(l-onnx-doc-Sum)=

# Sum


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

## Sum - 13

### Version

- **name**: [Sum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sum)
- **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

Element-wise sum of each of the input tensors (with Numpy-style broadcasting support).
All inputs and outputs must have the same data type.
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

Between 1 and 2147483647 inputs.

- **data_0** (variadic, heterogeneous) - **T**:

  List of tensors for sum.

### Outputs

- **sum** (heterogeneous) - **T**:

  Output tensor.

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.

```{toctree}
text_diff_Sum_8_13
```

(l-onnx-op-sum-8)=

## Sum - 8

### Version

- **name**: [Sum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sum)
- **domain**: `main`
- **since_version**: `8`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 8**.

### Summary

Element-wise sum of each of the input tensors (with Numpy-style broadcasting support).
All inputs and outputs must have the same data type.
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

Between 1 and 2147483647 inputs.

- **data_0** (variadic, heterogeneous) - **T**:

  List of tensors for sum.

### Outputs

- **sum** (heterogeneous) - **T**:

  Output tensor.

### Type Constraints

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

  Constrain input and output types to float tensors.

```{toctree}
text_diff_Sum_6_13
text_diff_Sum_6_8
```

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

## Sum - 6

### Version

- **name**: [Sum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sum)
- **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

Element-wise sum of each of the input tensors. All inputs and outputs must
have the same shape and data type.

### Inputs

Between 1 and 2147483647 inputs.

- **data_0** (variadic, heterogeneous) - **T**:

  List of tensors for Sum.

### Outputs

- **sum** (heterogeneous) - **T**:

  Output tensor. Same dimension as inputs.

### Type Constraints

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

  Constrain input and output types to float tensors.

```{toctree}
text_diff_Sum_1_13
text_diff_Sum_1_8
text_diff_Sum_1_6
```

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

## Sum - 1

### Version

- **name**: [Sum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sum)
- **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

Element-wise sum of each of the input tensors. All inputs and outputs must
have the same shape and data type.

### Attributes

* **consumed_inputs - INTS** :

  legacy optimization attribute.

### Inputs

Between 1 and 2147483647 inputs.

- **data_0** (variadic, heterogeneous) - **T**:

  List of tensors for Sum.

### Outputs

- **sum** (heterogeneous) - **T**:

  Output tensor. Same dimension as inputs.

### Type Constraints

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

  Constrain input and output types to float tensors.