| 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 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 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) |
| |
| __radd__ = __add__ = _C._VariableBase.add |
| |
| def __iadd__(self, other): |
| return self.add_(other) |
| |
| __sub__ = _C._VariableBase.sub |
| |
| def __isub__(self, other): |
| return self.sub_(other) |
| |
| def __rsub__(self, other): |
| return -self + other |
| |
| __rmul__ = __mul__ = _C._VariableBase.mul |
| |
| def __imul__(self, other): |
| return self.mul_(other) |
| |
| def __matmul__(self, other): |
| if not isinstance(other, Variable): |
| return NotImplemented |
| return self.matmul(other) |
| |
| __truediv__ = __div__ = _C._VariableBase.div |
| |
| def __rdiv__(self, other): |
| return self.reciprocal() * other |
| __rtruediv__ = __rdiv__ |
| |
| def __idiv__(self, other): |
| return self.div_(other) |
| |
| __pow__ = _C._VariableBase.pow |
| |
| 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 normal(means, std=1): |
| return Normal.apply(means, std) |
| |
| |
| 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() |
