.. _op_ai_onnx_Gelu:

Gelu
====

- **Domain**: ``ai.onnx``
- **Since version**: 20

Gelu takes one input data (Tensor ) and produces one
output data (Tensor ) where the gaussian error linear units function,
$y = 0.5 \* x \* (1 + erf(x/sqrt(2)))$ is applied to the tensor elementwise.
If the attribute "approximate" is set to "tanh", the function estimation,
$y = 0.5 \* x \* (1 + Tanh(sqrt(2/\pi) \* (x + 0.044715 \* x^3)))$ is used and applied
to the tensor elementwise.

**Inputs**

- **X** (*T*): Input tensor

**Outputs**

- **Y** (*T*): Output tensor

**Attributes**

- **approximate** (*string*): Gelu approximation algorithm: ``"tanh"``, ``"none"``(default).``"none"``: do not use approximation.``"tanh"``: use tanh approximation.

**Type Constraints**

- **T**: Constrain input and output types to float tensors.
  Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16).

Examples
--------

**test_cc_gelu_default_1**

.. code-block:: text

    Node:
      Gelu(X) -> (Y)

.. code-block:: text

    Inputs:
      X: shape=(3,), dtype=float32
        [-1.,  0.,  1.]

    Outputs:
      Y: shape=(3,), dtype=float32
        [-0.15865526,  0.        ,  0.8413447 ]

**test_cc_gelu_default_2**

.. code-block:: text

    Node:
      Gelu(X) -> (Y)
      Attributes:
        approximate = "none"

.. code-block:: text

    Inputs:
      X: shape=(2, 3), dtype=float32
        [[-2. , -1. , -0.5],
         [ 0.5,  1. ,  2. ]]

    Outputs:
      Y: shape=(2, 3), dtype=float32
        [[-0.04550028, -0.15865526, -0.15426877],
         [ 0.34573123,  0.8413447 ,  1.9544997 ]]

**test_cc_gelu_default_bfloat16**

.. code-block:: text

    Node:
      Gelu(X) -> (Y)

.. code-block:: text

    Inputs:
      X: shape=(4,), dtype=bfloat16
        [-1, 0, 1, 0.5]

    Outputs:
      Y: shape=(4,), dtype=bfloat16
        [-0.158203, 0, 0.839844, 0.345703]

**test_cc_gelu_default_float16**

.. code-block:: text

    Node:
      Gelu(X) -> (Y)

.. code-block:: text

    Inputs:
      X: shape=(3,), dtype=float16
        [-1.,  0.,  1.]

    Outputs:
      Y: shape=(3,), dtype=float16
        [-0.1587,  0.    ,  0.8413]

**test_cc_gelu_tanh_1**

.. code-block:: text

    Node:
      Gelu(X) -> (Y)
      Attributes:
        approximate = "tanh"

.. code-block:: text

    Inputs:
      X: shape=(3,), dtype=float32
        [-1.,  0.,  1.]

    Outputs:
      Y: shape=(3,), dtype=float32
        [-0.158808,  0.      ,  0.841192]

**test_cc_gelu_tanh_2**

.. code-block:: text

    Node:
      Gelu(X) -> (Y)
      Attributes:
        approximate = "tanh"

.. code-block:: text

    Inputs:
      X: shape=(2, 3), dtype=float32
        [[-2. , -1. , -0.5],
         [ 0.5,  1. ,  2. ]]

    Outputs:
      Y: shape=(2, 3), dtype=float32
        [[-0.04540229, -0.158808  , -0.154286  ],
         [ 0.345714  ,  0.841192  ,  1.9545977 ]]

**test_cc_gelu_tanh_float16**

.. code-block:: text

    Node:
      Gelu(X) -> (Y)
      Attributes:
        approximate = "tanh"

.. code-block:: text

    Inputs:
      X: shape=(2, 3), dtype=float16
        [[-2. , -1. , -0.5],
         [ 0.5,  1. ,  2. ]]

    Outputs:
      Y: shape=(2, 3), dtype=float16
        [[-0.0454, -0.1588, -0.1543],
         [ 0.3457,  0.8413,  1.955 ]]