mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
583 lines
20 KiB
Python
583 lines
20 KiB
Python
import sys
|
|
import torch
|
|
import torch._C as _C
|
|
from collections import OrderedDict
|
|
import torch.sparse as sparse
|
|
import torch.utils.hooks as hooks
|
|
import warnings
|
|
import weakref
|
|
from torch._six import imap
|
|
|
|
|
|
class Variable(_C._VariableBase):
|
|
"""Wraps a tensor and records the operations applied to it.
|
|
|
|
Variable is a thin wrapper around a Tensor object, that also holds
|
|
the gradient w.r.t. to it, and a reference to a function that created it.
|
|
This reference allows retracing the whole chain of operations that
|
|
created the data. If the Variable has been created by the user, its grad_fn
|
|
will be ``None`` and we call such objects *leaf* Variables.
|
|
|
|
Since autograd only supports scalar valued function differentiation, grad
|
|
size always matches the data size. Also, grad is normally only allocated
|
|
for leaf variables, and will be always zero otherwise.
|
|
|
|
Attributes:
|
|
data: Wrapped tensor of any type.
|
|
grad: Variable holding the gradient of type and location matching
|
|
the ``.data``. This attribute is lazily allocated and can't
|
|
be reassigned.
|
|
requires_grad: Boolean indicating whether the Variable has been
|
|
created by a subgraph containing any Variable, that requires it.
|
|
See :ref:`excluding-subgraphs` for more details.
|
|
Can be changed only on leaf Variables.
|
|
volatile: Boolean indicating that the Variable should be used in
|
|
inference mode, i.e. don't save the history. See
|
|
:ref:`excluding-subgraphs` for more details.
|
|
Can be changed only on leaf Variables.
|
|
is_leaf: Boolean indicating if the Variable is a graph leaf (i.e
|
|
if it was created by the user).
|
|
grad_fn: Gradient function graph trace.
|
|
|
|
Parameters:
|
|
data (any tensor class): Tensor to wrap.
|
|
requires_grad (bool): Value of the requires_grad flag. **Keyword only.**
|
|
volatile (bool): Value of the volatile flag. **Keyword only.**
|
|
"""
|
|
|
|
_fallthrough_methods = {
|
|
'size',
|
|
'stride',
|
|
'nelement',
|
|
'ndimension',
|
|
'element_size',
|
|
'is_contiguous',
|
|
'is_set_to',
|
|
'is_signed',
|
|
'numel',
|
|
'dim',
|
|
'get_device',
|
|
'is_cuda',
|
|
'shape'
|
|
}
|
|
|
|
def __getattr__(self, name):
|
|
if name in self._fallthrough_methods:
|
|
return getattr(self.data, name)
|
|
return object.__getattribute__(self, name)
|
|
|
|
def __getitem__(self, key):
|
|
if torch.is_tensor(key):
|
|
key = Variable(key) # auto-wrap tensors
|
|
if isinstance(key, Variable):
|
|
if type(key.data).__name__ == 'ByteTensor':
|
|
return MaskedSelect.apply(self, key)
|
|
elif type(key.data).__name__ == 'LongTensor':
|
|
return IndexSelect.apply(self, 0, key)
|
|
# else fall through and raise an error in Index
|
|
return Index.apply(self, key)
|
|
|
|
def __setitem__(self, key, value):
|
|
if isinstance(key, Variable) and type(key.data).__name__ == 'ByteTensor':
|
|
if isinstance(value, Variable):
|
|
return MaskedScatter.apply(self, key, value, True)
|
|
else:
|
|
return MaskedFill.apply(self, key, value, True)
|
|
else:
|
|
return SetItem.apply(self, key, value)
|
|
|
|
def __deepcopy__(self, memo):
|
|
if not self.is_leaf:
|
|
raise RuntimeError("Only Variables created explicitly by the user "
|
|
"(graph leaves) support the deepcopy protocol at the moment")
|
|
result = type(self)(self.data.clone())
|
|
result.requires_grad = self.requires_grad
|
|
result.volatile = self.volatile
|
|
memo[id(self)] = result
|
|
return result
|
|
|
|
def __reduce_ex__(self, proto):
|
|
state = (self.requires_grad, self.volatile, self._backward_hooks)
|
|
if proto > 1:
|
|
return type(self), (self.data,), state
|
