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4fa08535ed
these two operators use a Tensor to hold the sizes, which allows symbolic implementations to be attached
419 lines
16 KiB
Python
419 lines
16 KiB
Python
import sys
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import torch
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import torch._C as _C
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from collections import OrderedDict
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import torch.sparse as sparse
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import torch.utils.hooks as hooks
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import warnings
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import weakref
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from torch._six import imap
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from torch._C import _add_docstr
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import torch.onnx
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def _size_as_tensor(g, input):
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return g.op('Shape', input)
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def _resize_from_tensor(g, input, shape):
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return g.op('ReshapeDynamic', input, shape)
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class Variable(_C._VariableBase):
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r"""Wraps a tensor and records the operations applied to it.
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Variable is a thin wrapper around a Tensor object, that also holds
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the gradient w.r.t. to it, and a reference to a function that created it.
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This reference allows retracing the whole chain of operations that
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created the data. If the Variable has been created by the user, its grad_fn
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will be ``None`` and we call such objects *leaf* Variables.
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Since autograd only supports scalar valued function differentiation, grad
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size always matches the data size. Also, grad is normally only allocated
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for leaf variables, and will be always zero otherwise.
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Attributes:
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data: Wrapped tensor of any type.
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grad: Variable holding the gradient of type and location matching
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the ``.data``. This attribute is lazily allocated and can't
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be reassigned.
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requires_grad: Boolean indicating whether the Variable has been
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created by a subgraph containing any Variable, that requires it.
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See :ref:`excluding-subgraphs` for more details.
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Can be changed only on leaf Variables.
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is_leaf: Boolean indicating if the Variable is a graph leaf (i.e
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if it was created by the user).
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grad_fn: Gradient function graph trace.
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Parameters:
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data (any tensor class): Tensor to wrap.
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requires_grad (bool): Value of the requires_grad flag. **Keyword only.**
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"""
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def __deepcopy__(self, memo):
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if not self.is_leaf:
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raise RuntimeError("Only Variables created explicitly by the user "
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"(graph leaves) support the deepcopy protocol at the moment")
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if id(self) in memo:
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return memo[id(self)]
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with torch.no_grad():
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if self.is_sparse:
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new_tensor = self.clone()
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else:
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new_storage = self.storage().__deepcopy__(memo)
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new_tensor = self.new()
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new_tensor.set_(new_storage, self.storage_offset(), self.size(), self.stride())
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memo[id(self)] = new_tensor
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new_tensor.requires_grad = self.requires_grad
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return new_tensor
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def __reduce_ex__(self, proto):
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args = (self.storage(),
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self.storage_offset(),
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tuple(self.size()),
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self.stride(),
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self.requires_grad,
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self._backward_hooks)
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return (torch._utils._rebuild_tensor_v2, args)
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def __setstate__(self, state):
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if len(state) == 5:
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# legacy serialization of Variable
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self.data = state[0]
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state = (state[3], state[4], state[2])
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if not self.is_leaf:
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raise RuntimeError('__setstate__ can be only called on leaf variables')
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self.requires_grad, _, self._backward_hooks = state
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def __repr__(self):
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# All strings are unicode in Python 3, while we have to encode unicode
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# strings in Python2. If we can't, let python decide the best
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# characters to replace unicode characters with.
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if sys.version_info > (3,):
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return torch._tensor_str._str(self)
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else:
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if hasattr(sys.stdout, 'encoding'):
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return torch._tensor_str._str(self).encode(
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sys.stdout.encoding or 'UTF-8', 'replace')
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else:
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return torch._tensor_str._str(self).encode('UTF-8', 'replace')
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def backward(self, gradient=None, retain_graph=None, create_graph=False):
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r"""Computes the gradient of current variable w.r.t. graph leaves.
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The graph is differentiated using the chain rule. If the variable is
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non-scalar (i.e. its data has more than one element) and requires
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gradient, the function additionally requires specifying ``gradient``.
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It should be a tensor of matching type and location, that contains
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the gradient of the differentiated function w.r.t. ``self``.
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This function accumulates gradients in the leaves - you might need to
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zero them before calling it.
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Arguments:
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gradient (Tensor, Variable or None): Gradient w.r.t. the
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variable. If it is a tensor, it will be automatically converted
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to a Variable that does not require grad unless ``create_graph`` is True.
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None values can be specified for scalar Variables or ones that
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don't require grad. If a None value would be acceptable then
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this argument is optional.
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retain_graph (bool, optional): If ``False``, the graph used to compute
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the grads will be freed. Note that in nearly all cases setting
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this option to True is not needed and often can be worked around
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in a much more efficient way. Defaults to the value of
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``create_graph``.
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create_graph (bool, optional): If ``True``, graph of the derivative will
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be constructed, allowing to compute higher order derivative
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products. Defaults to ``False``.
