def grad(
outputs: Union[Tensor, Sequence[Tensor]],
inputs: Union[Tensor, Sequence[Tensor]],
out_grads: Union[Tensor, Sequence[Tensor], None] = None,
retain_graph: bool = False,
create_graph: bool = False,
) -> Tuple[Tensor]:
in_grads = grad_api(
convert_to_tensor_tuple(outputs),
convert_to_tensor_tuple(inputs),
convert_to_tensor_tuple(out_grads),
retain_graph,
create_graph,
)
return tuple([Tensor(x) for x in in_grads])
def grad(
outputs: Union[Tensor, Sequence[Tensor]],
inputs: Union[Tensor, Sequence[Tensor]],
grad_outputs: Union[Tensor, Sequence[Tensor], None] = None,
retain_graph: bool = False,
create_graph: bool = False,
) -> Tuple[Tensor]:
r"""
The documentation is referenced from:
https://pytorch.org/docs/1.10/generated/torch.autograd.grad.html.
Computes and returns the sum of gradients of outputs with respect to the inputs.
The graph is differentiated using the chain rule. ``grad_outputs`` should be a sequence of
length matching ``outputs``, containing the "vector" in the Jacobian-vector product.
(``None`` is an acceptable value for that tensor don't require gradient.)
Args:
outputs (Sequence[Tensor]): Tensors of which the derivative will be computed.
inputs (Sequence[Tensor]): Inputs w.r.t. which the derivative will be returned(and not
accumulated into ``.grad``).
grad_outputs (Sequence[Tensor], optional): 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. Defaults to None.
retain_graph (bool, optional): If ``False``, the graph used to compute the grads will be
reset after backward is complete. Defaults to ``False``. 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``.
Returns:
Tuple(Tensor): A tuple of tensors containing the gradients for each ``inputs``.
"""
in_grads = grad_api(
convert_to_tensor_tuple(outputs),
convert_to_tensor_tuple(inputs),
convert_to_tensor_tuple(grad_outputs),
retain_graph,
create_graph,
)
return tuple([Tensor(x) for x in in_grads])