.. _l-onnx-doccom.microsoft-QOrderedLayerNormalization:

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com.microsoft - QOrderedLayerNormalization
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.. _l-onnx-opcom-microsoft-qorderedlayernormalization-1:

QOrderedLayerNormalization - 1 (com.microsoft)
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**Version**

* **name**: `QOrderedLayerNormalization (GitHub) <https://github.com/onnx/onnx/blob/main/docs/Operators.md#com.microsoft.QOrderedLayerNormalization>`_
* **domain**: **com.microsoft**
* **since_version**: **1**
* **function**:
* **support_level**:
* **shape inference**:

This version of the operator has been available
**since version 1 of domain com.microsoft**.

**Summary**

QOrderedLayerNormalization

**Attributes**

* **axis**:
  The first normalization dimension: normalization will be performed
  along dimensions axis : rank(inputs). Default value is ``?``.
* **epsilon**:
  The epsilon value to use to avoid division by zero. Default value is ``?``.
* **order_X**:
  cublasLt order of input X. Default is ROW MAJOR. See the schema of
  QuantizeWithOrder for order definition. Default value is ``?``.
* **order_Y**:
  cublasLt order of matrix Y, must be same as order_X. Default is ROW
  MAJOR. Default value is ``?``.

**Inputs**

* **X** (heterogeneous) - **Q**:
  Input data tensor from the previous layer.
* **scale_X** (heterogeneous) - **S**:
  scale of the quantized X
* **scale** (heterogeneous) - **F**:
  Scale tensor, i.e., gamma vector.
* **B** (optional, heterogeneous) - **F**:
  Bias tensor.
* **scale_Y** (heterogeneous) - **S**:
  scale of the quantized X

**Outputs**

* **Y** (heterogeneous) - **Q**:
  Output data tensor.

**Examples**