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"""
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torch.autograd.backward(self, gradient, retain_graph, create_graph)
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def register_hook(self, hook):
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r"""Registers a backward hook.
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The hook will be called every time a gradient with respect to the
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variable is computed. The hook should have the following signature::
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hook(grad) -> Variable or None
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The hook should not modify its argument, but it can optionally return
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a new gradient which will be used in place of :attr:`grad`.
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This function returns a handle with a method ``handle.remove()``
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that removes the hook from the module.
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Example:
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>>> v = Variable(torch.Tensor([0, 0, 0]), requires_grad=True)
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>>> h = v.register_hook(lambda grad: grad * 2) # double the gradient
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>>> v.backward(torch.Tensor([1, 1, 1]))
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>>> v.grad.data
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2
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2
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2
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[torch.FloatTensor of size 3]
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>>> h.remove() # removes the hook
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"""
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if not self.requires_grad:
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raise RuntimeError("cannot register a hook on a variable that "
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"doesn't require gradient")
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if self._backward_hooks is None:
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self._backward_hooks = OrderedDict()
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if self.grad_fn is not None:
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self.grad_fn._register_hook_dict(self)
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handle = hooks.RemovableHandle(self._backward_hooks)
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self._backward_hooks[handle.id] = hook
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return handle
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def reinforce(self, reward):
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def trim(str):
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return '\n'.join([line.strip() for line in str.split('\n')])
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raise RuntimeError(trim(r"""reinforce() was removed.
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Use torch.distributions instead.
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See http://pytorch.org/docs/master/distributions.html
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Instead of:
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probs = policy_network(state)
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action = probs.multinomial()
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next_state, reward = env.step(action)
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action.reinforce(reward)
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action.backward()
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Use:
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probs = policy_network(state)
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# NOTE: categorical is equivalent to what used to be called multinomial
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m = torch.distributions.Categorical(probs)
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action = m.sample()
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next_state, reward = env.step(action)
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loss = -m.log_prob(action) * reward
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loss.backward()
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"""))
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detach = _add_docstr(_C._VariableBase.detach, r"""
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Returns a new Variable, detached from the current graph.
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The result will never require gradient.
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.. note::
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Returned Variable uses the same data tensor as the original one.
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In-place modifications on either of them will be seen, and may trigger
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errors in correctness checks.
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""")
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detach_ = _add_docstr(_C._VariableBase.detach_, r"""
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Detaches the Variable from the graph that created it, making it a leaf.
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Views cannot be detached in-place.
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""")
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def retain_grad(self):
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"""Enables .grad attribute for non-leaf Variables."""
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if self.grad_fn is None: # no-op for leaves
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return
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if not self.requires_grad:
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raise RuntimeError("can't retain_grad on Variable that has requires_grad=False")
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if hasattr(self, 'retains_grad'):
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return
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weak_self = weakref.ref(self)
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def retain_grad_hook(grad):
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var = weak_self()
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if var is None:
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return
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if var._grad is None:
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var._grad = grad.clone()
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else:
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var._grad = var._grad + grad
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self.register_hook(retain_grad_hook)
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self.retains_grad = True
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def is_pinned(self):
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r"""Returns true if this tensor resides in pinned memory"""
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storage = self.storage()
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return storage.is_pinned() if storage else False
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def is_shared(self):
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r"""Checks if tensor is in shared memory.
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This is always ``True`` for CUDA tensors.
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"""
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return self.storage().is_shared()
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def share_memory_(self):
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r"""Moves the underlying storage to shared memory.
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This is a no-op if the underlying storage is already in shared memory
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and for CUDA tensors. Tensors in shared memory cannot be resized.
