signature=e7ed2d7c7c0935f26c013481ed38233d,pytorch/tensor.py at ac0d13cc88d269567007b11fa9e7bb71ed77...

import torch

import torch._C as _C

from torch._namedtensor_internals import update_names, check_serializing_named_tensor, resolve_ellipsis

from torch._namedtensor_internals import unzip_namedshape, single_ellipsis_index, is_ellipsis

from collections import OrderedDict

import torch.utils.hooks as hooks

import warnings

import weakref

from torch._C import _add_docstr

from numbers import Number

import functools

def _wrap_type_error_to_not_implemented(f):

# functools.wraps doesn't work well with methods in python 2

method_assignments = ('__name__', '__doc__')

assigned = functools.WRAPPER_ASSIGNMENTS

@functools.wraps(f, assigned=assigned)

def wrapped(*args, **kwargs):

from torch.overrides import has_torch_function, handle_torch_function

if not all(type(t) is Tensor for t in args) and has_torch_function(args):

return handle_torch_function(wrapped, args, *args, **kwargs)

try:

return f(*args, **kwargs)

except TypeError:

return NotImplemented

return wrapped

# NB: If you subclass Tensor, and want to share the subclassed class

# across processes, you must also update torch/multiprocessing/reductions.py

# to define a ForkingPickler serialization mode for the class.

#

# NB: If you add a new method to Tensor, you must update

# torch/__init__.py.in to add a type annotation for your method;

# otherwise, it will not show up in autocomplete.

class Tensor(torch._C._TensorBase):

def __deepcopy__(self, memo):

from torch.overrides import has_torch_function, handle_torch_function

relevant_args = (self,)

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__deepcopy__, relevant_args, self, memo)

if not self.is_leaf:

raise RuntimeError("Only Tensors created explicitly by the user "

"(graph leaves) support the deepcopy protocol at the moment")

if id(self) in memo:

return memo[id(self)]

with torch.no_grad():

if self.is_sparse or self.device.type == 'xla':

new_tensor = self.clone()

else:

new_storage = self.storage().__deepcopy__(memo)

if self.is_quantized:

if self.qscheme() == torch.per_tensor_affine:

quantizer_params = self.qscheme(), self.q_scale(), self.q_zero_point()

elif self.qscheme() in (torch.per_channel_affine, torch.per_channel_affine_float_qparams):

quantizer_params = self.qscheme(), \

self.q_per_channel_scales(), \

self.q_per_channel_zero_points(), \

self.q_per_channel_axis()

else:

raise RuntimeError(f"Unsupported qscheme {self.qscheme()} in deepcopy")

new_tensor = torch._utils._rebuild_qtensor(

new_storage,

self.storage_offset(),

self.size(),

self.stride(),

quantizer_params,

self.requires_grad,

self._backward_hooks)

else:

new_tensor = self.new()

new_tensor.set_(new_storage, self.storage_offset(), self.size(), self.stride())

new_tensor.requires_grad = self.requires_grad

memo[id(self)] = new_tensor

return new_tensor

def __reduce_ex__(self, proto):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__reduce_ex__, relevant_args, self, proto)

check_serializing_named_tensor(self)

# See Note [Don't serialize hooks]

torch.utils.hooks.warn_if_has_hooks(self)

# Note: Numpy array is chosen to be the rebuild component for XLA Tensor.

# We considered a few options:

# 1. CPU tensor can't be used here.

# Otherwise in torch.load CPU storage is reconstructed with randomly

# initialized data, moved onto XLA device, and then storage is updated

# to the serialized content. This works perfectly for CPU/CUDA but not XLA.

# XLA tensor is disconnected with storage so it doesn't get the update.

# 2. Python list is not a good fit due to performance reason.

# `tolist()` converts every single element in the tensor into python objects

# and serialize them one by one.

if self.device.type == 'xla':

args = (self.cpu().numpy(),

self.dtype,

str(self.device),

self.requires_grad)

return (torch._utils._rebuild_xla_tensor, args)

if self.is_quantized:

if self.qscheme() == torch.per_tensor_affine:

quantizer_params = (torch.per_tensor_affine,

self.q_scale(),

self.q_zero_point())

elif self.qscheme() in (torch.per_channel_affine, torch.per_channel_affine_float_qparams):

