Shortcuts

torch.autograd.functional.jvp(func, inputs, v=None, create_graph=False, strict=False)[source]

Function that computes the dot product between the Jacobian of the given function at the point given by the inputs and a vector v.

Parameters
• func (function) – a Python function that takes Tensor inputs and returns a tuple of Tensors or a Tensor.

• inputs (tuple of Tensors or Tensor) – inputs to the function func.

• v (tuple of Tensors or Tensor) – The vector for which the Jacobian vector product is computed. Must be the same size as the input of func. This argument is optional when the input to func contains a single element and (if it is not provided) will be set as a Tensor containing a single 1.

• create_graph (bool, optional) – If True, both the output and result will be computed in a differentiable way. Note that when strict is False, the result can not require gradients or be disconnected from the inputs. Defaults to False.

• strict (bool, optional) – If True, an error will be raised when we detect that there exists an input such that all the outputs are independent of it. If False, we return a Tensor of zeros as the jvp for said inputs, which is the expected mathematical value. Defaults to False.

Returns

tuple with:

func_output (tuple of Tensors or Tensor): output of func(inputs)

jvp (tuple of Tensors or Tensor): result of the dot product with the same shape as the output.

Return type

output (tuple)

Example

>>> def exp_reducer(x):
...   return x.exp().sum(dim=1)
>>> inputs = torch.rand(4, 4)
>>> v = torch.ones(4, 4)
>>> jvp(exp_reducer, inputs, v)
(tensor([6.3090, 4.6742, 7.9114, 8.2106]),
tensor([6.3090, 4.6742, 7.9114, 8.2106]))

>>> jvp(exp_reducer, inputs, v, create_graph=True)

>>> def adder(x, y):
...   return 2 * x + 3 * y
>>> inputs = (torch.rand(2), torch.rand(2))
>>> v = (torch.ones(2), torch.ones(2))
(tensor([2.2399, 2.5005]),
tensor([5., 5.]))


Note

The jvp is currently computed by using the backward of the backward (sometimes called the double backwards trick) as we don’t have support for forward mode AD in PyTorch at the moment. ## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials