# tensorplay.autograd.jvp

Source: https://www.tensorplay.cn/docs/generated/tensorplay.autograd.jvp.html

# tensorplay.autograd.jvp

tensorplay.autograd.jvp(func, inputs, v=None, create_graph=False, strict=False, mode='reversed')[[source]](../_modules/tensorplay/autograd/functional.html#jvp)

Compute 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](https://docs.python.org/3/library/stdtypes.html#tuple) of Tensors or Tensor ) – inputs to the function func .

- v ([tuple](https://docs.python.org/3/library/stdtypes.html#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](https://docs.python.org/3/library/functions.html#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](https://docs.python.org/3/library/functions.html#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](https://docs.python.org/3/library/stdtypes.html#tuple))

Note

autograd.functional.jvp computes the jvp by using the backward of
the backward (sometimes called the double backwards trick). This is not
the most performant way of computing the jvp.

Example

```
>>> def exp_reducer(x):
...     return x.exp().sum(dim=1)
>>> inputs = tensorplay.rand(4, 4)
>>> v = tensorplay.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]))
```

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

mode (str, optional): “reversed” computes the jvp via the double

backwards trick; “forward” uses native forward-mode AD kernels and
propagates tangents in a single pass per op (requires func to be
written with operators/methods supported by forward-mode, see
tensorplay.autograd._forward).  Defaults to “reversed”.