.. _op_ai_onnx_Ceil:

Ceil
====

- **Domain**: ``ai.onnx``
- **Since version**: 13

Ceil takes one input data (Tensor ) and produces one output data
(Tensor ) where the ceil is, y = ceil(x), is applied to
the tensor elementwise. If x is integral, +0, -0, NaN,  or infinite, x itself is returned.

**Inputs**

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

**Outputs**

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

**Type Constraints**

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

Examples
--------

**test_cc_ceil**

.. code-block:: text

    Node:
      Ceil(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=float32
        [[-1.5, -0.5,  0. ],
         [ 0.5,  1.2,  2. ]]

    Outputs:
      y: shape=(2, 3), dtype=float32
        [[-1., -0.,  0.],
         [ 1.,  2.,  2.]]

**test_cc_ceil_bfloat16**

.. code-block:: text

    Node:
      Ceil(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=bfloat16
        [[-1.5, -0.5, 0],
         [0.5, 1.5, 2.70312]]

    Outputs:
      y: shape=(2, 3), dtype=bfloat16
        [[-1, -0, 0],
         [1, 2, 3]]

**test_cc_ceil_float16**

.. code-block:: text

    Node:
      Ceil(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2, 3), dtype=float16
        [[-1.5, -0.5,  0. ],
         [ 0.5,  1.5,  2.7]]

    Outputs:
      y: shape=(2, 3), dtype=float16
        [[-1., -0.,  0.],
         [ 1.,  2.,  3.]]

**test_ceil**

.. code-block:: text

    Node:
      Ceil(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(3, 4, 5), dtype=float32
        [[[ 1.4243481 , -0.61890423, -0.5907667 ,  1.4329695 ,  0.5837956 ],
          [-1.3854368 ,  1.2791865 ,  0.32735094,  0.6038593 ,  0.24222691],
          [-0.89236253, -1.1303798 , -0.09180629, -0.12591071, -1.2615176 ],
          [-0.55638343, -0.747256  , -0.59118223, -0.9279915 , -0.73401135]],
        
         [[ 1.1054384 , -0.69560546, -2.1534553 ,  0.11396561, -0.8268097 ],
          [ 1.2137318 , -0.22223468,  0.32949635, -0.21049212,  1.3518724 ],
          [ 0.01262847,  0.6841954 , -1.2623075 ,  0.20052178, -1.1255072 ],
          [-0.6395738 ,  1.5355366 , -1.1466801 , -0.42676184, -0.74427605]],
        
         [[-1.5989435 ,  2.3646672 , -1.0641551 ,  0.90345967, -0.24993172],
          [-1.784248  , -0.47239977,  0.09873669, -0.36464727,  0.6651279 ],
          [-1.01641   , -0.39525023,  0.45574856, -0.3439513 , -0.5487247 ],
          [ 0.06280329, -0.14411083, -1.1603392 ,  0.49200374, -0.16951095]]]

    Outputs:
      y: shape=(3, 4, 5), dtype=float32
        [[[ 2., -0., -0.,  2.,  1.],
          [-1.,  2.,  1.,  1.,  1.],
          [-0., -1., -0., -0., -1.],
          [-0., -0., -0., -0., -0.]],
        
         [[ 2., -0., -2.,  1., -0.],
          [ 2., -0.,  1., -0.,  2.],
          [ 1.,  1., -1.,  1., -1.],
          [-0.,  2., -1., -0., -0.]],
        
         [[-1.,  3., -1.,  1., -0.],
          [-1., -0.,  1., -0.,  1.],
          [-1., -0.,  1., -0., -0.],
          [ 1., -0., -1.,  1., -0.]]]

**test_ceil_example**

.. code-block:: text

    Node:
      Ceil(x) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(2,), dtype=float32
        [-1.5,  1.2]

    Outputs:
      y: shape=(2,), dtype=float32
        [-1.,  2.]

Differences with previous version (6)
-------------------------------------

**SchemaDiff**: ``Ceil`` (domain ``'ai.onnx'``)

* old version: 6
* new version: 13
* breaking: no

**Type constraints:**

* changed 'T': added types: ['tensor(bfloat16)']

**Documentation:**

* line similarity: 0.75 (+1/-1 lines)

.. code-block:: diff

    --- Ceil v6
    +++ Ceil v13
    @@ -1,4 +1,4 @@
     
     Ceil takes one input data (Tensor<T>) and produces one output data
     (Tensor<T>) where the ceil is, y = ceil(x), is applied to
    -the tensor elementwise.
    +the tensor elementwise. If x is integral, +0, -0, NaN,  or infinite, x itself is returned.

Version History
---------------

- :doc:`Version 6 <Ceil-6>`
- :doc:`Version 1 <Ceil-1>`