"""
``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.
As of now, we only support autograd for floating point :class:`Tensor` types (
half, float, double and bfloat16) and complex :class:`Tensor` types (cfloat, cdouble).
"""
import torch
import warnings
from torch.types import _TensorOrTensors
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
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 ..overrides import has_torch_function, handle_torch_function
from . import profiler
from . import functional
__all__ = ['Variable', 'Function', 'backward', 'grad_mode']
_OptionalTensor = Optional[torch.Tensor]
def _make_grads(outputs: Sequence[torch.Tensor], grads: Sequence[_OptionalTensor]) -> Tuple[_OptionalTensor, ...]:
new_grads: List[_OptionalTensor] = []
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) + ".")
if out.dtype.is_complex != grad.dtype.is_complex:
raise RuntimeError("For complex Tensors, both grad_output and output"
" are required to have the same dtype."
" Mismatch in dtype: grad_output["
+ str(grads.index(grad)) + "] has a dtype of "
+ str(grad.dtype) + " and output["
+ str(outputs.index(out)) + "] has a dtype of "
+ str(out.dtype) + ".")
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)
def _tensor_or_tensors_to_tuple(tensors: Optional[_TensorOrTensors], length: int) -> Tuple[_OptionalTensor, ...]:
if tensors is None:
return (None, ) * length
if isinstance(tensors, torch.Tensor):
return (tensors, )
return tuple(tensors)
[docs]def backward(
tensors: _TensorOrTensors,
grad_tensors: Optional[_TensorOrTensors] = None,
retain_graph: Optional[bool] = None,
create_graph: bool = False,
grad_variables: Optional[_TensorOrTensors] = None,
) -> 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
``.grad`` attributes or set them to ``None`` before calling it.
See :ref:`Default gradient layouts<default-grad-layouts>`
for details on the memory layout of accumulated gradients.
.. note::
Using this method with ``create_graph=True`` will create a reference cycle
between the parameter and its gradient which can cause a memory leak.
We recommend using ``autograd.grad`` when creating the graph to avoid this.
If you have to use this function, make sure to reset the ``.grad`` fields of your
parameters to ``None`` after use to break the cycle and avoid the leak.
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)
grad_tensors_ = _tensor_or_tensors_to_tuple(grad_tensors, len(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: _TensorOrTensors,
inputs: _TensorOrTensors,
grad_outputs: Optional[_TensorOrTensors] = None,
retain_graph: Optional[bool] = None,
create_graph: bool = False,
only_inputs: bool = True,
allow_unused: bool = False
) -> Tuple[torch.Tensor, ...]:
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``.
"""
outputs = (outputs,) if isinstance(outputs, torch.Tensor) else tuple(outputs)
inputs = (inputs,) if isinstance(inputs, torch.Tensor) else tuple(inputs)
overridable_args = outputs + inputs
if has_torch_function(overridable_args):
return handle_torch_function(
grad,
overridable_args,
outputs,
inputs,
grad_outputs=grad_outputs,
retain_graph=retain_graph,
create_graph=create_graph,
only_inputs=only_inputs,
allow_unused=allow_unused,
)
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.")
grad_outputs_ = _tensor_or_tensors_to_tuple(grad_outputs, len(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")
# Import all native method/classes
from torch._C._autograd import (ProfilerState, ProfilerConfig, ProfilerEvent,
_enable_profiler, _disable_profiler, _profiler_enabled,
_enable_record_function, _set_empty_test_observer)
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