# convert scales and zero points to tuple to avoid recursive calls

# when/if we get multi-axis quantized tensors in the future, the shape

# is recoverable from the main tensor shape

quantizer_params = (torch.per_channel_affine,

self.q_per_channel_scales(),

self.q_per_channel_zero_points(),

self.q_per_channel_axis())

else:

raise RuntimeError(f"Serialization is not supported for tensors of type {self.qscheme()}")

args = (self.storage(),

self.storage_offset(),

tuple(self.size()),

self.stride(),

quantizer_params,

self.requires_grad,

OrderedDict())

return (torch._utils._rebuild_qtensor, args)

elif self.is_sparse:

if self.layout == torch.sparse_coo:

args = (self.layout,

(self._indices(),

self._values(),

self.size()))

else:

raise NotImplementedError(

'sparse tensor __reduce_ex__ for layout `%s`' % (self.layout))

return (torch._utils._rebuild_sparse_tensor, args)

else:

args = (self.storage(),

self.storage_offset(),

tuple(self.size()),

self.stride(),

self.requires_grad,

OrderedDict()) # previously was self._backward_hooks

return (torch._utils._rebuild_tensor_v2, args)

def __setstate__(self, state):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__setstate__, relevant_args, self, state)

# Warning: this method is NOT called when you torch.load() a tensor;

# that is managed by _rebuild_tensor_v2

if not self.is_leaf:

raise RuntimeError('__setstate__ can be only called on leaf Tensors')

if len(state) == 4:

# legacy serialization of Tensor

self.set_(*state)

return

elif len(state) == 5:

# legacy serialization of Variable

self.data = state[0]

state = (state[3], state[4], state[2])

# The setting of _backward_hooks is expected to be a no-op.

# See Note [Don't serialize hooks]

self.requires_grad, _, self._backward_hooks = state

def __repr__(self):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__repr__, relevant_args, self)

# All strings are unicode in Python 3.

return torch._tensor_str._str(self)

def backward(self, gradient=None, retain_graph=None, create_graph=False):

r"""Computes the gradient of current tensor w.r.t. graph leaves.

The graph is differentiated using the chain rule. If the tensor is

non-scalar (i.e. its data has more than one element) and requires

gradient, the function additionally requires specifying ``gradient``.

It should be a tensor of matching type and location, that contains

the gradient of the differentiated function w.r.t. ``self``.

This function accumulates gradients in the leaves - you might need to zero

``.grad`` attributes or set them to ``None`` before calling it.

See :ref:`Default gradient layouts`

for details on the memory layout of accumulated gradients.

Arguments:

gradient (Tensor or None): Gradient w.r.t. the

tensor. If it is a tensor, it will be automatically converted

to a Tensor that does not require grad unless ``create_graph`` is True.

None values can be specified for scalar Tensors or ones that

don't require grad. If a None value would be acceptable then

this argument is optional.

retain_graph (bool, optional): If ``False``, the graph used to compute

the grads will be freed. Note that in nearly all cases setting

this option to True is not needed and often can be worked around

in a much more efficient way. Defaults to the value of

``create_graph``.

create_graph (bool, optional): If ``True``, graph of the derivative will

be constructed, allowing to compute higher order derivative

products. Defaults to ``False``.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(

Tensor.backward,

relevant_args,

self,

gradient=gradient,

retain_graph=retain_graph,

create_graph=create_graph)

torch.autograd.backward(self, gradient, retain_graph, create_graph)

def register_hook(self, hook):

r"""Registers a backward hook.

The hook will be called every time a gradient with respect to the

Tensor is computed. The hook should have the following signature::

hook(grad) -> Tensor or None

The hook should not modify its argument, but it can optionally return

a new gradient which will be used in place of :attr:`grad`.

This function returns a handle with a method ``handle.remove()``

that removes the hook from the module.

Example::

>>> v = torch.tensor([0., 0., 0.], requires_grad=True)

>>> h = v.register_hook(lambda grad: grad * 2) # double the gradient

>>> v.backward(torch.tensor([1., 2., 3.]))

>>> v.grad

2

4

6

[torch.FloatTensor of size (3,)]

>>> h.remove() # removes the hook

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.register_hook, relevant_args, self, hook)

if not self.requires_grad:

raise RuntimeError("cannot register a hook on a tensor that "

"doesn't require gradient")

if self._backward_hooks is None:

self._backward_hooks = OrderedDict()

if self.grad_fn is not None:

self.grad_fn._register_hook_dict(self)

handle = hooks.RemovableHandle(self._backward_hooks)

self._backward_hooks[handle.id] = hook

return handle

def reinforce(self, reward):

def trim(str):

return '\n'.join([line.strip() for line in str.split('\n')])

raise RuntimeError(trim(r"""reinforce() was removed.

