forward_grad¶

paddle.incubate.autograd. forward_grad ( outputs, inputs, grad_inputs=None ) [source]

Forward mode of automatic differentiation.

Note

ONLY available in the static graph mode and primitive operators.

Parameters

• outputs (Tensor|Sequence[Tensor]) – The output tensor or tensors.

• inputs (Tensor|Sequence[Tensor]) – The input tensor or tensors.

• grad_inputs (Tensor|Sequence[Tensor]) – Optional, the gradient Tensor or Tensors of inputs which has the same shape with inputs, Defaults to None, in this case is equivalent to all ones.

Returns

The gradients for outputs.

Return type

grad_outputs(Tensor|Sequence[Tensor])

Examples

>>> import numpy as np
>>> import paddle

>>> paddle.enable_static()
>>> paddle.incubate.autograd.enable_prim()

>>> startup_program = paddle.static.Program()
>>> main_program = paddle.static.Program()

>>> with paddle.static.program_guard(main_program, startup_program):
...     x = paddle.static.data('x', shape=[1], dtype='float32')
...     y = x * x
...     y_grad = paddle.incubate.autograd.forward_grad(y, x)
...     paddle.incubate.autograd.prim2orig()
...
>>> exe = paddle.static.Executor()
>>> exe.run(startup_program)
>>> y_grad = exe.run(main_program, feed={'x': np.array([2.]).astype('float32')}, fetch_list=[y_grad])
>>> print(y_grad)
[array([4.], dtype=float32)]

>>> paddle.incubate.autograd.disable_prim()
>>> paddle.disable_static()