Source code for torch

"""
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 >= 2.0.
"""

import sys
from ._utils import _import_dotted_name
from .version import __version__

__all__ = [
    'typename', 'is_tensor', 'is_storage', 'set_default_tensor_type',
    'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed',
    'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
    'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
    'ShortStorage', 'CharStorage', 'ByteStorage',
    'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
    'ShortTensor', 'CharTensor', 'ByteTensor',
]

################################################################################
# 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
except:
    pass

# first check if the os package has the required flags
if not hasattr(_dl_flags, 'RTLD_GLOBAL') or not hasattr(_dl_flags, 'RTLD_NOW'):
    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_NOW)

from torch._C import *

__all__ += [name for name in dir(_C)
            if name[0] != '_' and
            not name.endswith('Base')]

sys.setdlopenflags(old_flags)
del _dl_flags
del old_flags

################################################################################
# Define basic utilities
################################################################################


def typename(o):
    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 type(obj) in _tensor_classes
[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): global Tensor global Storage Tensor = _import_dotted_name(t) Storage = _import_dotted_name(t.replace('Tensor', 'Storage')) _C._set_default_tensor_type(Tensor)
[docs]def set_rng_state(new_state): r"""Sets the random number generator state. Args: new_state (torch.ByteTensor): The desired state """ default_generator.set_state(new_state)
[docs]def get_rng_state(): r"""Returns the random number generator state as a ByteTensor.""" return default_generator.get_state()
[docs]def manual_seed(seed): r"""Sets the seed for generating random numbers. And returns a `torch._C.Generator` object. Args: seed (int or long): The desired seed. """ if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) return default_generator.manual_seed(seed)
[docs]def initial_seed(): r"""Returns the initial seed for generating random numbers as a python `long`. """ return default_generator.initial_seed()
from .serialization import save, load from ._tensor_str import set_printoptions ################################################################################ # Define Storage and Tensor classes ################################################################################ from .storage import _StorageBase from .tensor import _TensorBase 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 DoubleTensor(_C.DoubleTensorBase, _TensorBase): def is_signed(self): return True @classmethod def storage_type(cls): return DoubleStorage class FloatTensor(_C.FloatTensorBase, _TensorBase): def is_signed(self): return True @classmethod def storage_type(cls): return FloatStorage class HalfTensor(_C.HalfTensorBase, _TensorBase): def is_signed(self): return True @classmethod def storage_type(cls): return HalfStorage class LongTensor(_C.LongTensorBase, _TensorBase): def is_signed(self): return True @classmethod def storage_type(cls): return LongStorage class IntTensor(_C.IntTensorBase, _TensorBase): def is_signed(self): return True @classmethod def storage_type(cls): return IntStorage class ShortTensor(_C.ShortTensorBase, _TensorBase): def is_signed(self): return True @classmethod def storage_type(cls): return ShortStorage class CharTensor(_C.CharTensorBase, _TensorBase): def is_signed(self): # TODO return False @classmethod def storage_type(cls): return CharStorage class ByteTensor(_C.ByteTensorBase, _TensorBase): def is_signed(self): return False @classmethod def storage_type(cls): return ByteStorage _storage_classes = { DoubleStorage, FloatStorage, LongStorage, IntStorage, ShortStorage, CharStorage, ByteStorage, } _tensor_classes = { DoubleTensor, FloatTensor, LongTensor, IntTensor, ShortTensor, CharTensor, ByteTensor, } set_default_tensor_type('torch.FloatTensor') ################################################################################ # Import interface functions defined in Python ################################################################################ from .functional import * ################################################################################ # Initialize extension ################################################################################ def manager_path(): import os path = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'lib', 'torch_shm_manager') 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 ################################################################################ # Remove unnecessary members ################################################################################ del DoubleStorageBase del FloatStorageBase del LongStorageBase del IntStorageBase del ShortStorageBase del CharStorageBase del ByteStorageBase del DoubleTensorBase del FloatTensorBase del LongTensorBase del IntTensorBase del ShortTensorBase del CharTensorBase del ByteTensorBase del SparseDoubleTensorBase del SparseFloatTensorBase del SparseLongTensorBase del SparseIntTensorBase del SparseShortTensorBase del SparseCharTensorBase del SparseByteTensorBase ################################################################################ # Import most common subpackages ################################################################################ import torch.cuda import torch.autograd import torch.nn import torch.optim import torch.multiprocessing import torch.sparse import torch.utils.backcompat _C._init_names(list(torch._tensor_classes) + 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