# @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
import ctypes
if sys.version_info < (3,):
raise Exception("Python 2 has reached end-of-life and is no longer supported by PyTorch.")
from ._utils import _import_dotted_name
from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
USE_RTLD_GLOBAL_WITH_LIBTORCH
from .version import __version__
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',
'lobpcg',
]
################################################################################
# Load the extension module
################################################################################
if platform.system() == 'Windows':
is_conda = os.path.exists(os.path.join(sys.prefix, 'conda-meta'))
py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
if not os.path.exists(os.path.join(th_dll_path, 'nvToolsExt64_1.dll')) and \
not os.path.exists(os.path.join(py_dll_path, 'nvToolsExt64_1.dll')):
nvtoolsext_dll_path = os.path.join(
os.getenv('NVTOOLSEXT_PATH', 'C:\\Program Files\\NVIDIA Corporation\\NvToolsExt'), 'bin', 'x64')
else:
nvtoolsext_dll_path = ''
from .version import cuda as cuda_version
import glob
if cuda_version and len(glob.glob(os.path.join(th_dll_path, 'cudart64*.dll'))) == 0 and \
len(glob.glob(os.path.join(py_dll_path, 'cudart64*.dll'))) == 0:
cuda_version_1 = cuda_version.replace('.', '_')
cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
default_path = 'C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v' + cuda_version
cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
else:
cuda_path = ''
if sys.version_info >= (3, 8):
dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, nvtoolsext_dll_path, cuda_path]))
for dll_path in dll_paths:
os.add_dll_directory(dll_path)
if is_conda or sys.version_info < (3, 8):
dll_paths = [th_dll_path, py_dll_path, nvtoolsext_dll_path, cuda_path]
dll_paths = list(filter(os.path.exists, dll_paths)) + [os.environ['PATH']]
os.environ['PATH'] = ';'.join(dll_paths)
import glob
dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
for dll in dlls:
ctypes.CDLL(dll)
# See Note [Global dependencies]
def _load_global_deps():
if platform.system() == 'Windows':
return
lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
here = os.path.abspath(__file__)
lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
platform.system() != 'Windows':
# Do it the hard way. You might want to load libtorch with RTLD_GLOBAL in a
# few circumstances:
#
# 1. You're in a build environment (e.g., fbcode) where
# libtorch_global_deps is not available, but you still need
# to get mkl to link in with RTLD_GLOBAL or it will just
# not work.
#
# 2. You're trying to run PyTorch under UBSAN and you need
# to ensure that only one copy of libtorch is loaded, so
# vptr checks work properly
#
# If you're using this setting, you must verify that all the libraries
# you load consistently use the same libstdc++, or you may have
# mysterious segfaults.
#
import os as _dl_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)
from torch._C import *
sys.setdlopenflags(old_flags)
del old_flags
del _dl_flags
else:
# Easy way. You want this most of the time, because it will prevent
# C++ symbols from libtorch clobbering C++ symbols from other
# libraries, leading to mysterious segfaults.
#
# See Note [Global dependencies]
_load_global_deps()
from torch._C import *
__all__ += [name for name in dir(_C)
if name[0] != '_' and
not name.endswith('Base')]
################################################################################
# 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
[docs]def is_tensor(obj):
r"""Returns True if `obj` is a PyTorch tensor.
Args:
obj (Object): Object to test
"""
return isinstance(obj, torch.Tensor)
[docs]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
[docs]def set_default_tensor_type(t):
r"""Sets the default ``torch.Tensor`` type to floating point tensor type
``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)
[docs]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
[docs]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
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.mkldnn
import torch.backends.openmp
import torch.backends.quantized
import torch.quantization
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
[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
from torch._classes import classes
# Import the quasi random sampler
import torch.quasirandom
# If you are seeing this, it means that this call site was not checked if
# the memory format could be preserved, and it was switched to old default
# behaviour of contiguous
legacy_contiguous_format = contiguous_format
# Register fork handler to initialize OpenMP in child processes (see gh-28389)
from torch.multiprocessing._atfork import register_after_fork
register_after_fork(torch.get_num_threads)
del register_after_fork
# Import tools that require fully imported torch (for applying
# torch.jit.script as a decorator, for instance):
from ._lobpcg import lobpcg
