grad(outputs, inputs, grad_outputs=None, retain_graph=None, create_graph=False, only_inputs=True, allow_unused=False, is_grads_batched=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 vector-Jacobian 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 vector-Jacobian 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
is_grads_batched (bool, optional) – If
True, the first dimension of each tensor in
grad_outputswill be interpreted as the batch dimension. Instead of computing a single vector-Jacobian product, we compute a batch of vector-Jacobian products for each “vector” in the batch. We use the vmap prototype feature as the backend to vectorize calls to the autograd engine so that this computation can be performed in a single call. This should lead to performance improvements when compared to manually looping and performing backward multiple times. Note that due to this feature being experimental, there may be performance cliffs. Please use
torch._C._debug_only_display_vmap_fallback_warnings(True)to show any performance warnings and file an issue on github if warnings exist for your use case. Defaults to