStorage(_C.CharStorageBase, _StorageBase):
pass
class ByteStorage(_C.ByteStorageBase, _StorageBase):
pass
class BoolStorage(_C.BoolStorageBase, _StorageBase):
pass
class BFloat16Storage(_C.BFloat16StorageBase, _StorageBase):
pass
class QUInt8Storage(_C.QUInt8StorageBase, _StorageBase):
pass
class QInt8Storage(_C.QInt8StorageBase, _StorageBase):
pass
class QInt32Storage(_C.QInt32StorageBase, _StorageBase):
pass
_storage_classes = {
DoubleStorage, FloatStorage, LongStorage, IntStorage, ShortStorage,
CharStorage, ByteStorage, HalfStorage, BoolStorage, QUInt8Storage, QInt8Storage,
QInt32Storage, BFloat16Storage
}
# The _tensor_classes set is initialized by the call to _C._initialize_tensor_type_bindings()
_tensor_classes = set()
################################################################################
# Initialize extension
################################################################################
def manager_path():
if platform.system() == 'Windows':
return b""
path = get_file_path('torch', 'bin', 'torch_shm_manager')
prepare_multiprocessing_environment(get_file_path('torch'))
if not os.path.exists(path):
raise RuntimeError("Unable to find torch_shm_manager at " + path)
return path.encode('utf-8')
# Shared memory manager needs to know the exact location of manager executable
_C._initExtension(manager_path())
del manager_path
for name in dir(_C._VariableFunctions):
if name.startswith('__'):
continue
globals()[name] = getattr(_C._VariableFunctions, name)
################################################################################
# Import interface functions defined in Python
################################################################################
# needs to be after the above ATen bindings so we can overwrite from Python side
from .functional import *
################################################################################
# Remove unnecessary members
################################################################################
del DoubleStorageBase
del FloatStorageBase
del LongStorageBase
del IntStorageBase
del ShortStorageBase
del CharStorageBase
del ByteStorageBase
del BoolStorageBase
del QUInt8StorageBase
del BFloat16StorageBase
################################################################################
# Import most common subpackages
################################################################################
import torch.cuda
import torch.autograd
from torch.autograd import no_grad, enable_grad, set_grad_enabled # noqa: F401
import torch.nn
import torch.nn._intrinsic
import torch.nn.quantized
import torch.optim
import torch.multiprocessing
import torch.sparse
import torch.utils.backcompat
import torch.onnx
import torch.jit
import torch.hub
import torch.random
import torch.distributions
import torch.testing
import torch.backends.cuda
import torch.backends.mkl
import torch.backends.openmp
import torch.utils.data
import torch.__config__
import torch.__future__
_C._init_names(list(torch._storage_classes))
# attach docstrings to torch and tensor functions
from . import _torch_docs, _tensor_docs, _storage_docs
del _torch_docs, _tensor_docs, _storage_docs
def compiled_with_cxx11_abi():
r"""Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1"""
return _C._GLIBCXX_USE_CXX11_ABI
# Import the ops "namespace"
from torch._ops import ops # noqa: F401
# Import the quasi random sampler
import torch.quasirandom