# Div¶

## Div - 14¶

### Version¶

• name: Div (GitHub)

• 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 division (with Numpy-style broadcasting support).

(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.

## Div - 13¶

### Version¶

• name: Div (GitHub)

• 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 division (with Numpy-style broadcasting support).

### 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.

## Div - 7¶

### Version¶

• name: Div (GitHub)

• 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 division (with Numpy-style broadcasting support).

### 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.

## Div - 6¶

### Version¶

• name: Div (GitHub)

• 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 division (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'):

### 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.

## Div - 1¶

### Version¶

• name: Div (GitHub)

• 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 division (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'):

• 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.