mindspore.ops.rms_norm

mindspore.ops.rms_norm(x, gamma, epsilon=1e-6)

The RmsNorm(Root Mean Square Layer Normalization) operator is a normalization operation. Compared to LayerNorm, it retains scaling invariance and removes translation invariance. Its formula is:

\[y=\frac{x_i}{\sqrt{\frac{1}{n}\sum_{i=1}^{n}{ x_i^2}+\varepsilon}}\gamma_i\]

Warning

After version 2.9.0, the signature will change to rms_norm(input, normalized_shape, weight=None, eps=1e-6), and the parameter and return value semantics will change accordingly.

Parameters:

• x (Tensor) – Input data of RmsNorm. Support data type: float16, float32, bfloat16.

• gamma (Tensor) – Learnable parameter \(\gamma\) . Support data type: float16, float32, bfloat16.

• epsilon (float, optional) – A float number in the range (0, 1] to prevent division by 0. Default value is 1e-6.

Returns:

• Tensor, denotes the normalized result, has the same type and shape as x.

• Tensor, with the float data type, denotes the reciprocal of the input standard deviation, used by gradient calculation.

Raises:

• TypeError – If data type of x is not one of the following: float16, float32, bfloat16.

• TypeError – If data type of gamma is not one of the following: float16, float32, bfloat16.

• TypeError – If data type of x is not the same as the data type of gamma.

• ValueError – If epsilon is not a float between 0 and 1.

• ValueError – If the rank of gamma is larger than the rank of x.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]), mindspore.float32)
>>> gamma = Tensor(np.ones([3]), mindspore.float32)
>>> y, rstd = ops.rms_norm(x, gamma)
>>> print(y)
[[0.46290997  0.92581993  1.3887299]
 [0.46290997  0.92581993  1.3887299]]
>>> print(rstd)
[[0.46290997]
 [0.46290997]]