(l-onnx-doc-Mul)=

# Mul


(l-onnx-op-mul-14)=

## Mul - 14

### Version

- **name**: [Mul (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mul)
- **domain**: `main`
- **since_version**: `14`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 14**.

### Summary

Performs element-wise binary multiplication (with Numpy-style broadcasting support).

This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

(Opset 14 change): Extend supported types to include uint8, int8, uint16, and int16.

### Inputs

- **A** (heterogeneous) - **T**:

  First operand.
- **B** (heterogeneous) - **T**:

  Second operand.

### Outputs

- **C** (heterogeneous) - **T**:

  Result, has same element type as two inputs

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Constrain input and output types to all numeric tensors.

```{toctree}
text_diff_Mul_13_14
```

(l-onnx-op-mul-13)=

## Mul - 13

### Version

- **name**: [Mul (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mul)
- **domain**: `main`
- **since_version**: `13`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 13**.

### Summary

Performs element-wise binary multiplication (with Numpy-style broadcasting support).

This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

### Inputs

- **A** (heterogeneous) - **T**:

  First operand.
- **B** (heterogeneous) - **T**:

  Second operand.

### Outputs

- **C** (heterogeneous) - **T**:

  Result, has same element type as two inputs

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ):

  Constrain input and output types to high-precision numeric tensors.

```{toctree}
text_diff_Mul_7_14
text_diff_Mul_7_13
```

(l-onnx-op-mul-7)=

## Mul - 7

### Version

- **name**: [Mul (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mul)
- **domain**: `main`
- **since_version**: `7`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 7**.

### Summary

Performs element-wise binary multiplication (with Numpy-style broadcasting support).

This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).

### Inputs

- **A** (heterogeneous) - **T**:

  First operand.
- **B** (heterogeneous) - **T**:

  Second operand.

### Outputs

- **C** (heterogeneous) - **T**:

  Result, has same element type as two inputs

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ):

  Constrain input and output types to high-precision numeric tensors.

```{toctree}
text_diff_Mul_6_14
text_diff_Mul_6_13
text_diff_Mul_6_7
```

(l-onnx-op-mul-6)=

## Mul - 6

### Version

- **name**: [Mul (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mul)
- **domain**: `main`
- **since_version**: `6`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 6**.

### Summary

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):

  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.

### Attributes

* **axis - INT** :

  If set, defines the broadcast dimensions. See doc for details.

* **broadcast - INT** (default is `'0'`):

  Pass 1 to enable broadcasting

### Inputs

- **A** (heterogeneous) - **T**:

  First operand, should share the type with the second operand.
- **B** (heterogeneous) - **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** (heterogeneous) - **T**:

  Result, has same dimensions and type as A

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int32)`, `tensor(int64)`, `tensor(uint32)`, `tensor(uint64)` ):

  Constrain input and output types to high-precision numeric tensors.

```{toctree}
text_diff_Mul_1_14
text_diff_Mul_1_13
text_diff_Mul_1_7
text_diff_Mul_1_6
```

(l-onnx-op-mul-1)=

## Mul - 1

### Version

- **name**: [Mul (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mul)
- **domain**: `main`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `False`

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

### Summary

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):

  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.

### Attributes

* **axis - INT** :

  If set, defines the broadcast dimensions. See doc for details.

* **broadcast - INT** (default is `'0'`):

  Pass 1 to enable broadcasting

* **consumed_inputs - INTS** :

  legacy optimization attribute.

### Inputs

- **A** (heterogeneous) - **T**:

  First operand, should share the type with the second operand.
- **B** (heterogeneous) - **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** (heterogeneous) - **T**:

  Result, has same dimensions and type as A

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.