Use torch.distributions instead.

See https://pytorch.org/docs/master/distributions.html

Instead of:

probs = policy_network(state)

action = probs.multinomial()

next_state, reward = env.step(action)

action.reinforce(reward)

action.backward()

Use:

probs = policy_network(state)

# NOTE: categorical is equivalent to what used to be called multinomial

m = torch.distributions.Categorical(probs)

action = m.sample()

next_state, reward = env.step(action)

loss = -m.log_prob(action) * reward

loss.backward()

"""))

detach = _add_docstr(_C._TensorBase.detach, r"""

Returns a new Tensor, detached from the current graph.

The result will never require gradient.

.. note::

Returned Tensor shares the same storage with the original one.

In-place modifications on either of them will be seen, and may trigger

errors in correctness checks.

IMPORTANT NOTE: Previously, in-place size / stride / storage changes

(such as `resize_` / `resize_as_` / `set_` / `transpose_`) to the returned tensor

also update the original tensor. Now, these in-place changes will not update the

original tensor anymore, and will instead trigger an error.

For sparse tensors:

In-place indices / values changes (such as `zero_` / `copy_` / `add_`) to the

returned tensor will not update the original tensor anymore, and will instead

trigger an error.

""")

detach_ = _add_docstr(_C._TensorBase.detach_, r"""

Detaches the Tensor from the graph that created it, making it a leaf.

Views cannot be detached in-place.

""")

def retain_grad(self):

r"""Enables .grad attribute for non-leaf Tensors."""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.retain_grad, relevant_args, self)

if not self.requires_grad:

raise RuntimeError("can't retain_grad on Tensor that has requires_grad=False")

if self.is_leaf: # no-op for leaves

return

if hasattr(self, 'retains_grad'):

return

weak_self = weakref.ref(self)

def retain_grad_hook(grad):

var = weak_self()

if var is None:

return

if var._grad is None:

if grad.is_sparse:

var._grad = grad.clone()

else:

var._grad = grad.clone(memory_format=torch.contiguous_format)

else:

var._grad = var._grad + grad

self.register_hook(retain_grad_hook)

self.retains_grad = True

def is_shared(self):

r"""Checks if tensor is in shared memory.

This is always ``True`` for CUDA tensors.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.is_shared, relevant_args, self)

return self.storage().is_shared()

def share_memory_(self):

r"""Moves the underlying storage to shared memory.

This is a no-op if the underlying storage is already in shared memory

and for CUDA tensors. Tensors in shared memory cannot be resized.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.share_memory_, relevant_args, self)

self.storage().share_memory_()

return self

def __reversed__(self):

r"""Reverses the tensor along dimension 0."""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__reversed__, relevant_args, self)

if self.dim() == 0:

return self

else:

return self.flip(0)

def norm(self, p="fro", dim=None, keepdim=False, dtype=None):

r"""See :func:`torch.norm`"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.norm, relevant_args, self, p=p, dim=dim, keepdim=keepdim, dtype=dtype)

return torch.norm(self, p, dim, keepdim, dtype=dtype)

def lu(self, pivot=True, get_infos=False):

r"""See :func:`torch.lu`"""

# If get_infos is True, then we don't need to check for errors and vice versa

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.lu, relevant_args, self, pivot=pivot, get_infos=get_infos)

LU, pivots, infos = torch._lu_with_info(self, pivot=pivot, check_errors=(not get_infos))

if get_infos:

return LU, pivots, infos

else:

return LU, pivots

def stft(self, n_fft, hop_length=None, win_length=None, window=None,

center=True, pad_mode='reflect', normalized=False, onesided=True):

r"""See :func:`torch.stft`

.. warning::

This function changed signature at version 0.4.1. Calling with

the previous signature may cause error or return incorrect result.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(

Tensor.stft, relevant_args, self, n_fft, hop_length=hop_length,

win_length=win_length, window=window, center=center, pad_mode=pad_mode, normalized=normalized,

onesided=onesided

)

return torch.stft(self, n_fft, hop_length, win_length, window, center,

pad_mode, normalized, onesided)

def istft(self, n_fft, hop_length=None, win_length=None, window=None,

center=True, normalized=False, onesided=True, length=None):

r"""See :func:`torch.istft`"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(

