grad(outputs, inputs, grad_outputs=None, retain_graph=None, create_graph=False, only_inputs=True, allow_unused=False)¶
Computes and returns the sum of gradients of outputs with respect to the inputs.
grad_outputsshould be a sequence of length matching
outputcontaining 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
If you run any forward ops, create
grad_outputs, and/or call
gradin a user-specified CUDA stream context, see Stream semantics of backward passes.
only_inputsargument is deprecated and is ignored now (defaults to
True). To accumulate gradient for other parts of the graph, please use
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_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
Trueis not needed and often can be worked around in a much more efficient way. Defaults to the value of
create_graph (bool, optional) – If
True, graph of the derivative will be constructed, allowing to compute higher order derivative products. Default:
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