ai.onnx.Slice

ai.onnx · standard ONNX operator · ONNX opset ≥ 13

Description

Produces a slice of the input tensor along multiple axes, using starts, ends, axes, and steps to select a sub-tensor. Negative indices are resolved relative to the dimension size, and out-of-range values are clamped. Omitting axes defaults to all axes in order; omitting steps defaults to stride 1.

See the ONNX Slice spec for the reference semantics.

Inputs

Outputs

Type constraints

Files

• metadata.json — kernel metadata (id, digests, provenance)
• manifest.json — the op contract (source of truth)
• test.json — correctness cases
• bench.json — benchmark + tuning cases
• datamove-slice-block.wgsl.jinja
• slice-rank2-single-axis-x4.wgsl.jinja
• slice.wgsl.jinja

Use with @huggingface/kernels

The loader automatically allocates outputs whose metadata it can derive from the manifest contract and this call.

The explicit outputs entries provide shape and logical dtype metadata for the results listed below:

• output

Each entry either requests an optional result or supplies metadata that cannot be inferred from the inputs.

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version.

Replace each *Data placeholder with a typed array containing the corresponding input data.

import { getKernel } from "@huggingface/kernels";

const kernel = await getKernel("webgpu-kernels/ai.onnx.Slice", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { output } = await kernel({
  data: { data: dataData, shape: [2, 2] },
  starts: { data: startsData, shape: [1] },
  ends: { data: endsData, shape: [1] },
}, {
  outputs: { output: { shape: [1, 2], dtype: "float32" } },
});