Tensor.istft, relevant_args, self, n_fft, hop_length=hop_length, win_length=win_length,

window=window, center=center, normalized=normalized, onesided=onesided, length=None

)

return torch.istft(self, n_fft, hop_length, win_length, window, center,

normalized, onesided, length)

def resize(self, *sizes):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.resize, relevant_args, self, *sizes)

warnings.warn("non-inplace resize is deprecated")

from torch.autograd._functions import Resize

return Resize.apply(self, sizes)

def resize_as(self, tensor):

relevant_args = (self, tensor)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and type(tensor) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.resize_as, relevant_args, self, tensor)

warnings.warn("non-inplace resize_as is deprecated")

from torch.autograd._functions import Resize

return Resize.apply(self, tensor.size())

def split(self, split_size, dim=0):

r"""See :func:`torch.split`

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.split, relevant_args, self, split_size, dim=dim)

if isinstance(split_size, int):

return super(Tensor, self).split(split_size, dim)

elif isinstance(split_size, Tensor):

try:

split_size = int(split_size)

return super(Tensor, self).split(split_size, dim)

except ValueError:

return super(Tensor, self).split_with_sizes(split_size, dim)

else:

return super(Tensor, self).split_with_sizes(split_size, dim)

def unique(self, sorted=True, return_inverse=False, return_counts=False, dim=None):

r"""Returns the unique elements of the input tensor.

See :func:`torch.unique`

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(

Tensor.unique, relevant_args, self, sorted=sorted, return_inverse=return_inverse,

return_counts=return_counts, dim=dim

)

return torch.unique(self, sorted=sorted, return_inverse=return_inverse, return_counts=return_counts, dim=dim)

def unique_consecutive(self, return_inverse=False, return_counts=False, dim=None):

r"""Eliminates all but the first element from every consecutive group of equivalent elements.

See :func:`torch.unique_consecutive`

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(

Tensor.unique_consecutive, relevant_args, self, return_inverse=return_inverse,

return_counts=return_counts, dim=dim

)

return torch.unique_consecutive(self, return_inverse=return_inverse, return_counts=return_counts, dim=dim)

def __rsub__(self, other):

relevant_args = (self, other)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and type(other) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__rsub__, relevant_args, self, other)

return _C._VariableFunctions.rsub(self, other)

def __rdiv__(self, other):

relevant_args = (self, other)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and type(other) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__rdiv__, relevant_args, self, other)

if self.dtype.is_floating_point or self.dtype.is_complex:

return self.reciprocal() * other

else:

return (self.double().reciprocal() * other).type_as(self)

__rtruediv__ = __rdiv__

__itruediv__ = _C._TensorBase.__idiv__

__pow__ = _C._TensorBase.pow

def __format__(self, format_spec):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__format__, relevant_args, self, format_spec)

if self.dim() == 0:

return self.item().__format__(format_spec)

return object.__format__(self, format_spec)

def __ipow__(self, other):

relevant_args = (self, other)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and type(other) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__ipow__, relevant_args, self, other)

return NotImplemented

@_wrap_type_error_to_not_implemented

def __rpow__(self, other):

dtype = torch.result_type(other, self)

return torch.tensor(other, dtype=dtype, device=self.device) ** self

@_wrap_type_error_to_not_implemented

def __floordiv__(self, other):

return torch.floor_divide(self, other)

@_wrap_type_error_to_not_implemented

def __rfloordiv__(self, other):

result = other / self

if result.dtype.is_floating_point:

result = result.trunc()

return result

__neg__ = _C._TensorBase.neg

__eq__ = _wrap_type_error_to_not_implemented(_C._TensorBase.eq)

__ne__ = _wrap_type_error_to_not_implemented(_C._TensorBase.ne)

__lt__ = _wrap_type_error_to_not_implemented(_C._TensorBase.lt)

__le__ = _wrap_type_error_to_not_implemented(_C._TensorBase.le)

__gt__ = _wrap_type_error_to_not_implemented(_C._TensorBase.gt)

__ge__ = _wrap_type_error_to_not_implemented(_C._TensorBase.ge)

__abs__ = _C._TensorBase.abs

def __len__(self):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__len__, relevant_args, self)

if self.dim() == 0:

raise TypeError("len() of a 0-d tensor")

return self.shape[0]

def __iter__(self):

