ai.onnx.NonZero

ai.onnx · standard ONNX operator · ONNX opset ≥ 9

Description

Returns the indices of all non-zero elements in X, in row-major order, as a 2-D tensor of shape [rank(X), nnz] where each column is an N-dimensional index. This follows ONNX NonZero, including scalar output shape [0, N] and logical int64 output type. Because every emitted coordinate is bounded by a WebGPU-addressable input dimension, the backend stores this logical int64 tensor losslessly as uint32. The exact data-dependent output shape must be supplied as output metadata. String, float64, 64-bit input, and other ONNX input types not listed below are unsupported.

See the ONNX NonZero 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
• nonzero-flag-block-scan-vec4.wgsl.jinja
• nonzero-scatter-vec4.wgsl.jinja
• nonzero.wgsl.jinja
• scan-block-prefix-u32.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.NonZero", { version: 1 });
// Explicit destinations request optional results or supply metadata that cannot be inferred.
const { y } = await kernel({ x: { data: xData, shape: [4] } }, {
  outputs: { y: { shape: [1, 2], dtype: "int64" } },
});