mindspore.ops.InsertGradientOf

class mindspore.ops.InsertGradientOf(f)[source]

Attaches callback to the graph node that will be invoked on the node's gradient.

Parameters: f (Function) – MindSpore's Function. Callback function.

Inputs:

input_x (Any) - The graph node to attach to.

Outputs:

Tensor, returns input_x directly. InsertGradientOf does not affect the forward result.

Raises: TypeError – If f is not a function of MindSpore.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops, jit
>>> a = Tensor(np.array([1.0]).astype(np.float32))
>>> b = Tensor(np.array([0.2]).astype(np.float32))
>>> def clip_gradient(dx):
...     ret = dx
...     if ret > a:
...         ret = a
...
...     if ret < b:
...         ret = b
...
...     return ret
...
>>> clip = ops.InsertGradientOf(clip_gradient)
>>> grad_all = ops.GradOperation(get_all=True)
>>> def InsertGradientOfClipDemo():
...     def clip_test(x, y):
...         x = clip(x)
...         y = clip(y)
...         c = x * y
...         return c
...
...     @jit
...     def f(x, y):
...         return clip_test(x, y)
...
...     def fd(x, y):
...         return grad_all(clip_test)(x, y)
...
...     print("forward: ", f(Tensor(np.array([1.1]).astype(np.float32)),
...         Tensor(np.array([0.1]).astype(np.float32))))
...     print("clip_gradient:", fd(Tensor(np.array([1.1]).astype(np.float32)),
...         Tensor(np.array([0.1]).astype(np.float32))))
>>> InsertGradientOfClipDemo()
forward: [0.11000001]
clip_gradient: (Tensor(shape=[1], dtype=Float32, value= [ 2.00000003e-01]),
                Tensor(shape=[1], dtype=Float32, value= [ 1.00000000e+00]))