# NB: we use 'imap' and not 'map' here, so that in Python 2 we get a

# generator and don't eagerly perform all the indexes. This could

# save us work, and also helps keep trace ordering deterministic

# (e.g., if you zip(*hiddens), the eager map will force all the

# indexes of hiddens[0] before hiddens[1], while the generator

# map will interleave them.)

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__iter__, relevant_args, self)

if self.dim() == 0:

raise TypeError('iteration over a 0-d tensor')

if torch._C._get_tracing_state():

warnings.warn('Iterating over a tensor might cause the trace to be incorrect. '

'Passing a tensor of different shape won\'t change the number of '

'iterations executed (and might lead to errors or silently give '

'incorrect results).', category=RuntimeWarning)

return iter(self.unbind(0))

def __hash__(self):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__hash__, relevant_args, self)

return id(self)

def __dir__(self):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__dir__, relevant_args, self)

if self.is_quantized:

warnings.warn('Only a small subset of methods are supported for quantized tensors.')

tensor_methods = dir(self.__class__)

tensor_methods.remove('volatile') # deprecated

attrs = list(self.__dict__.keys())

keys = tensor_methods + attrs

# property only available dense, cuda tensors

if (not self.is_cuda) or self.is_sparse:

keys.remove("__cuda_array_interface__")

return sorted(keys)

# Numpy array interface, to support `numpy.asarray(tensor) -> ndarray`

__array_priority__ = 1000 # prefer Tensor ops over numpy ones

def __array__(self, dtype=None):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__array__, relevant_args, self, dtype=dtype)

if dtype is None:

return self.numpy()

else:

return self.numpy().astype(dtype, copy=False)

# Wrap Numpy array again in a suitable tensor when done, to support e.g.

# `numpy.sin(tensor) -> tensor` or `numpy.greater(tensor, 0) -> ByteTensor`

def __array_wrap__(self, array):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__array_wrap__, relevant_args, self, array=array)

if array.dtype == bool:

# Workaround, torch has no built-in bool tensor

array = array.astype('uint8')

return torch.from_numpy(array)

def __contains__(self, element):

r"""Check if `element` is present in tensor

Arguments:

element (Tensor or scalar): element to be checked

for presence in current tensor"

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__contains__, relevant_args, self, element)

if isinstance(element, (torch.Tensor, Number)):

return (element == self).any().item()

raise RuntimeError(

"Tensor.__contains__ only supports Tensor or scalar, but you passed in a %s." %

type(element)

)

@property

def __cuda_array_interface__(self):

"""Array view description for cuda tensors.

See:

https://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.__cuda_array_interface__.__get__, relevant_args, self)

# raise AttributeError for unsupported tensors, so that

# hasattr(cpu_tensor, "__cuda_array_interface__") is False.

if not self.is_cuda:

raise AttributeError(

"Can't get __cuda_array_interface__ on non-CUDA tensor type: %s "

"If CUDA data is required use tensor.cuda() to copy tensor to device memory." %

self.type()

)

if self.is_sparse:

raise AttributeError(

"Can't get __cuda_array_interface__ on sparse type: %s "

"Use Tensor.to_dense() to convert to a dense tensor first." %

self.type()

)

# RuntimeError, matching tensor.__array__() behavior.

if self.requires_grad:

raise RuntimeError(

"Can't get __cuda_array_interface__ on Variable that requires grad. "

"If gradients aren't required, use var.detach() to get Variable that doesn't require grad."

)

# CUDA devices are little-endian and tensors are stored in native byte

# order. 1-byte entries are endian-agnostic.

typestr = {

torch.complex64: "

torch.complex128: "

torch.float16: "

torch.float32: "

torch.float64: "

torch.uint8: "|u1",

torch.int8: "|i1",

torch.int16: "

torch.int32: "

torch.int64: "

}[self.dtype]

itemsize = self.storage().element_size()

shape = tuple(self.shape)

if self.is_contiguous():

# __cuda_array_interface__ v2 requires the strides to be omitted

# (either not set or set to None) for C-contiguous arrays.

strides = None

else:

strides = tuple(s * itemsize for s in self.stride())

data_ptr = self.data_ptr() if self.numel() > 0 else 0

data = (data_ptr, False) # read-only is false

return dict(typestr=typestr, shape=shape, strides=strides, data=data, version=2)

def refine_names(self, *names):

r"""Refines the dimension names of :attr:`self` according to :attr:`names`.

