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 caller must provide the exact data-dependent output shape. String, float64, 64-bit input, and other ONNX input types not listed below are not yet implemented.

See the ONNX NonZero 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
• 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

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:

• y

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.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" } },
});