# CumSum¶

## CumSum - 14¶

### Version¶

• name: CumSum (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 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

## CumSum - 11¶

### Version¶

• name: CumSum (GitHub)

• 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