ai.onnx.Transpose

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

Description

Transposes the input tensor by permuting its axes according to the perm attribute. Axis i of the output corresponds to axis perm[i] of the input; if perm is omitted, the axes are reversed (n-1, ..., 0).

See the ONNX Transpose spec for the reference semantics.

Inputs

Outputs

Attributes

Attributes and default values (overridable per request):

Type constraints

Implementation variants

One implementation is selected per call from the device capabilities, the request shapes and the dtypes; these notes say what each one covers.

• tiled_scalar — Stages ragged two-dimensional transpose planes through a padded workgroup tile so reads and writes remain coalesced when vec4 alignment is unavailable.
• inner_vec4 — Vectorizes the contiguous innermost dimension for permutations that leave that axis in place. It assigns multiple vectors per invocation only when enough invocations remain to keep the dispatch populated.

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-flat-copy.wgsl.jinja
• datamove-transpose-2d-tiled-scalar.wgsl.jinja
• datamove-transpose-2d-tiled.wgsl.jinja
• datamove-transpose-vec4.wgsl.jinja
• transpose.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:

• y

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.Transpose", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [] } }, {
  outputs: { y: { shape: [], dtype: "float32" } },
});