Refining is a special case of renaming that "lifts" unnamed dimensions.

A ``None`` dim can be refined to have any name; a named dim can only be

refined to have the same name.

Because named tensors can coexist with unnamed tensors, refining names

gives a nice way to write named-tensor-aware code that works with both

named and unnamed tensors.

:attr:`names` may contain up to one Ellipsis (``...``).

The Ellipsis is expanded greedily; it is expanded in-place to fill

:attr:`names` to the same length as ``self.dim()`` using names from the

corresponding indices of ``self.names``.

Python 2 does not support Ellipsis but one may use a string literal

instead (``'...'``).

Arguments:

names (iterable of str): The desired names of the output tensor. May

contain up to one Ellipsis.

Examples::

>>> imgs = torch.randn(32, 3, 128, 128)

>>> named_imgs = imgs.refine_names('N', 'C', 'H', 'W')

>>> named_imgs.names

('N', 'C', 'H', 'W')

>>> tensor = torch.randn(2, 3, 5, 7, 11)

>>> tensor = tensor.refine_names('A', ..., 'B', 'C')

>>> tensor.names

('A', None, None, 'B', 'C')

.. warning::

The named tensor API is experimental and subject to change.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.refine_names, relevant_args, self, *names)

names = resolve_ellipsis(names, self.names, 'refine_names')

return super(Tensor, self).refine_names(names)

def align_to(self, *names):

r"""Permutes the dimensions of the :attr:`self` tensor to match the order

specified in :attr:`names`, adding size-one dims for any new names.

All of the dims of :attr:`self` must be named in order to use this method.

The resulting tensor is a view on the original tensor.

All dimension names of :attr:`self` must be present in :attr:`names`.

:attr:`names` may contain additional names that are not in ``self.names``;

the output tensor has a size-one dimension for each of those new names.

:attr:`names` may contain up to one Ellipsis (``...``).

The Ellipsis is expanded to be equal to all dimension names of :attr:`self`

that are not mentioned in :attr:`names`, in the order that they appear

in :attr:`self`.

Python 2 does not support Ellipsis but one may use a string literal

instead (``'...'``).

Arguments:

names (iterable of str): The desired dimension ordering of the

output tensor. May contain up to one Ellipsis that is expanded

to all unmentioned dim names of :attr:`self`.

Examples::

>>> tensor = torch.randn(2, 2, 2, 2, 2, 2)

>>> named_tensor = tensor.refine_names('A', 'B', 'C', 'D', 'E', 'F')

# Move the F and E dims to the front while keeping the rest in order

>>> named_tensor.align_to('F', 'E', ...)

.. warning::

The named tensor API is experimental and subject to change.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.align_to, relevant_args, self, *names)

ellipsis_idx = single_ellipsis_index(names, 'align_to')

if ellipsis_idx is None:

return super(Tensor, self).align_to(names)

return super(Tensor, self).align_to(

[name for name in names if not is_ellipsis(name)],

ellipsis_idx)

def unflatten(self, dim, sizes):

r"""Expands the dimension :attr:`dim` of the :attr:`self` tensor over multiple dimensions

of sizes given by :attr:`sizes`.

* :attr:`sizes` is the new shape of the unflattened dimension and it can be a `Tuple[int]` as well

as `torch.Size` if :attr:`self` is a `Tensor`, or `namedshape` (Tuple[(name: str, size: int)])

if :attr:`self` is a `NamedTensor`. The total number of elements in sizes must match the number

of elements in the original dim being unflattened.

Arguments:

dim (Union[int, str]): Dimension to unflatten

sizes (Union[Tuple[int] or torch.Size, Tuple[Tuple[str, int]]]): New shape of the unflattened dimension

Examples:

>>> torch.randn(3, 4, 1).unflatten(1, (2, 2)).shape

torch.Size([3, 2, 2, 1])

>>> torch.randn(2, 4, names=('A', 'B')).unflatten('B', (('B1', 2), ('B2', 2)))

tensor([[[-1.1772, 0.0180],

[ 0.2412, 0.1431]],

[[-1.1819, -0.8899],

[ 1.5813, 0.2274]]], names=('A', 'B1', 'B2'))

.. warning::