|
if sys.version_info[0] == 2:
|
|
from copy_reg import __newobj__
|
|
else:
|
|
from copyreg import __newobj__
|
|
return __newobj__, (type(self), self.data), state
|
|
|
|
def __setstate__(self, state):
|
|
if len(state) == 5:
|
|
# legacy serialization of Variable
|
|
self.data = state[0]
|
|
state = (state[3], state[4], state[2])
|
|
if not self.is_leaf:
|
|
raise RuntimeError('__setstate__ can be only called on leaf variables')
|
|
self.requires_grad, self.volatile, self._backward_hooks = state
|
|
|
|
def __repr__(self):
|
|
return 'Variable containing:' + self.data.__repr__()
|
|
|
|
def __bool__(self):
|
|
if self.data.numel() == 0:
|
|
return False
|
|
raise RuntimeError("bool value of Variable objects containing non-empty " +
|
|
torch.typename(self.data) + " is ambiguous")
|
|
|
|
__nonzero__ = __bool__
|
|
|
|
def backward(self, gradient=None, retain_graph=None, create_graph=None, retain_variables=None):
|
|
"""Computes the gradient of current variable w.r.t. graph leaves.
|
|
|
|
The graph is differentiated using the chain rule. If the variable 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 them before calling it.
|
|
|
|
Arguments:
|
|
gradient (Tensor, Variable or None): Gradient w.r.t. the
|
|
variable. If it is a tensor, it will be automatically converted
|
|
to a Variable that is volatile unless ``create_graph`` is True.
|
|
None values can be specified for scalar Variables 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, unless ``gradient`` is a volatile
|
|
Variable.
|
|
"""
|
|
torch.autograd.backward(self, gradient, retain_graph, create_graph, retain_variables)
|
|
|
|
def register_hook(self, hook):
|
|
"""Registers a backward hook.
|
|
|
|
The hook will be called every time a gradient with respect to the
|
|
variable is computed. The hook should have the following signature::
|
|
|
|
hook(grad) -> Variable 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 = Variable(torch.Tensor([0, 0, 0]), requires_grad=True)
|
|
>>> h = v.register_hook(lambda grad: grad * 2) # double the gradient
|
|
>>> v.backward(torch.Tensor([1, 1, 1]))
|
|
>>> v.grad.data
|
|
2
|
|
2
|
|
2
|
|
[torch.FloatTensor of size 3]
|
|
>>> h.remove() # removes the hook
|
|
"""
|
|
if self.volatile:
|
|
raise RuntimeError("cannot register a hook on a volatile variable")
|
|
if not self.requires_grad:
|
|
raise RuntimeError("cannot register a hook on a variable 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):
|
|
"""Registers a reward obtained as a result of a stochastic process.
|
|
|
|
Differentiating stochastic nodes requires providing them with reward
|
|
value. If your graph contains any stochastic operations, you should
|
|
call this function on their outputs. Otherwise an error will be raised.
|
|
|
|
Parameters:
|
|
reward(Tensor): Tensor with per-element rewards. It has to match
|
|
the device location and shape of Variable's data.
|
|
"""
|
|
if not isinstance(self.grad_fn, StochasticFunction):
|
|
raise RuntimeError("reinforce() can be only called on outputs "
|
|
"of stochastic functions")
|
|
self.grad_fn._reinforce(reward)
|
|
|
|
def detach(self):
|
|
"""Returns a new Variable, detached from the current graph.
|
|
|
|
Result will never require gradient. If the input is volatile, the output
|
|
will be volatile too.
|
|
|
|
.. note::
|
|
|
|
Returned Variable uses the same data tensor, as the original one, and
|
|
in-place modifications on either of them will be seen, and may trigger
|
|
errors in correctness checks.
|
|
"""
|
|
result = NoGrad()(self) # this is needed, because it merges version counters
|
|
result._grad_fn = None
|
|
return result
|
|
|
|
def detach_(self):
|
|
"""Detaches the Variable from the graph that created it, making it a
|
|
leaf.
