[object Object]

[object Object][object Object]undefined[object Object]

Description: Extracts the sub-tensor $out$ from the input tensor $self$ based on the specified range $[start, end]$ and step $step$ on the specified dimension $dim$. $start$ and $end$ can be set to values other than $[0, self.shape[dim]]$. After the values are set, they are converted to valid values using the following formula (assume self.shape[dim] = N):

$$start = \begin{cases} &0, & if\;start < -N \\ &N, & if\;start >= N\\ &(start+N) \% N, & otherwise \end{cases}$$ $$end = \begin{cases} &N, & if\; end >= N\\ &start, & else\;if\; (end+N)\%N < start \\ &(end+N)\%N, & otherwise\\ \end{cases}$$

$out.shape$ is the same as $self.shape$ except on the [object Object] axis.

$$out.shape[dim] = ⌊\frac{end - start + step - 1}{step}⌋$$ [object Object]

Each operator has calls. First, [object Object] is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, [object Object] is called to perform computation.

• [object Object]
• [object Object]

[object Object]

• Parameters:

  • [object Object] (aclTensor*, computation input): $self$ in the formula, aclTensor on the device. The shape can have 1 to 8 dimensions. The data types of [object Object] and [object Object] are the same. are supported. The can be ND.

    • [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, INT32, INT64, INT8, UINT8, or BOOL.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be FLOAT, FLOAT16, INT32, INT64, INT8, UINT8, BOOL, or BFLOAT16.

  • [object Object] (int64_t, computation input): $dim$ in the formula, a specified dimension, an integer on the host. The value range is [–self.dim(), self.dim() – 1].

  • [object Object] (int64_t, computation input): $start$ in the formula, start index of the slice position, an integer on the host.

  • [object Object] (int64_t, computation input): $end$ in the formula, end index of the slice position, an integer on the host.

  • [object Object] (int64_t, computation input): $step$ in the formula, slice step, an integer greater than 0 on the host.

  • [object Object] (aclTensor*, computation output): $out$ in the formula, aclTensor on the device. The shape can have 1 to 8 dimensions and must meet the deduction rules in the formula. The data types of [object Object] and [object Object] are the same. are supported. The can be ND.

    • [object Object]Atlas training products[object Object]: The data type can be FLOAT, FLOAT16, INT32, INT64, INT8, UINT8, or BOOL.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be FLOAT, FLOAT16, INT32, INT64, INT8, UINT8, BOOL, or BFLOAT16.

  • [object Object] (uint64_t*, output): size of the workspace to be allocated on the device.

  • [object Object] (aclOpExecutor**, output): operator executor, containing the operator computation process.

• Returns:

  [object Object]: status code. For details, see .

[object Object]

• Parameters:

  • [object Object] (void*, input): address of the workspace to be allocated on the device.

  • [object Object] (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by calling [object Object].

  • [object Object] (aclOpExecutor*, input): operator executor, containing the operator computation process.

  • [object Object] (aclrtStream, input): stream for executing the task.

• Returns:

  [object Object]: status code. For details, see .

[object Object]

• Deterministic compute:
  • [object Object] defaults to a deterministic implementation.

[object Object]

The following example is for reference only. For details, see .