The named tensor API is experimental and subject to change.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.unflatten, relevant_args, self, dim, namedshape)

if not sizes:

raise RuntimeError("unflatten: sizes must be non-empty")

names = None

if isinstance(sizes, OrderedDict) or (isinstance(sizes, (tuple, list)) and isinstance(sizes[0], (tuple, list))):

names, sizes = unzip_namedshape(sizes)

return super(Tensor, self).unflatten(dim, sizes, names)

def rename_(self, *names, **rename_map):

"""In-place version of :meth:`~Tensor.rename`."""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.rename_, relevant_args, self, *names, **rename_map)

# Note [rename_ / rename API]

# The Python API for these is different from the C++ API. In Python:

# 1) tensor.rename(*names) takes a vararglist of names

# 2) tensor.rename(**rename_map) takes a map of names to rename.

# C++ is static, making it difficult to implement similar behavior.

return update_names(self, names, rename_map, inplace=True)

def rename(self, *names, **rename_map):

"""Renames dimension names of :attr:`self`.

There are two main usages:

``self.rename(**rename_map)`` returns a view on tensor that has dims

renamed as specified in the mapping :attr:`rename_map`.

``self.rename(*names)`` returns a view on tensor, renaming all

dimensions positionally using :attr:`names`.

Use ``self.rename(None)`` to drop names on a tensor.

One cannot specify both positional args :attr:`names` and keyword args

:attr:`rename_map`.

Examples::

>>> imgs = torch.rand(2, 3, 5, 7, names=('N', 'C', 'H', 'W'))

>>> renamed_imgs = imgs.rename(N='batch', C='channels')

>>> renamed_imgs.names

('batch', 'channels', 'H', 'W')

>>> renamed_imgs = imgs.rename(None)

>>> renamed_imgs.names

(None,)

>>> renamed_imgs = imgs.rename('batch', 'channel', 'height', 'width')

>>> renamed_imgs.names

('batch', 'channel', 'height', 'width')

.. warning::

The named tensor API is experimental and subject to change.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.rename, relevant_args, self, *names, **rename_map)

# See Note [rename_ / rename API]

return update_names(self, names, rename_map, inplace=False)

def _update_names(self, names, inplace):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor._update_names, relevant_args, self, names, inplace)

# See Note [rename_ / rename API]

if inplace:

return super(Tensor, self).rename_(names)

else:

return super(Tensor, self).rename(names)

@property

def grad(self):

"""

This attribute is ``None`` by default and becomes a Tensor the first time a call to

:func:`backward` computes gradients for ``self``.

The attribute will then contain the gradients computed and future calls to

:func:`backward` will accumulate (add) gradients into it.

"""

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.grad.__get__, relevant_args, self)

if self.requires_grad and not hasattr(self, "retains_grad") and not self.is_leaf and self._grad is None:

warnings.warn("The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad "

"attribute won't be populated during autograd.backward(). If you indeed want the gradient "

"for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the "

"non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See "

"github.com/pytorch/pytorch/pull/30531 for more informations.", stacklevel=2)

return self._grad

@grad.setter

def grad(self, new_grad):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.grad.__set__, relevant_args, self, new_grad)

self._grad = new_grad

@grad.deleter

def grad(self):

relevant_args = (self,)

from torch.overrides import has_torch_function, handle_torch_function

if type(self) is not Tensor and has_torch_function(relevant_args):

return handle_torch_function(Tensor.grad.__delete__, relevant_args, self)

del self._grad

@classmethod

def __torch_function__(cls, func, types, args=(), kwargs=None):

"""

This __torch_function__ implementation wraps subclasses such that

methods called on subclasses return a subclass instance instead of

a ``torch.Tensor`` instance.

One corollary to this is that you need coverage for torch.Tensor

methods if implementing __torch_function__ for subclasses.

We recommend always calling ``super().__torch_function__`` as the base

case when doing the above.

While not mandatory, we recommend making `__torch_function__` a classmethod.

"""

if kwargs is None:

kwargs = {}

if not all(issubclass(cls, t) for t in types):

return NotImplemented

with _C.DisableTorchFunction():

ret = func(*args, **kwargs)

return _convert(ret, cls)

__module__ = 'torch'

def _convert(ret, cls):

if cls is Tensor:

return ret

if isinstance(ret, Tensor):

ret = ret.as_subclass(cls)

if isinstance(ret, tuple):

ret = tuple(_convert(r, cls) for r in ret)

return ret