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, per-variant templates, 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

npm install --save-exact @huggingface/kernels@0.0.1-preview.2

Outputs with inferable metadata are allocated automatically. Explicit outputs entries request optional results or provide metadata that cannot be inferred from the supplied inputs and attributes.

This example supplies explicit metadata for:

• output

The version: 1 option selects the published kernel contract; it is independent of any operator opset, contrib since_version, or model version. It follows the v1 branch as fixes land. To pin exact artifact bytes, pass a 40-character commit revision instead of 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" } },
});