(l-onnx-doc-CumSum)=

# CumSum


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

## CumSum - 14

### Version

- **name**: [CumSum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#CumSum)
- **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 cumulative sum of the input elements along the given axis.
By default, it will do the sum inclusively meaning the first element is copied as is.
Through an `exclusive` attribute, this behavior can change to exclude the first element.
It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1.

Example:
```
input_x = [1, 2, 3]
axis=0
output = [1, 3, 6]
exclusive=1
output = [0, 1, 3]
exclusive=0
reverse=1
output = [6, 5, 3]
exclusive=1
reverse=1
output = [5, 3, 0]
```

### Attributes

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

  If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.

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

  If set to 1 will perform the sums in reverse direction.

### Inputs

- **x** (heterogeneous) - **T**:

  An input tensor that is to be processed.
- **axis** (heterogeneous) - **T2**:

  A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.

### Outputs

- **y** (heterogeneous) - **T**:

  Output tensor of the same type as 'x' with cumulative sums of the x's elements

### 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.
* **T2** in ( `tensor(int32)`, `tensor(int64)` ):

  axis tensor can be int32 or int64 only

```{toctree}
text_diff_CumSum_11_14
```

(l-onnx-op-cumsum-11)=

## CumSum - 11

### Version

- **name**: [CumSum (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#CumSum)
- **domain**: `main`
- **since_version**: `11`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 11**.

### Summary

Performs cumulative sum of the input elements along the given axis.
By default, it will do the sum inclusively meaning the first element is copied as is.
Through an `exclusive` attribute, this behavior can change to exclude the first element.
It can also perform summation in the opposite direction of the axis. For that, set `reverse` attribute to 1.

Example:
```
input_x = [1, 2, 3]
axis=0
output = [1, 3, 6]
exclusive=1
output = [0, 1, 3]
exclusive=0
reverse=1
output = [6, 5, 3]
exclusive=1
reverse=1
output = [5, 3, 0]
```

### Attributes

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

  If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.

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

  If set to 1 will perform the sums in reverse direction.

### Inputs

- **x** (heterogeneous) - **T**:

  An input tensor that is to be processed.
- **axis** (heterogeneous) - **T2**:

  A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.

### Outputs

- **y** (heterogeneous) - **T**:

  Output tensor of the same type as 'x' with cumulative sums of the x's elements

### Type Constraints

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

  Input can be of any tensor type.
* **T2** in ( `tensor(int32)`, `tensor(int64)` ):

  axis tensor can be int32 or int64 only