|
|
"""
|
|
self._grad_fn = None
|
|
self.requires_grad = False
|
|
|
|
def retain_grad(self):
|
|
"""Enables .grad attribute for non-leaf Variables."""
|
|
if self.grad_fn is None: # no-op for leaves
|
|
return
|
|
if not self.requires_grad:
|
|
raise RuntimeError("can't retain_grad on Variable that has requires_grad=False")
|
|
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:
|
|
var._grad = grad.clone()
|
|
else:
|
|
var._grad = var._grad + grad
|
|
|
|
self.register_hook(retain_grad_hook)
|
|
self.retains_grad = True
|
|
|
|
def contiguous(self):
|
|
self.data = self.data.contiguous()
|
|
return self
|
|
|
|
def type(self, t):
|
|
if t != type(self.data):
|
|
return Type.apply(self, t)
|
|
return self
|
|
|
|
def type_as(self, t):
|
|
if isinstance(t, Variable):
|
|
t = t.data
|
|
return self.type(type(t))
|
|
|
|
def _get_type(self, name):
|
|
module = torch._import_dotted_name(self.data.__module__)
|
|
return getattr(module, name)
|
|
|
|
def cuda(self, device=None, async=False):
|
|
return CudaTransfer.apply(self, device, async)
|
|
|
|
def cpu(self):
|
|
return self.type(getattr(torch, type(self.data).__name__))
|
|
|
|
def double(self):
|
|
return self.type(self._get_type('DoubleTensor'))
|
|
|
|
def float(self):
|
|
return self.type(self._get_type('FloatTensor'))
|
|
|
|
def half(self):
|
|
return self.type(self._get_type('HalfTensor'))
|
|
|
|
def long(self):
|
|
return self.type(self._get_type('LongTensor'))
|
|
|
|
def int(self):
|
|
return self.type(self._get_type('IntTensor'))
|
|
|
|
def short(self):
|
|
return self.type(self._get_type('ShortTensor'))
|
|
|
|
def char(self):
|
|
return self.type(self._get_type('CharTensor'))
|
|
|
|
def byte(self):
|
|
return self.type(self._get_type('ByteTensor'))
|
|
|
|
def clamp(self, min=None, max=None):
|
|
if min is None and max is None:
|
|
raise ValueError("clamp requires specifying at least one of "
|
|
"min and max arguments")
|
|
elif min is None and max is not None:
|
|
return CminConstant.apply(self, max)
|
|
elif min is not None and max is None:
|
|
return CmaxConstant.apply(self, min)
|
|
else:
|
|
return Clamp.apply(self, min, max)
|
|
|
|
def prod(self, dim=None, keepdim=None):
|
|
return Prod.apply(self, dim, keepdim)
|
|
|
|
def view_as(self, tensor):
|
|
return self.view(tensor.size())
|
|
|
|
def split(self, split_size, dim=0):
|
|
return torch.split(self, split_size, dim)
|
|
|
|
def repeat(self, *repeats):
|
|
if len(repeats) == 1 and isinstance(repeats[0], torch.Size):
|
|
repeats = repeats[0]
|
|
else:
|
|
repeats = torch.Size(repeats)
|
|
return Repeat.apply(self, repeats)
|
|
|
|
def cumsum(self, dim):
|
|
return Cumsum.apply(self, dim)
|
|
|
|
def cumprod(self, dim):
|
|
return Cumprod.apply(self, dim)
|
|
|
|
def var(self, dim=None, keepdim=False, unbiased=True):
|
|
if dim is None:
|
|
mean = self.mean().view(*(1 for s in self.size()))
|
|
else:
|
|
mean = self.mean(dim, keepdim)
|
|
# we could just set keepdim to True, but this preserves some fidelity
|
|
if keepdim is False and self.dim() != 1:
|
|
mean = mean.unsqueeze(dim)
|
|
mean_expanded = mean.expand_as(self)
|
|
zero_centered = self.sub(mean_expanded)
|
|
if dim is None:
|
|
var = zero_centered.mul(zero_centered).sum()
|
|
else:
|
|
var = zero_centered.mul(zero_centered).sum(dim, keepdim=keepdim)
|
|
numel = self.numel() if dim is None else self.size(dim)
|
|
return var.div(numel - int(unbiased))
|
|
|
|
def std(self, dim=None, keepdim=False, unbiased=True):
|
|
return self.var(dim, keepdim, unbiased).sqrt()
|
|
|
|
def renorm(self, p, dim, maxnorm):
|
|
t = self.transpose(dim, 0)
|
|
flat = t.contiguous().view(self.size(0), -1)
|
|
norms = flat.norm(p, 1, True)
|
|
