# Source code for torch.autograd

```
"""
``torch.autograd`` provides classes and functions implementing automatic
differentiation of arbitrary scalar valued functions. It requires minimal
changes to the existing code - you only need to declare :class:`Tensor` s
for which gradients should be computed with the ``requires_grad=True`` keyword.
"""
import torch
import warnings
from .variable import Variable
from .function import Function, NestedIOFunction
from .gradcheck import gradcheck, gradgradcheck
from .grad_mode import no_grad, enable_grad, set_grad_enabled
from .anomaly_mode import detect_anomaly, set_detect_anomaly
from . import profiler
__all__ = ['Variable', 'Function', 'backward', 'grad_mode']
def _make_grads(outputs, grads):
new_grads = []
for out, grad in zip(outputs, grads):
if isinstance(grad, torch.Tensor):
if not out.shape == grad.shape:
raise RuntimeError("Mismatch in shape: grad_output["
+ str(grads.index(grad)) + "] has a shape of "
+ str(grad.shape) + " and output["
+ str(outputs.index(out)) + "] has a shape of "
+ str(out.shape) + ".")
new_grads.append(grad)
elif grad is None:
if out.requires_grad:
if out.numel() != 1:
raise RuntimeError("grad can be implicitly created only for scalar outputs")
new_grads.append(torch.ones_like(out, memory_format=torch.preserve_format))
else:
new_grads.append(None)
else:
raise TypeError("gradients can be either Tensors or None, but got " +
type(grad).__name__)
return tuple(new_grads)
[docs]def backward(tensors, grad_tensors=None, retain_graph=None, create_graph=False, grad_variables=None):
r"""Computes the sum of gradients of given tensors w.r.t. graph leaves.
The graph is differentiated using the chain rule. If any of ``tensors``
are non-scalar (i.e. their data has more than one element) and require
gradient, then the Jacobian-vector product would be computed, in this
case the function additionally requires specifying ``grad_tensors``.
It should be a sequence of matching length, that contains the "vector"
in the Jacobian-vector product, usually the gradient of the differentiated
function w.r.t. corresponding tensors (``None`` is an acceptable value for
all tensors that don't need gradient tensors).
This function accumulates gradients in the leaves - you might need to zero
them before calling it.
Arguments:
tensors (sequence of Tensor): Tensors of which the derivative will be
computed.
grad_tensors (sequence of (Tensor or None)): The "vector" in the Jacobian-vector
product, usually gradients w.r.t. each element of corresponding tensors.
None values can be specified for scalar Tensors or ones that don't require
grad. If a None value would be acceptable for all grad_tensors, then this
argument is optional.
retain_graph (bool, optional): If ``False``, the graph used to compute the grad
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``.
"""
if grad_variables is not None:
warnings.warn("'grad_variables' is deprecated. Use 'grad_tensors' instead.")
if grad_tensors is None:
grad_tensors = grad_variables
else:
raise RuntimeError("'grad_tensors' and 'grad_variables' (deprecated) "
"arguments both passed to backward(). Please only "
"use 'grad_tensors'.")
tensors = (tensors,) if isinstance(tensors, torch.Tensor) else tuple(tensors)
if grad_tensors is None:
grad_tensors = [None] * len(tensors)
elif isinstance(grad_tensors, torch.Tensor):
grad_tensors = [grad_tensors]
else:
grad_tensors = list(grad_tensors)
grad_tensors = _make_grads(tensors, grad_tensors)
if retain_graph is None:
retain_graph = create_graph
Variable._execution_engine.run_backward(
tensors, grad_tensors, retain_graph, create_graph,
allow_unreachable=True) # allow_unreachable flag
[docs]def grad(outputs, inputs, grad_outputs=None, retain_graph=None, create_graph=False,
only_inputs=True, allow_unused=False):
r"""Computes and returns the sum of gradients of outputs w.r.t. the inputs.
``grad_outputs`` should be a sequence of length matching ``output``
containing the "vector" in Jacobian-vector product, usually the pre-computed
gradients w.r.t. each of the outputs. If an output doesn't require_grad,
then the gradient can be ``None``).
If ``only_inputs`` is ``True``, the function will only return a list of gradients
w.r.t the specified inputs. If it's ``False``, then gradient w.r.t. all remaining
leaves will still be computed, and will be accumulated into their ``.grad``
attribute.
Arguments:
outputs (sequence of Tensor): outputs of the differentiated function.
inputs (sequence of Tensor): Inputs w.r.t. which the gradient will be
returned (and not accumulated into ``.grad``).
grad_outputs (sequence of Tensor): The "vector" in the Jacobian-vector product.
Usually gradients w.r.t. each output. None values can be specified for scalar
Tensors or ones that don't require grad. If a None value would be acceptable
for all grad_tensors, then this argument is optional. Default: None.
retain_graph (bool, optional): If ``False``, the graph used to compute the grad
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.
Default: ``False``.
allow_unused (bool, optional): If ``False``, specifying inputs that were not
used when computing outputs (and therefore their grad is always zero)
is an error. Defaults to ``False``.
"""
if not only_inputs:
warnings.warn("only_inputs argument is deprecated and is ignored now "
"(defaults to True). To accumulate gradient for other "
"parts of the graph, please use torch.autograd.backward.")
outputs = (outputs,) if isinstance(outputs, torch.Tensor) else tuple(outputs)
inputs = (inputs,) if isinstance(inputs, torch.Tensor) else tuple(inputs)
if grad_outputs is None:
grad_outputs = [None] * len(outputs)
elif isinstance(grad_outputs, torch.Tensor):
grad_outputs = [grad_outputs]
else:
grad_outputs = list(grad_outputs)
grad_outputs = _make_grads(outputs, grad_outputs)
if retain_graph is None:
retain_graph = create_graph
return Variable._execution_engine.run_backward(
outputs, grad_outputs, retain_graph, create_graph,
inputs, allow_unused)
# This function applies in case of gradient checkpointing for memory
# optimization. Currently, for gradient checkpointing, we only support imperative
# backwards call i.e. torch.autograd.backward() and the torch.autograd.grad() won't
# work. The reason being that: torch.autograd.grad() only calculates the grads
# for the inputs that are passed by user but it doesn't calculate grad for
# anything else e.g. model parameters like weights, bias etc. However, for
# torch.autograd.backward(), we would actually compute the grad for the weights as well.
#
# This function returns whether the checkpointing is valid i.e. torch.autograd.backward
# or not i.e. torch.autograd.grad. The implementation works by maintaining a thread
# local variable in torch/csrc/autograd/engine.cpp which looks at the NodeTask
# in the stack and before a NodeTask is executed in evaluate_function, it
# checks for whether reentrant backwards is imperative or not.
# See https://github.com/pytorch/pytorch/pull/4594 for more discussion/context
def _is_checkpoint_valid():
return Variable._execution_engine.is_checkpoint_valid()
def variable(*args, **kwargs):
warnings.warn("torch.autograd.variable(...) is deprecated, use torch.tensor(...) instead")
return torch.tensor(*args, **kwargs)
if not torch._C._autograd_init():
raise RuntimeError("autograd initialization failed")
```