.. _op_ai_onnx_Mul-6:

Mul - version 6
===============

This page documents version **6** of operator **Mul**. See :doc:`Mul` for the latest version (since version 14).

- **Domain**: ``ai.onnx``
- **Since version**: 6

Performs element-wise binary multiplication (with limited broadcast support).
If necessary the right-hand-side argument will be broadcasted to match the
shape of left-hand-side argument. When broadcasting is specified, the second
tensor can either be of element size 1 (including a scalar tensor and any
tensor with rank equal to or smaller than the first tensor), or having its
shape as a contiguous subset of the first tensor's shape. The starting of the
mutually equal shape is specified by the argument "axis", and if it is not set,
suffix matching is assumed. 1-dim expansion doesn't work yet.

For example, the following tensor shapes are supported (with broadcast=1):

.. code-block:: text

    shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
    shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
    shape(A) = (2, 3, 4, 5), shape(B) = (5,)
    shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
    shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
    shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0

Attribute ``broadcast=1`` needs to be passed to enable broadcasting.

**Inputs**

- **A** (*T*): First operand, should share the type with the second operand.
- **B** (*T*): Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.

**Outputs**

- **C** (*T*): Result, has same dimensions and type as A

**Type Constraints**

- **T**: Constrain input and output types to high-precision numeric tensors.
  Allowed types: tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64).

Differences with previous version (1)
-------------------------------------

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

* old version: 1
* new version: 6
* breaking: no

**Type constraints:**

* changed 'T': added types: ['tensor(int32)', 'tensor(int64)', 'tensor(uint32)', 'tensor(uint64)']