# Slice¶

## Slice - 13¶

### Version¶

• name: Slice (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¶

Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/user/basics.indexing.html?highlight=slice#slicing-and-striding

Slice uses the starts, ends, axes and steps inputs to select a sub-tensor of its input data tensor.

An effective starts[i], ends[i], and steps[i] must be computed for each i in [0, ... r-1] where r = rank(input) as follows:

If axes are omitted, they are set to [0, ..., r-1]. If steps are omitted, they are set to [1, ..., 1] of length len(starts)

The effective values are initialized as start[i] = 0, ends[i] = dims[i] where dims are the dimensions of input and steps[i] = 1.

All negative elements of axes are made non-negative by adding r to them, where r =rank(input).

All negative values in starts[i] and ends[i] have dims[axes[i]] added to them, where dims are the dimensions of input. Then start[axes[i]] is the adjusted starts[i] is clamped into the range [0, dims[axes[i]]] for positive stepping and [0, dims[axes[i]]-1] for negative stepping.

The clamping for the adjusted ends[i] depends on the sign of steps[i] and must accommodate copying 0 through dims[axes[i]] elements, so for positive stepping ends[axes[i]] is clamped to [0, dims[axes[i]]], while for negative stepping it is clamped to [-1, dims[axes[i]]-1].

Finally, steps[axes[i]] = steps[i].

For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX when slicing forward and ‘INT_MIN’ when slicing backward.

Example 1:

data = [
[1, 2, 3, 4],
[5, 6, 7, 8],
]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
steps = [1, 2]
result = [
[5, 7],
]


Example 2:

data = [
[1, 2, 3, 4],
[5, 6, 7, 8],
]
starts = [0, 1]
ends = [-1, 1000]
result = [
[2, 3, 4],
]


### Inputs¶

Between 3 and 5 inputs.

• data (heterogeneous) - T:

Tensor of data to extract slices from.

• starts (heterogeneous) - Tind:

1-D tensor of starting indices of corresponding axis in axes

• ends (heterogeneous) - Tind:

1-D tensor of ending indices (exclusive) of corresponding axis in axes

• axes (optional, heterogeneous) - Tind:

1-D tensor of axes that starts and ends apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Behavior is undefined if an axis is repeated.

• steps (optional, heterogeneous) - Tind:

1-D tensor of slice step of corresponding axis in axes. Negative value means slicing backward. ‘steps’ cannot be 0. Defaults to 1s.

### Outputs¶

• output (heterogeneous) - T:

Sliced data tensor.

### Type Constraints¶

• T in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

Constrain input and output types to all tensor types.

• Tind in ( tensor(int32), tensor(int64) ):

Constrain indices to integer types

## Slice - 11¶

### Version¶

• name: Slice (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¶

Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses starts, ends, axes and steps inputs to specify the start and end dimension and step for each axis in the list of axes, it uses this information to slice the input data tensor. If a negative value is passed for any of the start or end indices, it represents number of elements before the end of that dimension. If the value passed to start or end is larger than the n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX when slicing forward and ‘INT_MIN’ when slicing backward. If a negative value is passed for step, it represents slicing backward. However step value cannot be 0. If axes are omitted, they are set to [0, ..., ndim-1]. If steps are omitted, they are set to [1, ..., 1] of length len(starts) Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ]

### Inputs¶

Between 3 and 5 inputs.

• data (heterogeneous) - T:

Tensor of data to extract slices from.

• starts (heterogeneous) - Tind:

1-D tensor of starting indices of corresponding axis in axes

• ends (heterogeneous) - Tind:

1-D tensor of ending indices (exclusive) of corresponding axis in axes

• axes (optional, heterogeneous) - Tind:

1-D tensor of axes that starts and ends apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).

• steps (optional, heterogeneous) - Tind:

1-D tensor of slice step of corresponding axis in axes. Negative value means slicing backward. ‘steps’ cannot be 0. Defaults to 1.

### Outputs¶

• output (heterogeneous) - T:

Sliced data tensor.

### Type Constraints¶

• T in ( tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

Constrain input and output types to all tensor types.

• Tind in ( tensor(int32), tensor(int64) ):

Constrain indices to integer types

## Slice - 10¶

### Version¶

• name: Slice (GitHub)

• domain: main

• since_version: 10

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 10.

### Summary¶

Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses starts, ends, axes and steps inputs to specify the start and end dimension and step for each axis in the list of axes, it uses this information to slice the input data tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. If a negative value is passed for step, it represents slicing backward. If axes are omitted, they are set to [0, ..., ndim-1]. If steps are omitted, they are set to [1, ..., 1] of length len(starts) Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] steps = [1, 2] result = [ [5, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ]

### Inputs¶

Between 3 and 5 inputs.

• data (heterogeneous) - T:

Tensor of data to extract slices from.

• starts (heterogeneous) - Tind:

1-D tensor of starting indices of corresponding axis in axes

• ends (heterogeneous) - Tind:

1-D tensor of ending indices (exclusive) of corresponding axis in axes

• axes (optional, heterogeneous) - Tind:

1-D tensor of axes that starts and ends apply to.

• steps (optional, heterogeneous) - Tind:

1-D tensor of slice step of corresponding axis in axes. Default to 1.

### Outputs¶

• output (heterogeneous) - T:

Sliced data tensor.

### Type Constraints¶

• T in ( tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

Constrain input and output types to all tensor types.

• Tind in ( tensor(int32), tensor(int64) ):

Constrain indices to integer types

## Slice - 1¶

### Version¶

• name: Slice (GitHub)

• domain: main

• since_version: 1

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 1.

### Summary¶

Produces a slice of the input tensor along multiple axes. Similar to numpy: https://numpy.org/doc/stable/reference/routines.indexing.html Slices uses axes, starts and ends attributes to specify the start and end dimension for each axis in the list of axes, it uses this information to slice the input data tensor. If a negative value is passed for any of the start or end indices, it represent number of elements before the end of that dimension. If the value passed to start or end is larger than the n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. If axes are omitted, they are set to [0, ..., ndim-1]. Example 1: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] result = [ [5, 6, 7], ] Example 2: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] starts = [0, 1] ends = [-1, 1000] result = [ [2, 3, 4], ]

### Attributes¶

• axes - INTS :

Axes that starts and ends apply to. It’s optional. If not present, will be treated as [0, 1, …, len(starts) - 1].

• ends - INTS (required) :

Ending indices (exclusive) of corresponding axis in axes

• starts - INTS (required) :

Starting indices of corresponding axis in axes

### Inputs¶

• data (heterogeneous) - T:

Tensor of data to extract slices from.

### Outputs¶

• output (heterogeneous) - T:

Sliced data tensor.

### Type Constraints¶

• T in ( tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)` ):

Constrain input and output types to all tensor types.