pypto.arange

Applicable Products

Ascend 950PR/Ascend 950DT: Supported
Atlas A3 training products/Atlas A3 inference products: Supported
Atlas A2 training products/Atlas A2 inference products: Supported

Description

Creates a one-dimensional tensor of length $\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil$ , containing an arithmetic sequence within the interval [start, end) with step as the step size.

Prototype

arange(start: Union[int, float] = 0, end: Union[int, float], step: Union[int, float] = 1) -> Tensor

Parameters

Parameter	Input/Output	Description
start	Input	Source operand. Supported data types: DT_FP16, DT_BF16, DT_INT16, DT_INT32, and DT_FP32. Default value: 0.
end	Input	Source operand. Supported data types: DT_FP16, DT_BF16, DT_INT16, DT_INT32, and DT_FP32. This parameter is mandatory.
step	Input	Source operand. Supported data types: DT_FP16, DT_BF16, DT_INT16, DT_INT32, and DT_FP32. Default value: 1.

Return Value

Returns a one-dimensional output tensor. If any input value is of a floating-point data type, the output tensor data type is DT_FP32; otherwise, it is DT_INT32.

Constraints

step cannot be 0. As a floating-point number, abs(step)>1e-8.
(end-start)/step must be greater than 0.
If start, end, and step are all int inputs, all three must remain within the int32 range.

Examples

TileShape Setting Example

Before calling this operation API, set the TileShape through set_vec_tile_shapes.

The TileShape and the output have the same dimensions, both being one-dimensional.

If the input start is m, end is n, and step is p, the output shape is [q], and the TileShape is set to [q1], then q1 is used to split the q axis.

pypto.set_vec_tile_shapes(16)

API Call Example

y1 = pypto.arange(1.0, 4.0, 0.5)
y2 = pypto.arange(1.0, 4.0)
y3 = pypto.arange(4)

The results are as follows:

Output data y1: [1.0, 1.5, 2.0, 2.5, 3.0, 3.5]
Output data y2: [1.0, 2.0, 3.0]
Output data y3: [0, 1, 2, 3]