torch.autograd.grad¶
- torch.autograd.grad(outputs, inputs, grad_outputs=None, retain_graph=None, create_graph=False, only_inputs=True, allow_unused=False, is_grads_batched=False)[source]¶
Computes and returns the sum of gradients of outputs with respect to the inputs.
grad_outputs
should be a sequence of length matchingoutput
containing 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 beNone
).Note
If you run any forward ops, create
grad_outputs
, and/or callgrad
in a user-specified CUDA stream context, see Stream semantics of backward passes.Note
only_inputs
argument is deprecated and is ignored now (defaults toTrue
). To accumulate gradient for other parts of the graph, please usetorch.autograd.backward
.- 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 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 toTrue
is not needed and often can be worked around in a much more efficient way. Defaults to the value ofcreate_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 toFalse
.is_grads_batched (bool, optional) – If
True
, the first dimension of each tensor ingrad_outputs
will 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 usetorch._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 toFalse
.
- Return type: