(l-onnx-doc-Div)=
# Div
(l-onnx-op-div-14)=
## Div - 14
### Version
- **name**: [Div (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Div)
- **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).
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_Div_13_14
```
(l-onnx-op-div-13)=
## Div - 13
### Version
- **name**: [Div (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Div)
- **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).
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_Div_7_14
text_diff_Div_7_13
```
(l-onnx-op-div-7)=
## Div - 7
### Version
- **name**: [Div (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Div)
- **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).
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_Div_6_14
text_diff_Div_6_13
text_diff_Div_6_7
```
(l-onnx-op-div-6)=
## Div - 6
### Version
- **name**: [Div (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Div)
- **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'`):
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_Div_1_14
text_diff_Div_1_13
text_diff_Div_1_7
text_diff_Div_1_6
```
(l-onnx-op-div-1)=
## Div - 1
### Version
- **name**: [Div (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Div)
- **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'`):
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.