norms = norms.clamp(max=maxnorm).div(norms.add(1e-7))
|
|
flat_out = flat.mul(norms.expand_as(flat))
|
|
return flat_out.view(t.size()).transpose(dim, 0)
|
|
|
|
def matmul(self, other):
|
|
return torch.matmul(self, other)
|
|
|
|
def resize(self, *sizes):
|
|
return Resize.apply(self, sizes)
|
|
|
|
def resize_as(self, variable):
|
|
return Resize.apply(self, variable.size())
|
|
|
|
def norm(self, p=2, dim=None, keepdim=False):
|
|
if dim is None:
|
|
return super(Variable, self).norm(p)
|
|
else:
|
|
return super(Variable, self).norm(p, dim, keepdim)
|
|
|
|
def index_add(self, dim, index, tensor):
|
|
return self.clone().index_add_(dim, index, tensor)
|
|
|
|
def _advanced_index_add(self, index, tensor):
|
|
return AdvancedIndexAdd.apply(self, index, tensor)
|
|
|
|
def index_copy(self, dim, index, tensor):
|
|
return self.clone().index_copy_(dim, index, tensor)
|
|
|
|
def index_fill(self, dim, index, value):
|
|
return self.clone().index_fill_(dim, index, value)
|
|
|
|
def scatter(self, dim, index, source):
|
|
return self.clone().scatter_(dim, index, source)
|
|
|
|
def scatter_add(self, dim, index, source):
|
|
return self.clone().scatter_add_(dim, index, source)
|
|
|
|
def masked_copy(self, mask, variable):
|
|
warnings.warn("masked_copy is deprecated and renamed to masked_scatter, and will be removed in v0.3")
|
|
return self.masked_scatter(mask, variable)
|
|
|
|
def masked_copy_(self, mask, variable):
|
|
warnings.warn("masked_copy_ is deprecated and renamed to masked_scatter_, and will be removed in v0.3")
|
|
return self.masked_scatter_(mask, variable)
|
|
|
|
def masked_scatter(self, mask, variable):
|
|
return self.clone().masked_scatter_(mask, variable)
|
|
|
|
def masked_fill(self, mask, value):
|
|
return self.clone().masked_fill_(mask, value)
|
|
|
|
def expand_as(self, tensor):
|
|
return self.expand(tensor.size())
|
|
|
|
def select(self, dim, _index):
|
|
dim = dim if dim >= 0 else dim + self.dim()
|
|
index = tuple(slice(None, None) for _ in range(dim)) + (_index,)
|
|
return Index.apply(self, index)
|
|
|
|
def chunk(self, num_chunks, dim=0):
|
|
return Chunk.apply(self, num_chunks, dim)
|
|
|
|
def permute(self, *permutation):
|
|
return Permute.apply(self, permutation)
|
|
|
|
def multinomial(self, num_samples=1, replacement=False):
|
|
return Multinomial.apply(self, num_samples, replacement)
|
|
|
|
def bernoulli(self):
|
|
return Bernoulli.apply(self)
|
|
|
|
def __add__(self, other):
|
|
return self.add(other)
|
|
__radd__ = __add__
|
|
|
|
def __iadd__(self, other):
|
|
return self.add_(other)
|
|
|
|
def __sub__(self, other):
|
|
return self.sub(other)
|
|
|
|
def __isub__(self, other):
|
|
return self.sub_(other)
|
|
|
|
def __rsub__(self, other):
|
|
return -self + other
|
|
|
|
def __mul__(self, other):
|
|
return self.mul(other)
|
|
__rmul__ = __mul__
|
|
|
|
def __imul__(self, other):
|
|
return self.mul_(other)
|
|
|
|
def __matmul__(self, other):
|
|
if not isinstance(other, Variable):
|
|
return NotImplemented
|
|
return self.matmul(other)
|
|
|
|
def __div__(self, other):
|
|
return self.div(other)
|
|
__truediv__ = __div__
|
|
|
|
def __rdiv__(self, other):
|
|
return self.reciprocal() * other
|
|
__rtruediv__ = __rdiv__
|
|
|
|
def __idiv__(self, other):
|
|
return self.div_(other)
|
|
|
|
def __pow__(self, other):
|
|
return self.pow(other)
|
|
|
|
def __ipow__(self, other):
|
|
raise NotImplementedError("in-place pow not implemented")
|
|
|
|
def __rpow__(self, other):
|
|
return PowConstant.apply(other, self)
|
|
|
|
def __neg__(self):
|
|
return Negate.apply(self)
|
|
|
|
def __len__(self):
|
|
return len(self.data)
|
|
|
|
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.)