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"""
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self.storage().share_memory_()
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return self
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def view_as(self, tensor):
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return self.view(tensor.size())
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def btrifact(self, info=None, pivot=True):
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r"""See :func:`torch.btrifact`
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"""
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if info is not None:
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warnings.warn("info option in btrifact is deprecated and will be removed in v0.4, "
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"consider using btrifact_with_info instead", stacklevel=2)
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factorization, pivots, _info = super(Variable, self).btrifact_with_info(pivot=pivot)
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if not isinstance(info, Variable) or info.type() != _info.type():
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raise ValueError('btrifact expects info to be a Variable of IntTenor')
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info.resize_as_(_info).copy_(_info)
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return factorization, pivots
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else:
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return super(Variable, self).btrifact(pivot=pivot)
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@torch.onnx.symbolic_override(_size_as_tensor)
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def size_as_tensor(self):
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return torch.Tensor(tuple(self.size()))
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@torch.onnx.symbolic_override(_resize_from_tensor)
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def resize_from_tensor(self, size):
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from ._functions import Resize
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return Resize.apply(self, torch.Size(size()))
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def resize(self, *sizes):
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warnings.warn("non-inplace resize is deprecated")
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from ._functions import Resize
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return Resize.apply(self, sizes)
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def resize_as(self, variable):
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warnings.warn("non-inplace resize_as is deprecated")
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from ._functions import Resize
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return Resize.apply(self, variable.size())
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def split(self, split_size, dim=0):
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r"""See :func:`torch.split`
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"""
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if isinstance(split_size, int):
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return super(Variable, self).split(split_size, dim)
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else:
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return super(Variable, self).split_with_sizes(split_size, dim)
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def index_add(self, dim, index, tensor):
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return self.clone().index_add_(dim, index, tensor)
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def index_copy(self, dim, index, tensor):
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return self.clone().index_copy_(dim, index, tensor)
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def index_fill(self, dim, index, value):
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return self.clone().index_fill_(dim, index, value)
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def scatter(self, dim, index, source):
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return self.clone().scatter_(dim, index, source)
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def scatter_add(self, dim, index, source):
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return self.clone().scatter_add_(dim, index, source)
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def masked_copy(self, mask, variable):
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warnings.warn("masked_copy is deprecated and renamed to masked_scatter, and will be removed in v0.3")
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return self.masked_scatter(mask, variable)
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def masked_copy_(self, mask, variable):
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warnings.warn("masked_copy_ is deprecated and renamed to masked_scatter_, and will be removed in v0.3")
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return self.masked_scatter_(mask, variable)
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def masked_scatter(self, mask, variable):
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return self.clone().masked_scatter_(mask, variable)
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def masked_fill(self, mask, value):
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return self.clone().masked_fill_(mask, value)
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def expand_as(self, tensor):
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return self.expand(tensor.size())
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def unique(self, sorted=False, return_inverse=False):
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r"""Returns the unique scalar elements of the tensor as a 1-D tensor.
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See :func:`torch.unique`
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"""
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output, inverse_indices = self._unique(
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sorted=sorted, return_inverse=return_inverse)
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if return_inverse:
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return output, inverse_indices
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else:
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return output
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def __rsub__(self, other):
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return -self + other
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def __rdiv__(self, other):
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return self.reciprocal() * other
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__rtruediv__ = __rdiv__
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__itruediv__ = _C._VariableBase.__idiv__
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__pow__ = _C._VariableBase.pow
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def __format__(self, format_spec):
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if self.dim() == 0:
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return self.item().__format__(format_spec)
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return object.__format__(self, format_spec)
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def __ipow__(self, other):
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raise NotImplementedError("in-place pow not implemented")
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def __rpow__(self, other):
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return self.new([other]) ** self
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__neg__ = _C._VariableBase.neg
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__eq__ = _C._VariableBase.eq
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__ne__ = _C._VariableBase.ne
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__lt__ = _C._VariableBase.lt
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__le__ = _C._VariableBase.le
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__gt__ = _C._VariableBase.gt
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__ge__ = _C._VariableBase.ge
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__abs__ = _C._VariableBase.abs
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def __len__(self):
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if self.dim() == 0:
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raise TypeError("len() of a 0-d tensor")
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return self.shape[0]
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def __iter__(self):
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# NB: we use 'imap' and not 'map' here, so that in Python 2 we get a
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# generator and don't eagerly perform all the indexes. This could
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# save us work, and also helps keep trace ordering deterministic
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# (e.g., if you zip(*hiddens), the eager map will force all the
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# indexes of hiddens[0] before hiddens[1], while the generator
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# map will interleave them.)
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if self.dim() == 0:
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raise TypeError('iteration over a 0-d tensor')
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return iter(imap(lambda i: self[i], range(self.size(0))))
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def __hash__(self):
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return id(self)
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def __dir__(self):
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variable_methods = dir(self.__class__)
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variable_methods.remove('volatile') # deprecated
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attrs = list(self.__dict__.keys())
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keys = variable_methods + attrs
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return sorted(keys)
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# Numpy array interface, to support `numpy.asarray(tensor) -> ndarray`
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def __array__(self, dtype=None):
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if dtype is None:
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return self.cpu().numpy()
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else:
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return self.cpu().numpy().astype(dtype, copy=False)
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# Wrap Numpy array again in a suitable tensor when done, to support e.g.
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# `numpy.sin(tensor) -> tensor` or `numpy.greater(tensor, 0) -> ByteTensor`
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def __array_wrap__(self, array):
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if array.dtype == bool:
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# Workaround, torch has no built-in bool tensor
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array = array.astype('uint8')
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return torch.from_numpy(array)
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_torch = torch._C._VariableFunctions
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from torch._C import _ImperativeEngine as ImperativeEngine
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Variable._execution_engine = ImperativeEngine()
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