vjp

paddle.incubate.autograd. vjp ( func: Callable[..., _OutputT], xs: Tensor, v: TensorOrTensors | None = None ) → tuple[_OutputT, Tensor] [source]

paddle.incubate.autograd. vjp ( func: Callable[..., _OutputT], xs: Sequence[Tensor], v: TensorOrTensors | None = None ) → tuple[_OutputT, tuple[Tensor, ...]]

Computes the Vector-Jacobian product, a functional form of reverse mode automatic differentiation.

Warning

This API is in beta, the signatures could be changed in future version.

Parameters

func (Callable) – A function that takes xs as inputs parameter and returns a sequence of Tensors or a Tensor.
xs (Tensor|Sequence[Tensor]) – Used as positional arguments to evaluate func. xs is accepted as one Tensor or a sequence of Tensors.
v (Tensor|Sequence[Tensor]|None, optional) – The cotangent vector involved in the VJP computation. v matches the size and shape of func ‘s output. Defaults to None, which is equivalent to all ones the same size of func ‘s output.

Returns

func_out(Tensor|tuple[Tensor]): The output of func(xs) .
vjp(Tensor|tuple[Tensor]): The vjp result.

Return type

output(tuple)

Examples

           >>> import paddle

>>> def func(x):
...     return paddle.matmul(x, x)
...
>>> x = paddle.ones(shape=[2, 2], dtype='float32')
>>> _, vjp_result = paddle.incubate.autograd.vjp(func, x)
>>> print(vjp_result)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
       [[4., 4.],
        [4., 4.]])

>>> v = paddle.to_tensor([[1.0, 0.0], [0.0, 0.0]])
>>> _, vjp_result = paddle.incubate.autograd.vjp(func, x, v)
>>> print(vjp_result)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
       [[2., 1.],
        [1., 0.]])

          