|
|
return iter(imap(lambda i: self[i], range(self.size(0))))
|
|
|
|
def __mod__(self, other):
|
|
return self.remainder(other)
|
|
|
|
def __eq__(self, other):
|
|
return self.eq(other)
|
|
|
|
def __ne__(self, other):
|
|
return self.ne(other)
|
|
|
|
def __lt__(self, other):
|
|
return self.lt(other)
|
|
|
|
def __le__(self, other):
|
|
return self.le(other)
|
|
|
|
def __gt__(self, other):
|
|
return self.gt(other)
|
|
|
|
def __ge__(self, other):
|
|
return self.ge(other)
|
|
|
|
def __hash__(self):
|
|
return id(self)
|
|
|
|
class _torch(object):
|
|
|
|
@staticmethod
|
|
def cat(iterable, dim=0):
|
|
return Concat.apply(dim, *iterable)
|
|
|
|
@staticmethod
|
|
def normal(means, std=1):
|
|
return Normal.apply(means, std)
|
|
|
|
@staticmethod
|
|
def _blas(cls, args, inplace):
|
|
num_args = len(args)
|
|
alpha = beta = 1
|
|
if num_args > 5:
|
|
raise RuntimeError("too many args")
|
|
if num_args == 5:
|
|
alpha, beta = args[0], args[2]
|
|
tensors = args[1:2] + args[3:]
|
|
elif num_args == 4:
|
|
alpha = args[0]
|
|
tensors = args[1:]
|
|
else:
|
|
tensors = args
|
|
return cls.apply(*(tensors + (alpha, beta, inplace)))
|
|
|
|
@classmethod
|
|
def addmm(cls, *args):
|
|
return cls._blas(Addmm, args, False)
|
|
|
|
@classmethod
|
|
def addbmm(cls, *args):
|
|
return cls._blas(Addbmm, args, False)
|
|
|
|
@classmethod
|
|
def baddbmm(cls, *args):
|
|
return cls._blas(Baddbmm, args, False)
|
|
|
|
@classmethod
|
|
def addmv(cls, *args):
|
|
return cls._blas(Addmv, args, False)
|
|
|
|
@classmethod
|
|
def addr(cls, *args):
|
|
return cls._blas(Addr, args, False)
|
|
|
|
|
|
for method in dir(Variable):
|
|
# This will also wrap some methods that normally aren't part of the
|
|
# functional interface, but we don't care, as they won't ever be used
|
|
if method.startswith('_') or method.endswith('_'):
|
|
continue
|
|
if hasattr(Variable._torch, method):
|
|
continue
|
|
as_static = staticmethod(getattr(Variable, method))
|
|
setattr(Variable._torch, method, as_static)
|
|
|
|
|
|
from ._functions import *
|
|
from torch._C import _ImperativeEngine as ImperativeEngine
|
|
Variable._execution_engine = ImperativeEngine()
|