mindspore.ops.rms_norm

mindspore.ops.rms_norm(x, gamma, epsilon=1e-06)[source]

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

This is an experimental API that is subject to change or deletion. This API is only supported in Atlas A2 training series for now.

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 ranged in (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 with the data type of gamma.
ValueError – If epsilon is not a float between 0 and 1.
ValueError – If the rank of gamma is lagger 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]]