[Op][Spec] RMSNorm Operator Specification

docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/normalization/rms-norm-15.rst

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+.. {#openvino_docs_ops_normalization_RMS_15}
+
+RMS
+===
+
+
+.. meta::
+  :description: Learn about RMS-15 - a normalization operation.
+
+**Versioned name**: *RMS-15*
+
+**Category**: *Normalization*
+
+**Short description**: Calculates Root Mean Square (RMS) normalization of the input tensor.
+
+**Detailed description**
+
+*RMSNorm* operation performs Root Mean Square (RMS) normalization on a given input ``data`` along dimensions specified by ``axes`` input.
+`Reference <https://arxiv.org/abs/1910.07467>`__.
+
+.. code-block:: py
+
+    (x / Sqrt(ReduceMean(x^2, axes) + eps))
+
+
+ -   If the optional ``scale`` input is provided:
+
+.. code-block:: py
+
+    (x / Sqrt(ReduceMean(x^2, axes) + eps)) * scale
+
+
+**Attributes**
+
+* *epsilon*
+
+  * **Description**: A very small value added to the variance for numerical stability. Ensures that division by zero does not occur for any normalized element.
+  * **Range of values**: a positive floating-point number
+  * **Type**: ``float``
+  * **Required**: *yes*
+
+* *compute_type*
+
+  * **Description**: The precision for internal computation, before scaling.
+  * **Range of values**: Supported floating point type: "f32", "f16", ...
+  * **Type**: ``string``
+  * **Default value**: "undefined" (the same as the input type)
+  * **Required**: *no*
+
+
+**Inputs**
+
+* **1**: ``data`` - Input data to be normalized. A tensor of type *T* and arbitrary shape. **Required.**
+
+* **2**: ``axes`` - 1D or scalar tensor which specifies indices of dimensions in ``data`` that define normalization slices. Allowed range of axes is ``[-r; r-1]`` where ``r = rank(data)``, the order can be not sorted. Negative value means counting dimensions from the back. Type *T_AXES*. **Required.**
+
+* **3**: ``scale`` - A tensor of type *T* containing the scale values for . The shape should be broadcastable to the shape of ``data`` tensor. **Optional.**
+
+
+**Outputs**
+
+* **1**: Output tensor of the same shape and type as the ``data`` input tensor.
+
+**Types**
+
+* *T*: any floating point type.
+* *T_AXES*: ``int64`` or ``int32``.
+
+**Example**
+
+.. code-block:: xml
+   :force:
+
+   <layer ... type="RMS">
+       <data eps="1e-6"/>
+       <input>
+           <port id="0">
+               <dim>6</dim>
+               <dim>12</dim>
+               <dim>10</dim>
+               <dim>24</dim>
+           </port>
+           <port id="1">
+               <dim>1</dim> <!-- value of [-1] means normalization over the last dimension -->
+           </port>
+       </input>
+       <output>
+           <port id="2">
+               <dim>6</dim>
+               <dim>12</dim>
+               <dim>10</dim>
+               <dim>24</dim>
+           </port>
+       </output>
+   </layer>