torch.autograd.grad(outputs, inputs, grad_outputs=None, retain_graph=None, create_graph=False, only_inputs=True, allow_unused=False)[source]

Computes and returns the sum of gradients of outputs with respect to 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.

Note

If you run any forward ops, create grad_outputs, and/or call grad in a user-specified CUDA stream context, see Stream semantics of backward passes.

Parameters
• 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.